breast cancer research papers

Breast Cancer Research and Treatment

The journal's focus spans across various disciplines including surgery, radiotherapy, medical oncology, endocrinology, epidemiology, immunology and cell biology.
Provides an international platform for the discussion and resolution of ongoing controversies in breast cancer treatment.
For over 35 years, it has served as a reliable resource for a wide audience of researchers and professionals.
With 800K+ article downloads each year, it offers a high visibility platform for research.
Boasts high levels of author satisfaction with 93% of authors likely to publish with us again.
William J. Gradishar

Latest issue

Volume 207, Issue 2

Latest articles

The effects of the-optimal-lymph-flow health it system application on treatment-related high risk lymphedema in breast cancer patients: a randomized controlled trial.

SGLT2 inhibition improves PI3Kα inhibitor–induced hyperglycemia: findings from preclinical animal models and from patients in the BYLieve and SOLAR-1 trials

Manuel Ruiz Borrego
Yen-Shen Lu
Stephen Chia

Patient characteristics and treatment patterns of patients with locally advanced or metastatic HER2-low breast cancer, a single site descriptive study

Connor Willis
Chia Jie Tan
David Stenehjem

Cytokine levels in breast cancer are highly dependent on cytomegalovirus (CMV) status

Juliet V. Spencer
Jianfang Liu
Stella Somiari

Polycystic ovary syndrome and risk of breast cancer in premenopausal and postmenopausal women: a nationwide population-based cohort study

Clarissa L. B. Frandsen
Allan Jensen

Journal information

Biological Abstracts
CAB Abstracts
Chemical Abstracts Service (CAS)
Current Contents/Clinical Medicine
Current Contents/Life Sciences
Google Scholar
INIS Atomindex
Japanese Science and Technology Agency (JST)
OCLC WorldCat Discovery Service
Pathway Studio
Science Citation Index Expanded (SCIE)
Semantic Scholar
TD Net Discovery Service
UGC-CARE List (India)

Rights and permissions

Editorial policies

Find a journal
Publish with us
Track your research

Neoplasms by Site
breast neoplasms
Breast Cancer

Breast cancer

December 2019
Nature Reviews Disease Primers 5(1)
This person is not on ResearchGate, or hasn't claimed this research yet.

Centre Jean Perrin

Vall d’Hebron Institute of Oncology

Medical University of Vienna

Discover the world's research

25+ million members
160+ million publication pages
2.3+ billion citations

Ni Putu Eka Leliqia

Sugela Susana Blancas-Zugarazo

Frédéric Percevault

Hong-Jian Luo
Jia-Liang Ren
Xiao-Li Song
ACS APPL MATER INTER
Xingquan Ma
Yuqing Dong

Renyang Liu
Wenhui Zhou
Benjamin L Franc
Wendy B DeMartini
Eric L Rosen
BIOMED ENG ONLINE
Kaiyue Wang
Yiding Chen

CELL MOL BIOL LETT
LANCET ONCOL

Anne-Laure Vallier
Fatima Cardoso

Sacha Howell

Gail S. Wright
William J Gradishar

Yin-Hsun Feng

E.M. Ciruelos

Paul McGale

Recruit researchers
Join for free
Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up

Advances in Breast Cancer Research

A polyploid giant cancer cell from triple-negative breast cancer in which actin is red, mitochondria are green, and nuclear DNA is blue.

A polyploid giant cancer cell (PGCC) from triple-negative breast cancer.

NCI-funded researchers are working to advance our understanding of how to prevent, detect, and treat breast cancer. They are also looking at how to address disparities and improve quality of life for survivors of the disease.

This page highlights some of what's new in the latest research for breast cancer, including new clinical advances that may soon translate into improved care, NCI-supported programs that are fueling progress, and research findings from recent studies.

Early Detection of Breast Cancer

Breast cancer is one of a few cancers for which an effective screening test, mammography , is available. MRI ( magnetic resonance imaging ) and ultrasound are also used to detect breast cancer, but not as routine screening tools for people with average risk.

Ongoing studies are looking at ways to enhance current breast cancer screening options. Technological advances in imaging are creating new opportunities for improvements in both screening and early detection.

One technology advance is 3-D mammography , also called breast tomosynthesis . This procedure takes images from different angles around the breast and builds them into a 3-D-like image. Although this technology is increasingly available in the clinic, it isn’t known whether it is better than standard 2-D mammography , for detecting cancer at a less advanced stage.

NCI is funding a large-scale randomized breast screening trial, the Tomosynthesis Mammographic Imaging Screening Trial (TMIST) , to compare the number of advanced cancers detected in women screened for 5 years with 3-D mammography with the number detected in women screened with 2-D mammography.

Two concerns in breast cancer screening, as in all cancer screening, are:

the potential for diagnosing tumors that would not have become life-threatening ( overdiagnosis )
the possibility of receiving false-positive test results, and the anxiety that comes with follow-up tests or procedures

As cancer treatment is becoming more individualized, researchers are looking at ways to personalize breast cancer screening. They are studying screening methods that are appropriate for each woman’s level of risk and limit the possibility of overdiagnosis.

For example, the Women Informed to Screen Depending on Measures of Risk (WISDOM) study aims to determine if risk-based screening—that is, screening at intervals that are based on each woman’s risk as determined by her genetic makeup, family history , and other risk factors—is as safe, effective, and accepted as standard annual screening mammography.

WISDOM is also making a focused effort to enroll Black women in the trial. Past studies tended to contain a majority of White women and therefore, there is less data on how screening can benefit Black women. Researchers are taking a number of steps to include as many Black women as possible in the study while also increasing the diversity of all women enrolled.

Breast Cancer Treatment

The mainstays of breast cancer treatment are surgery , radiation , chemotherapy , hormone therapy , and targeted therapy . But scientists continue to study novel treatments and drugs, along with new combinations of existing treatments.

It is now known that breast cancer can be divided into subtypes based on whether they:

are hormone receptor (HR) positive which means they express estrogen and/or progesterone receptors ( ER , PR )

Illustrations of two forms of breast-conserving surgery

Shortening Radiation Therapy for Some with Early Breast Cancer

A condensed course was as effective and safe as the standard course for women with higher-risk early-stage breast cancer who had a lumpectomy.

As we learn more about the subtypes of breast cancer and their behavior, we can use this information to guide treatment decisions. For example:

The NCI-sponsored TAILORx clinical trial. The study, which included patients with ER-positive, lymph node-negative breast cancer, found that a test that looks at the expression of certain genes can predict which women can safely avoid chemotherapy.
The RxPONDER trial found that the same gene expression test can also be used to determine treatment options in women with more advanced breast cancer. The study found that some postmenopausal women with HR positive, HER-2 negative breast cancer that has spread to several lymph nodes and has a low risk of recurrence do not benefit from chemotherapy when added to their hormone therapy.
The OFSET trial is comparing the addition of chemotherapy to usual treatment ( ovarian function suppression plus hormone therapy) to usual treatment alone in treating premenopausal estrogen receptor (ER)-positive/HER2-negative breast cancer patients who are at high risk of their cancer returning. This will help determine whether or not adding chemotherapy helps prevent the cancer from returning.

Genomic analyses, such as those carried out through The Cancer Genome Atlas (TCGA) , have provided more insights into the molecular diversity of breast cancer and eventually could help identify even more breast cancer subtypes. That knowledge, in turn, may lead to the development of therapies that target the genetic alterations that drive those cancer subtypes.

HR-Positive Breast Cancer Treatment

Hormone therapies have been a mainstay of treatment for HR-positive cancer. However, there is a new focus on adding targeted therapies to hormone therapy for advanced or metastatic HR-positive cancers. These treatments could prolong the time until chemotherapy is needed and ideally, extend survival. Approved drugs include:

A woman in her 40s in her bedroom holding a pill bottle and her mobile phone

Drug Combo Effective for Metastatic Breast Cancer in Younger Women

Ribociclib plus hormone therapy were superior to standard chemotherapy combos in a recent trial.

Palbociclib (Ibrance) , ribociclib (Kisqali) , and everolimus (Afinitor) have all been approved by the FDA for use with hormone therapy for treatment of advanced or metastatic breast cancer. Ribociclib has been shown to increase the survival of patients with metastatic breast cancer . It has also shown to slow the growth of metastatic cancer in younger women when combined with hormone therapy.
Elacestrant (Orserdu) is approved for HR-positive and HER2-negative breast cancer that has a mutation in the ESR1 gene, and has spread. It is used in postmenopausal women and in men whose cancer has gotten worse after at least one type of hormone therapy.
Abemaciclib (Verzenio) can be used with or after hormone therapy to treat advanced or metastatic HR-positive, HER2-negative breast cancer. In October 2021, the Food and Drug Administration ( FDA ) approved abemaciclib in combination with hormone therapy to treat some people who have had surgery for early-stage HR-positive, HER2-negative breast cancer.
Alpelisib (Piqray) is approved to be used in combination with hormone therapy to treat advanced or metastatic HR-positive, HER2-negative breast cancers that have a mutation in the PIK3CA gene .
Sacituzumab govitecan-hziy (Trodelvy) is used for HR-positive and HER2-negative breast cancer that has spread or can't be removed with surgery. It is used in those who have received hormone therapy and at least two previous treatments. It has shown to extend the amount of time that the disease doesn't get worse ( progression-free survival ) and also shown to improve overall survival .

HER2-Positive Breast Cancer Treatment

The FDA has approved a number of targeted therapies to treat HER2-positive breast cancer , including:

Trastuzumab (Herceptin) has been approved to be used to prevent a relapse in patients with early-stage HER2-positive breast cancer.
Pertuzumab (Perjeta) is used to treat metastatic HER2-positive breast cancer, and also both before surgery ( neoadjuvant ) and after surgery ( adjuvant therapy ).
Trastuzumab and pertuzumab together can be used in combination with chemotherapy to prevent relapse in people with early-stage HER2-positive breast cancer. Both are also used together in metastatic disease, where they delay progression and improve overall survival.
Trastuzumab deruxtecan (Enhertu) is approved for patients with advanced or metastatic HER2-positive breast cancer who have previously received a HER2-targeted treatment. A 2021 clinical trial showed that the drug lengthened the time that people with metastatic HER2-positive breast cancer lived without their cancer progressing. The trial also showed that it was better at shrinking tumors than another targeted drug, trastuzumab emtansine (Kadcyla).
Tucatinib (Tukysa) is approved to be used in combination with trastuzumab and capecitabine (Xeloda) for HER2-positive breast cancer that cannot be removed with surgery or is metastatic. Tucatinib is able to cross the blood–brain barrier, which makes it especially useful for HER2-positive metastatic breast cancer, which tends to spread to the brain.
Lapatinib (Tykerb) has been approved for treatment of some patients with HER2-positive advanced or metastatic breast cancer, together with capecitabine or letrozole.
Neratinib Maleate (Nerlynx) can be used in patients with early-stage HER2-positive breast cancer and can also be used together with capecitabine (Xeloda) in some patients with advanced or metastatic disease.
Ado-trastuzumab emtansine (Kadcyla) is approved to treat patients with metastatic HER2-positive breast cancer who have previously received trastuzumab and a taxane . It's also used in some patients with early-stage HER2-positive breast cancer who have completed therapy before surgery ( neoadjuvant ) and have residual disease at the time of surgery.

HER2-Low Breast Cancer

A newly defined subtype, HER2-low, accounts for more than half of all metastatic breast cancers. HER2-low tumors are defined as those whose cells contain lower levels of the HER2 protein on their surface. Such tumors have traditionally been classified as HER2-negative because they did not respond to drugs that target HER2.

However, in a clinical trial, trastuzumab deruxtecan (Enhertu) improved the survival of patients with HER2-low breast cancer compared with chemotherapy , and the drug is approved for use in such patients.

Immunotherapy Improves Survival in Triple-Negative Breast Cancer

For patients whose tumors had high PD-L1 levels, pembrolizumab with chemo helped them live longer.

Triple-Negative Breast Cancer Treatment

Triple-negative breast cancers (TNBC) are the hardest to treat because they lack both hormone receptors and HER2 overexpression , so they do not respond to therapies directed at these targets. Therefore, chemotherapy is the mainstay for treatment of TNBC. However, new treatments are starting to become available. These include:

Sacituzumab govitecan-hziy (Trodelvy) is approved to treat patients with TNBC that has spread to other parts of the body . Patients must have received at least two prior therapies before receiving the drug.
Pembrolizumab (Keytruda) is an immunotherapy drug that is approved to be used in combination with chemotherapy for patients with locally advanced or metastatic TNBC that has the PD-L1 protein. It may also be used before surgery (called neoadjuvant ) for patients with early-stage TNBC, regardless of their PD-L1 status.
PARP inhibitors, which include olaparib (Lynparza) and talazoparib (Talzenna) , are approved to treat metastatic HER2-negative or triple-negative breast cancers in patients who have inherited a harmful BRCA gene mutation. Olaparib is also approved for use in certain patients with early-stage HER2-negative or triple-negative breast cancer.
Drugs that block the androgen receptors or prevent androgen production are being tested in a subset of TNBC that express the androgen receptor.

For a complete list of drugs for breast cancer, see Drugs Approved for Breast Cancer .

NCI-Supported Breast Cancer Research Programs

Many NCI-funded researchers working at the NIH campus, as well as across the United States and world, are seeking ways to address breast cancer more effectively. Some research is basic, exploring questions as diverse as the biological underpinnings of cancer and the social factors that affect cancer risk. And some are more clinical, seeking to translate this basic information into improving patient outcomes. The programs listed below are a small sampling of NCI’s research efforts in breast cancer.

TMIST is a randomized breast screening trial that compares two Food and Drug Administration (FDA)-approved types of digital mammography, standard digital mammography (2-D) with a newer technology called tomosynthesis mammography (3-D).

The Breast Specialized Programs of Research Excellence (Breast SPOREs) are designed to quickly move basic scientific findings into clinical settings. The Breast SPOREs support the development of new therapies and technologies, and studies to better understand tumor resistance, diagnosis, prognosis, screening, prevention, and treatment of breast cancer.

The NCI Cancer Intervention and Surveillance Modeling Network (CISNET) focuses on using modeling to improve our understanding of how prevention, early detection, screening, and treatment affect breast cancer outcomes.

The Confluence Project , from NCI's Division of Cancer Epidemiology and Genetics (DCEG) , is developing a research resource that includes data from thousands of breast cancer patients and controls of different races and ethnicities. This resource will be used to identify genes that are associated with breast cancer risk, prognosis, subtypes, response to treatment, and second breast cancers. (DCEG conducts other breast cancer research as well.)

The Black Women’s Health Study (BWHS) Breast Cancer Risk Calculator allows health professionals to estimate a woman’s risk of developing invasive breast cancer over the next 5 years. With the NCI-funded effort, researchers developed a tool to estimate the risk of breast cancer in US Black women. The team that developed the tool hopes it will help guide more personalized decisions on when Black women—especially younger women—should begin breast cancer screening.

The goal of the Breast Cancer Surveillance Consortium (BCSC) , an NCI-funded program launched in 1994, is to enhance the understanding of breast cancer screening practices in the United States and their impact on the breast cancer's stage at diagnosis, survival rates, and mortality.

There are ongoing programs at NCI that support prevention and early detection research in different cancers, including breast cancer. Examples include:

The Cancer Biomarkers Research Group , which promotes research in cancer biomarkers and manages the Early Detection Research Network (EDRN) . EDRN is a network of NCI-funded institutions that are collaborating to discover and validate early detection biomarkers. Within the EDRN, the Breast and Gynecologic Cancers Collaborative Group conducts research on breast and ovarian cancers.
NCI's Division of Cancer Prevention houses the Breast and Gynecologic Cancer Research Group which conducts and fosters the development of research on the prevention and early detection of breast and gynecologic cancers.

Breast Cancer Survivorship Research

NCI’s Office of Cancer Survivorship, part of the Division of Cancer Control and Population Sciences (DCCPS), supports research projects throughout the country that study many issues related to breast cancer survivorship. Examples of studies funded include the impact of cancer and its treatment on physical functioning, emotional well-being, cognitive impairment , sleep disturbances, and cardiovascular health. Other studies focus on financial impacts, the effects on caregivers, models of care for survivors, and issues such as racial disparities and communication.

Breast Cancer Clinical Trials

NCI funds and oversees both early- and late-phase clinical trials to develop new treatments and improve patient care. Trials are available for breast cancer prevention , screening , and treatment .

Breast Cancer Research Results

The following are some of our latest news articles on breast cancer research and study updates:

How Breast Cancer Risk Assessment Tools Work
Can Some People with Breast Cancer Safely Skip Lymph Node Radiation?
Study Adds to Debate about Mammography in Older Women
Pausing Long-Term Breast Cancer Therapy to Become Pregnant Appears to Be Safe
A Safer, Better Treatment Option for Some Younger Women with Breast Cancer
Shorter Course of Radiation Is Effective, Safe for Some with Early-Stage Breast Cancer

View the full list of Breast Cancer Research Results and Study Updates .

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Publications
Account settings
My Bibliography
Collections
Citation manager

Save citation to file

Email citation, add to collections.

Create a new collection
Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

Search in PubMed
Search in NLM Catalog
Add to Search

Current State of Breast Cancer Diagnosis, Treatment, and Theranostics

Affiliations.

1 Ladue Horton Watkins High School, St. Louis, MO 63124, USA.
2 Department of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, Kingston, RI 02881, USA.
PMID: 34069059
PMCID: PMC8156889
DOI: 10.3390/pharmaceutics13050723

Breast cancer is one of the leading causes of cancer-related morbidity and mortality in women worldwide. Early diagnosis and effective treatment of all types of cancers are crucial for a positive prognosis. Patients with small tumor sizes at the time of their diagnosis have a significantly higher survival rate and a significantly reduced probability of the cancer being fatal. Therefore, many novel technologies are being developed for early detection of primary tumors, as well as distant metastases and recurrent disease, for effective breast cancer management. Theranostics has emerged as a new paradigm for the simultaneous diagnosis, imaging, and treatment of cancers. It has the potential to provide timely and improved patient care via personalized therapy. In nanotheranostics, cell-specific targeting moieties, imaging agents, and therapeutic agents can be embedded within a single formulation for effective treatment. In this review, we will highlight the different diagnosis techniques and treatment strategies for breast cancer management and explore recent advances in breast cancer theranostics. Our main focus will be to summarize recent trends and technologies in breast cancer diagnosis and treatment as reported in recent research papers and patents and discuss future perspectives for effective breast cancer therapy.

Keywords: breast cancer; breast specific gamma imaging; imaging modalities; mammography; theranostics; triple-negative breast cancer.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Representation of the various imaging…

Representation of the various imaging techniques that can be used in breast cancer…

Novel FDA-approved targeted therapies for…

Novel FDA-approved targeted therapies for the treatment of molecular subtypes of breast cancer.

Schematic diagram representing theranostic approaches…

Schematic diagram representing theranostic approaches in breast cancer management.

Publication types

Search in MeSH

Related information

Cited in Books

Grants and funding

P20 GM103430/GM/NIGMS NIH HHS/United States
P20GM103430/GM/NIGMS NIH HHS/United States

LinkOut - more resources

Full text sources.

Europe PubMed Central
PubMed Central

Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

View all journals
Explore content
About the journal
Publish with us
Sign up for alerts
Open access
Published: 29 August 2019

Deep Learning to Improve Breast Cancer Detection on Screening Mammography

Li Shen ORCID: orcid.org/0000-0002-5190-2851 1 ,
Laurie R. Margolies 2 ,
Joseph H. Rothstein 3 ,
Eugene Fluder 4 ,
Russell McBride ORCID: orcid.org/0000-0002-3890-529X 5 &
Weiva Sieh 3

Scientific Reports volume 9 , Article number: 12495 ( 2019 ) Cite this article

111k Accesses

538 Citations

75 Altmetric

Metrics details

Computational science
Computer science
Predictive markers

The rapid development of deep learning, a family of machine learning techniques, has spurred much interest in its application to medical imaging problems. Here, we develop a deep learning algorithm that can accurately detect breast cancer on screening mammograms using an “end-to-end” training approach that efficiently leverages training datasets with either complete clinical annotation or only the cancer status (label) of the whole image. In this approach, lesion annotations are required only in the initial training stage, and subsequent stages require only image-level labels, eliminating the reliance on rarely available lesion annotations. Our all convolutional network method for classifying screening mammograms attained excellent performance in comparison with previous methods. On an independent test set of digitized film mammograms from the Digital Database for Screening Mammography (CBIS-DDSM), the best single model achieved a per-image AUC of 0.88, and four-model averaging improved the AUC to 0.91 (sensitivity: 86.1%, specificity: 80.1%). On an independent test set of full-field digital mammography (FFDM) images from the INbreast database, the best single model achieved a per-image AUC of 0.95, and four-model averaging improved the AUC to 0.98 (sensitivity: 86.7%, specificity: 96.1%). We also demonstrate that a whole image classifier trained using our end-to-end approach on the CBIS-DDSM digitized film mammograms can be transferred to INbreast FFDM images using only a subset of the INbreast data for fine-tuning and without further reliance on the availability of lesion annotations. These findings show that automatic deep learning methods can be readily trained to attain high accuracy on heterogeneous mammography platforms, and hold tremendous promise for improving clinical tools to reduce false positive and false negative screening mammography results. Code and model available at: https://github.com/lishen/end2end-all-conv .

Intelligent breast cancer diagnosis with two-stage using mammogram images

A convolutional deep learning model for improving mammographic breast-microcalcification diagnosis

Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach

Introduction.

The rapid advancement of machine learning and especially deep learning continues to fuel the medical imaging community’s interest in applying these techniques to improve the accuracy of cancer screening. Breast cancer is the second leading cause of cancer deaths among U.S. women 1 and screening mammography has been found to reduce mortality 2 . Despite the benefits, screening mammography is associated with a high risk of false positives as well as false negatives. The average sensitivity of digital screening mammography in the U.S. is 86.9% and the average specificity is 88.9% 3 . To help radiologists improve the predictive accuracy of screening mammography, computer-assisted detection and diagnosis (CAD) software 4 have been developed and in clinical use since the 1990s. Unfortunately, data suggested that early commercial CAD systems had not led to significant improvement in performance 5 , 6 , 7 and progress stagnated for more than a decade since they were introduced. With the remarkable success of deep learning in visual object recognition and detection, and many other domains 8 , there is much interest in developing deep learning tools to assist radiologists and improve the accuracy of screening mammography 9 , 10 , 11 , 12 , 13 , 14 . Recent studies 15 , 16 have shown that a deep learning based CAD system performed as well as radiologists in standalone mode and improved the radiologists’ performance in support mode.

Detection of subclinical breast cancer on screening mammography is challenging as an image classification task because the tumors themselves occupy only a small portion of the image of the entire breast. For example, a full-field digital mammography (FFDM) image is typically 4000 × 3000 pixels while a potentially cancerous region of interest (ROI) can be as small as 100 × 100 pixels. For this reason, many studies 13 , 17 , 18 , 19 , 20 , 21 have limited their focus to the classification of annotated lesions. Although classifying manually annotated ROIs is an important first step, a fully automated software system must be able to operate on the entire mammogram to provide additional information beyond the known lesions and augment clinical interpretations. If ROI annotations were widely available in mammography databases then established object detection and classification methods such as the region-based convolutional neural network (R-CNN) 22 and its variants 23 , 24 , 25 could be readily applied. However, approaches that require ROI annotations 14 , 26 , 27 , 28 , 29 often cannot be transferred to large mammography databases that lack ROI annotations, which are laborious and costly to assemble. Indeed, few public mammography databases are fully annotated 30 . Other studies 9 , 10 have attempted to train neural networks using whole mammograms without relying on any annotations. However, it is hard to know if such networks were able to locate the clinically significant lesions and base predictions on the corresponding portions of the mammograms. It is well known that deep learning requires large training datasets to be most effective. Thus, it is essential to leverage both the few fully annotated datasets, as well as larger datasets labeled with only the cancer status of each image to improve the accuracy of breast cancer classification algorithms.

Pre-training is a promising method to address the problem of training a classifier when the ideal large and complete training datasets are not available. For example, Hinton et al . 31 used layer-wise pre-training to initialize the weight parameters of a deep belief net (DBN) with three hidden layers and then fine-tuned it for classification. They found that pre-training improved the training speed as well as the accuracy of handwritten digit recognition. Another popular training method is to first train a deep learning model on a large database such as the ImageNet 32 and then fine-tune the model for another task. Although the specific task may not be related to the initial training dataset, the model’s weight parameters are already initialized for recognizing primitive features, such as edges, corners and textures, which can be readily used for a different task. This often saves training time and improves the model’s performance 33 .

In this study, we propose an “end-to-end” approach in which a model to classify local image patches is pre-trained using a fully annotated dataset with ROI information. The patch classifier’s weight parameters are then used to initialize the weight parameters of the whole image classifier, which can be further fine-tuned using datasets without ROI annotations. We used a large public digitized film mammography database with thousands of images to develop the patch and whole image classifiers, and then transferred the whole image classifiers to a smaller public FFDM database with hundreds of images. We evaluated various network designs for constructing the patch and whole image classifiers to attain the best performance. The pipeline required to build a whole image classifier is presented here, as well as the pros and cons of different training strategies.

Converting a classifier from recognizing patches to whole images

To perform classification or segmentation on large complex images, a common strategy involves the use of a classifier in sliding window fashion to recognize local patches on an image to generate a grid of probabilistic outputs. This is followed by another process to summarize the patch classifier’s outputs to give the final classification or segmentation result. Such methods have been used to detect metastatic breast cancer using whole slide images of sentinel lymph node biopsies 34 and to segment neuronal membranes in microscopic images 35 . However, this strategy requires two steps that each needs to be optimized separately. Here, we propose a method to combine the two steps into a single step for training on the whole images (Fig. 1 ). Assume we have an input patch \(X\in {{\rm{IR}}}^{{\rm{p}}\times {\rm{q}}}\) and a patch classifier which is a function f so that \(f(X)\in {{\rm{IR}}}^{{\rm{c}}}\) , where the function’s output satisfies f ( X ) i ∈ [0, 1] and \({{\rm{\Sigma }}}_{i=1}^{c}f{(X)}_{i}=1\) and c is the number of classes of the patches. Here, c = 5 and the classes are: benign calcification, malignant calcification, benign mass, malignant mass and background for each patch from a mammogram. Assume the input patch is extracted from an image \(M\in {{\rm{IR}}}^{{\rm{r}}\times {\rm{s}}}\) where p ≪ r and q ≪ s . If the function f represents a convolutional neural network (CNN), then f can be applied to M without changing the network parameters so that \(f(M)\in {{\rm{IR}}}^{{\rm{u}}\times {\rm{v}}\times {\rm{c}}}\) , where u > 1 and v > 1 depend on the image size and the stride of the patch classifier. This is possible because of the weight sharing and locality properties of a CNN 36 . If the function f represents a different class of neural networks, such as the multilayer perceptron (MLP), then this becomes infeasible because a MLP requires the input to be fixed. Therefore, after changing the input from X to M , we have a u × v grid of probabilistic outputs of c classes (referred to as “heatmap”) instead of a single output of c classes. Hence the heatmap has a size of u × v × c . More layers can then be added on top of the heatmap to transform the outputs and connect with the final classification output of the image. Adding a convolutional layer on top of the patch classifier’s outputs turns the entire patch classifier into a filter and enlarges its receptive field. For example, if the patch classifier has a receptive field of 224 × 224 with a stride = 32, adding a 3 × 3 convolutional layer on top of it increases each side of the receptive field to 224 + (3 − 1) × 32 = 228. Thus, the top layers effectively use the patch classifier to “scan” the whole image, looking for cues of cancerous lesions and extracting higher level features that can finally be used for whole image classification. Using function g to represent the top layers, the whole image classification function can be written as \(h(M)=g(f(M))\in {{\rm{IR}}}^{{\rm{d}}}\) , where d is the number of classes of the whole image. Typically, d = 2 represents the two classes we want to predict: malignant and nonmalignant (benign or normal).

Converting a patch classifier to an end-to-end trainable whole image classifier using an all convolutional design. The function f was first trained on patches and then refined on whole images. We evaluated whether removing the heatmap improved information flow from the bottom layers of the patch classifier to the top convolutional layers in the whole image classifier. The magnifying glass shows an enlarged version of the heatmap. This figure is best viewed in color.

The function h accepts whole images as input and produces labels at the whole image level. Therefore, it is end-to-end trainable, providing two advantages over the two-step approach. First, the entire network can be jointly trained, avoiding sub-optimal solutions from each step; Second, the trained network can be transferred to another dataset without explicit reliance on ROI annotations. Large mammography databases with ROI annotations are rare and expensive to obtain. The largest public database with ROI annotations for digitized film mammograms – DDSM 37 – contains several thousand images with pixel-level annotations, which can be exploited to train a patch classifier f . Once the patch classifier is converted into a whole image classifier h , it can be fine-tuned on other databases using only image-level labels. This approach allows us to significantly reduce the requirement for ROI annotations, and has many applications in medical imaging in addition to breast cancer detection on screening mammograms.

Network design

A modern CNN is typically constructed by stacking convolutional layers on top of the input, followed by one or more fully connected (FC) layers to join with the classification output. Max pooling layers are often used amid convolutional layers to improve translational invariance and to reduce feature map size. In this study, two popular CNN structures are compared: the VGG network 38 and the residual network (Resnet) 39 . Consecutive network layers can be naturally grouped into “blocks” so that the feature map size is reduced (typically by a factor of 2) either at the beginning or at the end of a block but stays the same elsewhere in the block. For example, a “VGG block” is a stack of several 3 × 3 convolutional layers with the same depth followed by a 2 × 2 max pooling layer that reduces the feature map size by a factor of 2. Although other filter sizes can be used, 3 × 3 convolution and 2 × 2 max pooling are widely used, and employed throughout this study unless otherwise stated. Therefore, a VGG block can be represented by the pattern of N × K , where N represents the depth of each convolutional layer and K represents the number of convolutional layers. A “Resnet block” uses stride = 2 in the first convolutional layer instead of 2 × 2 max pooling to reduce feature map size at the beginning of the block, followed by the stacking of several convolutional layers. We use the “bottleneck design 39 ” which consists of repeated units of three convolutional layers that have filter sizes of 1 × 1, 3 × 3 and 1 × 1, respectively. A key feature of the Resnet block is that a shortcut is made between the two ends of each unit so that the features are directly carried over and therefore each unit can focus on learning the “residual” information 39 . Batch normalization (BN) is used in every convolutional layer in the Resnet, which is known to speedup convergence and also has a regularization effect 40 . A Resnet block can be represented by the pattern of [ L − M − N ] × K , where L , M and N represent the depths of the three convolutional layers in a unit and K represents the number of units. Here, the 16-layer VGG network (VGG16) and the 50-layer Resnet (Resnet50) are used as patch classifiers. The original design of the VGG16 38 consisted of five VGG blocks followed by two FC layers. To be consistent with the Resnet50, we replaced the two FC layers with a global average pooling layer which calculates the average activation of each feature map for the output of the last VGG block. For example, if the output of the last VGG block has a size of 7 × 7 × 512 (height × width × channel), after the global average pooling layer the output becomes 512. This output is then connected to the classification output with a FC layer.

A straightforward approach to construct a whole image classifier from a patch classifier involves flattening the heatmap and connecting it to the image’s classification output using FC layers. To increase the model’s translational invariance to the patch classifier’s output, a max pooling layer can be used after the heatmap. Further, a shortcut can be made between the heatmap and the output to make the training easier. The heatmap results directly from the patch classifier’s output which uses the softmax activation:

However, the softmax activation diminishes gradients for large inputs, which may impede gradient flow when it is used in an intermediate layer. Therefore, the rectified linear units (ReLU) can be used instead:

In the following, when we refer to the heatmap in a whole image classifier, the activation is always assumed to be ReLU unless otherwise stated.

We further propose to use convolutional layers as top layers, which preserve spatial information. Two blocks of convolutional layers (VGG or Resnet) can be added on top of the patch classifier layers, followed by a global average pooling layer and then the image’s classification output (Fig. 1 ). Therefore, this design creates an “all convolutional” network for whole image classification. As Fig. 1 shows, the heatmap abruptly reduces the depth of the feature map between the patch classifier layers and the top layers, which may cause information loss in the whole image classification. Therefore, we also evaluated the results when the heatmap is removed entirely from the whole image classifier to allow the top layers to fully utilize the features extracted from the patch classifier.

Computational environment

All experiments in this study were carried out on a Linux workstation equipped with an NVIDIA 8 GB Quadro M4000 GPU card.

Developing patch and whole image classifiers on CBIS-DDSM

Setup and processing of the dataset.

The DDSM 37 contains digitized film mammograms in a lossless-JPEG format that is now obsolete. We used a later version of the database called CBIS-DDSM 41 which contains images that are converted into the standard DICOM format. The dataset which consisted of 2478 mammography images from 1249 women was downloaded from the CBIS-DDSM website, and included both craniocaudal (CC) and mediolateral oblique (MLO) views for most of the exams. Each view was treated as a separate image in this study. We randomly split the CBIS-DDSM dataset 85:15 at the patient level to create independent training and test sets. The training data was further split 90:10 to create an independent validation set. The splits were done in a stratified fashion to maintain the same proportion of cancer cases in the training, validation and test sets. The total numbers of images in the training, validation and testing sets were: 1903, 199 and 376, respectively.

The CBIS-DDSM database contains the pixel-level annotations for the ROIs and their pathologically confirmed labels: benign or malignant. It further labels each ROI as a calcification or mass. Most mammograms contained only one ROI. All mammograms were converted into PNG format and downsized to 1152 × 896 using interpolation; no image cropping was performed. The downsizing was motivated by the limitation of GPU memory size. Two patch datasets were created by sampling image patches from ROIs and background regions. All patches had the same size of 224 × 224, which were large enough to cover most of the ROIs annotated. The first dataset (S1) consisted of sets of patches in which one was centered on the ROI and one is a random background patch from the same image. The second dataset (S10) consisted of 10 patches randomly sampled from around each ROI, with a minimum overlapping ratio of 0.9 with the ROI and inclusion of some background, to more completely capture the potentially informative region; and an equal number of background patches from the same image. All patches were classified into one of the five categories: background, malignant mass, benign mass, malignant calcification and benign calcification.

Network training

Training a whole image classifier was achieved in two steps. The first step was to train a patch classifier. We compared the networks with pre-trained weights using the ImageNet 32 database to those with randomly initialized weights. In a pre-trained network, the bottom layers represent primitive features that tend to be preserved across different tasks, whereas the top layers represent higher-order features that are more related to specific tasks and require further training. Using the same learning rate for all layers may destroy the features that were learned in the bottom layers. To prevent this, a 3-stage training strategy was employed in which the parameter learning is frozen for all but the final layer and progressively unfrozen from the top to the bottom layers, while simultaneously decreasing the learning rate. The 3-stage training strategy on the S10 patch set was as follows:

Set learning rate to 10 −3 and train the last layer for 3 epochs.

Set learning rate to 10 −4 , unfreeze the top layers and train for 10 epochs, where the top layer number is set to 46 for Resnet50 and 11 for VGG16.

Set learning rate to 10 −5 , unfreeze all layers and train for 37 epochs for a total of 50 epochs.

In the above, an epoch was defined as a sweep through the training set. For the S1 patch dataset, the total number of epochs was increased to 200 because it was much smaller and less redundant than the S10 patch dataset. For randomly initialized networks a constant learning rate of 10 −3 was used. Adam 42 was used as the optimizer and the batch size was set to be 32. The sample weights were adjusted within each batch to balance the five classes.

The second step was to train a whole image classifier converted from the patch classifier (Fig. 1 ). A 2-stage training strategy was employed to first train the newly added top layers (i.e. function g ) and then train all layers (i.e. function h ) with a reduced learning rate, which was as follows:

Set learning rate to 10 −4 , weight decay to 0.001 and train the newly added top layers for 30 epochs.

Set learning rate to 10 −5 , weight decay to 0.01 and train all layers for 20 epochs for a total of 50 epochs.

We found that the VGG-based image classifiers showed sign of continuing improvement towards the end of the 50 epochs, while the Resnet-based image classifiers had already converged. To be fair for the VGG-based image classifiers, we continued to train them with 200 additional epochs. Due to GPU memory limits, a batch size of 2 was used.

The average gray scale value of the whole image training set was subtracted from both patch and whole image datasets in training. No other preprocessing was applied. To improve the generalization of final models, data augmentation was performed using the following random transformations: horizontal and vertical flips, rotation in [−25, 25] degrees, zoom in [0.8, 1.2] ratio and intensity shift in [−20, 20] pixel values.

Development of patch classifiers

Table 1 shows the accuracy of the classification of image patches into 5 classes using Resnet50 and VGG16 in the CBIS-DDSM test set. A bootstrapping method with 3000 runs was used to derive the 95% confidence intervals for patch classification accuracy. The S10 set was more difficult to classify than the S1 set because it contained patches sampled from around ROIs, rather than centered on the ROI, that were more difficult to distinguish from background regions. On the S1 set, both randomly initialized and pre-trained Resnet50 classifiers achieved similar accuracy but the pre-trained network converged after half as many epochs as the randomly initialized one. On the S10 set, the pre-trained Resnet50 outperformed the randomly initialized one by a large margin, achieving an accuracy [95% confidence interval (CI)] of 0.89 [0.88, 0.90]. These results showed that pre-training can greatly help network convergence and performance. Therefore, pre-trained networks were used for the rest of the study. The accuracy of the pre-trained VGG16 (0.84 [0.83, 0.85]) on the S10 set was lower than that of the pre-trained Resnet50.

To further characterize performance, confusion matrix analyses were conducted on the Resnet50 and VGG16 patch classifiers in the S10 test set (Fig. 2 ). For both patch classifiers, all five classes were predicted into the correct categories with the highest probability. The background class was easiest, and malignant calcifications hardest to classify. Malignant calcifications were most likely to be misclassified as benign calcification, followed by malignant mass. Benign calcifications were most likely to be misclassified as background, followed by malignant calcification. Malignant masses were most likely to be misclassified as benign masses, while benign masses were most likely to be misclassified as malignant masses or background, depending on the patch classifier.

Confusion matrix analysis of 5-class patch classification for Resnet50 ( a ) and VGG16 ( b ) in the S10 test set. The matrices are normalized so that each row sums to one. This figure is best viewed in color.

Converting patch to whole image classifiers

Using pre-trained Resnet50 and VGG16 patch classifiers, we tested several different configurations for the top layers of the whole image classifiers. We also evaluated removal of the heatmap and adding two Resnet or VGG blocks on top of the patch classifier layers, followed by a global average pooling layer and the classification output. Model performance was assessed by computing the per-image AUCs on the independent test set.

Resnet-based networks: To evaluate whether the patch classifiers trained on the S1 and S10 datasets are equally useful for whole image classification, the Resnet50 patch classifiers were used. In the original design of the Resnet50 39 , L ≡ M , N is four times L and K is 3 or more; the L of the current block is also double of the L of the previous block. However, we found this design to exceed our GPU memory limit when it is used for the top layers of the whole image classifier. In the initial experiments, we used instead the same configuration of [512 − 512 − 2048] × 1 for two Resnet blocks on top of the patch classifier. A bootstrapping method with 3000 runs was used to derive 95% confidence intervals for AUCs and AUC differences. The whole image classifier trained using the S10 set (mean AUC = 0.85) performed much better than that trained using the S1 set (mean AUC = 0.63) (Table 2 ), despite its poorer patch classification accuracy (Table 1 ). The S10 dataset contains more information about the ROIs as well as their adjacent regions and other background regions on the image than the S1 dataset, which allows a patch classifier to extract more features that can be important for whole image classification. To test this hypothesis, we created another patch set (referred to as S1g) with one patch each from the ROI and background but a large patch size of 448 × 448 to include the surrounding area. The patch classification accuracy in S1g was much lower than that in S10 (Table 1 ). However, the image classification accuracy was similar for models trained on S1g and S10 (Table 2 ) with an estimated AUC difference [95% confidence interval] of −0.023 [−0.061, 0.016] supporting the hypothesis that the background regions contain useful information. For the rest of the study, only patch classifiers trained on the S10 dataset were used. Varying the configuration by using two Resnet blocks of [512 − 512 − 1024] × 2 yielded a mean AUC of 0.86, while reducing the depths and K of the two Resnet blocks to: [256 − 256 − 256] × 1 and [128 − 128 − 128] × 1 did not significantly decrease the AUC (Table 2 ). This result showed that the depths of the Resnet blocks were relatively uncorrelated with the performance of the whole image classifiers.

VGG-based networks: We tested whole image classifiers using VGG16 as the patch classifier and VGG blocks as the top layers. BN was used for the VGG blocks on the top except for the VGG16 patch classifier because it is a pre-trained network which cannot be modified. The VGG-based whole image classifiers performed similarly to the Resnet-based ones but took longer to achieve the same performance level (Table 3 ). In contrast to the Resnet, using more convolutional layers and higher depths in VGG blocks led to poorer performance: using two VGG blocks of 256 × 1 and 128 × 1 (mean AUC = 0.85) performed better than two VGG blocks of 512 × 3 (mean AUC = 0.81) with an AUC difference of 0.041 [0.011, 0.071]. Reducing the depths further to 128 and 64 did not further improve the AUC. This result illustrates that controlling model complexity (i.e., #layers and depths) is important for achieving good performance with the VGG-based networks, which are more likely to overfit.

Hybrid networks: We also created two “hybrid” networks by adding the VGG top layers that performed the best (two VGG blocks of 256 × 1 and 128 × 1) on top of the Resnet50 patch classifier; and the Resnet top layers that performed the best (two Resnet blocks of the same configuration of [512 − 512 − 1024] × 2) on top of the VGG16 patch classifier. The two hybrid networks achieved mean AUCs of 0.87 and 0.85, respectively, and were among the best performing models (Tables 2 and 3 ).

Augmented prediction and model averaging: Augmented prediction was implemented by horizontally and vertically flipping an image to obtain four images and taking an average of the four images’ scores. This technique increased the AUC (referred to as A-AUC) for each model by 0.01–0.03 (Tables 2 and 3 ), although only some of the models showed significant increase based on the 95% confidence intervals of AUC differences (Table S1 ). The four best performing models were combined into an ensemble model by taking the average of their augmented prediction scores. Two of the four best models used Resnet50 and VGG16 as patch classifiers and Resnet and VGG blocks as top layers, respectively (referred to as Resnet-Resnet and VGG-VGG); and the remaining two were hybrid models (referred to as Resnet-VGG and VGG-Resnet). Figure 3a shows the Receiver Operating Characteristic (ROC) curves of the four best models and the ensemble model, which yielded an AUC of 0.91. Because the clinical significance of a false negative (FN) is higher than that of a false positive (FP), a clinically useful system should not have significantly lower sensitivity than the current standard of care. Therefore, we evaluated model performance using a sensitivity of 86% as a benchmark based upon the estimated average for U.S. radiologists 3 , and determined the model specificity to be 80.1% at a similar sensitivity of 86.1%.

ROC curves for the four best individual models and ensemble model on the CBIS-DDSM ( a ) and INbreast ( b ) test sets. This figure is best viewed in color.

Saliency map and error analysis: Saliency maps were created using the Resnet-VGG model (Fig. 4a–c ), which showed the gradients of the input image with respect to the cancer class output. We used the guided back-propagation approach 43 that calculates only positive gradients for positive activations. A saliency map illustrates which area of the input image is considered to be responsible for the cancer prediction by a whole image classifier. Figure 4a shows the saliency map of a true positive (TP) image where the identified area is in or close to the malignant ROI. This shows that the image classifier was able to correctly locate the cancerous region on which its decision was based. Figure 4b shows a typical FP image where the identified region is located in a benign ROI that resembles a malignant ROI. Figure 4c shows a typical FN image where the malignant ROI is difficult to discern and no response passes the low cutoff.

Saliency maps of TP ( a ), FP ( b ) and FN ( c ) image classifications. The outlines represent the regions of interest annotated by the radiologist, and biopsy-confirmed to contain either malignant (blue) or benign (green) tissue. The red dots represent the gradients of the input image with respect to the cancer class output. The gradients were rescaled to be within [0, 1] and a low cutoff of 0.06 was used to remove background noise. Heatmaps ( d ) of the four non-background classes for input image ( a ). The colors of the heatmaps represent the activation values after ReLU. This figure is best viewed in color.

Combining the CC and MLO views for prediction: Combining the CC and MLO views may increase performance because each view can contain unique information. After removing the samples where only a single view was available, 90% of the test set remained for analysis of both views from each of 169 breasts. We used a simple approach of taking the average score of the two views. A breast-level bootstrapping method was used (3000 runs) to compare two-view vs. single-view AUCs for the four best models above: Resnet-Resnet, Resnet-VGG, VGG-VGG and VGG-Resnet. The mean AUC differences were 0.030 [0.018, 0.042], 0.027 [0.016, 0.037], 0.040 [0.028, 0.051] and 0.048 [0.032, 0.064], respectively. Thus, using two views when available significantly increased the AUCs in comparison to single views for all the models tested here.

Max-pooling, shortcut and FC layers: We tested an alternative design by using the heatmap followed by a max-pooling and two FC layers, including a shortcut between the heatmap and the classification output. The Resnet50 and VGG16 patch classifiers were used. The FC layer sizes were chosen to gradually reduce the layer outputs. When the pooling size increased from 1 × 1 (i.e. no pooling) to 5 × 5, the AUCs did not show significant changes with the exception of pooling size 1 × 1 for the Resnet50 patch classifier, in which case the AUC was significantly lower than the others (Tables 2 and 3 ). The best mean AUC for these models was 0.74, falling short of the performance of the all convolutional models.

Evaluation of the heatmap in all convolutional networks: To test our hypothesis that the heatmap can cause information loss in the whole image classification network, we inserted a heatmap in the Resnet-based whole image classifier with two [512 − 512 − 1024] × 2 blocks as top layers. The heatmap inserted was a 1 × 1 convolutional layer that reduces the number of filters from the previous convolutional layer (2048) to 5, which corresponds to the 5 classes of the patch classifier. To facilitate the back-propagation of gradients, ReLU was used to replace the softmax activation in the heatmap. Figure 4d shows an example heatmap that provides a rough segmentation of the input image; the top layers then use the segmentation to classify the whole image. This model achieved a mean AUC of 0.80 (Table 2 ), which was significantly lower than that of the same classifier without the heatmap with an AUC difference of −0.050 [−0.088, −0.012]. To exclude the possibility that the top layers were overfit due to the shallow depth of the heatmap, another model with reduced complexity using two Resnet blocks of [64 − 64 − 256] × 2 and [128 − 128 − 512] × 2 was tested, which achieved a similar mean AUC of 0.81 (AUC difference of −0.044 [−0.075, −0.012]). These results indicated that removing the heatmap was beneficial to the whole image classification networks.

Comparison to prior two-step approach: Finally, for comparison we tested a previously reported approach 34 that used a probability cutoff to binarize the heatmap into a binary image that represents each pixel as background (0) or ROI (1). This was repeated for each of the 4 foreground classes. We then extracted regional features (such as area, major axis length and mean intensity) from the ROIs of the binary images and trained a random forest classifier (#trees = 500, max depth = 9, min samples split = 300) on the regional features. The Resnet50 patch classifier was used and the softmax activation was used in the heatmap to obtain the probabilities for the 5 classes. Four cutoffs—0.3, 0.5, 0.7 and 0.9 were used to binarize the heatmaps and the regional features were combined. This approach achieved an AUC of 0.73, and was inferior to the all convolutional models.

Transfer learning for whole image classification on INbreast

The INbreast 30 dataset is a public database containing more recently acquired FFDM images. These images have different intensity profiles compared with digitized film mammograms from the CBIS-DDSM, as illustrated by example images from the two databases (Fig. 5 ). Therefore, INbreast provides an excellent opportunity to test the transferability of a whole image classifier across mammography platforms. The INbreast database contains 115 patients and 410 mammograms including both CC and MLO views. We analyzed each view separately like above. The INbreast database includes radiologists’ BI-RADS 44 assessment categories which are defined as follows: 0, incomplete exam; 1, no findings; 2, benign; 3, probably benign; 4, suspicious; 5, highly suggestive of malignancy; and 6, known biopsy-proven cancer. Because the database lacks reliable pathological confirmation of malignancy, we assigned all images with BI-RADS 1 and 2 as negative; BI-RADS 4, 5 and 6 as positive; and excluded 12 patients and 23 images with BI-RADS 3 since this assessment is typically not given at screening. We split the dataset 70:30 into training and test sets at the patient-level while maintaining the same ratio of positive and negative images. The total numbers of images in the training and test sets were 280 from 72 women and 107 from 31 women, respectively. We used the same processing steps on the INbreast images as for the CBIS-DDSM images.

Representative examples of a digitized film mammogram from CBIS-DDSM and a digital mammogram from INbreast.

Effectiveness and efficiency of transfer learning

Although the INbreast database contains ROI annotations, they were ignored to test the transferability of the whole image classifier across different mammography platforms and databases. The four best performing models (See Tables 2 and 3 ) were directly fine-tuned on the INbreast training set and evaluated by computing per-image AUCs on the test set. Adam 42 was used as the optimizer and the learning rate was set at 10 −5 . The number of epochs was set at 200 and the weight decay at 0.01. All four models achieved an AUC of 0.95 (Table 4 ). The ensemble model based on averaging the four best models improved the AUC to 0.98 with a corresponding sensitivity of 86.7% and specificity of 96.1% (Fig. 3b ).

We also sought to determine the minimum amount of data required to fine-tune a whole image classifier to a satisfactory level of performance, to guide future studies in minimizing the resource intensive process of obtaining labels. Training subsets with 20, 30, 40, 50 and 60 patients were sampled for fine-tuning and model performance was evaluated using the same test set (Table 4 ). With as few as 20 patients or 79 images, the four models already attained AUCs between 0.87 and 0.92. The AUCs quickly approached the maximum as the training subset size increased. These results suggest that the intensive part of learning is to recognize the shapes and textures of the benign and malignant ROIs and normal tissues, and that adjusting to different intensity profiles found in different mammography datasets may require much less data. Importantly, these results clearly demonstrate that the end-to-end training approach can be successfully used to fine-tune a whole image classifier using additional small training sets with image-level labels, greatly reducing the burden of training set construction for multiple different mammography platforms.

This study shows that accurate classification of screening mammograms can be achieved with a deep learning model trained in an end-to-end fashion that relies on clinical ROI annotations only in the initial stage. Once the whole image classifier is built, it can be fine-tuned using additional datasets that lack ROI annotations, even if the pixel intensity distributions differ as is often the case for datasets assembled from heterogeneous mammography platforms. These findings indicate that deep learning algorithms can improve upon classic commercial CAD systems, such as iCAD SecondLook 1.4 and R2 ImageChecker Cenova 1.0, that are not deep learning based and have been reported to attain an average AUC of 0.72 6 . Our all convolutional networks trained using an end-to-end approach have highly competitive performance and are more generalizable across different mammography platforms compared with previous deep learning methods that have achieved AUCs in the range of 0.65–0.97 on the DDSM and INbreast databases, as well as in-house datasets 12 . Two recent studies reported that a new commercial CAD system, Transpara 1.4.0, attained an AUC of 0.89 when used to support radiologists 16 and 0.84 in standalone mode 15 . This commercial CAD used CNNs trained using the lesion annotations from 9000 mammograms with cancer to generate scores at the patch level; the scores for all detected regions were then combined into a score at the examination level. To our knowledge, the commercial CAD cannot easily be fine-tuned on different mammography datasets without lesion annotations. Our approach has the advantage of requiring only image-level labels for fine-tuning once the whole image classifier is built to facilitate scaling to larger datasets and transferring to new mammography systems as they rapidly evolve.

Two recent studies 45 , 46 developed deep learning based methods for breast cancer classification using film and digital mammograms, which were end-to-end trainable. Both studies used multi-instance learning (MIL) and modified the whole image classifier cost functions to satisfy the MIL criterion. In contrast to our approach, neither study utilized ROI annotations to train the patch classifiers first and the AUCs were lower than reported in this study. We found that the quality of the patch classifiers is critical to the accuracy of the whole image classifiers. This was supported by two lines of evidence. First, the whole image classifier based on the S10 patch set performed far better than the one based on the S1 patch set because the S10 patch set contained more information about the background than the S1 patch set. Second, it took much longer for the VGG16-based whole image classifiers to achieve the same performance as the Resnet50-based classifiers because the VGG16 was less accurate than Resnet50 in patch classification.

We also found that the accuracy of whole image classification was improved by sampling more or larger patches to include neighboring regions around the ROI and additional background regions. However, the computational burden increases linearly with the number or size of patches sampled and the performance gain may quickly diminish. Using larger patches can decrease the signal-to-noise ratio, as indicated by the lower patch classification accuracy using the S1g vs. S10 patch sets. Using larger patches also requires higher GPU memory, which may limit network choices. The saliency map analysis showed that our whole image networks were able to correctly identify the ROIs and use the information therein to predict cancer. It also showed that classification errors typically occurred in difficult cases, such as benign lesions with malignant features, or malignant lesions that were difficult to distinguish from background. Further research is needed to investigate how to sample local patches more efficiently, perhaps by augmenting the training data with difficult cases and focusing on the patches that are more likely to be misclassified. This could help overcome the computational burden of training more accurate classifiers.

Although the VGG-based image classifiers were more prone to overfitting and required longer training, the performance of VGG-based and Resnet-based image classifiers was comparable. The fact that the ensemble model performed better than any of the individual models also suggests that the VGG-based and Resnet-based classifiers can complement each other. Moreover, the VGG16 (without the two FC layers), with 15 million weight parameters, is a much smaller network than the Resnet50, with 24 million weight parameters. Having fewer parameters reduces memory requirements and training time per epoch, which is important when computational resources are limited. The Resnet is a more recently developed deep learning method, which is enhanced by shortcuts and batch normalization, both techniques that may help the network train faster and generalize better. The same techniques can be used in the VGG-based networks as well in future work, which may improve the VGG-based classifiers.

This study had some limitations. Mammograms were downsized to fit the available GPU (8 GB). As more GPU memory becomes available, future studies will be able to train models using larger image sizes, or retain the original image resolution without the need for downsizing. Retaining the full resolution of modern digital mammography images will provide finer details of the ROIs and likely improve performance. Although the CBIS-DDSM dataset included pathological confirmation of all cancer diagnoses, the INbreast dataset did not. Therefore, we used the radiologists’ BI-RADS assessments to assign labels to the images in the INbreast dataset, which has the limitation of reproducing radiologists’ impressions instead of discovering new characteristics of malignant lesions. It would be of interest in future work to include interval breast cancers that were missed by radiologists, to help train algorithms to detect more subtle signs of malignancy that may not be visually apparent. Finally, the CBIS-DDSM and INbreast datasets were not nationally representative samples and performance metrics in these datasets are not directly comparable to national estimates of radiologists’ sensitivity and specificity. Future direct comparisons between algorithms and radiologists will be facilitated by public sharing of the code and greater availability of representative benchmarking datasets.

In conclusion, our study demonstrates that deep learning models trained in an end-to-end fashion can be highly accurate and potentially readily transferable across diverse mammography platforms. Deep learning methods have enormous potential to further improve the accuracy of breast cancer detection on screening mammography as the available training datasets and computational resources expand. Our approach may assist future development of superior CAD systems that could be used to help prioritize the most suspicious cases to be read by a radiologist, or as an automatic second reader after making an initial independent interpretation. Our end-to-end approach can also be applied to other medical imaging problems where ROI annotations are scarce.

Data Availability

A preprint version of this article is available at: https://arxiv.org/abs/1708.09427 .

American Cancer Society. How Common Is Breast Cancer? https://www.cancer.org/cancer/breast-cancer/about/howcommon-is-breast-cancer.html (2018).

Oeffinger, K. C. et al . Breast Cancer Screening for Women at Average Risk: 2015 Guideline Update From the American Cancer Society. JAMA 314 , 1599–1614 (2015).

Article CAS Google Scholar

Lehman, C. D. et al . National Performance Benchmarks for Modern Screening Digital Mammography: Update from the Breast Cancer Surveillance Consortium. Radiol. 283 , 49–58 (2016).

Article Google Scholar

Elter, M. & Horsch, A. CADx of mammographic masses and clustered microcalcifications: A review. Med. Phys. 36 , 2052–2068 (2009).

Fenton, J. J. et al . Influence of Computer-Aided Detection on Performance of Screening Mammography. New Engl. J. Medicine 356 , 1399–1409 (2007).

Cole, E. B. et al . Impact of Computer-Aided Detection Systems on Radiologist Accuracy With Digital Mammography. Am. J. Roentgenol. 203 , 909–916 (2014).

Lehman, C. D. et al . Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection. JAMA Intern. Medicine 175 , 1828–1837 (2015).

LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nat. 521 , 436–444 (2015).

Article ADS CAS Google Scholar

Aboutalib, S. S. et al . Deep Learning to Distinguish Recalled but Benign Mammography Images in Breast Cancer Screening. Clin . Cancer Res . (2018).

Kim, E.-K. et al . Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study. Sci. Reports 8 , 2762 (2018).

Article ADS Google Scholar

Hamidinekoo, A., Denton, E., Rampun, A., Honnor, K. & Zwiggelaar, R. Deep learning in mammography and breast histology, an overview and future trends. Med. Image Analysis 47 , 45–67 (2018).

Burt, J. R. et al . Deep learning beyond cats and dogs: Recent advances in diagnosing breast cancer with deep neural networks. The Br . J . Radiol . 20170545 (2018).

Kooi, T. et al . Large scale deep learning for computer aided detection of mammographic lesions. Med. Image Analysis 35 , 303–312 (2017).

Agarwal, R., Diaz, O., Lladó, X., Yap, M. H. & Martí, R. Automatic mass detection in mammograms using deep convolutional neural networks. J. Med. Imaging 6 , 031409 (2019).

Rodriguez-Ruiz, A. et al . Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists. JNCI: J . Natl . Cancer Inst (2019).

Rodríguez-Ruiz, A. et al . Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System. Radiol. 290 , 305–314 (2018).

Jamieson, A. R., Drukker, K. & Giger, M. L. Breast image feature learning with adaptive deconvolutional networks. Proc. SPIE 8315, (6–13 (2012).

Google Scholar

Arevalo, J., González, F. A., Ramos-Pollán, R., Oliveira, J. L. & Lopez, M. A. G. Convolutional neural networks for mammography mass lesion classification. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society ( EMBC ), 797–800, https://doi.org/10.1109/EMBC.2015.7318482 (2015).

Arevalo, J., González, F. A., Ramos-Pollán, R., Oliveira, J. L. & Guevara Lopez, M. A. Representation learning for mammography mass lesion classification with convolutional neural networks. Comput. Methods Programs Biomed. 127 , 248–257 (2016).

Lévy, D. & Jain, A. Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks. arXiv preprint arXiv:1612 . 00542 (2016).

Dhungel, N., Carneiro, G. & Bradley, A. P. The Automated Learning of Deep Features for Breast Mass Classification from Mammograms. In Ourselin, S., Joskowicz, L., Sabuncu, M. R., Unal, G. & Wells, W. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016: 19th International Conference , Athens , Greece , October 17 – 21 , 2016 , Proceedings , Part II , 106–114 (Springer International Publishing, Cham, 2016).

Girshick, R., Donahue, J., Darrell, T. & Malik, J. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. In Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition , CVPR’ 14, 580–587 (IEEE Computer Society, Washington, DC, USA, 2014).

Girshick, R. Fast r-cnn. In Proceedings of the IEEE International Conference on Computer Vision , 1440–1448 (2015).

Ren, S., He, K., Girshick, R. & Sun, J. Faster R-CNN: Towards real-time object detection with region proposal networks. In Advances in Neural Information Processing Systems , 91–99 (2015).

Dai, J., Li, Y., He, K. & Sun, J. R-FCN: Object Detection via Region-based Fully Convolutional Networks. arXiv:1605 . 06409 [ cs ], 1605.06409 (2016).

Dhungel, N., Carneiro, G. & Bradley, A. P. Automated Mass Detection in Mammograms Using Cascaded Deep Learning and Random Forests. In 2015 International Conference on Digital Image Computing: Techniques and Applications ( DICTA ), 1–8, https://doi.org/10.1109/DICTA.2015.7371234 (2015).

Ertosun, M. G. & Rubin, D. L. Probabilistic visual search for masses within mammography images using deep learning. In 2015 IEEE International Conference on Bioinformatics and Biomedicine ( BIBM ), 1310–1315, https://doi.org/10.1109/BIBM.2015.7359868 (2015).

Akselrod-Ballin, A. et al . A Region Based Convolutional Network for Tumor Detection and Classification in Breast Mammography. In Carneiro, G. et al . (eds) Deep Learning and Data Labeling for Medical Applications: First International Workshop , LABELS 2016 , and Second International Workshop , DLMIA 2016 , Held in Conjunction with MICCAI 2016 , Athens , Greece , October 21 , 2016 , Proceedings , 197–205 (Springer International Publishing, Cham, 2016).

Ribli, D., Horváth, A., Unger, Z., Pollner, P. & Csabai, I. Detecting and classifying lesions in mammograms with Deep Learning. arXiv:1707 . 08401 [ cs ], 1707.08401 (2017).

Moreira, I. C. et al . INbreast: Toward a Full-field Digital Mammographic Database. Acad. Radiol. 19 , 236–248 (2012).

Hinton, G. E., Osindero, S. & Teh, Y.-W. A fast learning algorithm for deep belief nets. Neural Comput. 18 , 1527–1554 (2006).

Article MathSciNet Google Scholar

Russakovsky, O. et al . ImageNet Large Scale Visual Recognition Challenge. Int. J. Comput. Vis. 115 , 211–252 (2015).

Oquab, M., Bottou, L., Laptev, I. & Sivic, J. Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 1717–1724 (2014).

Wang, D., Khosla, A., Gargeya, R., Irshad, H. & Beck, A. H. Deep Learning for Identifying Metastatic Breast Cancer. arXiv:1606 . 05718 [ cs , q-bio ], 1606.05718 (2016).

Ciresan, D., Giusti, A., Gambardella, L. M. & Schmidhuber, J. Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images. In Pereira, F., Burges, C. J. C., Bottou, L. & Weinberger, K. Q. (eds) Advances in Neural Information Processing Systems 25 , 2843–2851 (Curran Associates, Inc., 2012).

LeCun, Y. et al . Convolutional networks for images, speech, and time series. The handbook brain theory neural networks 3361 , 1995 (1995).

Heath, M., Bowyer, K., Kopans, D., Moore, R. & Kegelmeyer, W. The Digital Database for Screening Mammography. In Yaffe, M. (ed.) Proceedings of the Fifth International Workshop on Digital Mammography , 212–218 (Medical Physics Publishing, 2001).

Simonyan, K. & Zisserman, A. Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409 . 1556 [ cs ], 1409.1556 (2014).

He, K., Zhang, X., Ren, S. & Sun, J. Deep Residual Learning for Image Recognition. arXiv:1512 . 03385 [ cs ], 1512.03385 (2015).

Ioffe, S. & Szegedy, C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502 . 03167 [ cs ], 1502.03167 (2015).

Lee, R. S., Gimenez, F., Hoogi, A. & Rubin, D. Curated Breast Imaging Subset of DDSM. The Cancer Imaging Arch (2016).

Kingma, D. P. & Ba, J. Adam: A Method for Stochastic Optimization. arXiv:1412 . 6980 [ cs ], 1412.6980 (2014).

Springenberg, J. T., Dosovitskiy, A., Brox, T. & Riedmiller, M. Striving for Simplicity: The All Convolutional Net. arXiv:1412 . 6806 [ cs ], 1412.6806 (2014).

D’Orsi, C., Sickles, E., Mendelson, E. & Morris, E. ACR BI-RADS R Atlas , Breast Imaging Reporting and Data System , 5th edn (American College of Radiology, Reston, Va, 2013).

Zhu, W., Lou, Q., Vang, Y. S. & Xie, X. Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification. arXiv:1705 . 08550 [ cs ], 1705.08550 (2017).

Choukroun, Y. et al . Mammogram Classification and Abnormality Detection from Nonlocal Labels using Deep Multiple Instance Neural Network. In Eurographics Workshop on Visual Computing for Biology and Medicine (2017).

Download references

Acknowledgements

This work was partially supported by the Friedman Brain Institute, the Tisch Cancer Institute (NIH P30CA196521) and a Clinical and Translational Science Award (NIH UL1TR001433). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work was supported in part through the computational resources and staff expertise provided by the Department of Scientific Computing at the Icahn School of Medicine at Mount Sinai.

Author information

Authors and affiliations.

Icahn School of Medicine at Mount Sinai (ISMMS), Department of Neuroscience, New York, 10029, USA

ISMMS, Department of Diagnostic, Molecular, and Interventional Radiology, New York, 10029, USA

Laurie R. Margolies

ISMMS, Department of Population Health Science and Policy and Department of Genetics and Genomic Sciences, New York, 10029, USA

Joseph H. Rothstein & Weiva Sieh

ISMMS, Department of Scientific Computing, New York, 10029, USA

Eugene Fluder

ISMMS, Department of Pathology, New York, 10029, USA

Russell McBride

You can also search for this author in PubMed Google Scholar

Contributions

L.S. developed the algorithm, conceived and conducted the experiments and analyzed the results. L.R.M., J.R., R.M. and W.S. analyzed the results. E.F. helped with the computational resources. All authors contributed to writing and reviewing the manuscript.

Corresponding author

Correspondence to Li Shen .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Shen, L., Margolies, L.R., Rothstein, J.H. et al. Deep Learning to Improve Breast Cancer Detection on Screening Mammography. Sci Rep 9 , 12495 (2019). https://doi.org/10.1038/s41598-019-48995-4

Download citation

Received : 14 May 2019

Accepted : 07 August 2019

Published : 29 August 2019

DOI : https://doi.org/10.1038/s41598-019-48995-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Bibliometric analysis of the application of deep learning in cancer from 2015 to 2023.

Xiaowei Tang

Cancer Imaging (2024)

Are better AI algorithms for breast cancer detection also better at predicting risk? A paired case–control study

Ruggiero Santeramo
Celeste Damiani
Adam R. Brentnall

Breast Cancer Research (2024)

Dual view deep learning for enhanced breast cancer screening using mammography

Samuel Rahimeto Kebede
Fraol Gelana Waldamichael
Zelalem Chimdesa Merga

Scientific Reports (2024)

Deep learning system for true- and pseudo-invasion in colorectal polyps

Charles Ling

Machine vision-based detections of transparent chemical vessels toward the safe automation of material synthesis

Leslie Ching Ow Tiong
Hyuk Jun Yoo
Donghun Kim

npj Computational Materials (2024)

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

Explore articles by subject
Guide to authors
Editorial policies

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Research article
Open access
Published: 01 October 2013

Critical research gaps and translational priorities for the successful prevention and treatment of breast cancer

Suzanne A Eccles 17 ,
Eric O Aboagye 1 ,
Simak Ali 1 ,
Annie S Anderson 2 ,
Jo Armes 7 ,
Fedor Berditchevski 4 ,
Jeremy P Blaydes 3 ,
Keith Brennan 5 ,
Nicola J Brown 6 ,
Helen E Bryant 6 ,
Nigel J Bundred 5 ,
Joy M Burchell 7 ,
Anna M Campbell 2 ,
Jason S Carroll 9 ,
Robert B Clarke 5 ,
Charlotte E Coles 34 ,
Gary JR Cook 7 ,
Angela Cox 6 ,
Nicola J Curtin 10 ,
Lodewijk V Dekker 11 ,
Isabel dos Santos Silva 12 ,
Stephen W Duffy 13 ,
Douglas F Easton 9 ,
Diana M Eccles 3 ,
Dylan R Edwards 15 ,
Joanne Edwards 14 ,
D Gareth Evans 5 ,
Deborah F Fenlon 3 ,
James M Flanagan 1 ,
Claire Foster 3 ,
William M Gallagher 16 ,
Montserrat Garcia-Closas 17 ,
Julia M W Gee 18 ,
Andy J Gescher 28 ,
Vicky Goh 7 ,
Ashley M Groves 8 ,
Amanda J Harvey 33 ,
Michelle Harvie 5 ,
Bryan T Hennessy 20 ,
Stephen Hiscox 18 ,
Ingunn Holen 6 ,
Sacha J Howell 5 ,
Anthony Howell 5 ,
Gill Hubbard 21 ,
Nick Hulbert-Williams 22 ,
Myra S Hunter 7 ,
Bharat Jasani 18 ,
Louise J Jones 13 ,
Timothy J Key 23 ,
Cliona C Kirwan 5 ,
Anthony Kong 23 ,
Ian H Kunkler 24 ,
Simon P Langdon 24 ,
Martin O Leach 17 ,
David J Mann 1 ,
John F Marshall 13 ,
Lesley Ann Martin 17 ,
Stewart G Martin 11 ,
Jennifer E Macdougall 25 ,
David W Miles 7 ,
William R Miller 24 ,
Joanna R Morris 4 ,
Sue M Moss 13 ,
Paul Mullan 26 ,
Rachel Natrajan 17 ,
James PB O’Connor 5 ,
Rosemary O’Connor 27 ,
Carlo Palmieri 31 ,
Paul D P Pharoah 9 ,
Emad A Rakha 11 ,
Elizabeth Reed 29 ,
Simon P Robinson 17 ,
Erik Sahai 32 ,
John M Saxton 15 ,
Peter Schmid 30 ,
Matthew J Smalley 18 ,
Valerie Speirs 19 ,
Robert Stein 8 ,
John Stingl 9 ,
Charles H Streuli 5 ,
Andrew N J Tutt 7 ,
Galina Velikova 19 ,
Rosemary A Walker 28 ,
Christine J Watson 9 ,
Kaye J Williams 5 ,
Leonie S Young 20 &
Alastair M Thompson 2

Breast Cancer Research volume 15 , Article number: R92 ( 2013 ) Cite this article

132k Accesses

292 Citations

69 Altmetric

Metrics details

Introduction

Breast cancer remains a significant scientific, clinical and societal challenge. This gap analysis has reviewed and critically assessed enduring issues and new challenges emerging from recent research, and proposes strategies for translating solutions into practice.

More than 100 internationally recognised specialist breast cancer scientists, clinicians and healthcare professionals collaborated to address nine thematic areas: genetics, epigenetics and epidemiology; molecular pathology and cell biology; hormonal influences and endocrine therapy; imaging, detection and screening; current/novel therapies and biomarkers; drug resistance; metastasis, angiogenesis, circulating tumour cells, cancer ‘stem’ cells; risk and prevention; living with and managing breast cancer and its treatment. The groups developed summary papers through an iterative process which, following further appraisal from experts and patients, were melded into this summary account.

The 10 major gaps identified were: (1) understanding the functions and contextual interactions of genetic and epigenetic changes in normal breast development and during malignant transformation; (2) how to implement sustainable lifestyle changes (diet, exercise and weight) and chemopreventive strategies; (3) the need for tailored screening approaches including clinically actionable tests; (4) enhancing knowledge of molecular drivers behind breast cancer subtypes, progression and metastasis; (5) understanding the molecular mechanisms of tumour heterogeneity, dormancy, de novo or acquired resistance and how to target key nodes in these dynamic processes; (6) developing validated markers for chemosensitivity and radiosensitivity; (7) understanding the optimal duration, sequencing and rational combinations of treatment for improved personalised therapy; (8) validating multimodality imaging biomarkers for minimally invasive diagnosis and monitoring of responses in primary and metastatic disease; (9) developing interventions and support to improve the survivorship experience; (10) a continuing need for clinical material for translational research derived from normal breast, blood, primary, relapsed, metastatic and drug-resistant cancers with expert bioinformatics support to maximise its utility. The proposed infrastructural enablers include enhanced resources to support clinically relevant in vitro and in vivo tumour models; improved access to appropriate, fully annotated clinical samples; extended biomarker discovery, validation and standardisation; and facilitated cross-discipline working.

Conclusions

With resources to conduct further high-quality targeted research focusing on the gaps identified, increased knowledge translating into improved clinical care should be achievable within five years.

Globally, breast cancer is the most frequently diagnosed cancer in women, with an estimated 1.38 million new cases per year. Fifty thousand cases in women and 400 in men are recorded each year in the UK alone. There are 458,000 deaths per year from breast cancer worldwide making it the most common cause of female cancer death in both the developed and developing world [ 1 ].

In the UK, the age-standardised incidence of breast cancer in women has increased by 6% over the last decade, between 1999 to 2001 and 2008 to 2010 [ 2 ]. It is estimated that around 550,000-570,000 people are living with or after a diagnosis of breast cancer in the UK [ 3 ] and, based on current projections, this figure is expected to triple by 2040 due to an ageing population and continued improvements in survival [ 4 ]. Recent research indicates that the annual cost of breast cancer to the UK economy is £1.5bn, with just over a third of that cost (£0.6bn) from healthcare alone [ 5 ]. Yet the annual spend on breast cancer research by partners of the National Cancer Research Institute has reduced in recent years despite the level of cancer research spend being generally maintained [ 6 ].

In 2006, the charity Breast Cancer Campaign facilitated a meeting of leading breast cancer experts in the United Kingdom to explore which gaps in research, if filled, would make the most impact on patient benefit. The subsequent paper [ 7 ] has helped shape the direction of breast cancer research since that time. One overarching need identified was the ‘lack of access to appropriate and annotated clinical material’, which directly led to the formation of the UK’s first multi-centre, breast-specific tissue bank [ 8 ].

This new gap analysis represents an expanded, evidence-based follow-on developed collaboratively by clinicians, scientists and healthcare professionals. The aim is to ensure that the roadmap for breast cancer research remains a relevant, consensual and authoritative resource to signpost future needs. It builds upon the previous gap analysis by briefly reviewing the current status of key areas, critically assessing remaining issues and new challenges emerging from recent research findings and proposes strategies to aid their translation into practice. Whilst a survey of progress during the last five years is not the intention of this article, the preparatory detailed discussions and data analysis could provide the basis for such a retrospective review.

During 2012, Breast Cancer Campaign facilitated a series of workshops, each covering a specialty area of breast cancer (Figure 1 ). These working groups covered genetics, epigenetics and epidemiology; molecular pathology and cell biology; hormonal influences and endocrine therapy; imaging, detection and screening; current and novel therapies and associated biomarkers; drug resistance; invasion, metastasis, angiogenesis, circulating tumour cells, cancer ‘stem’ cells; breast cancer risk and prevention; living with and managing breast cancer and its treatment. Working group leaders and their multidisciplinary teams (comprising a representative cross-section of breast cancer clinicians, scientists, and healthcare professionals) participated in iterative cycles of presentation and discussion, offering a subjective consideration of the recent relevant peer-reviewed literature. Summary reports were prepared by each group, collated, condensed and edited into a draft, which was critically appraised by an external Executive Advisory Board of international experts. This position paper highlights the key gaps in breast cancer research that were identified, together with detailed recommendations for action.

Gap analysis methodology. The flow chart illustrates the concept, processes and procedures devised to generate the gap analysis review.

Genetics, epigenetics and epidemiology

Current status, genetic predisposition.

Our knowledge of the heritability of breast cancer has increased significantly since 2007. Known breast cancer genes (BRCA1, BRCA2, CHEK2, ATM, PALB2, BRIP1, TP53, PTEN, CDH1 and STK11) make up 25 to 30% of the heritability [ 9 ]. Genome-wide association studies (GWAS) and the recent international collaborative analyses have confirmed 77 common polymorphisms individually associated with breast cancer risk, which add a further 14% [ 9 – 11 ]. Evidence from an Illumina collaborative oncological gene-environment study (iCOGS) experiment suggests that further single nucleotide polymorphisms (SNPs) may contribute at least 14% to the heritability, leaving only approximately 50% as ‘missing heritability’ (Figure 2 ).

Familial cancer genetics. The proportion of the familial component of breast cancers that can be ascribed to specific genetic defects. The difference between June 2007 and 2013 shows the impact of genome-wide association studies (GWAS) that have now identified 77 common low-risk SNPs. Courtesy of Professor Douglas Easton (University of Cambridge). Reprinted by permission from Macmillan Publishers Ltd: Nature Genetics (45,345-348), copyright 2013.

If we assume the risk estimates for polygenic markers are log additive, the cumulative risk associated with these SNPs has a median of 9% to age 80 (95% confidence intervals 5 to 15%). In the familial setting, we have learnt that common genetic SNPs can modify the risk associated with BRCA2, which may be relevant when considering risk-reducing surgery [ 12 , 13 ].

BRCA1 and BRCA2

There is improved understanding of the function of BRCA1 and BRCA2 in relation to DNA repair and therapeutic responses. For example, BRCA2 functions in RAD51 loading and BRCA1 in countering 53BP1-mediated blocking of homologous recombinational (HR)-DNA repair; hence poly (ADP-ribose) polymerase (PARP) inhibitors have been developed and trialled against BRCA-driven cancers [ 14 ]. Several additional genes associated with breast cancer risk are part of the BRCA network and there is a clear relationship with the Fanconi pathway [ 9 ]. Genes in this network point to reduced HR-DNA repair as the mechanism underlying cancer susceptibility, although the precise functions of associated signalling proteins (for example PTEN, CHK2, ATM and N-terminal BRCA1) that relate to cancer development are unknown. Gene interactions of some higher risk alleles are recognised to be sub-multiplicative, whereas low risk alleles are log-additive [ 15 ]. Some susceptibility SNPs may function at the level of chromatin remodelling/enhancer activity related to nearby gene expression.

Epigenetics

Epigenetic alterations are frequent and cancer-specific methylation in circulating tumour (ct)DNA in serum can be used as an early detection biomarker, or as a prognostic indicator [ 16 , 17 ]. The recent ENCODE study provided a wide-ranging analysis of epigenetic marks on a small fraction of the genome [ 18 ]. The first candidate gene epigenetic risk factor that could usefully be included in breast cancer risk models (once fully validated) has been identified [ 19 ]. Epigenetic factors also provide molecular measures of long-term exposure to potentially oncogenic agents. Epigenetic alterations are reversible; preclinical and recent clinical testing of epigenetic-targeted therapies such as etinostat (a DNA methylation inhibitor) and vorinostat (a histone deacetylase inhibitor) indicate that such drugs may prove effective in combination with other therapies [ 20 , 21 ].

Psychosocial considerations

Predictive genetic testing for breast cancer predisposition genes can increase distress in the short term (which reduces over time) for those identified as gene carriers, whilst non-carriers report lower levels of concern following genetic testing [ 22 ]. A number of interventions have now been developed and tested to support the genetic testing process and have been shown to reduce distress, improve the accuracy of the perceived risk of breast cancer, and increase knowledge about breast cancer and genetics [ 23 ]. Examples introduced since the last gap analysis include education using tailored information technology to prepare women for genetic counselling [ 24 ]; interventions to support women’s decisions about whether or not to have genetic testing [ 25 ] and support for gene carriers thus identified [ 12 ].

What are the key gaps in our knowledge and how might they be filled?

Moderate risk alleles.

Remaining ‘moderate risk’ alleles will be found within the short term by exome sequencing and extended GWAS studies will identify additional lower risk alleles. If up to 28% of the risk from known SNPs could be explained, while the median of the risk distribution changes little, confidence limits would change dramatically, such that the women in the top 5% at risk would have >15% lifetime risk, compared with <3% lifetime risk at the lower end. A prospective analysis will be required to show that genetic risk assessment can predict risk when combined with mammographic screening. We need to determine if or how common SNPs modify the contributions of BRCA1-associated and moderate risk genes (such as CHEK2, ATM) and whether this is influenced by oestrogen levels or risk management using, for example, lifestyle or chemopreventive approaches.

Functional implications of unclassified variants in BRCA1/BRCA2, fine-mapping of risk-associated variants (from GWAS) and understanding the functional impact of the more common SNPs such as TOX3 and the role of FOXA1 remain to be determined. Similarly, deconvoluting the functional interactions between susceptibility genes and known breast cancer-associated proteins require systems biology approaches. Can we achieve a clear clinical use of the knowledge gained by GWAS, SNP and BRCA studies by validation of risk models incorporating SNPs and moderate risk alleles (in particular in the familial setting) to improve risk management? A randomised trial for population screening with mammography stratified on individual genetic risk estimates (combined with other key risk factors) is warranted.

BRCA1 and 2

A scheme to define categories of risk for variants in BRCA (and other) cancer genes is needed to provide specific clinical recommendations. BRCA variants of uncertain significance occur in approximately 5% of all genetic tests for BRCA1/BRCA2 mutations [ 26 ]. A range of in silico and functional assays is available to provide evidence for or against a genetic variant being pathogenic. A calculation combining all lines of evidence can estimate the posterior probability that a particular gene variant is predisposing to disease. The expression of breast cancer genes in normal breast tissue and pathways that may underlie cancer risk (such as DNA damage response) could be used to identify tractable markers and to direct treatment choice. Additional BRCA-deficient human tumour cell lines and animal models of breast cancer are required.

There is a gap in our understanding of cause or consequence between epigenetic traits and gene transcription. Translational studies are needed to investigate epigenetic patterns in clinical material and from clinical trials to identify and validate prognostic markers. The extent to which epigenetic markers can be incorporated into risk models alongside genetic and lifestyle factors is not yet known. Understanding how cancer risk factors impact on the epigenome and whether this provides a mechanism for increased risk associated with those exposures is poorly understood.

Further research is needed to support informed decision making about risk management options and to assess the psychosocial implications of changing behaviour and anxiety about cancer [ 27 ]. Interventions to support discussions with those newly diagnosed with breast cancer are being developed to improve understanding of risk to individuals and their families [ 28 ]. Interventions are also required to support conversations within the family about genetic risk and its implications, given that the onus is often on the patient [ 29 ]. Research involving women at increased genetic risk for breast cancer should assess the psychosocial impact on partners and the implications for their relationships [ 30 ]. Evidence from this research needs to inform services and direct resources to support those at increased risk of breast cancer.

Risk and prevention

Risk estimation.

We know little about the exact cause(s) of the majority of breast cancers. The major challenge for prevention is to identify women at risk as precisely as possible and then to apply measures such as chemoprevention and lifestyle changes. Current models can predict probable numbers of breast cancer cases in specific risk factor strata, but have modest discriminatory accuracy at the individual level [ 31 ]. The publication of more than 70 common genetic susceptibility factors via large-scale collaborative efforts [ 10 , 32 ] and the realisation that mammographic density is a major risk factor is important, but the major gap in our knowledge is how to incorporate these factors into our current risk prediction models [ 33 ].

Automated methods for estimation of mammographic density require further evaluation for its potential use as a biomarker for risk stratification in screening and changes in density as a biomarker of responsiveness to preventive approaches. Studies of chest irradiation for lymphomas and carcinogens in rodent models suggest the importance of exposure to radiation during puberty [ 34 , 35 ].

There is a need to assess the value of several new approaches to discovering biomarkers including adductomics, transcriptomics, metabolomics [ 36 ] and epigenomics and to determine how well-established measurements (for example oestrogen levels) can be incorporated into risk models [ 37 ].

Chemoprevention

An overview of all trials of selective oestrogen receptor modulators (SERMs) as chemopreventive agents indicates that risk is reduced by 38% for up to 10 years from the start of five years’ treatment [ 38 ]. An issue is predicting those women who will benefit from SERM treatment. Lasofoxifene appears to be the most active SERM and its further development is desirable [ 39 ]. In postmenopausal women, the MA P3 trial indicated that exemestane reduced risk by 65% after 35 months median follow-up [ 40 ] requiring confirmation with additional aromatase inhibitor (AI) prevention studies. The value of low-dose tamoxifen and fenretinide also needs to be established [ 41 ]. Since SERMs and AIs reduce only oestrogen receptor positive (ER+ve) disease, there is a need for agents to prevent ER negative (ER-ve) disease, to distinguish between ER- and progesterone receptor (PR)-related disease [ 42 ] and to develop better animal models [ 43 ]. There is a need to confirm that oestrogen-only hormone replacement therapy (HRT) reduces risk whereas combined HRT increases risk in the Women’s Health Initiative (WHI) trials and to establish the mechanism of this dichotomy [ 44 , 45 ].

Lifestyle changes

Most studies related to breast cancer risk and lifestyles are observational. Favourable changes in lifestyle including reduction of calorie excess, increasing exercise, reducing alcohol intake and less environmental exposures to disturbance of circadian rhythm could reduce breast cancer by one third [ 46 – 49 ]. Communicating the potential benefits of lifestyle change, identifying teachable moments and using health services to endorse lifestyle change for prevention will require additional studies to determine why health beliefs translate poorly into action [ 50 ].

Marked adult weight gain in premenopausal women is associated with a doubling of risk of postmenopausal breast cancer compared with no or little weight gain [ 51 ]. Conversely, weight loss of 3kg or more is associated with a 25 to 40% reduction of cancer in older women compared with those who continue to gain weight. [ 52 – 54 ]. It is not clear whether to focus on all overweight women, those with gynoid or abdominal obesity or those with metabolic syndrome. Weight gain after surgery for breast cancer increases risk of relapse [ 55 ]; there is a need for further randomised trials to determine whether reducing weight in the overweight, or preventing weight gain after surgery prevents relapse. Weight management strategies seeking efficacy in the long term may be particularly difficult to sustain.

The effect of individual components of diet is controversial. The risk of ER-ve tumours may be reduced by high vegetable intake [ 56 ] while lowering fat intake may reduce both breast cancer risk and relapse after surgery. However, two of the three randomised trials of lower fat intake are confounded by concomitant weight loss [ 57 , 58 ] and the one study without weight loss showed no effect of reduction of fat intake on breast cancer relapse after surgery [ 59 ].

There is evidence for breast cancer prevention with habitual exercise [ 60 ]. Observational evidence shows that a physically active lifestyle after cancer treatment prevents relapse and reduces the risk of all-cause mortality [ 61 ]. The optimal exercise regime and timing are uncertain and randomised trials are required to assess the preventive benefits. There is a need to understand the mechanism of the apparent beneficial effects of caloric restriction and exercise.

Effective and sustainable lifestyle changes (diet, exercise and weight) need to be agreed and effective routes to initiation and maintenance identified. Further work needs to be undertaken in chemoprevention strategies and adherence to effective agents.

Prospective cohort studies are needed to develop and validate risk models, which may need to incorporate polygenic risks, mammographic density and measures of body composition. Risks may be refined by the discovery and validation of novel biomarkers such as epigenetic markers [ 19 ] and prospective validation of known markers such as serum oestrogen [ 62 , 63 ]. Effectiveness and cost-effectiveness, analyses to evaluate possible personalised screening and prevention programmes [ 64 ] and pilot studies to evaluate delivery options followed by large randomised trials are required. Polygenic and other biomarkers should be used to distinguish between the development of ER +ve, ER+ve/PR +ve and ER–ve cancers.

Many breast cancers arise in women without apparent risk factors; current studies suggest that polygenic risk factors and mammographic density add only a little to the Gail model [ 65 ]. Precision is required using polygenic approaches to decide whether or not to give preventive tamoxifen. Currently, about 10% of breast cancers arise in women with a 10-year risk above 5%. Taking this at-risk group and increasing the frequency of screening would be of some benefit, but more effective risk-adapted screening will depend upon a better definition of risk.

Further improvement and cost-effectiveness of the NHS breast cancer screening programme could include tomography, ultrasound and automated methods for the measurement of volumetric mammographic density (using software programs such as Quantra or Volpara) and automatically using these for risk stratification to adapt screening interval to risk. Experimentally, there are now opportunities for determining whether high breast density alters the response of breast epithelial cells to DNA damage or oncogene activation. This may provide prognostic value if we can define novel biomarkers to distinguish which women with high mammographic density will develop cancer [ 66 , 67 ].

Uptake of tamoxifen and raloxifene is variable and optimal methods need to be developed to explain risk, the benefit/risk ratio of treatment and to identify women who will benefit. The benefit from tamoxifen may be determined by changes in mammographic density [ 68 ] but needs confirmation. Identification of women who could develop ER-ve tumours should become possible (for example by polygenic scores). Work is required to corroborate the efficacy of lasofoxifene; the use of AIs in the preventive setting should be clarified by the International Breast Cancer Intervention Study II (IBIS II) trial, while the use of low-dose tamoxifen and retinoids also await trial results. Further studies are required to develop new preventive agents; those which might be pursued further include rexinoids, omega 3 fatty acids, sulphorophane, antiprogestins and insulin-like growth factor 1 (IGF1) inhibitors [ 409 ].

The widespread introduction of preventive agents depends upon efficient methods for identifying risk and effective counselling. Neither has been widely taken up, particularly in postmenopausal women, but the recently published NICE guidelines may signal a change for the use of tamoxifen in chemoprevention. Identification within screening programmes may be a valid approach [ 64 ]. However, since trials of chemoprevention require long duration and are costly, the development of biomarkers as indicators of effectiveness and their acceptance by regulatory agencies is attractive.

Lifestyle change for breast cancer prevention

A precise definition of interventions for diet and exercise and the relative importance for reduction of ER+ve or ER-ve breast cancer is unclear. The effect of caloric restriction by age and the duration of interventions remain unknown as do the underlying mechanisms of action. Identifying successful methods to translate prevention evidence into public health policy including effective behaviour change programmes and convincing clinicians to change practice in favour of prevention are required. Most evidence for lifestyle change is observational and confirmatory data from prospective randomised controlled trials (RCTs) with long-term follow-up and clinical endpoints may be needed. A breast cancer prevention trial using exercise would require a sample size of 25,000 to 35,000 and an eight to ten-year follow-up to observe a 20 to 25% decrease in risk for a moderate-to-vigorous physical activity programme. Such a large-scale study is not currently possible so the focus has been on a RCT of exercise in breast cancer patients to determine how exercise influences survival. The AMBER cohort study in 1,500 breast cancer patients measures physical activity, fitness and other indicators to determine exactly how physical activity influences survival [ 69 ].

Nevertheless, the beneficial effects demonstrated in randomised trials to prevent diabetes and cardiovascular disease need to be balanced against the enormous size and cost that would be required for such trials in breast cancer. For secondary prevention of disease recurrence after surgery, trials are due to report on caloric restriction and exercise in 2014 and 2018 [ 70 , 71 ].

There are teachable moments within the breast screening programmes for links to prevention through changes in lifestyle [ 50 , 64 ]. Reduction in alcohol consumption using community/class/cultural approaches, analogous to those for smoking, needs to be explored using social marketing approaches within a research context. It is likely that energy restriction and exercise will not be a complete answer to prevention and efforts should be made to design lifestyle prevention trials with and without energy restriction mimetic agents such as mTOR inhibitors, resveratrol, and metformin. mTOR inhibitors such as everolimus (RAD001) are effective in advanced breast cancer [ 72 ] although toxicities will prevent its use as a preventive agent; rapamycin in animal models reduces tumour incidence and increases longevity [ 73 ]. There is a need to translate these important findings into the clinic, perhaps by low dose or intermittent regimens to avoid toxicity [ 74 ]. Metformin is in clinical trial as an adjuvant for breast cancer treatment and demonstration of effectiveness in this situation could lead to assessment for prevention including in prediabetic populations [ 75 ].

Molecular pathology

Breast cancer classification and issues of heterogeneity.

During the last five years several high-profile studies have significantly advanced the molecular subclassification of breast cancer (reviewed in [ 76 ] and [ 77 ]). Intratumoral heterogeneity in both pre-malignant and invasive breast cancer is well documented. It is likely that both genetic and epigenetic instability, combined with microenvironmental and therapy-induced selective pressures lead to clonal evolution, which continues during metastatic progression. However, whether heterogeneity arises from cancer stem cell plasticity and a hierarchy of aberrant differentiation or stochastic events is a moot point (Figure 3 ). Genomic studies have been used to develop both prognostic biomarkers and to identify biomarkers to predict response to therapy. Nevertheless, ‘driver’ genetic changes in breast cancer will need to be filtered from the background, clinically inconsequential changes [ 78 ].

Tumour heterogeneity. (A) Recent molecular and genetic profiling has demonstrated significant intratumoural heterogeneity that can arise through genomic instability (leading to mutations), epigenetic events and/or microenvironmental influences. The stem cell hypothesis proposes that tumour-initiating cells are pluripotent and can thus give rise to progeny of multiple phenotypes; alternatively heterogeneity could be due to stochastic events. Temporal heterogeneity can be exacerbated by therapy (theoretically due to clonal evolution as some clones are eliminated whilst others expand). The significant molecular/genetic differences between cells in different areas within individual cancers, between primary and metastatic tumours (and potentially between cancer cells that successfully colonise different organs) have implications for the reliability of primary tumour biopsies for diagnosis, seeking biomarkers for treatment planning and responses to therapy. In addition, there is substantial inter-tumour heterogeneity. (B) shows images of two patients who presented with breast cancers of identical histological type and biochemical parameters. Four years later, one patient is clear of disease, while the other has evidence of multiple distant metastases, illustrative of between-patient heterogeneity in terms of response to therapy (clinical images kindly provided by Professor William Gallagher, with thanks to Dr Rut Klinger and Dr Donal Brennan (UCD Conway Institute).

Exploring the diversity and inter-tumour heterogeneity of breast cancer has led to the development of a novel classification that integrates genomic and transcriptomic information to classify 10 subtypes with distinct clinical outcomes [ 79 ]. Triple-negative breast cancer (TNBC) in particular is now recognised to demonstrate heterogeneity at the molecular, pathological and clinical levels. [ 80 ]. Such analyses, together with advanced next-generation sequencing have significant implications for improved understanding of basic tumour biology and will potentially enable the identification of new molecular targets for personalised treatment plans [ 81 , 82 ] Additionally, identification of non-coding RNAs is showing potential in diagnosis, prognosis and therapy [ 83 ].

Microenvironmental influences and tumour - host interactions

Breast development is critically reliant upon cell polarity [ 84 ], choreographed cell death pathways and interactions between epithelial cells and stroma; all processes which when deregulated are implicated in oncogenesis and tumour progression [ 85 – 87 ]. The tumour microenvironment, comprising a community of both malignant and non-malignant cells, significantly influences breast cancer cell behaviour [ 88 , 89 ]. Recently, progress has been made in understanding the bidirectional interplay between tumours and surrounding stromal cells/extracellular matrix (ECM), which can potentiate resistance to targeted therapies including endocrine therapy [ 90 , 91 ]. Consequently, components of the tumour microenvironment may represent targets for therapeutic intervention alongside the tumour to improve response to treatment [ 92 ].

Hypoxia reflects dynamic microenvironmental conditions in solid tumours, limits responses to radiotherapy [ 93 ] and some chemotherapeutic and anti-endocrine agents [ 94 , 95 ], drives genomic instability and is generally associated with progression to invasive/metastatic disease [ 96 , 97 ]. Tumour-stromal interactions change under hypoxic conditions to promote tumour progression via the activity of enzymes such as LOX [ 98 ], angiogenic factors and infiltrating macrophages [ 99 , 100 ]. A stem-like breast cancer cell subpopulation with an epithelial-mesenchymal transition (EMT) phenotype is expanded during repetitive hypoxia/reoxygenation cycles [ 101 ]. Hypoxia also contributes to cancer stem cell plasticity and niche formation [ 102 ] potentially explaining the relationship between hypoxia and chemotherapy resistance [ 103 ]. Finally, at the physiological level, host metabolic, inflammatory and immunological factors can impact on cancer development and progression, and these processes are further modified by the physical environments in which we live (Figure 4 ).

Microenvironmental influences on breast cancer. Breast cancer biology, progression and response to therapy is influenced at many levels from epigenetic effects on gene expression (for example methylation) through soluble and cell-mediated stromal interactions, intratumoural inflammatory and angiogenic components, hypoxia, host endocrinological and immunological status through to exposure to multiple agents in the environment in which we live.

What are the key gaps in our knowledge and how might these be filled?

Normal breast development and the origins of cancer.

It is not known how many breast epithelial cell subpopulations function as stem cells (capable of self-renewal) or progenitor cells (which proliferate expansively) [ 104 – 106 ]. Clearer understanding of cell lineages, changes in transcription factor expression during breast development and definition of the nature of stem and progenitor cells is fundamental to delineating relationships between normal and malignant cells.

Current cancer stem cell (CSC) assays have limitations: dormant cells cannot be detected and cell subpopulations that give rise to clones in vivo may not be active in ‘mammosphere’ cultures. There is no clear consensus on markers that define functional breast CSC in mouse and human. Indeed, they may not represent a fixed subpopulation, but instead exist in specific niches in flexible equilibrium with non-CSCs, with the balance depending on interactions between them as well as external selective pressures [ 107 – 109 ]. Understanding this plasticity [ 110 ] and its therapeutic implications are key areas for future investigation.

Breast cancer subtypes: genomics and bioinformatics

Several large-scale, cross-sectional, integrated molecular studies have established comprehensive molecular portraits of invasive primary breast cancers [ 111 – 114 ]. The International Cancer Genome Consortium (ICGC), The Cancer Genome Atlas (TCGA) and individual studies have released sequence data; however, gaining access to and interrogating this information requires expert bioinformatic collaborations. Relating these advances in genomic knowledge to improving clinical care has yet to be achieved. Knowledge of genetic, epigenetic and host factors underpinning distinct subtypes of breast cancer (plus their associated aberrant signalling pathways) and predictive biomarkers will be essential in targeting new therapeutic agents to the right patients.

For ductal carcinoma in situ (DCIS), an increased understanding is required of molecular markers of prognosis, thus providing key information to avoid overtreatment. We need to know which DCIS lesions will recur if adequate surgery is performed with wide, clear margins. Biological markers of DCIS should aim at defining which lesions are likely to progress, in order to avoid radiotherapy or even surgery if the risk of invasive cancer is sufficiently remote [ 115 ]. Markers for response to radiotherapy or endocrine therapy and the need for these therapies (particularly in low-risk patients) remain unclear.

Tumour microenvironment and stromal influences

Paget’s venerable ‘seed and soil’ analogy - recognising that tumour-initiating cells require a permissive host environment to thrive - is beginning to be deciphered at the molecular level. [ 42 ]. The composition and biophysical characteristics of the breast matrisome [ 116 ] and how it controls different stages of gland development and in early breast cancer requires definition. It is important to identify the transcription factors that define luminal and myoepithelial cells and to understand whether additional microenvironmental factors such as the ECM and fibroblast growth factor (FGF), Notch or Wnt signalling can switch their fate. Specialised niches defined by specific cell-cell/cell-matrix interactions in the microenvironment together with soluble, ECM-bound and microvesicle-associated host factors regulate CSC activation [ 117 ]. Further research on such CSC niches, their role in dormancy and the complex relationships between CSCs and metastasis is essential [ 118 – 120 ].

Stromal changes predict early progression of disease [ 121 ] and in-depth knowledge of how these conditions can be manipulated for therapeutic benefit is required [ 122 ]. Advances in the field of mechanotransduction are shedding light on the mechanisms by which altered matrix density or ‘stiffness’ can influence cell behaviour, and enzymes such as lysyl oxidases (LOX) are potential targets for therapy [ 123 ].

There is a need for better biomarkers of hypoxia including gene expression profiles [ 124 ] serum proteins, circulating tumour cells (CTCs) or functional imaging that could be used non-invasively in patients to enable more rigorous testing of its prognostic/predictive value. Although hypoxia-targeted therapies have proven disappointing to date, new approaches are emerging. In common with other targeted therapies for systemic disease, methods for measuring efficacy will need to be redesigned [ 124 – 126 ].

Tumours have an increased dependence on aerobic glycolysis. We need to understand how hypoxia affects the tumour metabolome and thus may determine therapeutic responses [ 96 ]. The dependence of metabolically adapted breast cancer cells on altered biochemical pathways presents new therapeutic targets linked to aerobic glycolysis, acidosis and the hypoxic response [ 127 , 128 ]. Since these pathways also interact with classical survival and proliferation signalling pathways via PKB/mTOR, there are opportunities to develop new combinatorial therapeutic strategies.

Breast cancer development and progression

Mammary stem cells.

There is increased understanding of stem cell hierarchies and their potential roles in breast development [ 129 – 131 ], but debate continues on the relationship between normal stem and progenitor cells, their dysregulation in cancer and the nature of putative CSCs [ 132 – 135 ]. Most data suggest that breast CSCs are a defined population with basal-like or mesenchymal-like features [ 136 – 138 ]. There is emerging data from cell line models that the CSC state is dynamic and can be induced by the tumour microenvironment [ 110 ], and this requires further investigation in human cancers. It is not known whether there are differences in CSC phenotype between breast cancer subtypes such as luminal vs. TNBC [ 139 , 140 ]. An emerging consensus is that CSCs initiate metastases and tumour regrowth after therapy, but do not necessarily generate the majority cell population in primary tumours.

Circulating tumour cells

Blood-borne tumour cells are routinely identified in breast cancer patients but their scoring can depend upon the method used [ 141 ]. Their relationship to disseminated tumour cells (DTCs) in tissues is unclear, although a recent publication showed that the presence of CD44+CD24 -/lo cells (putative CSCs) in the bone marrow is an independent adverse prognostic indicator in patients with early stage breast cancer [ 142 ]. A population of CTCs from patients with primary luminal cancer (expressing EPCAM, CD44, CD47 and MET) generated multi-site metastases when injected into mice. Hence it is likely that a subset of CTCs have metastatic potential [ 143 ], which may equate to CSCs. CTCs may occur in heterogeneous emboli of multiple cell types; perhaps those containing stem-like cells and/or ‘feeder’ cells are more likely to survive and grow at distant sites.

This key hallmark of breast cancer occurs when cancer cells access lymphatic and vascular systems, enabling dissemination via lymph nodes and then via the venous and arterial vascular system to distant organs. Once the disease has spread, it becomes life-threatening and patients require systemic treatment. Metastatic relapse typically occurs many months to decades after surgery, thus we need a greater understanding of the processes that occur following tumour cell dissemination, including the phenomenon of dormancy. Recent mathematical modelling using relapse data has provided interesting insights and proposals for hypothesis testing [ 144 ]. CTCs and DTCs that generate metastases are, by definition, tumour-initiating cells; hence their study needs to relate to CSC research [ 145 , 146 ]. Since the last gap analysis, there has been a paradigm shift in this area with the discovery of ‘pre-metastatic niches’ (analogous to stem cell niches) in organs destined to develop metastases [ 147 , 148 ].

In addition, seminal research using animal models has identified tumour and host genes associated with metastatic capacity (quite distinct from tumorigenic potential), and also organotropism [ 149 – 151 ]. The relevance of these experimental observations to human breast cancer and the translation of these findings into clinical studies require confirmation but may provide additional predictive value [ 152 ].

Reversible EMT, regulated by many factors including transforming growth factor beta (TGFβ) signalling, Slug and Snail transcription factors and hypoxia may be linked to invasion, dissemination and drug resistance [ 153 – 156 ]. The role of EMT in human cancer metastasis is still controversial and the underlying molecular mechanisms are not fully understood [ 157 ]. However, mesenchymal/stromal gene signatures have been identified which relate to TNBC subtypes, bone metastasis and resistance to neoadjuvant therapies [ 158 ].

Circulating tumour cells and nucleic acids

It is unclear whether CTCs originate from primary tumours, micro-metastases or multiple primary and secondary sites. Indeed, CTCs from distant metastases can potentially reseed the primary tumour [ 159 , 160 ]. More research is needed to define the origins of these cells. Importantly, analysis of CTCs needs to be carried out as far as possible in the clinical context, where their biology can be correlated with patient outcomes. CTCs and ctDNA are particularly useful where accessible breast cancer material is not available, or to obtain serial samples during therapy, providing a window on response and relapse.

To enable further progress, systems and protocols for isolating and characterising CTCs need to be rigorously defined and standardised, with an analysis of whether all systems identify/isolate the same cells (or indeed all CTCs, since EMT may preclude identification using epithelial markers [ 141 , 161 – 163 ]). We need to know the proportion of live, quiescent and apoptotic CTCs, their characteristics and malignant potential and to understand their relationship to the primary tumour and whether different subsets of CTCs have different predictive value.

The use of ctDNA is increasing as a potentially useful further source of information on breast cancer biology and response to therapy [ 164 – 166 ]. miRNAs identified in the systemic circulation (free or exosome-associated) [ 167 ] may also serve as diagnostic or prognostic biomarkers and/or as therapeutic targets. Indeed, it has been suggested that exosomes themselves, with their emerging roles in bidirectional signalling, immune suppression, subversion of targeted therapy and potentiation of metastasis [ 168 ] could be removed (for example by plasmapheresis) for therapeutic benefit [ 169 ].

Metastatic disease

Metastasis is the major cause of treatment failure, but it is far from clear why some patients with apparently similar disease succumb and not others [ 170 ]. We need to identify key signalling pathways linked to organotropism [ 171 ] and to develop new therapies for micro-and macro-metastatic disease [ 172 ]. Given the multiple breast cancer subtypes (and associated oncogenic drivers), it will be important to try to align genotypes/epigenotypes to metastatic patterns, in order to predict likely sites of relapse. Treatment decisions are generally based on the profile of the primary cancer, but information about the evolution of the disease from CTC, DTC or (where possible) metastases at different sites is essential, since both gains and losses of potential therapeutic targets have been observed in these distinct tumour cell populations.

We need to understand how the host microenvironment at secondary sites influences tumour cell survival and to define similarities and differences between ‘permissive’ microenvironments in organs favoured by breast cancer cells such brain, bone or liver. We have learned a good deal since the last gap analysis about the ‘vicious cycle’ of bone metastasis, whereby tumour cell interactions within this unique microenvironment mutually promote metastatic outgrowth and bone remodelling via hormonal, immunological and inflammatory mediators. These findings need to be translated into new therapies targeting both tumour and host components [ 173 ] with the paradigm extended to other specialised sites such as brain [ 174 ].

Current therapies

Clinical therapies.

Current clinical therapies for breast cancer are offered on an individual patient basis via a multidisciplinary team and comprise surgery, radiotherapy and drug therapies targeting oncogenic processes. Selection of therapy is based on Level 1 evidence from large RCTs or meta-analyses of such RCTs [ 175 – 177 ]. Increasingly, correlative translational studies are integrated prospectively into clinical trials, aiming to define the optimal target population and provide insight into mechanisms of resistance. The individualisation of treatment, optimal duration of treatments, prediction of metastasis or drug resistance remain challenging and reflect incomplete understanding of the underlying biology of breast cancer. However, up-to-date guidelines are useful to determine the best therapy for individual patients [ 178 ].

Immunohistochemical (IHC) analyses for selecting therapeutic options generally lack reproducibility and standardization resulting in poor concordance between laboratories. The Quality Assurance programme for ER, PR and human epidermal growth factor receptor 2 (HER2) in the UK has to some extent addressed this, but for other biomarkers, including Ki67, there clearly remain problems. We need to develop standardised protocols for better quantification of biomarkers [ 179 ], especially optimised methods of sample collection/storage to ensure that unstable or transient biomarkers (such as phosphoproteins or histone marks) are retained. This is especially important for predictive markers such as HER2, together with those which report on the efficacy of HER2-directed therapies and other emerging targets.

Health inequalities remain in relation to treatment. Older people diagnosed with cancer are more likely to experience undertreatment, potentially having poorer clinical outcomes than younger women for example [ 180 , 181 ]. Indeed, there is a lack of data to inform decision making about treatment for the elderly patient with breast cancer in part attributable to their under-representation in trials, but clinical teams may make inadvertent ageist decisions [ 182 , 183 ]. In addition, breast cancer and its treatment can have a considerable impact on women and their families [ 184 ]. Psychological distress is common, although not inevitable, and is associated with poorer quality of life [ 185 , 186 ]. Regular distress screening is recommended as a core component of good quality cancer care [ 187 , 188 ] in order to provide appropriate support.

Surgery remains the primary treatment for most women, with breast conservation (plus whole breast radiotherapy) providing similar outcomes to mastectomy. Following mastectomy, breast reconstruction should be considered, although uptake is incomplete. Axillary surgery has moved from clearance via node sampling techniques to sentinel node biopsy as the preferred means for assessment of axillary metastasis in early breast cancer. Neoadjuvant therapy, initially implemented to down-stage inoperable cancers, is increasingly used to assess drug efficacy in individuals and to reduce the extent of surgery required in good responders [ 189 ].

Radiotherapy

Radiotherapy is both clinically effective and cost-effective in the adjuvant and palliative settings. The Oxford overview of adjuvant radiotherapy trials [ 177 ] showed a halving of risk of first recurrence in all risk groups and favourable effects of local control on long-term survival. There is long-term confirmation of the value of boost irradiation to the site of excision after breast-conserving surgery in all subgroups, including women >60 years [ 190 ]. The long-term safety and efficacy of hypo-fractionated radiotherapy after breast-conserving surgery and mastectomy for operable breast cancer has recently been confirmed: (10-year results of Canadian [ 191 ] and Standardisation of Breast Radiotherapy (START) trials also suggesting generalisability to all subgroups of patients [ 192 , 193 ].

Trials of partial breast irradiation evaluating intraoperative radiotherapy in comparison to external beam radiotherapy [ 194 , 195 ] or brachytherapy [ 196 ] have short follow-up, but guidelines on partial breast irradiation [ 197 , 198 ] have encouraged off-study use of partial breast irradiation in advance of clinical trial results. Omission of postoperative radiotherapy after breast-conserving surgery in older, lower-risk women suggests the differential in local recurrence rates may be acceptable with a cumulative in breast recurrence of 2.5% in breast conservation surgery alone vs. 0.7% for surgery and postoperative radiotherapy (median follow-up 53 months age 55 to 75 years [ 199 ]) and at 10 years local recurrence, nine for conservation alone vs. 2% for surgery and radiotherapy in the =/>70 years, ER+ve group [ 200 ].

Decision making

Clinical decision-making tools to support individualised treatment can influence patients’ treatment choices and experiences [ 201 ] and communication training for oncology professionals is now widely available throughout the UK to improve the delivery of information and support to patients [ 202 ]. A recent national survey of over 40,000 patients with a broad range of cancers identified the fact that younger patients and ethnic minorities in particular reported substantially less positive experiences of involvement in decision making [ 203 ].

Overtreatment

A significant number of patients are overtreated to achieve the improved survival overall in early breast cancer, since we cannot define individual risks of disease recurrence or sensitivity to treatment. For survivors, the long-term side effects of treatment may be significant; individualised treatment so that patients only receive the treatment they require to achieve cure remains elusive. This is relevant to surgery, radiotherapy, chemotherapy and endocrine therapy.

With the widespread adoption of sentinel node biopsy (SNB)-limiting surgery to the axilla has substantially reduced arm morbidity [ 204 ]. A detailed understanding of underlying tumour biology is required to support decisions around surgical management, (for example axillary node clearance or not after positive sentinel nodes). No further axillary surgery even for one to two positive nodes [ 205 ] and the equivalence of axillary clearance to axillary radiotherapy for local disease recurrence (despite the differing morbidities) in the presence of a low disease burden [ 206 ] demonstrate further progress in this surgical setting. However, the optimal design of radiation treatment fields for SNB-positive patients is not known.

For postoperative radiotherapy after breast-conserving therapy, we do not have reliable ways of identifying low risk, particularly in elderly patients for whom radiotherapy might be omitted. While even low-risk patients have an approximately 50% reduction in first recurrence [ 177 ], the absolute gain for low-risk breast cancer patients (older age, small, ER+ve cancers) after breast-conserving surgery is very modest. We need reliable molecular markers of identifying such low-risk groups or individuals.

Further work is required to clarify whether the response to neoadjuvant chemotherapy can be used to guide the selection of patients for regional nodal irradiation [ 207 ] or whether patients who are clinically node positive before neoadjuvant chemotherapy and are converted to node negative after neoadjuvant chemotherapy on SNB require axillary nodal irradiation.

Individualisation of treatment

Understanding the optimal treatment strategies for an individual patient remains elusive. A number of genomic (for example Mammaprint, Oncotype Dx, PAM50) and immunohistochemical (for example IHC 4) tests have been developed to predict prognosis and latterly, response to chemotherapy; however, prospective trial evidence is still awaited [ 208 ]. Recently, serum metabolite profiling using a combination of nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS) correctly identified 80% of breast cancer patients whose tumours failed to respond adequately to chemotherapy, showing promise for more personalized treatment protocols [ 209 ].

Increased understanding of the dynamic changes that occur over time is critical and will require repeated assessment of tumour profiles. Genomic tests predict response to endocrine or chemotherapy and those at highest risk of relapse [ 210 – 212 ], but prospective trials are required to determine whether axillary clearance or chemotherapy can be avoided in node-positive patients. Similarly, biological markers of radiosensitivity (tumour and normal tissue) require better characterisation and implementation into clinical strategies to allow personalisation of treatment and avoidance of late radiation-induced toxicity [ 213 ].

CNS metastatic disease

As a result of improved outcome for patients with metastatic breast cancer (MBC), central nervous system (CNS) metastatic disease is an increasing therapeutic challenge [ 214 ]. Optimal treatment strategies have yet to be defined including sequencing or combination of stereotactic and whole brain radiotherapy, systemic treatments, intrathecal treatment approaches for leptomeningeal disease and prophylactic interventions.

Bone metastatic disease

Bisphosphonates reduce the risk of developing breast cancer in osteoporotic and osteopenic women by approximately 30% and the risk of recurrence in early breast cancer when used at the time of diagnosis [ 215 , 216 ].The interaction between the internal endocrine environment and the effect of bisphosphonates is complex and poorly understood. While negative results overall were reported in the large UK AZURE trial [ 217 ] women more than five years postmenopausal benefitted, consistent with data from the NSABP-34 trial [ 218 ]. In premenopausal women, bisphosphonates can abrogate the bone loss associated with use of an AI. In addition, recurrence and death rates were reduced when used in combination with either tamoxifen or an AI after treatment with the LHRH agonist goserelin (ABCSG12: [ 219 ]. Taken together, these studies suggest that a bisphosphonate may have its greatest effect in a low-oestrogen environment.

The impact of bone-targeted therapy on extra-skeletal metastases and locoregional relapse also highlights the need to better understand experimental observations concerning reseeding of tumours from dormant cells within the bone microenvironment [ 220 ]. Additionally, the role of RANK-RANKL signalling in mammary stem cell biology allows for the possibility that targeting this pathway with agents such as denosumab may offer a prevention strategy for bone metastasis [ 221 , 222 ].

Oligometastatic disease

The role of localised treatment of oligometastatic disease for example in the form of selective stereotactic body radiotherapy, radiofrequency ablation or surgery is currently unclear. The impact of irradiating the primary tumour, biological communications between treated primary site and distant metastases and whether radiation therapy can convert the primary tumour into an in situ vaccine [ 223 ] are relatively unexplored. Prospective randomised trials are required, which should ideally incorporate comprehensive molecular studies to define subtypes most likely to respond; a related question is how to treat primary breast cancer in patients presenting with metastatic disease.

The molecular basis of chemo-radiosensitivity, biomarkers (including specific gene signatures, proteomic markers) of tumour and/or normal tissue sensitivity is required to allow selection of patients who may benefit from adjuvant radiotherapy and avoid toxicity to those who will not. Explanations for the mechanism(s) of favourable impacts of locoregional control from radiotherapy (RT) on survival are needed [ 224 ] and may include in vivo real time biosensors of tumour biology to capture transient changes in the tumour microenvironment that drive metastasis.

Hypofractionated adjuvant radiotherapy

Even shorter-dose fractionation schedules (that is one week of whole breast radiotherapy) might achieve equivalent locoregional control with comparable toxicity [ 225 , 226 ]. Partial breast irradiation appears promising, but the long-term safety and efficacy is still uncertain [ 197 , 198 ]. In addition, it appears likely that there is a subgroup of low-risk, older patients from whom postoperative radiotherapy can be safely omitted [ 227 , 228 ]. The role of postmastectomy radiotherapy in intermediate risk breast cancer [ 229 ], axillary irradiation in sentinel node positive macro- or micro-metastases [ 230 ] or boost dose in DCIS following breast-conserving surgery [ 231 ] are all currently unclear. Further definition of the role of stereotactic body radiotherapy, accounting for tumour motion [ 232 ], in combination with neoadjuvant systemic therapy, to liver or bone metastases for oligometastatic disease are required. Similarly, the optimal dose fractionation for locally advanced disease needs to be established [ 233 ].

Molecularly targeted therapies

Anti-endocrine agents.

Multiple lines of clinical and translational evidence have increased our knowledge of the risk of recurrence, particularly for ER+ve disease [ 212 , 234 – 236 ]. The optimal duration of treatment remains incompletely defined but several RCTs have provided important new data: eight to ten years of adjuvant treatment for ER+ve breast cancers is more effective than five years of letrozole or tamoxifen [ 237 – 239 ].

Endocrine therapy resistance

Comprehensive guidelines to define endocrine resistance have now been agreed [ 240 ]. Clinical studies of various agents alone and in combination with signalling inhibitors have been completed since the last gap analysis. [ 241 – 243 ]. The biology of ERs, including the importance of phosphorylation [ 244 ], ER co-regulators [ 245 ], cross-talk with kinases [ 246 ] and altered ER-binding events [ 247 ] nevertheless requires further elucidation. MicroRNAs regulate ER activity and endocrine responses, [ 248 ], while epigenetic events promote ER loss or tumour suppressor silencing [ 249 ]. Cancer stem cells may also be implicated in endocrine resistance [ 250 ].

The multiple cell-signalling changes driving resistance and associated disease progression, nevertheless reveal potential cancer cell vulnerabilities [ 251 ] for example mTOR [ 72 ], EGFR/HER2 [ 252 ] and Src kinase [ 253 ]. New methodologies such as large-scale siRNA screens have also provided novel therapeutic targets such as CDK10 and fibroblast growth factor receptor 1(FGFR1) [ 254 , 255 ].

Oncogenic signalling inhibitors

Several molecularly targeted therapies have been licensed since the last gap analysis including lapatinib and pertuzumab in HER2+ cancers [ 31 ] and the mTOR inhibitor everolimus in ER+ve disease [ 72 , 256 ], which can overcome endocrine resistance [ 257 ]. Agents targeting signal transduction pathways (notably HER2) have had a significant impact in the treatment of certain breast cancer subtypes [ 258 ]. However, there is still limited understanding of the oncogenic pathways that control the progression of premalignant breast diseases or rare, but often aggressive, breast cancers (for example metaplastic breast cancer) [ 259 ]. Molecules may have distinct functions in different cellular contexts, therefore rigorous target validation is critical [ 260 , 261 ]; if a signalling protein has a scaffold function, disruption of protein-protein interactions may be required for efficacy. This requires a detailed biophysical analysis of protein structures and their key interactions.

For HER-2 positive disease, dual HER-receptor blockade is more effective than monotherapy and may help prevent or overcome resistance [ 262 , 263 ]. Two years of adjuvant trastuzumab offers no benefit over one year [ 264 ] but the utility of shorter trastuzumab therapy is, as yet, unconfirmed [ 265 ]. In metastatic breast cancer, serum metabolomic analyses may help to select patients with HER2+ cancers with greater sensitivity to paclitaxel plus lapatinib [ 266 ]. Multiple clinical trials are evaluating PI3K pathway inhibitors; other new agents under development include HSP90 inhibitors (for example NVP-AUY922 and ganetespib); panHER, irreversible inhibitors including neratinib and afatinib; monoclonal antibodies directed against human epidermal growth factor receptor 3 (HER3) and Src inhibitors such as saracatinib.

Resistance to signalling inhibitors

Resistance to targeted signal transduction agents is common, arising via multiple mechanisms including utilisation of compensatory feedback loops or alternative signalling pathways. Systems biology applications have begun to describe these dynamic changes [ 267 , 268 ], and are critical to identify key target points for effective therapeutic intervention.

Robust guidelines (akin to REMARK) are not yet employed in studies assessing the efficacy of novel therapeutics. Such rigour is essential to ensure that both appropriate models and quantitative outputs are fully utilised. The best drug combinatorial approaches could then be developed based on mechanistic insight into opportunities afforded by synthetic lethality [ 269 , 270 ]. More sophisticated experimental models of DNA-damage response (DDR) defects and those that accurately reflect mechanisms of therapy resistance will enable the design of targeted therapies to overcome these clinically relevant issues.

Drug responses

We lack a comprehensive understanding of the exact mechanisms (both on- and off-target) by which drugs exert anti-cancer effects in vivo ; this is exacerbated by our incomplete appreciation of networks, cross-talk and redundancy in cell signalling. Given that multiple inhibitors of specific pathways are now available (for example PI3K/PKB/mTOR), harmonised approaches to prioritisation of specific inhibitors/inhibitor classes and of research objectives in clinical trials are required.

Clinical determinants of intrinsic and acquired resistance

There is incomplete understanding of the role of diverse gene expression, epigenetic, protein and non-coding RNA changes in the heterogeneous manifestations of clinical resistance, [ 271 ]. There is a lack of equivalence between clinical, pathological, proliferative and molecular resistance that needs to be addressed and single genes or a canonical pathway are unlikely to be responsible. Furthermore, multiple mechanisms have also been implicated in acquired resistance, but their relationship to intrinsic resistance remains to be defined. Figure 5 illustrates the heterogeneity in patterns of gene expression in clinical endocrine resistance, suggesting that at least three major molecular mechanisms could be involved [ 272 ].

Molecular heterogeneity of endocrine resistance. Unsupervised hierarchical clustering of mRNA from 60 endocrine-resistant breast cancers shows heterogeneity in gene expression suggesting a multiplicity of underlying mechanisms including changes in oestrogen and interferon signalling and stromal genes. Courtesy of Professor William Miller and Dr Alexey Larionov, based on a poster presentation at the thirty-second annual CTRC-AACR San Antonio Breast Cancer Symposium, Dec 10–13, 2009 [ 272 ].

There is a need to understand the clinical impact of additional hormone receptors besides ERα, especially the progesterone receptor (PR): whilst PR is prognostic, the TEAM study has not demonstrated a predictive value [ 273 ]. Similar considerations apply to ERβ [ 274 , 275 ] and the androgen receptor (AR) [ 276 ], since trials of anti-androgens are currently underway in metastatic breast cancer [ 277 ].

It is not clear whether there are differences in ER+ve premenopausal vs. postmenopausal endocrine resistance [ 278 ]. As with other targeted therapies, the microenvironment, therapy-induced signalling reprogramming and stem cells are likely to play key roles. Proteomic profiling and protein functionality are particularly poorly characterised in the clinical resistance setting and such measurements remain challenging but essential.

It is important to define the contribution of CSCs to relapse on endocrine therapy, determine their sensitivity to existing agents or identify the unique signalling pathways that sustain their clonogenic potential. Diagnostic or prognostic tests based on ‘whole’ tumour samples may fail to address these potentially significant minority subpopulations of cells.

The few prospective studies to date have demonstrated that changes in management for one in six patients could be advised based on changes in breast cancer biomarkers on relapse, particularly ER, PR and HER2 [ 279 – 281 ]. Consequently, important clinical questions such as whether changes in the frequency of drug administration or alternating drug therapy could avoid or contribute to this process need to be addressed. Considering host factors such as adherence to medication [ 282 ], drug metabolism [ 283 ] and immune mechanisms [ 284 ], alongside molecular characteristics of tumours and the host microenvironment is essential.

Combinations and sequencing of targeted agents with conventional agents

Despite high-level evidence for isolated treatment situations (for example adjuvant treatment with AIs) [ 210 , 285 , 286 ], these have not been integrated into sequential treatment strategies, for example for adjuvant or first- or second-line palliative treatment. As treatment standards change (with AIs as standard adjuvant therapy), the sequence of tamoxifen as adjuvant therapy with AIs for first-line metastatic ER+ve disease may require adaptation. Such trials apply standard treatments that manufacturers may have little interest in supporting; new ways of supporting these trials will need to be explored.

Models are needed for the longitudinal study of hypoxic ‘microniches’ to inform timing of delivery of sequential targeted therapies or chemotherapy with radiation; to test real-time robotically controlled RT delivery to motion-affected hypoxic regions of primary breast tumours; and RT in combination with novel agents targeting pH regulatory mechanisms. Similarly, novel early-phase clinical trials of preoperative RT + targeted therapy or neoadjuvant hormonal therapy with baseline on-treatment biopsies for markers and gene signatures of radiosensitivity (the window of opportunity design) could complement the development of trials of stereotactic body RT to primary + neoadjuvant systemic therapy for limited-volume metastases in liver and bone.

Practical considerations include the risk/benefit of combining signalling inhibitors with anti-hormones, sequencing of tamoxifen and AIs [ 287 ] and targeting additional steroidogenic enzymes [ 288 ]. Recent randomised clinical studies have demonstrated substantial benefits for combinations of targeted agents such as endocrine therapy and mTOR inhibitors in ER+ve MBC [ 72 ] or horizontal dual HER-receptor blockade [ 289 – 292 ]. This results in several new challenges. Many patients benefit from single agent endocrine therapy or HER2-blockade and could avoid, at least initially, the toxicity of combination therapy if these cancers could be identified. There is a clear need to identify patients who respond adequately to targeted therapy (for example anti-HER-2 agents +/− endocrine agents) and do not need chemotherapy. Rational combinations need to be explored in the appropriate setting, taking into consideration compensatory induction of alternative signal transduction pathways bypassing targeted treatments. Treatment benefits in MBC or the neoadjuvant setting need converting into a potential survival benefit in early breast cancer.

New therapeutic approaches

Although phenotypically similar to BRCA1 mutant breast cancers, TNBC are heterogeneous and lack of expression of ER, PR and HER2 is not a good predictor of homologous recombination repair (HRR) status [ 293 ] Prognostic and predictive biomarkers of response for TNBC are obvious gaps which need to be addressed [ 294 ], complemented by an expanded and representative panel of fully characterised tumour cell lines and models [ 295 ]. More emphasis should be directed at developing markers of drug resistance and markers of resistance to current basal-like breast cancer/TNBC therapies [ 296 ]. Better biomarker-led characterisation could assist in patient stratification and hopefully improved treatment responses. Similarly, additional targets are required for other molecular subtypes that fail to respond to existing therapies.

Lymphangiogenesis and angiogenesis

Current understanding the role of lymphangiogenesis in metastasis (and thus its potential as a therapeutic target akin to neoangiogenesis) is limited [ 297 ]. In contrast, given the morbidity associated with lymphoedema following extensive lymph node dissection, identifying a means of inducing local regeneration of lymphatic vessels postoperatively could be envisaged. The contribution of the lymphatic system to immune responses to tumours is also underexplored [ 298 ]. Better in vitro and in vivo models are required to understand the cellular and molecular complexities of pathological angiogenesis and lymphangiogenesis, tumour cell intravasation, extravasation, organ colonisation and strategies for effective therapeutic interventions [ 299 ].

Anti-angiogenic therapies have been extensively trialled but have not yet lived up to their promise, with bevacizumab no longer approved for breast cancer by the FDA [ 300 – 302 ]. Tumour vasculature is heterogeneous [ 303 ] and multiple, temporally dynamic mechanisms contribute to the lack of durable responses [ 304 ]. The main focus has been vascular endothelial growth factor (VEGF)-driven angiogenesis but there is considerable redundancy in angiogenic signalling pathways [ 305 ]. Also, there are no validated biomarkers of response to anti-angiogenic therapies and it is likely that the vasculature of anatomically dispersed metastases will demonstrate further functional heterogeneity.

Exploiting the immune system

Although generally considered to be immunosuppressive, some chemotherapeutic agents (and indeed monoclonal antibodies) may involve an immune element; thus the combination of immunotherapy and chemotherapy becomes a real possibility [ 306 , 307 ]. In node-positive, ER-/HER2- disease, lymphocytic infiltration was associated with good prognosis in the BIG 02–98 adjuvant phase III trial [ 284 ]. There needs to be a systematic quantification of immune infiltration of breast cancer subtypes and how this relates to tumour progression, response to therapy or changes during treatment.

Cancer immunotherapy is gaining ground, whether antibody-based or cell-based, with an increasing emphasis on targeting the tumour microenvironment (for example macrophages or cancer-associated fibroblast (CAFs)) with DNA vaccines [ 308 ]. In addition, several immunogenic antigens (such as cancer testis antigens) have been detected in poor-prognosis breast cancers, which may serve as targets for therapy or chemoprevention [ 309 , 310 ]. New strategies for enhancing natural immunity or eliminating suppressor functions are required. There is a need for better animal models for evaluating immunotherapeutic strategies and in deciphering possible contributions to lack of responsiveness.

Living with and managing breast cancer and its treatment

Survivorship.

Cancer and its treatment have a considerable and long-term impact on everyday life [ 311 – 313 ]. Consequences may be physical (for example pain, fatigue, lymphoedema, hot flushes, night sweats and sexual problems), or psychological (cognitive function, anxiety, depression, fear of recurrence) and directly affect relationships, social activities and work. The relationship between the cancer patient and his/her partner will have a bearing on the level of distress: if communication is good, psychological distress will be lower [ 314 ]. Women may feel abandoned once treatment is completed with low confidence as a result [ 312 , 315 ]. The current system does not meet their needs [ 184 ] and the National Cancer Survivorship Initiative has been established to investigate new models of aftercare.

A recent framework publication highlights the importance of providing support to enable people to self-manage their aftercare [ 315 ]. Patients benefit from improved sense of control and ability to effect change together with an increased likelihood of seeking health information [ 316 , 317 ].

Living with advanced breast cancer

Quality of life in women with metastatic breast cancer is poor [ 318 ] with many experiencing uncontrolled symptoms [ 319 ]. Pain is a significant problem throughout the illness, not just with the end of life [ 318 ]. Depression, anxiety and traumatic stress also require intervention [ 320 , 321 ]. Those with metastatic breast cancer receiving social support report more satisfaction and a sense of fulfilment. Fewer avoidance-coping strategies are associated with better social functioning and a larger social network. Social stress has been found to increase pain and mood disturbance and has been associated with isolation. In addition, self-image and a decrease in sexual functioning challenge self-esteem and relationships at a time when support is most needed [ 322 ].

The impact of medical management on quality of life and decision making regarding palliative chemotherapy [ 323 , 324 ] and a lack of rehabilitation services [ 325 , 326 ] has been recognised. The convergence of palliative treatments and the end of life may impact on symptom control and care provision as well as place of death [ 327 , 328 ].

Supportive interventions

The main physical symptoms associated with breast cancer treatment are fatigue, pain, hot flushes, night sweats, cognitive and sexual problems and lymphoedema. Some interventions have demonstrated benefit with specific side effects [ 329 – 331 ]. Meta-analysis demonstrates that psychological interventions can reduce distress and anxiety [ 332 ], provide some physiological benefit, but with weak evidence regarding survival benefit [ 333 ]. Overall the evidence focuses on short-term benefit while the longer-term implications are unknown.

Group interventions are less effective in reducing anxiety and depression than individualised interventions such as cognitive behaviour therapy (CBT); [ 334 ], but do result in social and emotional improvements [ 335 ] and greater patient satisfaction [ 336 ]. Psycho-educational interventions show improvements in physical and psychosocial wellbeing [ 337 ] and reduced anxiety [ 338 ].

CBT reduces fatigue [ 339 ], insomnia [ 340 ] improves physical activity and quality of life [ 341 ]. CBT appears to be effective at all stages of breast cancer: group CBT can significantly reduce the impact of menopausal symptoms in breast cancer patients [ 342 , 343 ] with effects maintained over six months. Care packages to help improve coping skills, including group counselling sessions and/or telephone-based prompts has shown supportive care in the extended and permanent phases of survival to be effective [ 344 ]. Mindfulness-based stress reduction and cognitive therapy can improve mood, endocrine-related quality of life, and wellbeing at least in the short term [ 345 ].

Much evidence demonstrates the benefits of physical activity for breast cancer patients [ 346 ]. RCTs show that physical activity interventions during treatment show small to moderate beneficial effects on cardiovascular fitness, muscular strength and can reduce deconditioning. Post treatment, physical activity interventions result in a reduction in body fat and increase in fat-free mass, a moderate to large effect on cardiovascular and muscular strength, small to moderate effect on quality of life, fatigue, anxiety and depression and some evidence of reduced lymphoedema and osteoporosis [ 347 , 348 ].

The translation of physical activity research into clinical practice is a challenge. Currently, exercise-based cancer rehabilitation is not routinely incorporated into breast cancer care. However, from the National Cancer Survivorship Initiative, Macmillan Cancer Support is evaluating around 12 physical activity programmes and evaluating physical, psychological and cost benefits. One exercise intervention during therapy reassessed participants after five years and showed that those from the exercise group were still incorporating approximately 2.5 hours more physical activity a week and were more positive than control patients [ 349 ]. Furthermore, other charities are starting up similar programmes, such as Breast Cancer Care’s ‘Best Foot Forward’. There are very few intervention studies involving women with advanced metastatic cancer; these predominantly focus on supportive-expressive therapy and have been found to reduce distress [ 350 ] but the benefits are not maintained in the long term [ 334 ].

Inadequate translation of research findings into practice

While the problems are well recognised, there is inadequate clinical translation: for example, recognising the benefits of physical activity requires incorporating and testing intervention(s) in clinical practice. There is also a lack of representation and sensitivity to the needs of diverse groups. Similarly, the impact of breast cancer goes beyond the patient; more attention should be paid to their families, partners and children.

CBT is becoming integrated into clinical practice with training for clinical nurse specialists but there is still a need to consider how CBT and other interventions can be better integrated to widen access. Novel interventions must be developed and validated using methods based upon sound theoretical principles, with demonstrable effectiveness (both clinical and financial) that can be deployed as widely as possible to maximise benefit. A clear understanding of the components of interventions that promote uptake, adherence and long-term benefit is required. Funding for research into living with and managing the consequences of breast cancer and its treatment is very limited, adversely impacting the building of research capacity and expertise.

Establishing a multidisciplinary research consortium to develop a theoretical framework to inform research addressing the needs of those living with and managing the broad ranging consequences of breast cancer and its treatment would inform choice of outcome measures, innovative approaches to intervention design and testing. Alternative trial designs to RCTs need to be considered that incorporate patient preferences. It would also be of great benefit to the field to draw up guidance on implementing successful evidence into clinical practice.

Longitudinal studies are required to assess the recovery of health and wellbeing and the long-term adjustment of women and men who have a diagnosis of breast cancer. This will allow investigation of how unmet psychosocial needs and psychological morbidity during diagnosis and treatment relate to quality of life, sexuality, physical wellbeing and the effects of other illnesses later in life. The long-term impacts of breast cancer and therapy on everyday life need further investigation [ 351 ]. There are implications for cardiac functioning, osteoporosis, neuropathy, cognitive dysfunction, lymphoedema and shoulder mobility on the ability to maintain independence [ 352 ].

There is insufficient epidemiological data on the problems of women who have recurrence and metastatic disease. Research into integrated oncology and palliative care models are needed to determine which approaches improve quality of life, psychological wellbeing, palliation of symptoms, treatment decisions and end of life care. The needs of the families of women with advanced metastatic cancer and how to support them and their carers most effectively are unclear. Decision making at the end of life and the development of tools to assist women and healthcare professionals to choose appropriate treatment and place of death is needed.

Specialist breast care nurses have also been found to enhance the supportive care of women with metastatic breast cancer. [ 353 ]. However, there is a need to identify the active components of interventions and an individual’s preference for different types of interventions to determine what works best for him or her.

Development of mindfulness and third-wave approaches (for example Acceptance and Commitment Therapy) may be effective. More RCTs of theory-based interventions for treatment-related symptoms and innovative trial designs are needed (with longer follow-up, analysis of moderators and mediators and identified components) to support women to manage their everyday lives. Interventions to address specific psychological needs such as low self-confidence and fear of recurrence also need to be tested. Interventions are required to support women to increase their physical activity, reduce the risk of recurrence and examine the impact on late effects. The frequency, intensity, type and timing of physical activity for maximum benefit needs to be established. Effective means are required to support women to manage impaired sexuality/sexual function, altered body image, lymphoedema, weight gain [ 354 ], fear of recurrence, hormone therapy-related symptoms [ 341 , 343 , 355 , 356 ], cognitive problems [ 357 ][ 358 ] and post-surgical problems [ 359 , 360 ]. Alternative delivery of intervention needs to be explored, such as self-management, telephone or online support and non-specialist delivery: for example comparison of home-based versus hospital-based interventions on physical activity levels, patient satisfaction and motivation.

Strategic approaches to enable progress

Experimental models of breast cancer, improved tissue culture models.

There is now a greater appreciation of the importance of employing appropriate human cancer cells. [ 361 ]. Commonly used breast cancer cell lines are derived from metastases or pleural effusions and fail to adequately represent the diversity and complexity of breast cancer [ 362 ]. It has proven difficult to establish human tumour cell cultures representative of the major subtypes and to maintain their genomic and phenotypic integrity. In addition, inter-patient variability and inadvertent selection of the most malignant subtypes, skews availability of representative material.

Better representation of breast cancer subtypes is required. Material from normal mammary tissue, premalignant breast conditions, different ER+ve (and rare) subtypes of breast cancers and ideally metastases from all major sites are needed to cover the full spectrum of breast cancer development and progression. Primary or minimally passaged cell cultures will avoid issues of misidentification, contamination or long-term culture artefacts. Ideally, a central repository of well-annotated human primary breast cancer cells, associated host cells and cell lines should be available to researchers linked to a searchable, open-access database. Maintaining breast tumour tissue in culture with its essential characteristics intact will enable prognostic screening and testing of potential therapeutic agents.

Reliable cell-type-specific markers are required and it is also important to be able to recognise cancer stem cell subpopulations (or transient phenotypes). Identification of promoters for distinct cell subpopulations will enhance the number and scope of available in vitro models. [ 363 ] and enable conditional genetic modifications for mechanistic and target validation studies [ 364 ]. Ideally, co-cultures (of both normal and precancerous breast cells) with host cell populations such as fibroblasts, myoepithelial cells, macrophages, adipocytes or vascular endothelial cells are needed for studies of cellular interactions within the appropriate ECM microenvironment.

Three-dimensional culture models can recapitulate the tissue architecture of the breast and its characteristic invasion patterns [ 89 , 365 ] especially if host stromal components are incorporated [ 366 ]. Three-dimensional heterotypic model systems are also enabling dissection of the effect of cell-cell interactions and stromal elements in drug resistance. Three-dimensional cultures require additional refinement, higher throughput, quantitative assays [ 367 ] and a move towards more physiologically relevant conditions, for example by the use of bioreactors, enabling long-term cultures under flow conditions; especially appropriate for invasion assays [ 368 , 369 ].

Animal tumour models

In the last five years there has been an expansion in the use of orthotopic (anatomically correct) breast cancer xenografts [ 370 ] and significant advances in developing patient-derived xenografts (PDX) [ 371 ]. These models better reflect the human cancers from which they were derived and ER+ve tumours respond appropriately to oestrogen ablation [ 372 ]. Increased use of genetically engineered mouse (GEM) models driven by relevant abnormalities such as BRCA mutations, HER2 overexpression and so on have enabled the study of naturally occurring tumours in immunocompetent hosts and evaluation of new targeted therapies such as PARP inhibitors and the emergence of resistance [ 373 ]. Pros and cons of different models are shown in Figure 6 .

Comparative properties of experimental tumour models. In vitro assays of tumour growth and response to therapy can be conducted in two dimensions or three dimensions - the latter more closely approximating the biology of solid tumours than a simple monolayer. Cultures can be enhanced by the addition of matrix proteins and/or host cells and can be adapted to measure not only tumour cell proliferation, but also additional cancer hallmarks such as invasion. Standard in vivo assays depend upon the transplantation of established human tumour cell lines into athymic (immune-incompetent) hosts. These models are relatively simple and easy to use, but are increasingly complemented by genetically engineered mice harbouring targeted genetic mutations which render them susceptible to developing mammary cancers. The figure summarises key advantages and disadvantages of each model and means by which their clinical relevance and utility might be enhanced. Based on a figure provided courtesy of Claire Nash in Dr Valerie Speirs’ group (University of Leeds).

Expansion of PDX models will be required to cover all the main breast cancer phenotypes [ 374 ] and to address the contribution of ethnic diversity [ 375 ]. Advanced GEM models with multiple genetic abnormalities, able to generate both hormone sensitive and insensitive tumours and in which metastasis occurs at clinically relevant sites will also be a desirable refinement [ 376 , 377 ]. However, all such animal models will require validation of any findings in the clinical setting [ 296 , 378 , 379 ]. Models are also required to investigate mechanisms of the induction of (and escape from) long-term tumour dormancy [ 380 ], a unique feature of breast cancer.

Invasive behaviour does not occur uniformly or synchronously within a tumour [ 381 ] and this heterogeneity is not easily reproduced in vitro . Improved tumour models and methods are required to understand the localised and possibly transient factors involved in temporal and spatial heterogeneity that promote invasion and metastasis.

Models for testing novel targeted agents against disseminated disease

Novel agents designed for systemic administration are rarely tested against established invasive/metastatic disease in preclinical animal models [ 382 , 383 ]. There is an urgent need to develop better models for the discovery and development of therapies targeting metastases that are effective against all sites of disease [ 384 ].

In around 20% of women, complete resection of primary tumours does not prevent distant metastases because dissemination has already occurred. In these cases, agents targeting cell motility or invasion may have limited value. It is therefore critical that preclinical models used for testing such therapies incorporate established micrometastases [ 385 ]. Similarly, there is a preponderance of lung metastasis models in routine use. Other important sites of breast cancer metastasis (for example bone, brain and, liver) are relatively poorly represented, and this needs remedying in preclinical drug evaluation [ 386 – 388 ]. Human tissue (such as bone) transplanted into mice can provide a more relevant microenvironment [ 389 ].

Preclinical or clinical trials focused on tumour shrinkage are not appropriate for testing the efficacy of anti-invasive or anti-metastatic agents that may reduce metastasis without significantly impacting primary tumour growth [ 390 ]. Such approaches would likely fail current response evaluation criteria in solid tumors (RECIST) criteria and show little activity in the neoadjuvant setting or in late stage patients with advanced metastatic disease. The potential to utilise veterinary models for testing novel therapies or RT-systemic therapy combinations and cross-disciplinary collaboration with other scientific disciplines to develop real-time in vivo biosensors of tumour biology offer novel opportunities for significant progress.

Modelling drug resistance

While challenging, establishing cell lines, tissue slice models and PDX from relapsed and resistant cancers should be the ultimate goal in order to provide a window on the mechanisms that occur in patients where therapies fail. This would also allow ex vivo targeting studies, employing signalling analyses and imaging systems to track resistance mechanisms and progression.

Preclinical endocrine resistant models have largely been derived from ER+ve MCF7 cells in vitro , either by transfection of potential signalling molecules such as HER2 or from continuous exposure to anti-endocrine agents. Extensive panels of relapsed human tumour cell lines are required to reflect the heterogeneity of clinical resistant disease. This will allow assessment of the impact of genetic background, duration, sequence and type of endocrine agent (including AI) and rational evaluation of agents to reverse resistance [ 391 ]. It is critical to validate mechanisms identified in vitro with clinical resistance.

Longitudinal clinical samples and associated biological studies

Biobanking has substantially improved and is seen as a significant outcome of the last gap analysis [ 7 ] but the systematic analysis of clinical material collected from serial tumour biopsies/ fine-needle aspiration (FNA) (or ideally less invasive means such as ‘liquid biopsy’) before, during and following resistance development is lacking. Procurement of matched materials remains challenging but is critical to establishing clinically relevant signalling mechanisms that culminate in acquired resistance, allowing tracking of the dynamics and prevalence of molecular events during response through to any subsequent relapse. Care must be taken to provide adequate sampling of inherently heterogeneous tumours in their primary, recurrent and disseminated settings, which may also provide material for study of site-specific metastasis. [ 392 ] and samples must be full annotated, ideally with ‘omics’ profiling and immunohistochemistry. The biopsy of metastatic lesions is challenging and will require systematic introduction of a ‘warm autopsy’ programme [ 393 ]. A more realistic alternative is to further exploit the preoperative neoadjuvant setting, despite the potential issues of heterogeneity and sampling [ 394 ]. Collection of such samples is a particularly valuable resource to address mechanisms of intrinsic resistance and to track early therapy-associated signalling changes (Figure 7 ).

Longitudinal sampling and enhanced biobanks. The longitudinal collection of blood and samples from normal breasts, primary cancers and relapsed/metastatic/treatment-resistant disease is essential in order to address the origins, heterogeneity and evolution of breast cancers. Samples are required from as broad a patient population as possible to understand ethnic, age-related and gender differences in incidence, molecular subtypes, prognosis and response to treatment. Sequential samples (ideally patient-matched) from primary tumours and metastases will enable detailed studies of tumour evolution/progression and provide material for generating new cell lines and patient-derived xenografts for translational research. Multimodality imaging and metabolomic analyses will add further dimensions of valuable information. Based on a figure provided courtesy of Professor William Gallagher, with thanks to Dr Rut Klinger (UCD Conway Institute).

Increased use of clinical relapse material will determine the relevance of preclinical findings and identify potential candidates for detailed mechanistic evaluation in appropriate tumour model systems. Ultimately the goal is to determine if patients can be better stratified to allow rational, personalised choices for further therapy. This aspiration requires better integration between clinicians and scientists, trial providers and pharmaceutical companies and would benefit from data sharing. Tissue-based analyses from clinical trials need to be expanded to incorporate all of the next generation sequencing studies for research. These initiatives need to be co-ordinated with cancer registry/ British Association of Surgical Oncology (BASO) breast cancer data.

Blood samples for early diagnosis, monitoring treatment response, early indicators of disease relapse (and revealing increased heterogeneity) are imperative as our ability to generate new biomarkers through emerging technologies increases. These include detection of CTCs, miRNAs, ctDNA, exosomes, and so on. Serum HER2 measurement may be another promising biomarker with prognostic and predictive value [ 395 – 398 ].

Biomarkers of response or relapse

With the exception of ER and HER2, the availability of biomarkers to accurately identify which patients will receive benefit from targeted treatment, and indicators of patients at high risk of progression or relapse remains limited. Further advances in molecularly targeted and anti-endocrine therapy require clinically applicable predictive biomarkers to enable appropriate patient recruitment and to track responses to treatment [ 399 , 400 ]. These analyses should be applied both to primary tumours and recurrent/metastatic lesions to accommodate the profound heterogeneity within individual cancers, which increases further during disease progression. Understanding which molecular markers are ‘drivers’ of breast cancer and their functional roles at different stages of disease will be key to designing more effective targeted agents.

Validation of predictive markers for drug response could be better facilitated by the routine inclusion of such approaches into clinical trials rather than retrospective analyses of archived material. Any new biomarkers should have well-defined cut-off points, be thoroughly validated and robust. We require biomarkers to identify patients who will not respond to trastuzumab (primary resistance) in addition to the development of secondary acquired resistance. Discriminatory biomarkers are required for combination therapies such as lapatinib and trastuzumab in HER2-positive breast cancers. We lack preclinical data that can predict which combination of anti-HER2 therapies is optimal. There is also a need for biomarkers that can identify patients who may be more suitably treated with a tyrosine kinase inhibitor (TKI) rather than trastuzumab or combination anti-HER2 therapy. New irreversible TKIs currently in clinical trials, (for example afatinib and neratinib) have shown increased potency in preclinical studies - could these now become the mainstay for HER2-positive tumours?

Knowledge of the therapeutic benefits of mTOR inhibitors and of newer PI3K pathway inhibitors in breast cancer subtypes is rudimentary and we have no biomarkers that can be used to optimise their therapeutic index. In addition, knowledge of how important genomic (for example PIK3CA mutations) and proteomic (for example PTEN loss) biomarkers impact the efficacy of specific PI3K pathway inhibitors in the clinical setting is limited. Further preclinical research on the functional proteomic effects of genomic abnormalities in the PI3K pathway in breast cancer is essential.

ER+ve tumour heterogeneity remains a challenge: luminal A vs. luminal B subgroups impact on prognosis; however, the mechanisms of endocrine failure remain largely unknown. In ER+ve disease there is a lack of accepted biomarkers/signatures to distinguish endocrine-sensitive patients from those with intrinsic insensitivity or who will develop early or late resistance.

There is a need to develop non-invasive means of detecting risk of subsequent relapse. In addition to serial tumour samples, serum samples are warranted as these may ultimately provide less invasive indicators of acquisition of resistance. It remains unclear if single or multiple biomarkers or transcriptional profiles are optimal, or even if basic endocrinological markers may prove valuable in the context of predicting resistance.

While imaging (at least with some modalities) is routinely applied to the early detection and follow-up of breast cancers, there is a need to increase the use of functional screening techniques to better understand tumour heterogeneity, identify features associated with response or resistance to treatment and more rapidly translate promising new preclinical methodologies to clinical evaluation. It is important to evaluate emerging imaging biomarkers of primary and metastatic breast cancer and there is a requirement for new, more specific and clinically translatable radiotracers for positron emission tomography/single-photon emission computed tomography (PET/SPECT) [ 401 , 402 ]. We also need to identify and assess the utility of imaging biomarkers associated with other hallmarks of cancer beyond proliferation for example invasion, altered metabolism, hypoxia. Attention needs to be given as to how to validate novel imaging biomarkers in adequately powered multi-centre clinical trials. The funding available from most grant-awarding bodies is insufficient to cover this, suggesting the need to consider larger collaborative trials funded by more than one agency.

Imaging may also be able to report on intratumoural heterogeneity and identify the most significant region (for example more aggressive/invasive areas via diffusion-weighted magnetic resonance imaging (MRI)), to more accurately direct biopsies or radiotherapy. EMT could be addressed by the increased use of cluster, histogram and/or texture analyses, but it will be necessary to define the correct metrics to assess and quantify such phenotypes [ 403 ]. It would be desirable to extend these techniques to define different tumour subtypes such as DCIS, luminal or TNBC non-invasively (which may identify mixed lesions missed by homogenised or limited sample analyses) and assess heterogeneity between metastases. Ideally, imaging studies (both preclinical and clinical) should be co-registered with linked genomic and proteomic information in order to fully interpret the biological relevance of the images obtained [ 404 – 406 ]. However, tissue collection is often not co-ordinated with imaging studies and the added benefit not always appreciated.

A key achievable goal is to non-invasively evaluate predictive biomarkers of therapeutic responses. Increased adoption of more clinically relevant orthotopic xenograft and transgenic murine models of primary and metastatic breast cancer will demand robust preclinical imaging approaches. The use of such models in imaging-embedded trials of novel agents will improve the accuracy of preclinical data, accelerating the development of promising drugs, or enabling early closure of suboptimal programmes. Such refined preclinical trial designs will also prove highly informative in establishing combination and/or sequential treatment regimes.

Clinical trial design and patient involvement

Clinical trial design should be adapted to use preoperative and neoadjuvant models to allow novel therapies to be tested in patients [ 394 , 407 ], identify de novo resistant cancers and investigate how such resistance can be counteracted. These approaches are particularly relevant for therapeutic strategies that target cancer stem cells, residual (dormant) cancer cells or influence the tumour microenvironment. Future trial design will also have to incorporate dynamic strategies, such as using the response to short-term treatment to guide the use of additional preoperative treatment. Given the increasing focus on small target populations (for example molecular subtypes of breast cancer), clinical trial strategies for effective patient stratification or selection based on molecular characteristics are required to allow routine integration into large-scale clinical trials. In addition, the relatively long period between surgery and relapse in breast cancer patients impacts negatively on the economic feasibility of such clinical trials. New thinking will be required to modify clinical trial design, and to consider biomarkers that relate to invasive and metastatic phenotypes, for example as in trials with denosumab where the development of skeletal-related events (SRE) was an accepted and measurable endpoint [ 221 ].

Patient reported outcomes

There is a need to incorporate standardised patient-reported outcome measures (PROMs) both within clinical trials and in everyday clinical practice. Currently, many trial reports are reliant on the common terminology criteria for adverse events (CTCAE) gradings about side effects, which show alarming discrepancies with data actually collected from patients [ 408 ].

Further research is needed to support the use of decision aids around surgery and treatment and to define any benefits. There is also a need for prospective research to identify consequences of treatment and the impact of co-morbidities on the lives of women with breast cancer so that future patients can consider these as part of their decision making. The experiences of minority ethnic groups, younger (<45 years) and older (>70 years) women in relation to their treatment choices and management need further research. Addressing non-adherence to endocrine therapy and understanding the biological mechanisms of significant side effects such as menopausal symptoms are poorly understood. The value of incorporating lifestyle recommendations as part of routine care and its impact on recovery and quality of life should be further explored.

Multidisciplinary collaborations and resources

Increased resources are required to support core (for example biochemical/IHC) as well as new ‘omics technologies; to develop improved in vitro / in vivo / ex vivo model development, serial clinical sample collection, advanced bioinformatic/systems biology analysis, clinical biomarker validation and ‘bench to bedside’ drug development. Stronger multidisciplinary collaborations between laboratory scientists, clinicians, bioinformaticians and engineers (and in turn with funding bodies and industry) must be encouraged. Much better integration of computer science, database engineering, data analytics and visualisation, hardware and software engineering within biological research will be essential to effectively read and translate increasingly complex data. Convincing drug companies of the benefits of a co-ordinated approach (tissue collection before, during and after treatments) in clinical trials of new drugs is problematic, and access of material for research purposes is limited. Companies must be convinced of the benefits of accurate biomarkers to allow for the better stratification of patients. Even though this will limit their target population, this should be offset by higher response rates and faster regulatory approval.

Continued support is required for basic biological research and understanding of cell signalling processes with emphasis on interactions, cross-talk and microenvironmental regulation. It is important that approaches in this area are linked to systematic investigations and precise analyses of cell responses to a wide range (and combination) of inhibitors, tested in clinically relevant breast cancer model systems. A key element is open discussion and learning from negative results to avoid unnecessary duplication of research. Sharing of information, best practice, optimised model systems, technologies and resources is essential, perhaps through developing web-based analysis portals. Such approaches are needed to integrate and interpret diverse sources of data to understand the plasticity of signalling emerging during treatment though to resistance (Figure 8 ).

Integrated vision of multidisciplinary research. Enhanced integration and utilisation of the vast amount of clinical and experimental observations relating to breast cancer is urgently required. Clinical observations generate hypotheses relating to the origins of cancer, its underlying molecular pathology and potential vulnerabilities that could be exploited for therapeutic benefit. Such insights provide opportunities for testing and validation in in vitro, in vivo and in silico models. Drug discovery aims to provide inhibitors of major oncogenic ‘drivers’ for use singly or in combination with conventional therapies; such personalised medicine requires the co-development of predictive and pharmacodynamic biomarkers of response. Results from preclinical therapy studies and clinical trials should be fed back into searchable databases to reveal reasons for treatment failure and allow new strategies to be tested and deployed. Based on a figure provided courtesy of Professor William Gallagher, with thanks to Professor Walter Kolch (UCD Conway Institute).

A co-operative network of advanced radiotherapy facilities, analogous to the Experimental Cancer Medicine Centres is needed to ensure adequate patient numbers for clinical trials. Engaging patients and healthcare teams is critical to enable complex biological studies (especially longitudinal biomarker studies). Lack of academic clinicians (particularly in radiation oncology), radiobiology and physics staff nationally and rising service pressures on NHS staff are all detrimental to delivery of clinical translational research.

While substantial advances have been made in breast cancer research and treatment in the last five years, there remain significant gaps in translating this newly acquired knowledge into clinical improvements.

Understanding the specific functions and contextual interactions of genetic and epigenetic advances and applying this knowledge to clinical practice, including tailored screening, will require deeper understanding of molecular mechanisms and prospective clinical validation. Even with clinically actionable tests, decision making, support for patients and their families and overcoming the barriers to lifestyle change (diet, exercise and weight) alongside chemopreventive strategies are required to optimise health outcomes.

Genomic profiling of sequential clinical samples (primary, relapsed and secondary cancers, CTC, ctDNA, before, during and following therapy) is required to identify specific biomarkers of inter-/intra-tumour spatial and temporal heterogeneity, metastatic potential, sensitivity to radiotherapy and different forms of chemotherapy, de novo or acquired resistance. This will significantly improve patient stratification for existing therapies and identify key nodes in these dynamic processes as potential new therapeutic targets. Validated markers of these processes (including minimally invasive multimodality imaging and metabolomics methodologies) will benefit from synergies between laboratory and clinical interactions. Improved understanding of the interactions, duration, sequencing and optimal combinations of therapy should allow better stratification of patients and reduce overtreatment (or undertreatment) enhancing prevention or survival while reducing morbidity.

Further genetic, epigenetic and molecular profiling of breast cancers and their associated stroma would be significantly enhanced by expanded panels of cell lines representing all major breast cancer subtypes and three-dimensional tumour-host heterotypic co-culture systems. This would enable increased understanding of the molecular drivers behind specific cancer subtypes and their role (together with microenvironmental modifiers) in treatment resistance and metastasis. Deciphering tumour-stromal interactions incorporating metabolic and immunological host mechanisms and intracellular/extracellular signalling pathways would have therapeutic implications for prevention and therapy. Advanced high-content analytical methods will enable consideration of additional key cancer ‘hallmarks’ beyond proliferation (for example cell motility and invasion) and enable screening for inhibitors under more physiologically relevant conditions. Better preclinical animal models (for example genetically engineered mice expressing relevant human oncogenes, which develop widespread metastases; patient-derived xenografts) are required. Such models would enable testing of hypotheses derived from clinical observations and rigorous target validation and evaluation of novel therapies in the metastatic setting (and where desirable in immunocompetent hosts).

Underpinning these advances, optimised multimodality imaging for diagnosis and therapeutic monitoring should enable better evaluation of primary and metastatic disease. Clinically annotated tissues for translational research must be linked to bioinformatics as key contributors to interdisciplinary research, essential for rapid future advances. Increasing numbers of women and men are surviving breast cancer. Alongside advances in understanding the disease and using that knowledge for prevention, earlier detection and successful treatment of breast cancer, interventions to improve the survivorship experience require innovative approaches to address the consequences of diagnosis and treatment.

Top 10 gaps:

Understanding the specific functions and contextual interactions of genetic and epigenetic changes in the normal breast and the development of cancer

Effective and sustainable lifestyle changes (diet, exercise and weight) alongside chemopreventive strategies

Tailored screening approaches including clinically actionable tests

Molecular drivers behind breast cancer subtypes, treatment resistance and metastasis

Mechanisms of tumour heterogeneity, tumour dormancy, de novo or acquired resistance; how to target the key nodes in these dynamic processes

Validated markers of chemosensitivity and radiosensitivity

Interactions, duration, sequencing and optimal combinations of therapy for improved individualisation of treatment

Optimised multimodality imaging for diagnosis and therapeutic monitoring should enable better evaluation of primary and metastatic disease

Interventions and support to improve the survivorship experience including physical symptoms such as hot flushes and lymphoedema

Clinically annotated tissues for translational research including tumour, non-tumour and blood based materials from primary cancers, relapsed and metastatic disease

Proposed strategic solutions:

For significant progress to be made in treating and supporting those impacted by breast cancer (and ultimately preventing and overcoming this disease) basic and translational research scientists in academia and industry, funding bodies, government and patients need to work together to achieve the following key strategic solutions

To reverse the decline in resources targeted towards breast cancer research, funding must be increased and strategically directed to enhance our current knowledge, develop the talent pool, and apply evidence-based findings to improve clinical care

A fully cohesive and collaborative infrastructure must be developed to support breast cancer research; this requires improved access to appropriate, well-annotated clinical material including longitudinal sample collection with expert bioinformatics support and data sharing.

Building on sound investment and infrastructure, all stakeholders (researchers, funders, government, industry and patients) must work together on the clinical development and translation of research knowledge to patient benefit. For example, enhanced, clinically relevant, in vitro and in vivo models are required for evaluation of new therapies together with validated biomarkers, which should then be embedded in clinical practice.

Research funders, government and industry should provide innovative programmes to encourage collaborative cross-disciplinary working practices, including the training of more physician-scientists and integration of physical sciences, technology and engineering.

Improving clinical trial methodologies, including patient involvement, recognising that a changing global environment is required to ensure that all clinical developments can be tested and ultimately implemented for patient benefit.

Abbreviations

Aromatase inhibitor

Androgen receptor

Ataxia telangiectasia mutated

British Association of Surgical Oncology

Cancer-associated fibroblast

Cognitive behavioural therapy

Cyclin-dependent kinase 10

CHK2 checkpoint homolog

Checkpoint kinase 2

Central nervous system

Cancer stem cell

Circulating tumour cell (in blood)

Common terminology criteria for adverse events

Circulating tumour DNA

Ductal carcinoma in situ

DNA damage response

Deoxyribonucleic acid

Disseminated tumour cell (usually in marrow nodes or tissue)

Extracellular matrix

Epithelial-mesenchymal transition

Oestrogen receptor

Fibroblast growth factor

Fibroblast growth factor receptor 1

Fine-needle aspiration

Forkhead box protein A1

Genetically engineered mouse

Genome-wide association studies

Human epidermal growth factor receptor 2

Human epidermal growth factor receptor 3

Homologous recombination repair

Hormone replacement therapy

Heat shock protein 90

Ipsilateral breast tumour recurrence

International Cancer Genome Consortium

Illumina collaborative oncological gene-environment study

Insulin-like growth factor 1

Immunohistochemical

Induced pluripotent stem cells

Chromatography-mass spectrometry

Metastatic breast cancer

Magnetic resonance imaging

Nuclear magnetic resonance

Representing the whole HER family

Poly (ADP-ribose) polymerase

Patient-derived xenografts

Positron emission tomography/single-photon emission computed tomography

Phosphatidylinositide-3 kinase

Gene encoding PI3 kinase alpha

Protein kinase B

Progesterone receptor

Patient-reported outcome measures

Randomised controlled trial

Response evaluation criteria in solid tumors

Ribonucleic acid

Selective oestrogen receptor modulators

Short inhibitory RNAs

Sentinel node biopsy

Single nucleotide polymorphism

Skeletal-related events

Standardisation of Breast Radiotherapy (START) trial A

Standardisation of Breast Radiotherapy (START) trial B

The Cancer Genome Atlas

Transforming growth factor beta

Tyrosine kinase inhibitor

Tissue microarray

Triple-negative breast cancer

Vascular endothelial growth factor

Women’s Health Initiative.

Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM: Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer. 2010, 127: 2893-2917.

Article CAS PubMed Google Scholar

Breast cancer incidence statistics. http://www.cancerresearchuk.org/cancer-info/cancerstats/types/breast/incidence/#trends ,

Maddams JBD, Gavin A, Steward J, Elliott J, Utley M, Møller H: Cancer prevalence in the United Kingdom: estimates for 2008. Br J Cancer. 2009, 101: 541-547.

Article CAS PubMed PubMed Central Google Scholar

Maddams J, Utley M, Moller H: Projections of cancer prevalence in the United Kingdom, 2010–2040. Br J Cancer. 2012, 107: 1195-1202.

Leal J: The economic burden of cancer across the European Union. Proceedings of the National Cancer Research Institute Conference: 4–7. 2012, Liverpool, November

Google Scholar

Data package. http://www.ncri.org.uk/includes/Publications/general/Data_package_12.xls ,

Thompson A, Brennan K, Cox A, Gee J, Harcourt D, Harris A, Harvie M, Holen I, Howell A, Nicholson R, Steel M, Streuli C: Evaluation of the current knowledge limitations in breast cancer research: a gap analysis. Breast Cancer Res : BCR. 2008, 10: R26-

Article PubMed PubMed Central Google Scholar

Tissue Bank. http://breastcancertissuebank.org/about-tissue-bank.php ,

Melchor L, Benitez J: The complex genetic landscape of familial breast cancer. Hum Genet. 2013

Michailidou K, Hall P, Gonzalez-Neira A, Ghoussaini M, Dennis J, Milne RL, Schmidt MK, Chang-Claude J, Bojesen SE, Bolla MK, Wang Q, Dicks E, Lee A, Turnbull C, Rahman N, Fletcher O, Peto J, Gibson L, Dos Santos Silva I, Nevanlinna H, Muranen TA, Aittomäki K, Blomqvist C, Czene K, Irwanto A, Liu J, Waisfisz Q, Meijers-Heijboer H, Adank M, Breast and Ovarian Cancer Susceptibility Collaboration, et al: Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nat Genet. 2013, 45: 353-361. 361e351-352

Sakoda LC, Jorgenson E, Witte JS: Turning of COGS moves forward findings for hormonally mediated cancers. Nat Genet. 2013, 45: 345-348.

Antoniou AC, Beesley J, McGuffog L, Sinilnikova OM, Healey S, Neuhausen SL, Ding YC, Rebbeck TR, Weitzel JN, Lynch HT, Isaacs C, Ganz PA, Tomlinson G, Olopade OI, Couch FJ, Wang X, Lindor NM, Pankratz VS, Radice P, Manoukian S, Peissel B, Zaffaroni D, Barile M, Viel A, Allavena A, Dall'Olio V, Peterlongo P, Szabo CI, Zikan M, Claes K, et al: Common breast cancer susceptibility alleles and the risk of breast cancer for BRCA1 and BRCA2 mutation carriers: implications for risk prediction. Cancer Res. 2010, 70: 9742-9754.

Ingham S, Warwick J, Byers H, Lalloo F, Newman W, Evans D: Is multiple SNP testing in BRCA2 and BRCA1 female carriers ready for use in clinical practice? Results from a large Genetic Centre in the UK. Clin Genet. 2013, 84: 37-42.

Audeh MW, Carmichael J, Penson RT, Friedlander M, Powell B, Bell-McGuinn KM, Scott C, Weitzel JN, Oaknin A, Loman N, Lu K, Schmutzler RK, Matulonis U, Wickens M, Tutt A: Oral poly(ADP-ribose) polymerase inhibitor olaparib in patients with BRCA1 or BRCA2 mutations and recurrent ovarian cancer: a proof-of-concept trial. Lancet. 2010, 376: 245-251.

Turnbull C, Seal S, Renwick A, Warren-Perry M, Hughes D, Elliott A, Pernet D, Peock S, Adlard JW, Barwell J, Berg J, Brady AF, Brewer C, Brice G, Chapman C, Cook J, Davidson R, Donaldson A, Douglas F, Greenhalgh L, Henderson A, Izatt L, Kumar A, Lalloo F, Miedzybrodzka Z, Morrison PJ, Paterson J, Porteous M, Rogers MT, Shanley S, et al: Gene-gene interactions in breast cancer susceptibility. Hum Mol Genet. 2012, 21: 958-962.

Muller HM, Widschwendter A, Fiegl H, Ivarsson L, Goebel G, Perkmann E, Marth C, Widschwendter M: DNA methylation in serum of breast cancer patients: an independent prognostic marker. Cancer Res. 2003, 63: 7641-7645.

PubMed Google Scholar

Yazici H, Terry MB, Cho YH, Senie RT, Liao Y, Andrulis I, Santella RM: Aberrant methylation of RASSF1A in plasma DNA before breast cancer diagnosis in the Breast Cancer Family Registry. Cancer Epidemiol Biomarkers Prev. 2009, 18: 2723-2725.

Bernstein BE, Birney E, Dunham I, Green ED, Gunter C, Snyder M, ENCODE Project Consortium: An integrated encyclopedia of DNA elements in the human genome. Nature. 2012, 489: 57-74.

Article CAS Google Scholar

Brennan K, Garcia-Closas M, Orr N, Fletcher O, Jones M, Ashworth A, Swerdlow A, Thorne H, Investigators KC, Riboli E, Vineis P, Dorronsoro M, Clavel-Chapelon F, Panico S, Onland-Moret NC, Trichopoulos D, Kaaks R, Khaw KT, Brown R, Flanagan JM: Intragenic ATM methylation in peripheral blood DNA as a biomarker of breast cancer risk. Cancer Res. 2012, 72: 2304-2313.

Azad N, Zahnow CA, Rudin CM, Baylin SB: The future of epigenetic therapy in solid tumours–lessons from the past. Nat Rev Clin Oncol. 2013, 10: 256-266.

Tsai HC, Li H, Van Neste L, Cai Y, Robert C, Rassool FV, Shin JJ, Harbom KM, Beaty R, Pappou E, Harris J, Yen RW, Ahuja N, Brock MV, Stearns V, Feller-Kopman D, Yarmus LB, Lin YC, Welm AL, Issa JP, Minn I, Matsui W, Jang YY, Sharkis SJ, Baylin SB, Zahnow CA: Transient low doses of DNA-demethylating agents exert durable antitumor effects on hematological and epithelial tumor cells. Cancer Cell. 2012, 21: 430-446.

Foster C, Watson M, Eeles R, Eccles D, Ashley S, Davidson R, Mackay J, Morrison PJ, Hopwood P, Evans DG, Psychosocial Study Collaborators: Predictive genetic testing for BRCA1/2 in a UK clinical cohort: three-year follow-up. Br J Cancer. 2007, 96: 718-724.

Hilgart JS, Coles B, Iredale R: Cancer genetic risk assessment for individuals at risk of familial breast cancer. Cochrane Database Syst Rev. 2012, 2: CD003721

Albada A, Werrett J, Van Dulmen S, Bensing JM, Chapman C, Ausems MG, Metcalfe A: Breast cancer genetic counselling referrals: how comparable are the findings between the UK and the Netherlands?. J Comm Gen. 2011, 2: 233-247.

Article Google Scholar

Wakefield CE, Meiser B, Homewood J, Peate M, Taylor A, Lobb E, Kirk J, Young MA, Williams R, Dudding T, Tucker K, AGenDA Collaborative Group: A randomized controlled trial of a decision aid for women considering genetic testing for breast and ovarian cancer risk. Breast Cancer Res Treat. 2008, 107: 289-301.

Article PubMed Google Scholar

Lindor NM, Goldgar DE, Tavtigian SV, Plon SE, Couch FJ: BRCA1/2 Sequence variants of uncertain significance: a primer for providers to assist in discussions and in medical management. Oncol. 2013, 18: 518-524.

Hallowell N, Baylock B, Heiniger L, Butow PN, Patel D, Meiser B, Saunders C, Price MA, kConFab Psychosocial Group on behalf of the kConFab I: Looking different, feeling different: women’s reactions to risk-reducing breast and ovarian surgery. Fam Cancer. 2012, 11: 215-224.

Watts KJ, Meiser B, Mitchell G, Kirk J, Saunders C, Peate M, Duffy J, Kelly PJ, Gleeson M, Barlow-Stewart K, Rahman B, Friedlander M, Tucker K, TFGT Collaborative Group: How should we discuss genetic testing with women newly diagnosed with breast cancer? Design and implementation of a randomized controlled trial of two models of delivering education about treatment-focused genetic testing to younger women newly diagnosed with breast cancer. BMC Cancer. 2012, 12: 320-

Chivers Seymour K, Addington-Hall J, Lucassen AM, Foster CL: What facilitates or impedes family communication following genetic testing for cancer risk? A systematic review and meta-synthesis of primary qualitative research. J Genet Couns. 2010, 19: 330-342.

Mireskandari S, Sherman KA, Meiser B, Taylor AJ, Gleeson M, Andrews L, Tucker KM: Psychological adjustment among partners of women at high risk of developing breast/ovarian cancer. Genet Med. 2007, 9: 311-320.

Amir E, Freedman OC, Seruga B, Evans DG: Assessing women at high risk of breast cancer: a review of risk assessment models. J Natl Cancer Inst. 2010, 102: 680-691.

Dite GS, Mahmoodi M, Bickerstaffe A, Hammet F, Macinnis RJ, Tsimiklis H, Dowty JG, Apicella C, Phillips KA, Giles GG, Southey MC, Hopper JL: Using SNP genotypes to improve the discrimination of a simple breast cancer risk prediction model. Breast Cancer Res Treat. 2013, 139: 887-896.

Eriksson L, Hall P, Czene K, Dos Santos SI, McCormack V, Bergh J, Bjohle J, Ploner A: Mammographic density and molecular subtypes of breast cancer. Br J Cancer. 2012, 107: 18-23.

Swerdlow AJ, Cooke R, Bates A, Cunningham D, Falk SJ, Gilson D, Hancock BW, Harris SJ, Horwich A, Hoskin PJ, Linch DC, Lister TA, Lucraft HH, Radford JA, Stevens AM, Syndikus I, Williams MV: Breast cancer risk after supradiaphragmatic radiotherapy for Hodgkin’s lymphoma in England and Wales: a National Cohort Study. J Clin Oncol. 2012, 30: 2745-2752.

Aupperlee MD, Leipprandt JR, Bennett JM, Schwartz RC, Haslam SZ: Amphiregulin mediates progesterone-induced mammary ductal development during puberty. Breast Cancer Res BCR. 2013, 15: R44-

Denkert C, Bucher E, Hilvo M, Salek R, Oresic M, Griffin J, Brockmoller S, Klauschen F, Loibl S, Barupal DK, Budczies J, Iljin K, Nekljudova V, Fiehn O: Metabolomics of human breast cancer: new approaches for tumor typing and biomarker discovery. Genome Med. 2012, 4: 37-

CAS PubMed PubMed Central Google Scholar

Santen RJ, Boyd NF, Chlebowski RT, Cummings S, Cuzick J, Dowsett M, Easton D, Forbes JF, Key T, Hankinson SE, Howell A, Ingle J, Breast Cancer Prevention Collaborative Group: Critical assessment of new risk factors for breast cancer: considerations for development of an improved risk prediction model. Endocr Relat Cancer. 2007, 14: 169-187.

Cuzick J, Sestak I, Bonanni B, Costantino JP, Cummings S, DeCensi A, Dowsett M, Forbes JF, Ford L, LaCroix AZ, Mershon J, Mitlak BH, Powles T, Veronesi U, Vogel V, Wickerham DL, SERM Chemoprevention of Breast Cancer Overview Group: Selective oestrogen receptor modulators in prevention of breast cancer: an updated meta-analysis of individual participant data. Lancet. 2013, 381: 1827-1834.

LaCroix AZ, Powles T, Osborne CK, Wolter K, Thompson JR, Thompson DD, Allred DC, Armstrong R, Cummings SR, Eastell R, Ensrud KE, Goss P, Lee A, Neven P, Reid DM, Curto M, Vukicevic S, PEARL Investigators: Breast cancer incidence in the randomized PEARL trial of lasofoxifene in postmenopausal osteoporotic women. J Natl Cancer Inst. 2010, 102: 1706-1715.

Goss PE, Ingle JN, Ales-Martinez JE, Cheung AM, Chlebowski RT, Wactawski-Wende J, McTiernan A, Robbins J, Johnson KC, Martin LW, Winquist E, Sarto GE, Garber JE, Fabian CJ, Pujol P, Maunsell E, Farmer P, Gelmon KA, Tu D, Richardson H, NCIC CTG MAP.3 Study Investigators: Exemestane for breast-cancer prevention in postmenopausal women. N Engl J Med. 2011, 364: 2381-2391.

Decensi A, Gandini S, Serrano D, Cazzaniga M, Pizzamiglio M, Maffini F, Pelosi G, Daldoss C, Omodei U, Johansson H, Macis D, Lazzeroni M, Penotti M, Sironi L, Moroni S, Bianco V, Rondanina G, Gjerde J, Guerrieri-Gonzaga A, Bonanni B: Randomized dose-ranging trial of tamoxifen at low doses in hormone replacement therapy users. J Clin Oncol. 2007, 25: 4201-4209.

Rosner B, Glynn RJ, Tamimi RM, Chen WY, Colditz GA, Willett WC, Hankinson SE: Breast cancer risk prediction with heterogeneous risk profiles according to breast cancer tumor markers. Am J Epidemiol. 2013, 178: 296-308.

Uray IP, Brown PH: Chemoprevention of hormone receptor-negative breast cancer: new approaches needed. Recent Results Cancer Res. 2011, 188: 147-162.

Chlebowski RT, Anderson GL, Gass M, Lane DS, Aragaki AK, Kuller LH, Manson JE, Stefanick ML, Ockene J, Sarto GE, Johnson KC, Wactawski-Wende J, Ravdin PM, Schenken R, Hendrix SL, Rajkovic A, Rohan TE, Yasmeen S, Prentice RL, WHI Investigators: Estrogen plus progestin and breast cancer incidence and mortality in postmenopausal women. JAMA. 2010, 304: 1684-1692.

Anderson GL, Chlebowski RT, Aragaki AK, Kuller LH, Manson JE, Gass M, Bluhm E, Connelly S, Hubbell FA, Lane D, Martin L, Ockene J, Rohan T, Schenken R, Wactawski-Wende J: Conjugated equine oestrogen and breast cancer incidence and mortality in postmenopausal women with hysterectomy: extended follow-up of the Women’s Health Initiative randomised placebo-controlled trial. Lancet Oncol. 2012, 13: 476-486.

Wiseman M: The second World Cancer Research Fund/American Institute for Cancer Research expert report. Food, nutrition, physical activity, and the prevention of cancer: a global perspective. Proc Nutr Soc. 2008, 67: 253-256.

Parkin DM, Boyd L, Walker LC: 16. The fraction of cancer attributable to lifestyle and environmental factors in the UK in 2010. Br J Cancer. 2011, 105: S77-S81.

Li CI, Chlebowski RT, Freiberg M, Johnson KC, Kuller L, Lane D, Lessin L, O’Sullivan MJ, Wactawski-Wende J, Yasmeen S, Prentice R: Alcohol consumption and risk of postmenopausal breast cancer by subtype: the women’s health initiative observational study. J Natl Cancer Inst. 2010, 102: 1422-1431.

Hansen J, Stevens RG: Case–control study of shift-work and breast cancer risk in Danish nurses: impact of shift systems. Eur J Cancer. 2012, 48: 1722-1729.

Anderson AS, Mackison D, Boath C, Steele R: Promoting changes in diet and physical activity in breast and colorectal cancer screening settings: an unexplored opportunity for endorsing healthy behaviors. Cancer Prev Res. 2013, 6: 165-172.

Huang Z, Hankinson SE, Colditz GA, Stampfer MJ, Hunter DJ, Manson JE, Hennekens CH, Rosner B, Speizer FE, Willett WC: Dual effects of weight and weight gain on breast cancer risk. JAMA. 1997, 278: 1407-1411.

Harvie M, Howell A, Vierkant RA, Kumar N, Cerhan JR, Kelemen LE, Folsom AR, Sellers TA: Association of gain and loss of weight before and after menopause with risk of postmenopausal breast cancer in the Iowa women’s health study. Cancer Epidemiol Biomarkers Prev. 2005, 14: 656-661.

Eliassen AH, Colditz GA, Rosner B, Willett WC, Hankinson SE: Adult weight change and risk of postmenopausal breast cancer. JAMA. 2006, 296: 193-201.

Teras LR, Goodman M, Patel AV, Diver WR, Flanders WD, Feigelson HS: Weight loss and postmenopausal breast cancer in a prospective cohort of overweight and obese US women. CCC. 2011, 22: 573-579.

Niraula S, Ocana A, Ennis M, Goodwin PJ: Body size and breast cancer prognosis in relation to hormone receptor and menopausal status: a meta-analysis. Breast Cancer Res Treat. 2012, 134: 769-781.

Jung S, Spiegelman D, Baglietto L, Bernstein L, Boggs DA, van den Brandt PA, Buring JE, Cerhan JR, Gaudet MM, Giles GG, Goodman G, Hakansson N, Hankinson SE, Helzlsouer K, Horn-Ross PL, Inoue M, Krogh V, Lof M, McCullough ML, Miller AB, Neuhouser ML, Palmer JR, Park Y, Robien K, Rohan TE, Scarmo S, Schairer C, Schouten LJ, Shikany JM, Sieri S, et al: Fruit and vegetable intake and risk of breast cancer by hormone receptor status. J Natl Cancer Inst. 2013, 105: 219-236.

Prentice RL, Caan B, Chlebowski RT, Patterson R, Kuller LH, Ockene JK, Margolis KL, Limacher MC, Manson JE, Parker LM, Paskett E, Phillips L, Robbins J, Rossouw JE, Sarto GE, Shikany JM, Stefanick ML, Thomson CA, Van Horn L, Vitolins MZ, Wactawski-Wende J, Wallace RB, Wassertheil-Smoller S, Whitlock E, Yano K, Adams-Campbell L, Anderson GL, Assaf AR, Beresford SA, et al: Low-fat dietary pattern and risk of invasive breast cancer: the Women’s Health Initiative Randomized Controlled Dietary Modification Trial. JAMA. 2006, 295: 629-642.

Chlebowski RT, Rose D, Buzzard IM, Blackburn GL, Insull W, Grosvenor M, Elashoff R, Wynder EL: Adjuvant dietary fat intake reduction in postmenopausal breast cancer patient management. The Women’s Intervention Nutrition Study (WINS). Breast Cancer Res Treat. 1992, 20: 73-84.

Pierce JP, Natarajan L, Caan BJ, Parker BA, Greenberg ER, Flatt SW, Rock CL, Kealey S, Al-Delaimy WK, Bardwell WA, Carlson RW, Emond JA, Faerber S, Gold EB, Hajek RA, Hollenbach K, Jones LA, Karanja N, Madlensky L, Marshall J, Newman VA, Ritenbaugh C, Thomson CA, Wasserman L, Stefanick ML: Influence of a diet very high in vegetables, fruit, and fiber and low in fat on prognosis following treatment for breast cancer: the Women’s Healthy Eating and Living (WHEL) randomized trial. JAMA. 2007, 298: 289-298.

Friedenreich CM: Physical activity and breast cancer: review of the epidemiologic evidence and biologic mechanisms. Recent Results Cancer Res. 2011, 188: 125-139.

Fontein DB, de Glas NA, Duijm M, Bastiaannet E, Portielje JE, Van de Velde CJ, Liefers GJ: Age and the effect of physical activity on breast cancer survival: A systematic review. Cancer Treat Rev. 2013, 39: 958-965.

Key TJ: Endogenous oestrogens and breast cancer risk in premenopausal and postmenopausal women. Steroids. 2011, 76: 812-815.

Farhat GN, Cummings SR, Chlebowski RT, Parimi N, Cauley JA, Rohan TE, Huang AJ, Vitolins M, Hubbell FA, Manson JE, Cochrane BB, Lane DS, Lee JS: Sex hormone levels and risks of estrogen receptor-negative and estrogen receptor-positive breast cancers. J Natl Cancer Inst. 2011, 103: 562-570.

Evans DG, Warwick J, Astley SM, Stavrinos P, Sahin S, Ingham S, McBurney H, Eckersley B, Harvie M, Wilson M, Beetles U, Warren R, Hufton A, Sergeant JC, Newman WG, Buchan I, Cuzick J, Howell A: Assessing individual breast cancer risk within the U.K. National Health Service Breast Screening Program: a new paradigm for cancer prevention. Cancer Prev Res. 2012, 5: 943-951.

Darabi H, Czene K, Zhao W, Liu J, Hall P, Humphreys K: Breast cancer risk prediction and individualised screening based on common genetic variation and breast density measurement. BCR. 2012, 14: R25-

Brower V: Homing in on mechanisms linking breast density to breast cancer risk. J Natl Cancer Inst. 2010, 102: 843-845.

Martin LJ, Boyd NF: Mammographic density. Potential mechanisms of breast cancer risk associated with mammographic density: hypotheses based on epidemiological evidence. BCR. 2008, 10: 201-

Article PubMed PubMed Central CAS Google Scholar

Cuzick J, Warwick J, Pinney E, Duffy SW, Cawthorn S, Howell A, Forbes JF, Warren RM: Tamoxifen-induced reduction in mammographic density and breast cancer risk reduction: a nested case–control study. J Natl Cancer Inst. 2011, 103: 744-752.

Courneya KS, Karvinen KH, McNeely ML, Campbell KL, Brar S, Woolcott CG, McTiernan A, Ballard-Barbash R, Friedenreich CM: Predictors of adherence to supervised and unsupervised exercise in the Alberta Physical Activity and Breast Cancer Prevention Trial. J Phys Act Health. 2012, 9: 857-866.

Rack B, Andergassen U, Neugebauer J, Salmen J, Hepp P, Sommer H, Lichtenegger W, Friese K, Beckmann MW, Hauner D, Hauner H, Janni W: The German SUCCESS C Study - the first European lifestyle study on breast cancer. Breast Care (Basel). 2010, 5: 395-400.

Villarini A, Pasanisi P, Traina A, Mano MP, Bonanni B, Panico S, Scipioni C, Galasso R, Paduos A, Simeoni M, Bellotti E, Barbero M, Macellari G, Venturelli E, Raimondi M, Bruno E, Gargano G, Fornaciari G, Morelli D, Seregni E, Krogh V, Berrino F: Lifestyle and breast cancer recurrences: the DIANA-5 trial. Tumori. 2012, 98: 1-18.

CAS PubMed Google Scholar

Baselga J, Campone M, Piccart M, Burris HA, Rugo HS, Sahmoud T, Noguchi S, Gnant M, Pritchard KI, Lebrun F, Beck JT, Ito Y, Yardley D, Deleu I, Perez A, Bachelot T, Vittori L, Xu Z, Mukhopadhyay P, Lebwohl D, Hortobagyi GN: Everolimus in postmenopausal hormone-receptor-positive advanced breast cancer. N Engl J Med. 2012, 366: 520-529.

Anisimov VN, Zabezhinski MA, Popovich IG, Piskunova TS, Semenchenko AV, Tyndyk ML, Yurova MN, Rosenfeld SV, Blagosklonny MV: Rapamycin increases lifespan and inhibits spontaneous tumorigenesis in inbred female mice. Cell Cycle. 2011, 10: 4230-4236.

Longo VD, Fontana L: Intermittent supplementation with rapamycin as a dietary restriction mimetic. Aging. 2011, 3: 1039-1040.

Goodwin PJ, Thompson AM, Stambolic V: Diabetes, metformin, and breast cancer: lilac time?. J Clin Oncol. 2012, 30: 2812-2814.

Reis-Filho JS, Pusztai L: Gene expression profiling in breast cancer: classification, prognostication, and prediction. Lancet. 2011, 378: 1812-1823.

Baird RD, Caldas C: Genetic heterogeneity in breast cancer: the road to personalized medicine?. BMC Med. 2013, 11: 151-

Lawrence MS, Stojanov P, Polak P, Kryukov GV, Cibulskis K, Sivachenko A, Carter SL, Stewart C, Mermel CH, Roberts SA, Kiezun A, Hammerman PS, McKenna A, Drier Y, Zou L, Ramos AH, Pugh TJ, Stransky N, Helman E, Kim J, Sougnez C, Ambrogio L, Nickerson E, Shefler E, Cortés ML, Auclair D, Saksena G, Voet D, Noble M, DiCara D, et al: Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature. 2013, 499: 214-218.

Dawson SJ, Rueda OM, Aparicio S, Caldas C: A new genome-driven integrated classification of breast cancer and its implications. EMBO J. 2013, 32: 617-628.

Metzger-Filho O, Tutt A, de Azambuja E, Saini KS, Viale G, Loi S, Bradbury I, Bliss JM, Azim HA, Ellis P, Di Leo A, Baselga J, Sotiriou C, Piccart-Gebhart M: Dissecting the heterogeneity of triple-negative breast cancer. J Clin Oncol. 2012, 30: 1879-1887.

Russnes HG, Navin N, Hicks J, Borresen-Dale AL: Insight into the heterogeneity of breast cancer through next-generation sequencing. J Clin Invest. 2011, 121: 3810-3818.

Samuel N, Hudson TJ: Translating genomics to the clinic: implications of cancer heterogeneity. Clin Chem. 2013, 59: 127-137.

Jansson MD, Lund AH: MicroRNA and cancer. Mol Oncol. 2012, 6: 590-610.

Akhtar N, Streuli CH: An integrin-ILK-microtubule network orients cell polarity and lumen formation in glandular epithelium. Nat Cell Biol. 2013, 15: 17-27.

Bazzoun D, Lelievre S, Talhouk R: Polarity proteins as regulators of cell junction complexes: Implications for breast cancer. Pharmacol Ther. 2013, 138: 418-427.

Lelievre SA: Tissue polarity-dependent control of mammary epithelial homeostasis and cancer development: an epigenetic perspective. J Mammary Gland Biol Neoplasia. 2010, 15: 49-63.

Xue B, Krishnamurthy K, Allred DC, Muthuswamy SK: Loss of Par3 promotes breast cancer metastasis by compromising cell-cell cohesion. Nat Cell Biol. 2013, 15: 189-200.

Martin FT, Dwyer RM, Kelly J, Khan S, Murphy JM, Curran C, Miller N, Hennessy E, Dockery P, Barry FP, O'Brien T, Kerin MJ: Potential role of mesenchymal stem cells (MSCs) in the breast tumour microenvironment: stimulation of epithelial to mesenchymal transition (EMT). Breast Cancer Res Treat. 2010, 124: 317-326.

Weigelt B, Lo AT, Park CC, Gray JW, Bissell MJ: HER2 signaling pathway activation and response of breast cancer cells to HER2-targeting agents is dependent strongly on the 3D microenvironment. Breast Cancer Res Treat. 2010, 122: 35-43.

Pontiggia O, Sampayo R, Raffo D, Motter A, Xu R, Bissell MJ, Joffe EB, Simian M: The tumor microenvironment modulates tamoxifen resistance in breast cancer: a role for soluble stromal factors and fibronectin through beta1 integrin. Breast Cancer Res Treat. 2012, 133: 459-471.

Martinez-Outschoorn UE, Goldberg A, Lin Z, Ko YH, Flomenberg N, Wang C, Pavlides S, Pestell RG, Howell A, Sotgia F, Lisanti MP: Anti-estrogen resistance in breast cancer is induced by the tumor microenvironment and can be overcome by inhibiting mitochondrial function in epithelial cancer cells. Cancer Biol Ther. 2011, 12: 924-938.

Hanahan D, Coussens LM: Accessories to the crime: functions of cells recruited to the tumor microenvironment. Cancer Cell. 2012, 21: 309-322.

He WS, Dai XF, Jin M, Liu CW, Rent JH: Hypoxia-induced autophagy confers resistance of breast cancer cells to ionizing radiation. Oncol Res. 2012, 20: 251-258.

Article PubMed CAS Google Scholar

Tan EY, Yan M, Campo L, Han C, Takano E, Turley H, Candiloro I, Pezzella F, Gatter KC, Millar EK, O'Toole SA, McNeil CM, Crea P, Segara D, Sutherland RL, Harris AL, Fox SB: The key hypoxia regulated gene CAIX is upregulated in basal-like breast tumours and is associated with resistance to chemotherapy. Br J Cancer. 2009, 100: 405-411.

Milas L, Hittelman WN: Cancer stem cells and tumor response to therapy: current problems and future prospects. Semin Radiat Oncol. 2009, 19: 96-105.

Mimeault M, Batra SK: Hypoxia-inducing factors as master regulators of stemness properties and altered metabolism of cancer- and metastasis-initiating cells. J Cell Mol Med. 2013, 17: 30-54.

Rundqvist H, Johnson RS: Hypoxia and metastasis in breast cancer. Curr Top Microbiol Immunol. 2010, 345: 121-139.

Postovit LM, Abbott DE, Payne SL, Wheaton WW, Margaryan NV, Sullivan R, Jansen MK, Csiszar K, Hendrix MJ, Kirschmann DA: Hypoxia/reoxygenation: a dynamic regulator of lysyl oxidase-facilitated breast cancer migration. J Cell Biochem. 2008, 103: 1369-1378.

Obeid E, Nanda R, Fu YX, Olopade OI: The role of tumor-associated macrophages in breast cancer progression (Review). Int J Oncol. 2013, 43: 5-12.

Lewis CE, Hughes R: Inflammation and breast cancer. Microenvironmental factors regulating macrophage function in breast tumours: hypoxia and angiopoietin-2. BCR. 2007, 9: 209-

Louie E, Nik S, Chen JS, Schmidt M, Song B, Pacson C, Chen XF, Park S, Ju J, Chen EI: Identification of a stem-like cell population by exposing metastatic breast cancer cell lines to repetitive cycles of hypoxia and reoxygenation. BCR. 2010, 12: R94-

Dittmer J, Rody A: Cancer stem cells in breast cancer. Histol Histopathol. 2013, 28: 827-838.

Mao Q, Zhang Y, Fu X, Xue J, Guo W, Meng M, Zhou Z, Mo X, Lu Y: A tumor hypoxic niche protects human colon cancer stem cells from chemotherapy. J Cancer Res Clin Oncol. 2013, 139: 211-222.

Van Keymeulen A, Rocha AS, Ousset M, Beck B, Bouvencourt G, Rock J, Sharma N, Dekoninck S, Blanpain C: Distinct stem cells contribute to mammary gland development and maintenance. Nature. 2011, 479: 189-193.

van Amerongen R, Bowman AN, Nusse R: Developmental stage and time dictate the fate of Wnt/beta-catenin-responsive stem cells in the mammary gland. Cell Stem Cell. 2012, 11: 387-400.

de Visser KE, Ciampricotti M, Michalak EM, Tan DW, Speksnijder EN, Hau CS, Clevers H, Barker N, Jonkers J: Developmental stage-specific contribution of LGR5(+) cells to basal and luminal epithelial lineages in the postnatal mammary gland. J Pathol. 2012, 228: 300-309.

Smalley M, Piggott L, Clarkson R: Breast cancer stem cells: Obstacles to therapy. Cancer Lett. 2012, 338: 57-62.

Iliopoulos D, Hirsch HA, Wang G, Struhl K: Inducible formation of breast cancer stem cells and their dynamic equilibrium with non-stem cancer cells via IL6 secretion. Proc Natl Acad Sci U S A. 2011, 108: 1397-1402.

Sarrio D, Franklin CK, Mackay A, Reis-Filho JS, Isacke CM: Epithelial and mesenchymal subpopulations within normal basal breast cell lines exhibit distinct stem cell/progenitor properties. Stem Cells. 2012, 30: 292-303.

Chaffer CL, Marjanovic ND, Lee T, Bell G, Kleer CG, Reinhardt F, D’Alessio AC, Young RA, Weinberg RA: Poised chromatin at the ZEB1 promoter enables breast cancer cell plasticity and enhances tumorigenicity. Cell. 2013, 154: 61-74.

Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS, Thorsen T, Quist H, Matese JC, Brown PO, Botstein D, Lønning PE, Børresen-Dale AL: Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A. 2001, 98: 10869-10874.

Banerji S, Cibulskis K, Rangel-Escareno C, Brown KK, Carter SL, Frederick AM, Lawrence MS, Sivachenko AY, Sougnez C, Zou L, Cortes ML, Fernandez-Lopez JC, Peng S, Ardlie KG, Auclair D, Bautista-Piña V, Duke F, Francis J, Jung J, Maffuz-Aziz A, Onofrio RC, Parkin M, Pho NH, Quintanar-Jurado V, Ramos AH, Rebollar-Vega R, Rodriguez-Cuevas S, Romero-Cordoba SL, Schumacher SE, Stransky N: Sequence analysis of mutations and translocations across breast cancer subtypes. Nature. 2012, 486: 405-409.

Shah SP, Roth A, Goya R, Oloumi A, Ha G, Zhao Y, Turashvili G, Ding J, Tse K, Haffari G, Bashashati A, Prentice LM, Khattra J, Burleigh A, Yap D, Bernard V, McPherson A, Shumansky K, Crisan A, Giuliany R, Heravi-Moussavi A, Rosner J, Lai D, Birol I, Varhol R, Tam A, Dhalla N, Zeng T, Ma K, Chan SK, et al: The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature. 2012, 486: 395-399.

Cancer Genome Atlas N: Comprehensive molecular portraits of human breast tumours. Nature. 2012, 490: 61-70.

Solin LJ, Gray R, Baehner FL, Butler SM, Hughes LL, Yoshizawa C, Cherbavaz DB, Shak S, Page DL, Sledge GW, Davidson NE, Ingle JN, Perez EA, Wood WC, Sparano JA, Badve S: A multigene expression assay to predict local recurrence risk for ductal carcinoma in situ of the breast. J Natl Cancer Inst. 2013, 105: 701-710.

Naba A, Clauser KR, Hoersch S, Liu H, Carr SA, Hynes RO: The matrisome: in silico definition and in vivo characterization by proteomics of normal and tumor extracellular matrices. MCP. 2012, 11: M111.014647-

Glukhova MA, Streuli CH: How integrins control breast biology. Curr Opin Cell Biol. 2013, 25: 633-641.

Ito Y, Iwase T, Hatake K: Eradication of breast cancer cells in patients with distant metastasis: the finishing touches?. Breast Cancer. 2012, 19: 206-211.

Sampieri K, Fodde R: Cancer stem cells and metastasis. Semin Cancer Biol. 2012, 22: 187-193.

Takebe N, Warren RQ, Ivy SP: Breast cancer growth and metastasis: interplay between cancer stem cells, embryonic signaling pathways and epithelial-to-mesenchymal transition. BCR. 2011, 13: 211-

Finak G, Bertos N, Pepin F, Sadekova S, Souleimanova M, Zhao H, Chen H, Omeroglu G, Meterissian S, Omeroglu A, Hallett M, Park M: Stromal gene expression predicts clinical outcome in breast cancer. Nat Med. 2008, 14: 518-527.

Kalluri R, Zeisberg M: Fibroblasts in cancer. Nat Rev Cancer. 2006, 6: 392-401.

Barker HE, Cox TR, Erler JT: The rationale for targeting the LOX family in cancer. Nat Rev Cancer. 2012, 12: 540-552.

Favaro E, Lord S, Harris AL, Buffa FM: Gene expression and hypoxia in breast cancer. Genome Med. 2011, 3: 55-

Milani M, Harris AL: Targeting tumour hypoxia in breast cancer. Eur J Cancer. 2008, 44: 2766-2773.

Lundgren K, Holm C, Landberg G: Hypoxia and breast cancer: prognostic and therapeutic implications. CMLS. 2007, 64: 3233-3247.

Ward C, Langdon SP, Mullen P, Harris AL, Harrison DJ, Supuran CT, Kunkler IH: New strategies for targeting the hypoxic tumour microenvironment in breast cancer. Cancer Treat Rev. 2013, 39: 171-179.

Bailey KM, Wojtkowiak JW, Hashim AI, Gillies RJ: Targeting the metabolic microenvironment of tumors. Adv Pharmacol. 2012, 65: 63-107.

Dos Santos CO, Rebbeck C, Rozhkova E, Valentine A, Samuels A, Kadiri LR, Osten P, Harris EY, Uren PJ, Smith AD, Hannon GJ: Molecular hierarchy of mammary differentiation yields refined markers of mammary stem cells. Proc Natl Acad Sci U S A. 2013, 110: 7123-7130.

Makarem M, Spike BT, Dravis C, Kannan N, Wahl GM, Eaves CJ: Stem cells and the developing mammary gland. J Mammary Gland Biol Neoplasia. 2013, 18: 209-219.

Visvader JE: Keeping abreast of the mammary epithelial hierarchy and breast tumorigenesis. Genes Dev. 2009, 23: 2563-2577.

Ablett MP, Singh JK, Clarke RB: Stem cells in breast tumours: are they ready for the clinic?. Eur J Cancer. 2012, 48: 2104-2116.

Badve S, Nakshatri H: Breast-cancer stem cells-beyond semantics. Lancet Oncol. 2012, 13: e43-e48.

Kaimala S, Bisana S, Kumar S: Mammary gland stem cells: more puzzles than explanations. J Biosci. 2012, 37: 349-358.

La Porta CA: Thoughts about cancer stem cells in solid tumors. World J Stem Cells. 2012, 4: 17-20.

Mani SA, Guo W, Liao MJ, Eaton EN, Ayyanan A, Zhou AY, Brooks M, Reinhard F, Zhang CC, Shipitsin M, Campbell LL, Polyak K, Brisken C, Yang J, Weinberg RA: The epithelial-mesenchymal transition generates cells with properties of stem cells. Cell. 2008, 133: 704-715.

Polyak K, Weinberg RA: Transitions between epithelial and mesenchymal states: acquisition of malignant and stem cell traits. Nat Rev Cancer. 2009, 9: 265-273.

Scheel C, Weinberg RA: Phenotypic plasticity and epithelial-mesenchymal transitions in cancer and normal stem cells?. Int J Cancer. 2011, 129: 2310-2314.

Al-Hajj M, Wicha MS, Benito-Hernandez A, Morrison SJ, Clarke MF: Prospective identification of tumorigenic breast cancer cells. Proc Natl Acad Sci U S A. 2003, 100: 3983-3988.

Harrison H, Farnie G, Howell SJ, Rock RE, Stylianou S, Brennan KR, Bundred NJ, Clarke RB: Regulation of breast cancer stem cell activity by signaling through the Notch4 receptor. Cancer Res. 2010, 70: 709-718.

Muller V, Riethdorf S, Rack B, Janni W, Fasching P, Solomayer E, Aktas B, Kasimir-Bauer S, Pantel K, Fehm T, DETECT study group: Prognostic impact of circulating tumor cells assessed with the Cell Search AssayTM and AdnaTest BreastTM in metastatic breast cancer patients: the DETECT study. BCR. 2012, 14: R118-

Giordano A, Gao H, Cohen EN, Anfossi S, Khoury J, Hess K, Krishnamurthy S, Tin S, Cristofanilli M, Hortobagyi GN, Woodward WA, Lucci A, Reuben JM: Clinical of cancer stem cells in bone marrow of early breast cancer patients. Ann Oncol. 2013, [Epud ahead of print]

Baccelli I, Schneeweiss A, Riethdorf S, Stenzinger A, Schillert A, Vogel V, Klein C, Saini M, Bauerle T, Wallwiener M, Holland-Letz T, Höfner T, Sprick M, Scharpff M, Marmé F, Sinn HP, Pantel K, Weichert W, Trumpp A: Identification of a population of blood circulating tumor cells from breast cancer patients that initiates metastasis in a xenograft assay. Nat Biotechnol. 2013, 31: 539-544.

Willis L, Graham TA, Alarcon T, Alison MR, Tomlinson IP, Page KM: What can be learnt about disease progression in breast cancer dormancy from relapse data?. PloS one. 2013, 8: e62320-

Balic M, Lin H, Williams A, Datar RH, Cote RJ: Progress in circulating tumor cell capture and analysis: implications for cancer management. Expert Rev Mol Diagn. 2012, 12: 303-312.

Barriere G, Riouallon A, Renaudie J, Tartary M, Rigaud M: Mesenchymal and stemness circulating tumor cells in early breast cancer diagnosis. BMC Cancer. 2012, 12: 114-

Sceneay J, Smyth MJ, Moller A: The pre-metastatic niche: finding common ground. Cancer Metastasis Rev. 2013, [Epud ahead of print]

Peinado H, Lavotshkin S, Lyden D: The secreted factors responsible for pre-metastatic niche formation: old sayings and new thoughts. Semin Cancer Biol. 2011, 21: 139-146.

Nguyen DX, Bos PD, Massague J: Metastasis: from dissemination to organ-specific colonization. Nat Rev Cancer. 2009, 9: 274-284.

Hu G, Kang Y, Wang XF: From breast to the brain: unraveling the puzzle of metastasis organotropism. J Mole Cell Biol. 2009, 1: 3-5.

Hsieh SM, Look MP, Sieuwerts AM, Foekens JA, Hunter KW: Distinct inherited metastasis susceptibility exists for different breast cancer subtypes: a prognosis study. BCR. 2009, 11: R75-

Scheel C, Weinberg RA: Cancer stem cells and epithelial-mesenchymal transition: concepts and molecular links. Semin Cancer Biol. 2012, 22: 396-403.

Dave B, Mittal V, Tan NM, Chang JC: Epithelial-mesenchymal transition, cancer stem cells and treatment resistance. BCR. 2012, 14: 202-

Drasin DJ, Robin TP, Ford HL: Breast cancer epithelial-to-mesenchymal transition: examining the functional consequences of plasticity. BCR. 2011, 13: 226-

Giordano A, Gao H, Anfossi S, Cohen E, Mego M, Lee BN, Tin S, De Laurentiis M, Parker CA, Alvarez RH, Valero V, Ueno NT, De Placido S, Mani SA, Esteva FJ, Cristofanilli M, Reuben JM: Epithelial-mesenchymal transition and stem cell markers in patients with HER2-positive metastatic breast cancer. Mol Cancer Ther. 2012, 11: 2526-2534.

Kasimir-Bauer S, Hoffmann O, Wallwiener D, Kimmig R, Fehm T: Expression of stem cell and epithelial-mesenchymal transition markers in primary breast cancer patients with circulating tumor cells. BCR. 2012, 14: R15-

Chui MH: Insights into cancer metastasis from a clinicopathologic perspective: Epithelial-Mesenchymal Transition is not a necessary step. Int J Cancer. 2013, 132: 1487-1495.

Marchini C, Montani M, Konstantinidou G, Orru R, Mannucci S, Ramadori G, Gabrielli F, Baruzzi A, Berton G, Merigo F, Fin S, Iezzi M, Bisaro B, Sbarbati A, Zerani M, Galiè M, Amici A: Mesenchymal/stromal gene expression signature relates to basal-like breast cancers, identifies bone metastasis and predicts resistance to therapies. PloS one. 2010, 5: e14131-

Kim MY, Oskarsson T, Acharyya S, Nguyen DX, Zhang XH, Norton L, Massague J: Tumor self-seeding by circulating cancer cells. Cell. 2009, 139: 1315-1326.

Comen E, Norton L: Self-seeding in cancer. Recent Res Cancer Res. 2012, 195: 13-23.

Gorges TM, Tinhofer I, Drosch M, Rose L, Zollner TM, Krahn T, von Ahsen O: Circulating tumour cells escape from EpCAM-based detection due to epithelial-to-mesenchymal transition. BMC Cancer. 2012, 12: 178-

Kallergi G, Papadaki MA, Politaki E, Mavroudis D, Georgoulias V, Agelaki S: Epithelial to mesenchymal transition markers expressed in circulating tumour cells of early and metastatic breast cancer patients. BCR. 2011, 13: R59-

Yu M, Bardia A, Wittner BS, Stott SL, Smas ME, Ting DT, Isakoff SJ, Ciciliano JC, Wells MN, Shah AM, Concannon KF, Donaldson MC, Sequist LV, Brachtel E, Sgroi D, Baselga J, Ramaswamy S, Toner M, Haber DA, Maheswaran S: Circulating breast tumor cells exhibit dynamic changes in epithelial and mesenchymal composition. Science. 2013, 339: 580-584.

De Mattos-Arruda L, Cortes J, Santarpia L, Vivancos A, Tabernero J, Reis-Filho JS, Seoane J: Circulating tumour cells and cell-free DNA as tools for managing breast cancer. Nat Rev Clin Oncol. 2013, 10: 377-389.

Murtaza M, Dawson SJ, Tsui DW, Gale D, Forshew T, Piskorz AM, Parkinson C, Chin SF, Kingsbury Z, Wong AS, Marass F, Humphray S, Hadfield J, Bentley D, Chin TM, Brenton JD, Caldas C, Rosenfeld N: Non-invasive analysis of acquired resistance to cancer therapy by sequencing of plasma DNA. Nature. 2013, 497: 108-112.

Zhang F, Chen JY: Breast cancer subtyping from plasma proteins. BMC Med Genomics. 2013, 6: S6-

Corcoran C, Friel AM, Duffy MJ, Crown J, O’Driscoll L: Intracellular and extracellular microRNAs in breast cancer. Clin Chem. 2011, 57: 18-32.

Hendrix A, Hume AN: Exosome signaling in mammary gland development and cancer. Int J Dev Biol. 2011, 55: 879-887.

Marleau AM, Chen CS, Joyce JA, Tullis RH: Exosome removal as a therapeutic adjuvant in cancer. J Transl Med. 2012, 10: 134-

Eccles SA, Paon L: Breast cancer metastasis: when, where, how?. Lancet. 2005, 365: 1006-1007.

Eccles: Growth regulatory pathways contributing to organ selectivity of metastasis. Cancer Metastasis: Biologic Basis and Therapeutics. 2011, Cambridge: Cambridge University Press, 204-214.

Chapter Google Scholar

Mina LA, Sledge GW: Rethinking the metastatic cascade as a therapeutic target. Nat Rev Clin Oncol. 2011, 8: 325-332.

Wilson C, Holen I, Coleman RE: Seed, soil and secreted hormones: potential interactions of breast cancer cells with their endocrine/paracrine microenvironment and implications for treatment with bisphosphonates. Cancer Treat Rev. 2012, 38: 877-889.

Fidler IJ: The role of the organ microenvironment in brain metastasis. Semin Cancer Biol. 2011, 21: 107-112.

Peto R, Davies C, Godwin J, Gray R, Pan HC, Clarke M, Cutter D, Darby S, McGale P, Taylor C, Wang YC, Bergh J, Di Leo A, Albain K, Swain S, Piccart M, Pritchard K, Early Breast Cancer Trialists’ Collaborative G: Comparisons between different polychemotherapy regimens for early breast cancer: meta-analyses of long-term outcome among 100,000 women in 123 randomised trials. Lancet. 2012, 379: 432-444.

Darby S, McGale P, Correa C, Taylor C, Arriagada R, Clarke M, Cutter D, Davies C, Ewertz M, Godwin J, Gray R, Pierce L, Whelan T, Wang Y, Peto R, Early Breast Cancer Trialists’ Collaborative G: Effect of radiotherapy after breast-conserving surgery on 10-year recurrence and 15-year breast cancer death: meta-analysis of individual patient data for 10,801 women in 17 randomised trials. Lancet. 2011, 378: 1707-1716.

Davies C, Godwin J, Gray R, Clarke M, Cutter D, Darby S, McGale P, Pan HC, Taylor C, Wang YC, Dowsett M, Ingle J, Peto R, Early Breast Cancer Trialists’ Collaborative G: Relevance of breast cancer hormone receptors and other factors to the efficacy of adjuvant tamoxifen: patient-level meta-analysis of randomised trials. Lancet. 2011, 378: 771-784.

Senkus EKS, Penault-Llorca F, Poortmans P, Thompson A, Zackrisson S, Cardoso F: ESMO Guidelines Working Group. Ann Oncol. 2013, doi: 10.1093/annonc/mdt284

Khoury T: Delay to formalin fixation alters morphology and immunohistochemistry for breast carcinoma. Appl Immunohistochem Mol Morphol. 2012, 20: 531-542.

Dale DC: Poor prognosis in elderly patients with cancer: the role of bias and undertreatment. J Support Oncol. 2003, 1: 11-17.

Seah MD, Chan PM: Rethinking undertreatment in elderly breast cancer patients. Asian J Surg. 2009, 32: 71-75.

Harder H, Ballinger R, Langridge C, Ring A, Fallowfield LJ: Adjuvant chemotherapy in elderly women with breast cancer: patients’ perspectives on information giving and decision making. Psychooncology. 2013, doi: 10.1002/pon.3338

Ring A, Harder H, Langridge C, Ballinger RS, Fallowfield LJ: Adjuvant chemotherapy in elderly women with breast cancer (AChEW): an observational study identifying MDT perceptions and barriers to decision making. Ann Oncol. 2013, 24: 1211-1219.

Armes J, Crowe M, Colbourne L, Morgan H, Murrells T, Oakley C, Palmer N, Ream E, Young A, Richardson A: Patients’ supportive care needs beyond the end of cancer treatment: a prospective, longitudinal survey. J Clin Oncol. 2009, 27: 6172-6179.

Maguire P: Psychological aspects. ABC of Breast Diseases. 2002, London: BMJ Books, 150-153. 2nd Edition. Edited by Dixon M.

Hulbert-Williams N, Neal R, Morrison V, Hood K, Wilkinson C: Anxiety, depression and quality of life after cancer diagnosis: what psychosocial variables best predict how patients adjust?. Psychooncology. 2011, doi: 10.1002/pon.1980

Jacobsen PB: Screening for psychological distress in cancer patients: challenges and opportunities. J Clin Oncol. 2007, 25: 4526-4527.

The International. Psycho-Oncology Society. http://www.ipos-society.org/about/news/standards_news.aspx ,

Thompson AM, Moulder-Thompson SL: Neoadjuvant treatment of breast cancer. Ann Oncol. 2012, 23: x231-x236.

Bartelink H, Horiot JC, Poortmans PM, Struikmans H, Van den Bogaert W, Fourquet A, Jager JJ, Hoogenraad WJ, Oei SB, Warlam-Rodenhuis CC, Pierart M, Collette L: Impact of a higher radiation dose on local control and survival in breast-conserving therapy of early breast cancer: 10-year results of the randomized boost versus no boost EORTC 22881–10882 trial. J Clin Oncol. 2007, 25: 3259-3265.

Whelan TJ, Pignol JP, Levine MN, Julian JA, MacKenzie R, Parpia S, Shelley W, Grimard L, Bowen J, Lukka H, Perera F, Fyles A, Schneider K, Gulavita S, Freeman C: Long-term results of hypofractionated radiation therapy for breast cancer. N Engl J Med. 2010, 362: 513-520.

Group ST, Bentzen SM, Agrawal RK, Aird EG, Barrett JM, Barrett-Lee PJ, Bliss JM, Brown J, Dewar JA, Dobbs HJ, Haviland JS, Hoskin PJ, Hopwood P, Lawton PA, Magee BJ, Mills J, Morgan DA, Owen JR, Simmons S, Sumo G, Sydenham MA, Venables K, Yarnold JR: The UK Standardisation of Breast Radiotherapy (START) Trial A of radiotherapy hypofractionation for treatment of early breast cancer: a randomised trial. Lancet Oncol. 2008, 9: 331-341.

Group ST, Bentzen SM, Agrawal RK, Aird EG, Barrett JM, Barrett-Lee PJ, Bentzen SM, Bliss JM, Brown J, Dewar JA, Dobbs HJ, Haviland JS, Hoskin PJ, Hopwood P, Lawton PA, Magee BJ, Mills J, Morgan DA, Owen JR, Simmons S, Sumo G, Sydenham MA, Venables K, Yarnold JR: The UK Standardisation of Breast Radiotherapy (START) Trial B of radiotherapy hypofractionation for treatment of early breast cancer: a randomised trial. Lancet. 2008, 371: 1098-1107.

Vaidya JS, Joseph DJ, Tobias JS, Bulsara M, Wenz F, Saunders C, Alvarado M, Flyger HL, Massarut S, Eiermann W, Keshtgar M, Dewar J, Kraus-Tiefenbacher U, Sütterlin M, Esserman L, Holtveg HM, Roncadin M, Pigorsch S, Metaxas M, Falzon M, Matthews A, Corica T, Williams NR, Baum M: Targeted intraoperative radiotherapy versus whole breast radiotherapy for breast cancer (TARGIT-A trial): an international, prospective, randomised, non-inferiority phase 3 trial. Lancet. 2010, 376: 91-102.

Rampinelli C, Bellomi M, Ivaldi GB, Intra M, Raimondi S, Meroni S, Orecchia R, Veronesi U: Assessment of pulmonary fibrosis after radiotherapy (RT) in breast conserving surgery: comparison between conventional external beam RT (EBRT) and intraoperative RT with electrons (ELIOT). Technol Cancer Res Treat. 2011, 10: 323-329.

Hannoun-Levi JM, Resch A, Gal J, Kauer-Dorner D, Strnad V, Niehoff P, Loessl K, Kovacs G, Van Limbergen E, Polgar C, On behalf of the GEC-ESTRO Breast Cancer Working Group: Accelerated partial breast irradiation with interstitial brachytherapy as second conservative treatment for ipsilateral breast tumour recurrence: Multicentric study of the GEC-ESTRO Breast Cancer Working Group. Radiother Oncol. 2013, doi: 1016/j.radonc.2013.03.026

Smith BD, Arthur DW, Buchholz TA, Haffty BG, Hahn CA, Hardenbergh PH, Julian TB, Marks LB, Todor DA, Vicini FA, Whelan TJ, White J, Wo JY, Harris JR: Accelerated partial breast irradiation consensus statement from the American Society for Radiation Oncology (ASTRO). Int J Radiat Oncol Biol Phys. 2009, 74: 987-1001.

Polgar C, Van Limbergen E, Potter R, Kovacs G, Polo A, Lyczek J, Hildebrandt G, Niehoff P, Guinot JL, Guedea F, Johansson B, Ott OJ, Major T, Strnad V, GEC-ESTRO Breast Cancer Working Group: Patient selection for accelerated partial-breast irradiation (APBI) after breast-conserving surgery: recommendations of the Groupe Europeen de Curietherapie-European Society for Therapeutic Radiology and Oncology (GEC-ESTRO) breast cancer working group based on clinical evidence (2009). Radiother Oncol. 2010, 94: 264-273.

Tinterri C, Gatzemeier W, Zanini V, Regolo L, Pedrazzoli C, Rondini E, Amanti C, Gentile G, Taffurelli M, Fenaroli P, Tondini C, Saccetto G, Sismondi P, Murgo R, Orlandi M, Cianchetti E, Andreoli C: Conservative surgery with and without radiotherapy in elderly patients with early-stage breast cancer: a prospective randomised multicentre trial. Breast. 2009, 18: 373-377.

Hughes KS, Schnaper LA, Cirrincione C, Berry DA, McCormick B, Muss HB, Shank B, Hudis C, Winer EP, Smith BL: ASCO Annual Meeting 2010. 2010, Lumpectomy plus tamoxifen with or without irradiation in women age 70 or older with early breast cancer, Journal of Clinical Oncology,

Lipkus IM, Peters E, Kimmick G, Liotcheva V, Marcom P: Breast cancer patients’ treatment expectations after exposure to the decision aid program adjuvant online: the influence of numeracy. Med Decis Making. 2010, 30: 464-473.

Fallowfield L, Jenkins V, Farewell V, Saul J, Duffy A, Eves R: Efficacy of a Cancer Research UK communication skills training model for oncologists: a randomised controlled trial. Lancet. 2002, 359: 650-656.

El Turabi A, Abel GA, Roland M, Lyratzopoulos G: Variation in reported experience of involvement in cancer treatment decision making: evidence from the National Cancer Patient Experience Survey. Br J Cancer. 2013, 109: 780-787.

Fleissig A, Fallowfield LJ, Langridge CI, Johnson L, Newcombe RG, Dixon JM, Kissin M, Mansel RE: Post-operative arm morbidity and quality of life. Results of the ALMANAC randomised trial comparing sentinel node biopsy with standard axillary treatment in the management of patients with early breast cancer. Breast Cancer Res Treat. 2006, 95: 279-293.

Giuliano AE, Hunt KK, Ballman KV, Beitsch PD, Whitworth PW, Blumencranz PW, Leitch AM, Saha S, McCall LM, Morrow M: Axillary dissection vs no axillary dissection in women with invasive breast cancer and sentinel node metastasis: a randomized clinical trial. JAMA. 2011, 305: 569-575.

Rutgers EJ, Donker M, Straver ME, Meijnen P, Van De Velde CJ, Mansel RE, Westenberg H, Orzales L, Bouma WH, van der Mijle H, Nieuwenhuijzen P, Sanne C, Veltkamp LS, Messina CGM, Duez NJ, Hurkmans C, Bogaerts J, van Tienhoven G: ASCO Annual Meeting. 2013, Radiotherapy or surgery of the axilla after a positive sentinel node in breast cancer patients: final analysis of the EORTC AMAROS trial (10981/22023), Journal of Clinical Oncology,

Smith BD: Using chemotherapy response to personalize choices regarding locoregional therapy: a new era in breast cancer treatment?. J Clin Oncol. 2012, 30: 3913-3915.

Azim HA, Michiels S, Zagouri F, Delaloge S, Filipits M, Namer M, Neven P, Symmans WF, Thompson A, Andre F, Loi S, Swanton C: Utility of prognostic genomic tests in breast cancer practice: The IMPAKT 2012 Working Group Consensus Statement. Ann Oncol. 2013, 24: 647-654.

Wei S, Liu L, Zhang J, Bowers J, Gowda GA, Seeger H, Fehm T, Neubauer HJ, Vogel U, Clare SE, Raferty D: Metabolomics approach for predicting response to neoadjuvant chemotherapy for breast cancer. Mol Oncol. 2013, 7: 297-307.

Dowsett M, Cuzick J, Wale C, Forbes J, Mallon EA, Salter J, Quinn E, Dunbier A, Baum M, Buzdar A, Howell A, Bugarini R, Baehner FL, Shak S: Prediction of risk of distant recurrence using the 21-gene recurrence score in node-negative and node-positive postmenopausal patients with breast cancer treated with anastrozole or tamoxifen: a TransATAC study. J Clin Oncol. 2010, 28: 1829-1834.

Albain KS, Barlow WE, Shak S, Hortobagyi GN, Livingston RB, Yeh IT, Ravdin P, Bugarini R, Baehner FL, Davidson NE, Sledge GW, Winer EP, Hudis C, Ingle JN, Perez EA, Pritchard KI, Shepherd L, Gralow JR, Yoshizawa C, Allred DC, Osborne CK, Hayes DF, Breast Cancer Intergroup of North America: Prognostic and predictive value of the 21-gene recurrence score assay in postmenopausal women with node-positive, oestrogen-receptor-positive breast cancer on chemotherapy: a retrospective analysis of a randomised trial. Lancet Oncol. 2010, 11: 55-65.

Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, Baehner FL, Walker MG, Watson D, Park T, Hiller W, Fisher ER, Wickerham DL, Bryant J, Wolmark N: A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004, 351: 2817-2826.

Coates PJ, Appleyard MV, Murray K, Ackland C, Gardner J, Brown DC, Adamson DJ, Jordan LB, Purdie CA, Munro AJ, Wright EG, Dewar JA, Thompson AM: Differential contextual responses of normal human breast epithelium to ionizing radiation in a mouse xenograft model. Can Res. 2010, 70: 9808-9815.

Arslan UY, Oksuzoglu B, Aksoy S, Harputluoglu H, Turker I, Ozisik Y, Dizdar O, Altundag K, Alkis N, Zengin N: Breast cancer subtypes and outcomes of central nervous system metastases. Breast. 2011, 20: 562-567.

Rennert G, Pinchev M, Rennert HS: Use of bisphosphonates and risk of postmenopausal breast cancer. J Clin Oncol. 2010, 28: 3577-3581.

Chlebowski RT, Col N: Bisphosphonates and breast cancer prevention. Anticancer Agents Med Chem. 2012, 12: 144-150.

Coleman RE: Adjuvant bone-targeted therapy to prevent metastasis: lessons from the AZURE study. Curr Opin Support Palliat Care. 2012, 6: 322-329.

Paterson AH, Anderson SJ, Lembersky BC, Fehrenbacher L, Falkson CI, King KM, Weir LM, Brufsky AM, Dakhil S, Lad T, Baez-Diaz L, Gralow JR, Robidoux A, Perez EA, Zheng P, Geyer CE, Swain S, Costantino JP, Mamounas EP, Wolmark N: Oral clodronate for adjuvant treatment of operable breast cancer (National Surgical Adjuvant Breast and Bowel Project protocol B-34): a multicentre, placebo-controlled, randomised trial. Lancet Oncol. 2012, 13: 734-742.

Gnant M, Dubsky P, Hadji P: Bisphosphonates: prevention of bone metastases in breast cancer. Recent Results Cancer Res. 2012, 192: 65-91.

Comen E, Norton L, Massague J: Clinical implications of cancer self-seeding. Nat Rev Clin Oncol. 2011, 8: 369-377.

Azim H, Azim HA: Targeting RANKL in breast cancer: bone metastasis and beyond. Expert Rev Anticancer Ther. 2013, 13: 195-201.

Drooger JC, van der Padt A, Sleijfer S, Jager A: Denosumab in breast cancer treatment. Eur J Pharmacol. 2013, doi: 10.1016/j.ejphar.2013.03.034

Formenti SC, Demaria S: Radiation therapy to convert the tumor into an in situ vaccine. Int J Radiat Oncol Biol Phys. 2012, 84: 879-880.

Liauw SL, Connell PP, Weichselbaum RR: New paradigms and future challenges in radiation oncology: an update of biological targets and technology. Sci Transl Med. 2013, 5: 173sr172-

Coles CE, Brunt AM, Wheatley D, Mukesh MB, Yarnold JR: Breast radiotherapy: less is more?. Clin Oncol (R Coll Radiol). 2013, 25: 127-134.

Yarnold J, Bentzen SM, Coles C, Haviland J: Hypofractionated whole-breast radiotherapy for women with early breast cancer: myths and realities. Int J Radiat Oncol Biol Phys. 2011, 79: 1-9.

Mannino M, Yarnold JR: Local relapse rates are falling after breast conserving surgery and systemic therapy for early breast cancer: can radiotherapy ever be safely withheld?. Radiother Oncol. 2009, 90: 14-22.

Blamey RW, Bates T, Chetty U, Duffy SW, Ellis IO, George D, Mallon E, Mitchell MJ, Monypenny I, Morgan DA, Macmillan RD, Patnick J, Pinder SE: Radiotherapy or tamoxifen after conserving surgery for breast cancers of excellent prognosis: British Association of Surgical Oncology (BASO) II trial. Eur J Cancer. 2013, 49: 2294-2302.

Kunkler I: Adjuvant chest wall radiotherapy for breast cancer: black, white and shades of grey. Eur J Surg Oncol. 2010, 36: 331-334.

Critchley AC, Thompson AM, Chan HY, Reed MW: Current controversies in breast cancer surgery. Clin Oncol (R Coll Radiol). 2013, 25: 101-108.

Riou O, Lemanski C, Guillaumon V, Lauche O, Fenoglietto P, Dubois JB, Azria D: Role of the radiotherapy boost on local control in ductal carcinoma in situ. Int J Surg Oncol. 2012, 2012: 748196-

PubMed PubMed Central Google Scholar

Kirkbride P, Hoskin PJ: Implementation of stereotactic ablative radiotherapy (stereotactic body radiotherapy). Clin Oncol (R Coll Radiol). 2012, 24: 627-628.

Somaiah N, Yarnold J, Lagerqvist A, Rothkamm K, Helleday T: Homologous recombination mediates cellular resistance and fraction size sensitivity to radiation therapy. Radiother Oncol. 2013, 1008: 155-1561.

Dowsett M, Nielsen TO, A’Hern R, Bartlett J, Coombes RC, Cuzick J, Ellis M, Henry NL, Hugh JC, Lively T, McShane L, Paik S, Penault-Llorca E, Prudkin L, Regan M, Salter J, Sotiriou C, Smith IE, Viale G, Zujewski JA, Hayes DF, International Ki-67 in Breast Cancer Working Group: Assessment of Ki67 in breast cancer: recommendations from the International Ki67 in Breast Cancer working group. J Natl Cancer Inst. 2011, 103: 1656-1664.

Paik S, Tang G, Shak S, Kim C, Baker J, Kim W, Cronin M, Baehner FL, Watson D, Bryant J, Costantino JP, Geyer CE JR, Wickerham DL, Wolmark N: Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol. 2006, 24: 3726-3734.

van de Vijver MJ, He YD, van’t Veer LJ, Dai H, Hart AA, Voskuil DW, Schreiber GJ, Peterse JL, Roberts C, Marton MJ, Parrish M, Atsma D, Witteveen A, Glas A, Delahaye L, van der Velds T, Bartelink H, Rodenhuis S, Rutgers ET, Friend SH, Bernards R: A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002, 347: 1999-2009.

Goss PE, Ingle JN, Martino S, Robert NJ, Muss HB, Piccart MJ, Castiglione M, Tu D, Shepherd LE, Pritchard KI, Livingston RB, Davidson NE, Norton L, Peres ES, Abrams JS, Cameron DA, Palmer MJ, Pater JL, et al: Randomized trial of letrozole following tamoxifen as extended adjuvant therapy in receptor-positive breast cancer: updated findings from NCIC CTG MA.17. J Natl Cancer Inst. 2005, 97: 1262-1271.

Davies C, Pan H, Godwin J, Gray R, Arriagada R, Raina V, Abraham M, Medeiros Alencar VH, Badran A, Bonfill X, Bradbury J, Clarke M, Collins R, Davis SR, Delmestri A, Fores JF, Haddad P, Hou MF, Inbar M, Khaled H, Kielanowska J, Kwan WH, Mathew BS, Mittra I, Muller B, Nicolucci A, Peralta O, Pernas F, Petruzelka L, Pienkowski T, et al: Long-term effects of continuing adjuvant tamoxifen to 10 years versus stopping at 5 years after diagnosis of oestrogen receptor-positive breast cancer: ATLAS, a randomised trial. Lancet. 2013, 381: 805-816.

Jakesz R, Jonat W, Gnant M, Mittlboeck M, Greil R, Tausch C, Hilfrich J, Kwasny W, Menzel C, Samonigg H, Seifert M, Gademann G, Kaufmann M, Woldgang J, ABCSG and the GABG: Switching of postmenopausal women with endocrine-responsive early breast cancer to anastrozole after 2 years’ adjuvant tamoxifen: combined results of ABCSG trial 8 and ARNO 95 trial. Lancet. 2005, 366: 455-462.

Goldhirsch A, Ingle JN, Gelber RD, Coates AS, Thurlimann B, Senn HJ: Panel m: Thresholds for therapies: highlights of the St Gallen International Expert Consensus on the primary therapy of early breast cancer 2009. Ann Oncol. 2009, 20: 1319-1329.

Osborne CK, Neven P, Dirix LY, Mackey JR, Robert J, Underhill C, Schiff R, Gutierrez C, Migliaccio I, Anagnostou VK, Rimm DL, Magill P, Sellers M: Gefitinib or placebo in combination with tamoxifen in patients with hormone receptor-positive metastatic breast cancer: a randomized phase II study. Clin Cancer Res. 2011, 17: 1147-1159.

Carlson RW, O’Neill A, Vidaurre T, Gomez HL, Badve SS, Sledge GW: A randomized trial of combination anastrozole plus gefitinib and of combination fulvestrant plus gefitinib in the treatment of postmenopausal women with hormone receptor positive metastatic breast cancer. Breast Cancer Res Treat. 2012, 133: 1049-1056.

Baselga J, Bradbury I, Eidtmann H, Di Cosimo S, de Azambuja E, Aura C, Gomez H, Dinh P, Fauria K, Van Dooren V, Aktan G, Goldkirsch A, Chang TW, Horvath Z, Coccia-Portugal M, Dormont J, Tseng LM, Kunz G, Sohn JH, Semiglazov V, Lerzo G, Palacova M, Probachai V, Pusztai L, Untch M, Gelber RD, Piccart-Gebhart M, NeoALTTO Study Team: Lapatinib with trastuzumab for HER2-positive early breast cancer (NeoALTTO): a randomised, open-label, multicentre, phase 3 trial. Lancet. 2012, 379: 633-640.

Hamilton-Burke W, Coleman L, Cummings M, Green CA, Holliday DL, Horgan K, Maraqa L, Peter MB, Pollock S, Shaaban AM, Smith L, Speirs V: Phosphorylation of estrogen receptor beta at serine 105 is associated with good prognosis in breast cancer. Am J Pathol. 2010, 177: 1079-1086.

O’Hara J, Vareslija D, McBryan J, Bane F, Tibbitts P, Byrne C, Conroy RM, Hao Y, Gaora PO, Hill AD, McIlroy M, Young LS: AIB1:ERalpha transcriptional activity is selectively enhanced in aromatase inhibitor-resistant breast cancer cells. Clin Cancer Res. 2012, 18: 3305-3315.

Santen RJ, Fan P, Zhang Z, Bao Y, Song RX, Yue W: Estrogen signals via an extra-nuclear pathway involving IGF-1R and EGFR in tamoxifen-sensitive and -resistant breast cancer cells. Steroids. 2009, 74: 586-594.

Ross-Innes CS, Stark R, Teschendorff AE, Holmes KA, Ali HR, Dunning MJ, Brown GD, Gojis O, Ellis IO, Green AR, Ali S, Chin SF, Palmieri C, Caldas C, Carroll JS: Differential oestrogen receptor binding is associated with clinical outcome in breast cancer. Nature. 2012, 481: 389-393.

Di Leva G, Gasparini P, Piovan C, Ngankeu A, Garofalo M, Taccioli C, Iorio MV, Li M, Volinia S, Alder H, Nakamura T, Nuovo G, Liu Y, Nephew KP, Croce CM: MicroRNA cluster 221–222 and estrogen receptor alpha interactions in breast cancer. J Natl Cancer Inst. 2010, 102: 706-721.

Dunn BK, Jegalian K, Greenwald P: Biomarkers for early detection and as surrogate endpoints in cancer prevention trials: issues and opportunities. Recent Results Cancer Res. 2011, 188: 21-47.

Pece S, Tosoni D, Confalonieri S, Mazzarol G, Vecchi M, Ronzoni S, Bernard L, Viale G, Pelicci PG, Di Fiore PP: Biological and molecular heterogeneity of breast cancers correlates with their cancer stem cell content. Cell. 2010, 140: 62-73.

Giamas G, Filipovic A, Jacob J, Messier W, Zhang H, Yang D, Zhang W, Shifa BA, Photiou A, Tralau-Stewart C, Castellano L, Green AR, Coombes RC, Ellis IO, Ali S, Lenz HJ, Stebbing J: Kinome screening for regulators of the estrogen receptor identifies LMTK3 as a new therapeutic target in breast cancer. Nat Med. 2011, 17: 715-719.

Johnston S, Pippen J, Pivot X, Lichinitser M, Sadeghi S, Dieras V, Gomez HL, Romieu G, Manikhas A, Kennedy MJ, Press MF, Maltzman J, Florance A, O’Rourke L, Oliva C, Stein S, Pegram M: Lapatinib combined with letrozole versus letrozole and placebo as first-line therapy for postmenopausal hormone receptor-positive metastatic breast cancer. J Clin Oncol. 2009, 27: 5538-5546.

Elsberger B, Paravasthu DM, Tovey SM, Edwards J: Shorter disease-specific survival of ER-positive breast cancer patients with high cytoplasmic Src kinase expression after tamoxifen treatment. J Cancer Res Clin Oncol. 2012, 138: 327-332.

Iorns E, Turner NC, Elliott R, Syed N, Garrone O, Gasco M, Tutt AN, Crook T, Lord CJ, Ashworth A: Identification of CDK10 as an important determinant of resistance to endocrine therapy for breast cancer. Cancer Cell. 2008, 13: 91-104.

Turner N, Pearson A, Sharpe R, Lambros M, Geyer F, Lopez-Garcia MA, Natrajan R, Marchio C, Iorns E, Mackay A, Gillett C, Grigoriadis A, Tutt A, Reis-Filho JS: FGFR1 amplification drives endocrine therapy resistance and is a therapeutic target in breast cancer. Cancer Res. 2010, 70: 2085-2094.

Higgins MJ, Baselga J: Targeted therapies for breast cancer. J Clin Invest. 2011, 121: 3797-3803.

Gnant M: Overcoming endocrine resistance in breast cancer: importance of mTOR inhibition. Expert Rev Anticancer Ther. 2012, 12: 1579-1589.

Zardavas D, Baselga J, Piccart M: Emerging targeted agents in metastatic breast cancer. Nat Rev Clin Oncol. 2013, 10: 191-210.

Moulder S, Moroney J, Helgason T, Wheler J, Booser D, Albarracin C, Morrow PK, Koenig K, Kurzrock R: Responses to liposomal Doxorubicin, bevacizumab, and temsirolimus in metaplastic carcinoma of the breast: biologic rationale and implications for stem-cell research in breast cancer. J Clin Oncol. 2011, 29: e572-e575.

Hoelder S, Clarke PA, Workman P: Discovery of small molecule cancer drugs: successes, challenges and opportunities. Mol Oncol. 2012, 6: 155-176.

Kauselmann G, Dopazo A, Link W: Identification of disease-relevant genes for molecularly-targeted drug discovery. Curr Cancer Drug Targets. 2012, 12: 1-13.

Swain SM, Kim SB, Cortes J, Ro J, Semiglazov V, Campone M, Ciruelos E, Ferrero JM, Schneeweiss A, Knott A, Clark E, Ross G, Benyunes MC, Baselga J: Pertuzumab, trastuzumab, and docetaxel for HER2-positive metastatic breast cancer (CLEOPATRA study): overall survival results from a randomised, double-blind, placebo-controlled, phase 3 study. Lancet Oncol. 2013, 14: 461-471.

Criscitiello C, Azim HA, Agbor-Tarh D, de Azambuja E, Piccart M, Baselga J, Eidtmann H, Di Cosimo S, Bradbury I, Rubio IT: Factors associated with surgical management following neoadjuvant therapy in patients with primary HER2-positive breast cancer: results from the NeoALTTO phase III trial. Ann Oncol. 2013, 24: 1980-1985.

Goldhirsch A, Piccart-Gebhart MJ, Procter M, Azambuja E de, Weber HA, Untch M, Smith I, Gianni L, Jackisch C, Cameron D, Bell R, Dowsett M, Gelber RD, Leyland-Jones B, Baselga J: The HERA Study Team HERA TRIAL: 2 years versus 1 year of trastuzumab after adjuvant chemotherapy in women with HER2-positive early breast cancer at 8 years of median follow up. Cancer Research. 72 (24): December 15, 2012 Supplement 3;

Pivot X, Romieu G, Debled M, Pierga JY, Kerbrat P, Bachelot T, Lortholary A, Espie M, Fumoleau P, Serin D, Jacquin JP, Jouannaud C, Rios M, Abadie-Lacourtoisie S, Tubiana-Mathieu N, Cany L, Catala S, Khayat D, Pauporte I, Kramar A, PHARE trial investigators: 6 months versus 12 months of adjuvant trastuzumab for patients with HER2-positive early breast cancer (PHARE): a randomised phase 3 trial. Lancet Oncol. 2013, 14: 741-748.

Tenori L, Oakman C, Claudino WM, Bernini P, Cappadona S, Nepi S, Biganzoli L, Arbushites MC, Luchinat C, Bertini I, Di Leo A: Exploration of serum metabolomic profiles and outcomes in women with metastatic breast cancer: a pilot study. Mol Oncol. 2012, 6: 437-444.

Duncan JS, Whittle MC, Nakamura K, Abell AN, Midland AA, Zawistowski JS, Johnson NL, Granger DA, Jordan NV, Darr DB, Usary J, Kuan PF, Smalley DM, Major B, He X, Hoadley KA, Zhou B, Sharpless NE, Perou C, Kim WY, Gomez SM, Chen X, Jin J, Frye SV, Earp HS, Graves LM, Johnson GL: Dynamic reprogramming of the kinome in response to targeted MEK inhibition in triple-negative breast cancer. Cell. 2012, 149: 307-321.

Heiser LM, Sadanandam A, Kuo WL, Benz SC, Goldstein TC, Ng S, Gibb WJ, Wang NJ, Ziyad S, Tong F, Bayani N, Hu Z, Billig JI, Dueregger A, Lewis S, Jakkula L, Korkola JE, Durinck S, Pepin F, Guan Y, Purdom E, Neuvial P, Bengtsson H, Wood KW, Smith PG, Vassiley LT, Hennessy BT, Greshock J, Bachman KE, Hardwicke MA, et al: Subtype and pathway specific responses to anticancer compounds in breast cancer. Proc Natl Acad Sci U S A. 2012, 109: 2724-2729.

Kelly CM, Buzdar AU: Using multiple targeted therapies in oncology: considerations for use, and progress to date in breast cancer. Drugs. 2013, 73: 505-515.

Sultana R, Abdel-Fatah T, Abbotts R, Hawkes C, Albarakati N, Seedhouse C, Ball G, Chan S, Rakha EA, Ellis IO, Madhusudan S: Targeting XRCC1 deficiency in breast cancer for personalized therapy. Cancer Res. 2013, 73: 1621-1634.

Miller WR, Larionov A, Anderson TJ, Evans DB, Dixon JM: Sequential changes in gene expression profiles in breast cancers during treatment with the aromatase inhibitor, letrozole. Pharmacogenomics J. 2012, 12: 10-21.

Larionov AFD, Caldwell H, Sims A, Fawkes A, Murphy L, Renshaw L, Dixon J: Gene expression profiles of endocrine resistant breast tumours. Cancer Res. 2009, 69: 809-810.

Bartlett JM, Brookes CL, Robson T, van de Velde CJ, Billingham LJ, Campbell FM, Grant M, Hasenburg A, Hille ET, Kay C, Kieback DG, Putter H, Markopoulos C, Kranenbarg E, Mallon EA, Dirix L, Seynaeve C, Rea D: Estrogen receptor and progesterone receptor as predictive biomarkers of response to endocrine therapy: a prospectively powered pathology study in the Tamoxifen and Exemestane Adjuvant Multinational trial. J Clin Oncol. 2011, 29: 1531-1538.

Honma N, Horii R, Iwase T, Saji S, Younes M, Takubo K, Matsuura M, Ito Y, Akiyama F, Sakamoto G: Clinical importance of estrogen receptor-beta evaluation in breast cancer patients treated with adjuvant tamoxifen therapy. J Clin Oncol. 2008, 26: 3727-3734.

Yan Y, Li X, Blanchard A, Bramwell VH, Pritchard KI, Tu D, Shepherd L, Myal Y, Penner C, Watson PH, Leygue E, Murphy LC: Expression of both estrogen receptor-beta 1 (ER-beta1) and its co-regulator steroid receptor RNA activator protein (SRAP) are predictive for benefit from tamoxifen therapy in patients with estrogen receptor-alpha (ER-alpha)-negative early breast cancer (EBC). Ann Oncol. 2013, 24: 1986-1993.

De Amicis F, Thirugnansampanthan J, Cui Y, Selever J, Beyer A, Parra I, Weigel NL, Herynk MH, Tsimelzon A, Lewis MT, Chamness GC, Hilsenbeck SG, Ando S, Fuqua SA: Androgen receptor overexpression induces tamoxifen resistance in human breast cancer cells. Breast Cancer Res Treat. 2010, 121: 1-11.

Garay JP, Park BH: Androgen receptor as a targeted therapy for breast cancer. Am J Cancer Res. 2012, 2: 434-445.

Fan P, Yue W, Wang JP, Aiyar S, Li Y, Kim TH, Santen RJ: Mechanisms of resistance to structurally diverse antiestrogens differ under premenopausal and postmenopausal conditions: evidence from in vitro breast cancer cell models. Endocrinology. 2009, 150: 2036-2045.

Thompson AM, Jordan LB, Quinlan P, Anderson E, Skene A, Dewar JA, Purdie CA: Prospective comparison of switches in biomarker status between primary and recurrent breast cancer: the Breast Recurrence In Tissues Study (BRITS). Breast Cancer Res. 2010, 12: R92-

Amir E, Clemons M, Purdie CA, Miller N, Quinlan P, Geddie W, Coleman RE, Freedman OC, Jordan LB, Thompson AM: Tissue confirmation of disease recurrence in breast cancer patients: pooled analysis of multi-centre, multi-disciplinary prospective studies. Cancer Treat Rev. 2012, 38: 708-714.

Moussa O, Purdie C, Vinnicombe S, Thompson AM: Biomarker discordance: prospective and retrospective evidence that biopsy of recurrent disease is of clinical utility. Cancer Biomark. 2012, 12: 231-239.

Makubate B, Donnan PT, Dewar JA, Thompson AM, McCowan C: Cohort study of adherence to adjuvant endocrine therapy, breast cancer recurrence and mortality. Br J Cancer. 2013, 108: 1515-1524.

Thompson AM, Johnson A, Quinlan P, Hillman G, Fontecha M, Bray SE, Purdie CA, Jordan LB, Ferraldeschi R, Latif A, Hadfield KD, Clarke RB, Ashcroft L, Evans DG, Howell A, Nikoloff M, Lawrence J, Newman WG: Comprehensive CYP2D6 genotype and adherence affect outcome in breast cancer patients treated with tamoxifen monotherapy. Breast Cancer Res Treat. 2011, 125: 279-287.

Loi S, Sirtaine N, Piette F, Salgado R, Viale G, Van Eenoo F, Rouas G, Francis P, Crown JP, Hitre E, de Azambuja E, Quinaux E, Di Leo A, Michiels S, Piccart MJ, Sotiriou C: Prognostic and predictive value of tumor-infiltrating lymphocytes in a phase III randomized adjuvant breast cancer trial in node-positive breast cancer comparing the addition of docetaxel to doxorubicin with doxorubicin-based chemotherapy: BIG 02–98. J Clin Oncol. 2013, 31: 860-867.

Group BIGC, Mouridsen H, Giobbie-Hurder A, Goldhirsch A, Thurlimann B, Paridaens R, Smith I, Mauriac L, Forbes J, Price KN, Regan MM, Gelber RD, Coates AS: Letrozole therapy alone or in sequence with tamoxifen in women with breast cancer. N Engl J Med. 2009, 361: 766-776.

Coombes RC, Kilburn LS, Snowdon CF, Paridaens R, Coleman RE, Jones SE, Jassem J, Van de Velde CJ, Delozier T, Alvarez I, Del Mastro L, Ortmann O, Diedrich K, Coates AS, Bajetta E, Homberg SB, Dodwell D, Mickiewicz E, Anderson J, Lonning PE, Cocconi G, Forbes J, Castiglione M, Stuart N, Stewart A, Fallowfield LJ, Bertelli G, Hall E, Bogle RG, Carpentieri M, et al: Survival and safety of exemestane versus tamoxifen after 2–3 years’ tamoxifen treatment (Intergroup Exemestane Study): a randomised controlled trial. Lancet. 2007, 369: 559-570.

Palmieri C, Shah D, Krell J, Gojis O, Hogben K, Riddle P, Ahmad R, Tat T, Fox K, Porter A, Mahmoud S, Kirschke S, Shousha S, Gudi M, Coombes RC, Leonard R, Cleator S: Management and outcome of HER2-positive early breast cancer treated with or without trastuzumab in the adjuvant trastuzumab era. Clin Breast Cancer. 2011, 11: 93-102.

Fontein DB, Seynaeve C, Hadji P, Hille ET, van de Water W, Putter H, Kranenbarg EM, Hasenburg A, Paridaens RJ, Vannetzel JM, Markopoulos C, Hoxumi Y, Bartlett JM, Jones SE, Rea DW, Nortier JW, van de Velde CJ: Specific adverse events predict survival benefit in patients treated with tamoxifen or aromatase inhibitors: an international tamoxifen exemestane adjuvant multinational trial analysis. J Clin Oncol. 2013, 31: 2257-2264.

Blackwell KL, Burstein HJ, Storniolo AM, Rugo HS, Sledge G, Aktan G, Ellis C, Florance A, Vukelja S, Bischoff J, Baselga J, O’Shaughnessy J: Overall survival benefit with lapatinib in combination with trastuzumab for patients with human epidermal growth factor receptor 2-positive metastatic breast cancer: final results from the EGF104900 Study. J Clin Oncol. 2012, 30: 2585-2592.

Gianni L, Pienkowski T, Im YH, Roman L, Tseng LM, Liu MC, Lluch A, Staroslawska E, de la Haba-Rodriguez J, Im SA, Pedrini JL, Poirier B, Pedrini JL, Poirier B, Morandi P, Semiglazov V, Srimuninnimi V, Bianchi G, Szado T, Ratnayake J, Ross G, Valagussa P: Efficacy and safety of neoadjuvant pertuzumab and trastuzumab in women with locally advanced, inflammatory, or early HER2-positive breast cancer (NeoSphere): a randomised multicentre, open-label, phase 2 trial. Lancet Oncol. 2012, 13: 25-32.

Baselga J, Bradbury I, Eidtmann H, Di Cosimo S, de Azambuja E, Aura C, Gomez H, Dinh P, Fauria K, Van Dooren V, Aktan G, Goldhirsch A, Chang TW, Horvath Z, Coccia-Portugal M, Domant J, Tseng LM, Kunz G, Sohn JH, Semiglazov V, Lerzo G, Palacova M, Probachai V, Pusztai L, Untch M, Gelber RD, Piccart-Gebhart M, NeoALTTO Study Team: Lapatinib with trastuzumab for HER2-positive early breast cancer (NeoALTTO): a randomised, open-label, multicentre, phase 3 trial. Lancet. 2012, 379: 633-640.

Baselga J, Cortes J, Kim SB, Im SA, Hegg R, Im YH, Roman L, Pedrini JL, Pienkowski T, Knott A, Clark E, Benyunes MC, Ross G, Swain SM, CLEOPATRA Study Group: Pertuzumab plus trastuzumab plus docetaxel for metastatic breast cancer. N Engl J Med. 2012, 366: 109-119.

Gelmon KA, Tischkowitz M, Mackay H, Swenerton K, Robidoux A, Tonkin K, Hirte H, Huntsman D, Clemons M, Gilks B, Yerushalmi R, Macpherson E, Carmichael J, Oza A: Olaparib in patients with recurrent high-grade serous or poorly differentiated ovarian carcinoma or triple-negative breast cancer: a phase 2, multicentre, open-label, non-randomised study. Lancet Oncol. 2011, 12: 852-861.

Cleator S, Heller W, Coombes RC: Triple-negative breast cancer: therapeutic options. Lancet Oncol. 2007, 8: 235-244.

Molyneux G, Smalley MJ: The cell of origin of BRCA1 mutation-associated breast cancer: a cautionary tale of gene expression profiling. J Mammary Gland Biol Neoplasia. 2011, 16: 51-55.

Michalak EM, Jonkers J: Studying therapy response and resistance in mouse models for BRCA1-deficient breast cancer. J Mammary Gland Biol Neoplasia. 2011, 16: 41-50.

Ran S, Volk L, Hall K, Flister MJ: Lymphangiogenesis and lymphatic metastasis in breast cancer. Pathophysiology. 2010, 17: 229-251.

Ferris RL, Lotze MT, Leong SP, Hoon DS, Morton DL: Lymphatics, lymph nodes and the immune system: barriers and gateways for cancer spread. Clin Exp Metastasis. 2012, 29: 729-736.

Gomes FG, Nedel F, Alves AM, Nor JE, Tarquinio SB: Tumor angiogenesis and lymphangiogenesis: tumor/endothelial crosstalk and cellular/microenvironmental signaling mechanisms. Life Sci. 2013, 92: 101-107.

Lenzer J: FDA committee votes to withdraw bevacizumab for breast cancer. BMJ. 2011, 343: d4244-

D’Agostino RB: Changing end points in breast-cancer drug approval–the Avastin story. N Engl J Med. 2011, 365: e2-

Shojaei F: Anti-angiogenesis therapy in cancer: current challenges and future perspectives. Cancer Lett. 2012, 320: 130-137.

Nagy JA, Benjamin L, Zeng H, Dvorak AM, Dvorak HF: Vascular permeability, vascular hyperpermeability and angiogenesis. Angiogenesis. 2008, 11: 109-119.

Kerbel RS: Strategies for improving the clinical benefit of antiangiogenic drug based therapies for breast cancer. J Mammary Gland Biol Neoplasia. 2012, 17: 229-239.

Sitohy B, Nagy JA, Dvorak HF: Anti-VEGF/VEGFR therapy for cancer: reassessing the target. Cancer Res. 2012, 72: 1909-1914.

Chew V, Toh HC, Abastado JP: Immune microenvironment in tumor progression: characteristics and challenges for therapy. J Oncol. 2012, 2012: 608406-

Andre F, Dieci MV, Dubsky P, Sotiriou C, Curigliano G, Denkert C, Loi S: Molecular pathways: involvement of immune pathways in the therapeutic response and outcome in breast cancer. Clin Cancer Res. 2013, 19: 28-33.

Reisfeld RA: The tumor microenvironment: a target for combination therapy of breast cancer. Crit Rev Oncog. 2013, 18: 115-133.

Chen YT, Ross DS, Chiu R, Zhou XK, Chen YY, Lee P, Hoda SA, Simpson AJ, Old LJ, Caballero O, Neville A: Multiple cancer/testis antigens are preferentially expressed in hormone-receptor negative and high-grade breast cancers. PloS one. 2011, 6: e17876-

Adams S, Greeder L, Reich E, Shao Y, Fosina D, Hanson N, Tassello J, Singh B, Spagnoli GC, Demaria S, Jungbluth AA: Expression of cancer testis antigens in human BRCA-associated breast cancers: potential targets for immunoprevention?. Cancer Immunol Immunother. 2011, 60: 999-1007.

Corner J, Wright D, Hopkinson J, Gunaratnam Y, McDonald JW, Foster C: The research priorities of patients attending UK cancer treatment centres: findings from a modified nominal group study. Br J Cancer. 2007, 96: 875-881.

Hewitt M, Rowland JH, Yancik R: Cancer survivors in the United States: age, health, and disability. J Geront A, Biol Sci Med Sci. 2003, 58: 82-91.

Foster C, Wright D, Hill H, Hopkinson J, Roffe L: Psychosocial implications of living 5 years or more following a cancer diagnosis: a systematic review of the research evidence. Eur J Cancer Care (Engl). 2009, 18: 223-247.

Hubbard G, Menzies S, Flynn P, Adams S, Haseen F, Thomas I, Scanlon K, Reed L, Forbat L: Relational mechanisms and psychological outcomes in couples affected by breast cancer: a systematic review of the literature. BMJ, Supportive and Palliative Care. 2013, 3: 1-7.

Foster C, Fenlon D: Recovery and self-management support following primary cancer treatment. Br J Cancer. 2011, 105: S21-S28.

Cimprich B, Janz NK, Northouse L, Wren PA, Given B, Given CW: Taking CHARGE: A self-management program for women following breast cancer treatment. Psychooncology. 2005, 14: 704-717.

Bloom JR, Stewart SL, D’Onofrio CN, Luce J, Banks PJ: Addressing the needs of young breast cancer survivors at the 5 year milestone: can a short-term, low intensity intervention produce change?. J Cancer Surviv. 2008, 2: 190-204.

Reed E, Simmonds P, Haviland J, Corner J: Quality of life and experience of care in women with metastatic breast cancer: a cross-sectional survey. J Pain Symptom Manage. 2012, 43: 747-758.

Aranda S, Schofield P, Weih L, Yates P, Milne D, Faulkner R, Voudouris N: Mapping the quality of life and unmet needs of urban women with metastatic breast cancer. Eur J Cancer Care (Engl). 2005, 14: 211-222.

Hopwood P, Howell A, Maguire P: Psychiatric morbidity in patients with advanced cancer of the breast: prevalence measured by two self-rating questionnaires. Br J Cancer. 1991, 64: 349-352.

Pinder KL, Ramirez AJ, Black ME, Richards MA, Gregory WM, Rubens RD: Psychiatric disorder in patients with advanced breast cancer: prevalence and associated factors. Eur J Cancer. 1993, 29A: 524-527.

Kissane DW, Grabsch B, Love A, Clarke DM, Bloch S, Smith GC: Psychiatric disorder in women with early stage and advanced breast cancer: a comparative analysis. Aust N Z J Psychiatry. 2004, 38: 320-326.

Grunfeld EA, Maher EJ, Browne S, Ward P, Young T, Vivat B, Walker G, Wilson C, Potts HW, Westcombe AM, Richards MA, Ramirez AJ: Advanced breast cancer patients’ perceptions of decision making for palliative chemotherapy. J Clin Oncol. 2006, 24: 1090-1098.

Karamouzis MV, Ioannidis G, Rigatos G: Quality of life in metastatic breast cancer patients under chemotherapy or supportive care: a single-institution comparative study. Eur J Cancer Care. 2007, 16: 433-438.

Cheville AL, Troxel AB, Basford JR, Kornblith AB: Prevalence and treatment patterns of physical impairments in patients with metastatic breast cancer. J Clin Oncol. 2008, 26: 2621-2629.

Headley JA, Ownby KK, John LD: The effect of seated exercise on fatigue and quality of life in women with advanced breast cancer. Oncol Nurs forum. 2004, 31: 977-983.

Asola R, Huhtala H, Holli K: Intensity of diagnostic and treatment activities during the end of life of patients with advanced breast cancer. Breast Cancer Res Treat. 2006, 100: 77-82.

Gagnon B, Mayo NE, Hanley J, MacDonald N: Pattern of care at the end of life: does age make a difference in what happens to women with breast cancer?. J Clin Oncol. 2004, 22: 3458-3465.

Richardson A, Addington-Hall J, Amir Z, Foster C, Stark D, Armes J, Brearley SG, Hodges L, Hook J, Jarrett N, Stamataki Z, Scott I, Walker J, Ziegler L, Sharpe MS: Knowledge, ignorance and priorities for research in key areas of cancer survivorship: findings from a scoping review. Br J Cancer. 2011, 105: S82-S94.

Stanton AL, Luecken LJ, MacKinnon DP, Thompson EH: Mechanisms in psychosocial interventions for adults living with cancer: opportunity for integration of theory, research, and practice. J Consult Clin Psychol. 2013, 81: 318-335.

Fenlon DR, Corner JL, Haviland JS: A randomized controlled trial of relaxation training to reduce hot flashes in women with primary breast cancer. J Pain Symptom Manage. 2008, 35: 397-405.

Osborn RL, Demoncada AC, Feuerstein M: Psychosocial interventions for depression, anxiety, and quality of life in cancer survivors: meta-analyses. Int J Psychiatry Med. 2006, 36: 13-34.

Spiegel D, Bloom JR, Kraemer HC, Gottheil E: Effect of psychosocial treatment on survival of patients with metastatic breast cancer. Lancet. 1989, 2: 888-891.

Edwards AG, Hulbert-Williams N, Neal RD: Psychological interventions for women with metastatic breast cancer. Cochrane Database Syst Rev. 2008, 3: CD004253

Emilsson S, Svensk AC, Tavelin B, Lindh J: Support group participation during the post-operative radiotherapy period increases levels of coping resources among women with breast cancer. Eur J Cancer Care (Engl). 2012, 21: 591-598.

Hoey LM, Ieropoli SC, White VM, Jefford M: Systematic review of peer-support programs for people with cancer. Patient Educ Couns. 2008, 70: 315-337.

Ganz PA, Kwan L, Stanton AL, Bower JE, Belin TR: Physical and psychosocial recovery in the year after primary treatment of breast cancer. J Clin Oncol. 2011, 29: 1101-1109.

Capozzo MA, Martinis E, Pellis G, Giraldi T: An early structured psychoeducational intervention in patients with breast cancer: results from a feasibility study. Cancer Nurs. 2010, 33: 228-234.

Gielissen MF, Verhagen CA, Bleijenberg G: Cognitive behaviour therapy for fatigued cancer survivors: long-term follow-up. Br J Cancer. 2007, 97: 612-618.

Ritterband LM, Bailey ET, Thorndike FP, Lord HR, Farrell-Carnahan L, Baum LD: Initial evaluation of an Internet intervention to improve the sleep of cancer survivors with insomnia. Psychooncology. 2012, 21: 695-705.

Armes J, Chalder T, Addington-Hall J, Richardson A, Hotopf M: A randomized controlled trial to evaluate the effectiveness of a brief, behaviorally oriented intervention for cancer-related fatigue. Cancer. 2007, 110: 1385-1395.

Mann E, Smith M, Hellier J, Hunter MS: A randomised controlled trial of a cognitive behavioural intervention for women who have menopausal symptoms following breast cancer treatment (MENOS 1): trial protocol. BMC Cancer. 2011, 11: 44-

Duijts SF, van Beurden M, Oldenburg HS, Hunter MS, Kieffer JM, Stuiver MM, Gerritsma MA, Menke-Pluymers MB, Plaisier PW, Rijna H, Lopes Cardozo AM, Timmers G, van der Meij S, van der Veen H, Bijker N, de Widt-Levert LN, Geenen MM, Heuff G, van Dulken EJ, Aaronson NK BE: Efficacy of cognitive behavioral therapy and physical exercise in alleviating treatment-induced menopausal symptoms in patients with breast cancer: results of a randomized, controlled, multicenter trial. J Clin Oncol. 2012, 30: 4124-4133.

Thompson J, Cocker H, Coleman RE, Colwell B, Freeman JV, Holmes K, Reed MW, Anthony C, Greenfield D: Breast cancer aftercare; preparing patients for discharge from routine hospital follow-up (PREP). Proceedings of the British Psychosocial Oncology Society Conference: 3–4 December 2009. 2009, Cardiff, Wales: Psycho-Oncology, 19(Suppl. 3):S1–S20 (2010)

Shennan C, Payne S, Fenlon D: What is the evidence for the use of mindfulness-based interventions in cancer care? A review. Psychooncology. 2011, 20: 681-697.

Campbell KL, Neil SE, Winters-Stone KM: Review of exercise studies in breast cancer survivors: attention to principles of exercise training. Br J Sports Med. 2011, 46: 909-916.

Speck RM, Courneya KS, Masse LC, Duval S, Schmitz KH: An update of controlled physical activity trials in cancer survivors: a systematic review and meta-analysis. J Cancer Surviv. 2010, 4: 87-100.

Fong DY, Ho JW, Hui BP, Lee AM, Macfarlane DJ, Leung SS, Cerin E, Chan WY, Leung IP, Lam SH, Taylor AJ, Cheng KK: Physical activity for cancer survivors: meta-analysis of randomised controlled trials. BMJ. 2012, 344: e70-

Mutrie N, Campbell A, Barry S, Hefferon K, McConnachie A, Ritchie D, Tovey S: Five-year follow-up of participants in a randomised controlled trial showing benefits from exercise for breast cancer survivors during adjuvant treatment. Are there lasting effects?. J Cancer Surviv. 2012, 6: 420-430.

Classen C, Butler LD, Koopman C, Miller E, DiMiceli S, Giese-Davis J, Fobair P, Carlson RW, Kraemer HC, Spiegel D: Supportive-expressive group therapy and distress in patients with metastatic breast cancer: a randomized clinical intervention trial. Arch Gen Psychiatry. 2001, 58: 494-501.

Watson EK, Rose PW, Neal RD, Hulbert-Williams N, Donnelly P, Hubbard G, Elliott J, Campbell C, Weller D, Wilkinson C: Personalised cancer follow-up: risk stratification, needs assessment or both?. Br J Cancer. 2012, 106: 1-5.

Fenlon D, Frankland J, Foster CL, Brooks C, Coleman P, Payne S, Seymour J, Simmonds P, Stephens R, Walsh B, Addington-Hall JM: Living into old age with the consequences of breast cancer. Eur J Oncol Nurs. 2013, 17: 311-316.

Watts K, Meiser B, Conlon H, Rovelli S, Tiller K, Zorbas H, Lewis C, Neil G, Friedlander M: A specialist breast care nurse role for women with metastatic breast cancer: enhancing supportive care. Oncol Nurs Forum. 2011, 38: 627-631.

Absolom K, Eiser C, Michel G, Walters SJ, Hancock BW, Coleman RE, Snowden JA, Greenfield DM: Follow-up care for cancer survivors: views of the younger adult. Br J Cancer. 2009, 101: 561-567.

Fenlon DR, Corner JL, Haviland J: Menopausal hot flushes after breast cancer. Eur J Cancer Care (Engl). 2009, 18: 140-148.

Mann E, Smith MJ, Hellier J, Balabanovic JA, Hamed H, Grunfeld EA, Hunter MS: Cognitive behavioural treatment for women who have menopausal symptoms after breast cancer treatment (MENOS 1): a randomised controlled trial. Lancet Oncol. 2012, 13: 309-318.

Castellon SA, Ganz PA, Bower JE, Petersen L, Abraham L, Greendale GA: Neurocognitive performance in breast cancer survivors exposed to adjuvant chemotherapy and tamoxifen. J Clin Exp Neuropsychol. 2004, 26: 955-969.

Rausch R, Kraemer S, Pietras CJ, Le M, Vickrey BG, Passaro EA: Early and late cognitive changes following temporal lobe surgery for epilepsy. Neurology. 2003, 60: 951-959.

Oliveri JM, Day JM, Alfano CM, Herndon JE, Katz ML, Bittoni MA, Donohue K, Paskett ED: Arm/hand swelling and perceived functioning among breast cancer survivors 12 years post-diagnosis: CALGB 79804. J Cancer Surviv. 2008, 2: 233-242.

Fourie WJ, Robb KA: Physiotherapy management of axillary web syndrome following breast cancer treatment: discussing the use of soft tissue techniques. Physiotherapy. 2009, 95: 314-320.

Holliday DL, Speirs V: Choosing the right cell line for breast cancer research. Breast Cancer Res. 2011, 13: 215-

Lacroix M, Leclercq G: Relevance of breast cancer cell lines as models for breast tumours: an update. Breast Cancer Res Treat. 2004, 83: 249-289.

Liu X, Ory V, Chapman S, Yuan H, Albanese C, Kallakury B, Timofeeva OA, Nealon C, Dakic A, Simic V, Haddad BR, Rhim JS, Dritschilo A, Riegel A, McBride A, Schlegel R: ROCK inhibitor and feeder cells induce the conditional reprogramming of epithelial cells. Am J Pathol. 2012, 180: 599-607.

Yuan H, Myers S, Wang J, Zhou D, Woo JA, Kallakury B, Ju A, Bazylewicz M, Carter YM, Albanese C, Grant N, Shad A, Dritschilo A, Liu X, Schlegel R: Use of reprogrammed cells to identify therapy for respiratory papillomatosis. N Engl J Med. 2012, 367: 1220-1227.

Lee GY, Kenny PA, Lee EH, Bissell MJ: Three-dimensional culture models of normal and malignant breast epithelial cells. Nat Methods. 2007, 4: 359-365.

Calvo F, Sahai E: Cell communication networks in cancer invasion. Curr Opin Cell Biol. 2011, 23: 621-629.

Vinci M, Gowan S, Boxall F, Patterson L, Zimmermann M, Court W, Lomas C, Mendiola M, Hardisson D, Eccles SA: Advances in establishment and analysis of three-dimensional tumor spheroid-based functional assays for target validation and drug evaluation. BMC Biol. 2012, 10: 29-

Krishnan V, Shuman LA, Sosnoski DM, Dhurjati R, Vogler EA, Mastro AM: Dynamic interaction between breast cancer cells and osteoblastic tissue: comparison of two- and three-dimensional cultures. J Cell Physiol. 2011, 226: 2150-2158.

Quail DF, Maciel TJ, Rogers K, Postovit LM: A unique 3D in vitro cellular invasion assay. J Biomol Screen. 2012, 17: 1088-1095.

Ho KS, Poon PC, Owen SC, Shoichet MS: Blood vessel hyperpermeability and pathophysiology in human tumour xenograft models of breast cancer: a comparison of ectopic and orthotopic tumours. BMC Cancer. 2012, 12: 579-

DeRose YS, Gligorich KM, Wang G, Georgelas A, Bowman P, Courdy SJ, Welm AL, Welm BE, et al: Patient-derived models of human breast cancer: protocols for in vitro and in vivo applications in tumor biology and translational medicine. Current protocols in pharmacology. Edited by: Enna SJ, John Wiley & Sons . 2013, Chapter 14:Unit14 23

Kabos P, Finlay-Schultz J, Li C, Kline E, Finlayson C, Wisell J, Manuel CA, Edgerton SM, Harrell JC, Elias A, Sartorius CA: Patient-derived luminal breast cancer xenografts retain hormone receptor heterogeneity and help define unique estrogen-dependent gene signatures. Breast Cancer Res Treat. 2012, 135: 415-432.

Rottenberg S, Jaspers JE, Kersbergen A, van der Burg E, Nygren AO, Zander SA, Derksen PW, de Bruin M, Zevenhoven J, Lau A, Boulter R, Cranston A, O’Conner MJ, Martin NM, Borst P, Jonkers J: High sensitivity of BRCA1-deficient mammary tumors to the PARP inhibitor AZD2281 alone and in combination with platinum drugs. Proc Natl Acad Sci U S A. 2008, 105: 17079-17084.

Mollard S, Mousseau Y, Baaj Y, Richard L, Cook-Moreau J, Monteil J, Funalot B, Sturtz FG: How can grafted breast cancer models be optimized?. Cancer Biol Ther. 2011, 12: 855-864.

Zhang X, Claerhout S, Prat A, Dobrolecki LE, Petrovic I, Lai Q, Landis MD, Wiechmann L, Schiff R, Giuliano M, Wong H, Fuqua SW, Contreras A, Gutierrez C, Huang J, Mao S, Pavlick AC, Froehlich AM, Wu MF, Tsimelzon A, Hilsenbeck SG, Chen ES, Zuloaga P, Shaw CA, Rimawi MF, Perou CM, Mills GB, Chang JC, Lewis MT: A renewable tissue resource of phenotypically stable, biologically and ethnically diverse, patient-derived human breast cancer xenograft models. Cancer Res. 2013, 73: 4885-4897.

Borowsky AD: Choosing a mouse model: experimental biology in context–the utility and limitations of mouse models of breast cancer. Cold Spring Harb Perspect Biol. 2011, 3: a009670-

Andrechek ER, Nevins JR: Mouse models of cancers: opportunities to address heterogeneity of human cancer and evaluate therapeutic strategies. J Mol Med. 2010, 88: 1095-1100.

Caligiuri I, Rizzolio F, Boffo S, Giordano A, Toffoli G: Critical choices for modeling breast cancer in transgenic mouse models. J Cell Physiol. 2012, 227: 2988-2991.

Kirma NB, Tekmal RR: Transgenic mouse models of hormonal mammary carcinogenesis: advantages and limitations. J Steroid Biochem Mol Biol. 2012, 131: 76-82.

Uhr JW, Pantel K: Controversies in clinical cancer dormancy. Proc Natl Acad Sci U S A. 2011, 108: 12396-12400.

Giampieri S, Manning C, Hooper S, Jones L, Hill CS, Sahai E: Localized and reversible TGFbeta signalling switches breast cancer cells from cohesive to single cell motility. Nat Cell Biol. 2009, 11: 1287-1296.

Eccles SA, Welch DR: Metastasis: recent discoveries and novel treatment strategies. Lancet. 2007, 369: 1742-1757.

Francia G, Cruz-Munoz W, Man S, Xu P, Kerbel RS: Mouse models of advanced spontaneous metastasis for experimental therapeutics. Nat Rev Cancer. 2011, 11: 135-141.

Eckhardt BL, Francis PA, Parker BS, Anderson RL: Strategies for the discovery and development of therapies for metastatic breast cancer. Nature Rev Drug Dis. 2012, 11: 479-497.

Guerin E, Man S, Xu P, Kerbel RS: A model of postsurgical advanced metastatic breast cancer more accurately replicates the clinical efficacy of antiangiogenic drugs. Cancer Res. 2013, 73: 2743-2748.

Kievit FM, Stephen ZR, Veiseh O, Arami H, Wang T, Lai VP, Park JO, Ellenbogen RG, Disis ML, Zhang M: Targeting of primary breast cancers and metastases in a transgenic mouse model using rationally designed multifunctional SPIONs. ACS Nano. 2012, 6: 2591-2601.

Fang Y, Chen Y, Yu L, Zheng C, Qi Y, Li Z, Yang Z, Zhang Y, Shi T, Luo J, Liu M: Inhibition of breast cancer metastases by a novel inhibitor of TGFbeta receptor 1. J Natl Cancer Inst. 2013, 105: 47-58.

Palmieri D, Lockman PR, Thomas FC, Hua E, Herring J, Hargrave E, Johnson M, Flores N, Qian Y, Vega-Valle E, Tasker KS, Rudraraju V, Mittapalli RK, Gaasch JA, Bohn KA, Thorsheim HR, Liewehr DJ, Davis S, Reilly JF, Walker R, Bronder JL, Feigenbaum L, Steinberg S, Camphausen K, Meltzer PS, Richon VM, Smith QR, Steeq PS: Vorinostat inhibits brain metastatic colonization in a model of triple-negative breast cancer and induces DNA double-strand breaks. Clin Cancer Res. 2009, 15: 6148-6157.

Xia TS, Wang J, Yin H, Ding Q, Zhang YF, Yang HW, Liu XA, Dong M, Du Q, Ling LJ, Zha XM, Fu W, Wang S: Human tissue-specific microenvironment: an essential requirement for mouse models of breast cancer. Oncol Rep. 2010, 24: 203-211.

Steeg PS: Perspective: the right trials. Nature. 2012, 485: S58-S59.

Wong AL, Lee SC: Mechanisms of resistance to trastuzumab and novel therapeutic strategies in HER2-positive breast cancer. Int J Breast Cancer. 2012, 2012: 415170-

Polyak K: Heterogeneity in breast cancer. J Clin Invest. 2011, 121: 3786-3788.

Lindell KO, Erlen JA, Kaminski N: Lessons from our patients: development of a warm autopsy program. PLoS medicine. 2006, 3: e234-

Hadad S, Iwamoto T, Jordan L, Purdie C, Bray S, Baker L, Jellema G, Deharo S, Hardie DG, Pusztai L, Moulder-Thompson S, Dewar JA, Thompson AM: Evidence for biological effects of metformin in operable breast cancer: a pre-operative, window-of-opportunity, randomized trial. Breast Cancer Res Treat. 2011, 128: 783-794.

Leary AF, Hanna WM, van de Vijver MJ, Penault-Llorca F, Ruschoff J, Osamura RY, Bilous M, Dowsett M: Value and limitations of measuring HER-2 extracellular domain in the serum of breast cancer patients. J Clin Oncol. 2009, 27: 1694-1705.

Witzel I, Loibl S, von Minckwitz G, Mundhenke C, Huober J, Hanusch C, Henschen S, Hauschild M, Lantzsch T, Tesch H, Latos K, Just M, Hilfrich J, Barinoff J, Eulenburg CZ, Roller M, Untch M, Muller V: Monitoring serum HER2 levels during neoadjuvant trastuzumab treatment within the GeparQuattro trial. Breast Cancer Res Treat. 2010, 123: 437-445.

Thureau S, Clatot F, Laberge-Le-Couteulx S, Baron M, Basuyau JP, Blot E: Elevated HER2 extracellular domain level in primary breast cancer with HER2 overexpression predicts early failure of adjuvant trastuzumab. Anticancer Res. 2012, 32: 1429-1433.

Molina R, Escudero JM, Munoz M, Auge JM, Filella X: Circulating levels of HER-2/neu oncoprotein in breast cancer. Clin Chem Lab Med. 2012, 50: 5-21.

Dietel M, Johrens K, Laffert M, Hummel M, Blaker H, Muller BM, Lehmann A, Denkert C, Heppner FL, Koch A, Sers C, Anagnostopoulos I: Predictive molecular pathology and its role in targeted cancer therapy: a review focussing on clinical relevance. Cancer Gene Ther. 2013, 20: 211-221.

Modur V, Hailman E, Barrett JC: Evidence-based laboratory medicine in oncology drug development: from biomarkers to diagnostics. Clin Chem. 2013, 59: 102-109.

Knowles SM, Wu AM: Advances in immuno-positron emission tomography: antibodies for molecular imaging in oncology. J Clin Oncol. 2012, 30: 3884-3892.

Capala J, Bouchelouche K: Molecular imaging of HER2-positive breast cancer: a step toward an individualized ‘image and treat’ strategy. Curr Opin Oncol. 2010, 22: 559-566.

Asselin MC, O’Connor JP, Boellaard R, Thacker NA, Jackson A: Quantifying heterogeneity in human tumours using MRI and PET. Eur J Cancer. 2012, 48: 447-455.

Waterton JC, Pylkkanen L: Qualification of imaging biomarkers for oncology drug development. Eur J Cancer. 2012, 48: 409-415.

Segal E, Sirlin CB, Ooi C, Adler AS, Gollub J, Chen X, Chan BK, Matcuk GR, Barry CT, Chang HY, Kuo MD: Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol. 2007, 25: 675-680.

Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RGPM, Granton P, Zegers CML, Gillies R, Boellard R, Dekker A, Aerts HJ: Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012, 48: 441-446.

Macaskill EJ, Bartlett JM, Sabine VS, Faratian D, Renshaw L, White S, Campbell FM, Young O, Williams L, Thomas JS, Barber MD, Dixon JM: The mammalian target of rapamycin inhibitor everolimus (RAD001) in early breast cancer: results of a pre-operative study. Breast Cancer Res Treat. 2011, 128: 725-734.

Basch E, Jia X, Heller G, Barz A, Sit L, Fruscione M, Appawu M, Iasonos A, Atkinson T, Goldfarb S, Culkin A, Kris MG, Schrag D: Adverse symptom event reporting by patients vs clinicians: relationships with clinical outcomes. J Natl Cancer Inst. 2009, 101: 1624-1632.

Dietary Fish and Omega 3 Fatty Acids for Breast Cancer Prevention. [ http://clinicaltrials.gov/show/NCT01282580 ]

Download references

Acknowledgements

We would like to acknowledge the helpful contributions to the final manuscript from the Executive Advisory Board: Kevin Brindle, Robert E Coleman, Charles Coombes, Jack Cuzick, Mitchell Dowsett, Lesley Fallowfield, Christine Friedenreich, William J Gullick, Barry Gusterson, Craig Jordan, Sunil Lakhani, Bettina Meiser, Emma Pennery, Rebecca Riggins and Stephen Johnston. We would also like to acknowledge the contributions of the patient advocate representatives Mairead McKenzie and Marion Lewis from Breast Cancer Care’s Service User Research Panel.

SAE acknowledges support from the NIHR RM/ICR Biomedical Research Centre, ICR and Cancer Research UK.

AMT acknowledges support from Breast Cancer Campaign, Breakthrough Breast Cancer and CR-UK.

Breast Cancer Campaign staff Lisa Wilde, Phyllis Quinn and Stuart Griffiths assisted in the design and implementation of the gap analysis initiative and acted as facilitators throughout the process. Geraldine Byrne was responsible for co-ordinating and delivering the logistics and acted as a facilitator at the nine gap analysis workshops that were held at the Breast Cancer Campaign offices.

We thank Dr Alexis Willet who provided editorial assistance on behalf of Punch Consulting.

Author information

Authors and affiliations.

Imperial College London, Exhibition Rd, London, SW7 2AZ, UK

Eric O Aboagye, Simak Ali, James M Flanagan & David J Mann

University of Dundee, Perth Road, Dundee, DD1 4HN, UK

Annie S Anderson, Anna M Campbell & Alastair M Thompson

University of Southampton, University Road, Southampton, SO17 1BJ, UK

Jeremy P Blaydes, Diana M Eccles, Deborah F Fenlon & Claire Foster

University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK

Fedor Berditchevski & Joanna R Morris

University of Manchester, Oxford Road, Manchester, M13 9PL, UK

Keith Brennan, Nigel J Bundred, Robert B Clarke, D Gareth Evans, Michelle Harvie, Sacha J Howell, Anthony Howell, Cliona C Kirwan, James PB O’Connor, Charles H Streuli & Kaye J Williams

University of Sheffield, Western Bank, Sheffield, S10 2TN, UK

Nicola J Brown, Helen E Bryant, Angela Cox & Ingunn Holen

Kings College London, Strand, London, WC2R 2LS, UK

Jo Armes, Joy M Burchell, Gary JR Cook, Vicky Goh, Myra S Hunter, David W Miles & Andrew N J Tutt

University College London, Gower Street, London, WC1E 6BT, UK

Ashley M Groves & Robert Stein

Cancer Research UK, Cambridge Research Institute/University of Cambridge, Trinity Lane, Cambridge, CB2 1TN, UK

Jason S Carroll, Douglas F Easton, Paul D P Pharoah, John Stingl & Christine J Watson

Newcastle University, Claremont Road, Newcastle upon Tyne, NE1 7RU, UK

Nicola J Curtin

University of Nottingham, University Park, Nottingham, NG7 2RD, UK

Lodewijk V Dekker, Stewart G Martin & Emad A Rakha

London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 2HT, UK

Isabel dos Santos Silva

Queen Mary University of London, Mile End Road, London, E1 4NS, UK

Stephen W Duffy, Louise J Jones, John F Marshall & Sue M Moss

University of Glasgow, University Avenue, Glasgow, G12 8QQ, UK

Joanne Edwards

University of East Anglia, Earlham Road, Norwich, NR4 7TJ, UK

Dylan R Edwards & John M Saxton

University College Dublin, Belfield, Dublin 4, Ireland

William M Gallagher

The Institute of Cancer Research, 15 Cotswold Road, London, SM2 5MG, UK

Suzanne A Eccles, Montserrat Garcia-Closas, Martin O Leach, Lesley Ann Martin, Rachel Natrajan & Simon P Robinson

University of Cardiff, Park Place, Cardiff, CF10 3AT, UK

Julia M W Gee, Stephen Hiscox, Bharat Jasani & Matthew J Smalley

University of Leeds, Woodhouse Lane, Leeds, LS2 9JT, UK

Valerie Speirs & Galina Velikova

Royal College of Surgeons Ireland, 123, St Stephen’s Green, Dublin 2, Ireland

Bryan T Hennessy & Leonie S Young

University of Stirling, Stirling, FK9 4LA, UK

Gill Hubbard

University of Chester, Parkgate Road, Chester, CH1 4BJ, UK

Nick Hulbert-Williams

University of Oxford, Wellington Square, Oxford, OX1 2JD, UK

Timothy J Key & Anthony Kong

University of Edinburgh, South Bridge, Edinburgh, EH8 9YL, UK

Ian H Kunkler, Simon P Langdon & William R Miller

National Cancer Research Institute, 407 St John Street, London, EC1V 4AD, UK

Jennifer E Macdougall

Queen’s University Belfast, University Road, Belfast, BT7 1NN, UK

Paul Mullan

University College Cork, College Road, Cork, Ireland

Rosemary O’Connor

University of Leicester, University Road, Leicester, LE1 4RH, UK

Andy J Gescher & Rosemary A Walker

Princess Alice Hospice, West End Lane, Esher, KT10 8NA, UK

Elizabeth Reed

Brighton and Sussex Medical School, University of Sussex, Brighton, East Sussex, BN1 9PX, UK

Peter Schmid

The University of Liverpool, Brownlow Hill, Liverpool, L69 7ZX, UK

Carlo Palmieri

London Research Institute, 44 Lincoln’s Inn Fields, London, WC2A 3LY, UK

Brunel University, Kingston Lane, Uxbridge, UB8 3PH, UK

Amanda J Harvey

Cambridge University Hospitals NHS Foundation Trust, Hills Road, Cambridge, CB2 0QQ, UK

Charlotte E Coles

You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Suzanne A Eccles or Alastair M Thompson .

Additional information

Competing interests.

Dr Galina Velikova: Chair of a working group of the National Cancer Survivorship Initiative led by Macmillan Cancer Support.

Drs Helen Bryant and Dr Nicola Curtin: hold patents for PARP inhibitors.

Professor William Gallagher: co-Founder and part-time Chief Scientific Officer of OncoMark, a molecular diagnostics company.

Dr Martin Leach: director of Specialty Scanners plc, developing MRI-based diagnosis and treatment systems.

Dr Sacha Howell: Advisory Board honoraria from AstraZeneca, Roche, Novartis, Genomic Health and Celgene.

Dr Robert Stein: shareholder in GlaxoSmithKline and chief investigator of the OPTIMA study; travel funds received from Celgene, Roche, BristolMeyersSquibb, SanofiAventis and Novartis; Advisory Board fees from Novartis, Amgen, GSK, Roche and AstraZeneca.

Dr Nigel Bundred has received paid honoraria from Genomic Health.

The remaining authors declare that they have no competing interests.

Authors’ contributions

*denotes recipient of Breast Cancer Campaign funding in the last five years. ≠ denotes current Breast Cancer Campaign Scientific Advisory Board membership. # denotes current Breast Cancer Campaign Board of Trustees membership. Chairs: SAE # and AMT # conceived the overall strategy, designed the workshop formats and authored the manuscript on the basis of the final reports submitted by the nine working groups. Group Leaders: RBC, IDSS, DGE* ≠ , CF ≠ ,WMG ≠ , AH ≠ , IH* ≠ , LJJ*, SPL, SPR ≠ , PS* ≠ , and VS* led their respective groups with the help of the Deputy Group Leaders, co-ordinated responses from a pre-circulated questionnaire, and wrote and submitted final reports. Deputy Group Leaders: EOA, NJB a , JMF* ≠ , JMWG*, AJH*, MH, AK, JRM*, PM* ≠ , ES, MJS* ≠ , ER, and RN* supported the activities of the Group Leaders in contributing to collating workshop presentations and discussions and producing the final reports from each group. Working group members: SA*, ASA , JA*, FB*, JPB*, KB* ≠ , NJB b , HEB ≠ , JMB, AMC*, JSC*, CEC*, GJRC*, AC, NJC, LVD* ≠ , SWD, DFE, DME, DRE*, JE, DFF*, MGC, AJG, VG, AMG, BTH, SH, SJH ≠ , GH, NHW, MSH, BJ, TJK, CCK, IHK*, MOL, DJM, JFM* ≠ , LAM, SGM ≠ , JEM, DWM, WRM, JRM, SMM*, JPBOC, ROC*, CP, PDPP*, EAR ≠ , JMS*, RS ≠ , JS, CHS, ANJT, GV, RAW*, CJW, KJW ≠ and LSY all participated in/contributed to the gap analysis workshops, discussions and in generating the respective reports. NJB a Nigel J Bundred. NJB b Nicola J Brown. All authors read and approved the final manuscript.

Authors’ original submitted files for images

Below are the links to the authors’ original submitted files for images.

Authors’ original file for figure 1

Authors’ original file for figure 2, authors’ original file for figure 3, authors’ original file for figure 4, authors’ original file for figure 5, authors’ original file for figure 6, authors’ original file for figure 7, authors’ original file for figure 8, authors’ original file for figure 9, rights and permissions.

Reprints and permissions

About this article

Cite this article.

Eccles, S.A., Aboagye, E.O., Ali, S. et al. Critical research gaps and translational priorities for the successful prevention and treatment of breast cancer. Breast Cancer Res 15 , R92 (2013). https://doi.org/10.1186/bcr3493

Download citation

Received : 08 August 2013

Accepted : 12 September 2013

Published : 01 October 2013

DOI : https://doi.org/10.1186/bcr3493

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Breast Cancer
Circulating Tumor Cells (CTCs)
Current Cancer Stem Cell (CSC)
Mammographic Density
Triple-negative Breast Cancer (TNBC)

Breast Cancer Research

ISSN: 1465-542X

Submission enquiries: [email protected]

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Publications
Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

Advanced Search
Journal List
Int J Biol Sci
v.13(11); 2017

Risk Factors and Preventions of Breast Cancer

Yi-sheng sun.

1 Key Lab of Vaccine against Hemorrhagic Fever with Renal Syndrome, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China;

2 Centre of Laboratory Medicine, Zhejiang Provincial People's Hospital.

Zhang-Nv Yang

Hang-jing lu, zhi-yong zhu, jianmin jiang, ping-ping yao, han-ping zhu.

Breast cancer is the second leading cause of cancer deaths among women. The development of breast cancer is a multi-step process involving multiple cell types, and its prevention remains challenging in the world. Early diagnosis of breast cancer is one of the best approaches to prevent this disease. In some developed countries, the 5-year relative survival rate of breast cancer patients is above 80% due to early prevention. In the recent decade, great progress has been made in the understanding of breast cancer as well as in the development of preventative methods. The pathogenesis and tumor drug-resistant mechanisms are revealed by discovering breast cancer stem cells, and many genes are found related to breast cancer. Currently, people have more drug options for the chemoprevention of breast cancer, while biological prevention has been recently developed to improve patients' quality of life. In this review, we will summarize key studies of pathogenesis, related genes, risk factors and preventative methods on breast cancer over the past years. These findings represent a small step in the long fight against breast cancer.

Introduction

Breast cancer is one of the most common cancers in women worldwide, accounting for approximately 570,000 deaths in 2015. Over 1.5 million women (25% of all women with cancer) are diagnosed with breast cancer every year throughout the world 1 , 2 . In America, it is estimated that 30% of all new cancer cases (252,710) among women are breast cancer in 2017 3 . Breast cancer is a metastatic cancer and can commonly transfer to distant organs such as the bone, liver, lung and brain, which mainly accounts for its incurability. Early diagnosis of the disease can lead to a good prognosis and a high survival rate. In North American, the 5-year relative survival rate of breast cancer patients is above 80% due to the timely detection of this disease 4 . Mammography is a widely used screening approach in the detecting of breast cancer and proved to help reduce the mortality effectively. Other screening methods, such as Magnetic Resonance Imaging (MRI), which is more sensitive than mammography, have also been implemented and studied during the last decade 5 . There're numerous risk factors such as sex, aging, estrogen, family history, gene mutations and unhealthy lifestyle, which can increase the possibility of developing breast cancer 6 . Most breast cancer occur in women and the number of cases is 100 times higher in women than that in men 3 . Although the incidence rate of breast cancer in America increases year after year, the mortality rate decreases due to the widespread early screenings and advanced medical therapies. Biological therapies have been developed in recent years and proved to be beneficial for breast cancer. Here, we will focus on studies of the pathogenesis, related genes, risk factors and preventions of breast cancer over the past years.

Pathogenesis

Breast tumors usually start from the ductal hyperproliferation, and then develop into benign tumors or even metastatic carcinomas after constantly stimulation by various carcinogenic factors. Tumor microenvironments such as the stromal influences or macrophages play vital roles in breast cancer initiation and progression. The mammary gland of rats could be induced to neoplasms when only the stroma was exposed to carcinogens, not the extracellular matrix or the epithelium 7 , 8 . Macrophages can generate a mutagenic inflammatory microenvironment, which can promote angiogenesis and enable cancer cells to escape immune rejection 9 , 10 . Different DNA methylation patterns have been observed between the normal and tumor-associated microenvironments, indicating that epigenetic modifications in the tumor microenvironment can promote the carcinogenesis 11 , 12 . Recently, a new subclass of malignant cells within tumors called the cancer stem cells (CSCs) are observed and associated with tumor initiation, escape and recurrence. This small population of cells, which may develop from stem cells or progenitor cells in normal tissues, have self-renewal abilities and are resistant to conventional therapies such as chemotherapy and radiotherapy 13 - 15 . Breast cancer stem cells (bCSCs) were first identified by Ai Hajj and even as few as 100 bCSCs could form new tumors in the immunocompromised mice 16 . bCSCs are more likely to originate from luminal epithelial progenitors rather than from basal stem cells 17 . Signaling pathways including Wnt, Notch, Hedgehog, p53, PI3K and HIF are involved in the self-renewal, proliferation and invasion of bCSCs 18 - 21 . However, more studies are needed to understand bCSCs and to develop novel strategies to directly eliminate the bCSCs.

There're two hypothetical theories for breast cancer initiation and progression: the cancer stem cell theory and the stochastic theory 11 , 22 . The cancer stem cell theory suggests that all tumor subtypes are derived from the same stem cells or transit-amplifying cells (progenitor cells). Acquired genetic and epigenetic mutations in stem cells or progenitor cells will lead to different tumor phenotypes (Figure (Figure1A). 1 A). The stochastic theory is that each tumor subtype is initiated from a single cell type (stem cell, progenitor cell, or differentiated cell) (Figure (Figure1B). 1 B). Random mutations can gradually accumulate in any breast cells, leading to their transformation into tumor cells when adequate mutations have accumulated. Although both theories are supported by plenty of data, neither can fully explain the origin of human breast cancer.

An external file that holds a picture, illustration, etc.
Object name is ijbsv13p1387g001.jpg

Two hypothetical theories of breast cancer initiation and progression. (A) All subtypes of tumor are derived from the same stem cells or progenitor cells. Different tumor phenotypes are then determined by subtype-specific transforming events. (B) Each tumor subtype is initiated from a single cell type (stem cell, progenitor cell, or differentiated cell). Random mutations can gradually accumulate in any breast cells, leading to their transformation into tumor cells when an adequate number of mutations have accumulated.

Genes related to breast cancer

Lots of genes have been identified in relation to breast cancer. Mutations and abnormal amplification of both oncogenes and anti-oncogenes play key roles in the processes of tumor initiation and progression.

Breast cancer associated gene 1 and 2 ( BRCA1 and BRCA2 ) are two famous anti-oncogenes for breast cancer risk. BRCA1 and BRCA2 are located on chromosome 17q21 and 13q12, respectively. They both encode tumor suppressor proteins. BRCA1 deficiency leads to the dysregulation of cell cycle checkpoint, abnormal centrosome duplication, genetic instability and eventually apoptosis 23 , 24 . BRCA1 expression is repressed by “pocket proteins” such as p130, p107 and the retinoblastoma protein in an E2F-dependent manner. The BRCA1 gene has been shown to form a loop between the promoter, introns, and terminator regions, which regulates the expression of this gene via interactions with its own promoter 25 , 26 . BRCA2 protein regulates recombinational repair in DNA double-strand breaks by interacting with RAD51 and DMC1 27 , 28 . BRCA2-associated breast cancers are more likely to be high-grade invasive ductal carcinomas, but with a luminal phenotype 29 . The risk of breast cancer could be increased greatly if an individual inherits deleterious mutations in either BRCA1 or BRCA2 genes. BRCA1/2 mutations are inherited in an autosomal dominant manner even though the second allele is normal. Totally, about 20-25% of hereditary breast cancers and 5-10% of all breast cancers are caused by BRCA1/2 mutations 30 , 31 . A meta-analysis by Chen showed that the breast cancer risk ratio in women older than 70 years carrying BRCA1 or BRCA2 mutations was 57% and 49%, respectively 32 .

Human epidermal growth factor receptor 2, also known as c-erbB-2 , is an important oncogene in breast cancer and located on the long arm of human chromosome 17 (17q12). The homologene in mice is Neu , which was first identified in 3-methylcholanthrene induced rat neuroblastoma cells 33 . The expression of HER2 gene is activated mainly through the gene amplification and re-arrangement. HER2 protein is an epidermal growth factor receptor (EGFR) of tyrosine kinase family and form heterodimers with other ligand-bound EGFR family members such as Her3 and Her4, thus to activate downstream signaling pathways 34 . Knockout of HER2 in mouse models disrupts normal mammary duct formation. Overexpression of HER2, which is detected in about 20% of primary breast cancers, increases the number of cancer stem cells by PTEN/Akt/mTORC1 signaling, and indicates poor clinical outcomes 35 , 36 .

Epidermal Growth Factor Receptor (EGFR)

EGFR , also known as c-erbB-1 or Her1 in humans, is located on the short arm of chromosome 7 (7p12). The EGFR protein is a cell surface glycoprotein of tyrosine kinase family and is activated by binding to EGF, TGF-α, amphiregulin, betacellulin and so on. The downstream signaling pathways of EGFR including PI3K, Ras-Raf-MAPK and JNK are triggered to promote cell proliferation, cell invasion, angiogenesis and to protect cells against apoptosis 37 , 38 . Overexpression of EGFR is found in more than 30% of cases of the inflammatory breast cancer (IBC), a very aggressive subtype of breast cancer. Patients with EGFR -positive IBC have a poorer prognosis than those with EGFR -negative tumors 39 , 40 . More than half of triple-negative breast cancer (TNBC) cases, characterized by the absence of estrogen receptor (ER), progesterone receptor (PR) expression and HER2 amplification, also have EGFR overexpression 41 . Therefore, targeting the EGFR pathway might be a promising therapy for these malignant tumors.

This gene is located on the long arm of chromosome 8 (8q24) and encodes for the Myc protein, a transcription factor containing the bHLH/LZ (basic Helix-Loop-Helix Leucine Zipper) domain. Genome-wide screening shows that 15% of all genes are regulated by the Myc protein mainly through binding on the E-box consensus (CACGTG) and recruiting histone acetyltransferases (HATs) or DNA methyltransferases 42 , 43 . Some of the Myc-regulated genes such as MTA1 , hTERT and PEG10 play vital roles in breast cancer initiation and progression. The overexpression of c-Myc is predominantly observed in the high-grade, invasive stage of breast carcinomas, while no c-Myc amplification is detected in the benign tissues 44 , 45 .

Other related genes

There're three members in the Ras gene family: H-ras, K-ras and N-ras , located on the chromosome of 11 (11p15), 12 (12p12) and 1 (1p22) respectively. The proteins encoded by these genes are extremely homologous, and they belong to the small guanosine triphosphate (GTP)-binding protein (G protein) superfamily 46 . Point mutations are commonly associated with the overexpression of these three human Ras genes, and most are missense mutations located at the coding domain for GTP binding. Though mutations of Ras proteins are infrequently in breast cancer (<5%), the abnormality of Ras signal transduction pathway are observed in both benign and malignant mammary tissues 47 . H-ras can cooperate with B lymphoma moloney murine leukaemia virus insertion region-1 (BMI1) to promote proliferation, invasion, and to inhibit apoptosis in breast cancer cells 48 . H-ras overexpression is detected in both primary and advanced breast cancer patients, indicating a poor prognosis 49 , 50 .

Risk factors

A schematic diagram of risk factors is depicted in a pyramid-style structure (Figure (Figure2 2 ).

An external file that holds a picture, illustration, etc.
Object name is ijbsv13p1387g002.jpg

Schematic diagram of risk factors and preventions of breast cancer. Age, family history, reproductive factors, estrogen and life style are five important risk factors of breast cancer, represented in the pyramid chart. Screening (mammography and MRI), chemoprevention (with SERMs and AIs) and biological prevention (using Herceptin and pertuzumab) are currently being used to prevent breast cancer. PD1/PDL1 inhibitors are immunotherapy drugs and might be promising strategies in treating TNBC.

Besides sex, aging is one of the most important risk factors of breast cancer, because the incidence of breast cancer is highly related to the increasing age. In 2016, approximately 99.3% and 71.2% of all breast cancer-associated deaths in America were reported in women over the age of 40 and 60, respectively 3 . Therefore, it is necessary to have a mammography screening ahead of time in women aged 40 or older.

Family history

Nearly a quarter of all breast cancer cases are related to family history 65 . Women, whose mother or sister has a breast cancer, are prone to this disease. A cohort study of over 113,000 women in UK demonstrated that women with one first-degree relative with breast cancer have a 1.75-fold higher risk of developing this disease than women without any affected relatives. Moreover, the risk becomes 2.5-fold or higher in women with two or more first-degree relatives with breast cancer 65 . The inherited susceptibility to breast cancer is partially attributed to the mutations of breast cancer related genes such as BRCA1 and BRCA2 .

Reproductive factors

Reproductive factors such as early menarche, late menopause, late age at first pregnancy and low parity can increase the breast cancer risk. Each 1-year delay in menopause increases the risk of breast cancer by 3%. Each 1-year delay in menarche or each additional birth decreases the risk of breast cancer by 5% or 10%, respectively 66 - 68 . A recent Norwegian cohort study showed that a hazard ratio (HR) is 1.54 between late (≥35 years) and early (<20 years) age at first birth 69 . Reproductive factors are strongly associated with the ER status, with differences in the odds ratios (OR) between ER + and ER - breast cancer for parity (OR: 0.7 vs. 0.9 for ≥3 births vs. nulliparae) and age at the first birth (OR: 1.6 vs. 1.2 for age ≥30 vs. <25 years) 70 .

Both endogenous and exogenous estrogens are associated with the risk of breast cancer. The endogenous estrogen is usually produced by the ovary in premenopausal women and ovariectomy can reduce the risk of breast cancer 71 . The main sources of exogenous estrogen are the oral contraceptives and the hormone replacement therapy (HRT). The oral contraceptives have been widely used since 1960s and the formulations have been upgraded to reduce side-effects. However, the OR is still higher than 1.5 for African American women and Iranian populations 72 , 73 . Nevertheless, oral contraceptives do not increase the risk of breast cancer in women who stop to use them for more than 10 years 66 . HRT involves the administration of exogenous estrogen or other hormones for the menopausal or postmenopausal women. A number of studies have shown that the use of HRT can increase the breast cancer risk. The Million Women Study in UK reported a relative risk (RR) of 1.66 between current users of HRT and those who never used it 74 . A cohort study of 22,929 women in Asia demonstrated HRs of 1.48 and 1.95 after HRT use for 4 and 8 years, respectively 75 . However, the risk of breast cancer has been shown to significantly decrease after two years of stopping HRT 76 . The recurrence rate is also high among breast cancer survivors who take HRT, and the HR for a new breast tumor is 3.6 77 . Since the adverse effects of HRT were published in 2003 based on the Women's Health Initiative randomized controlled trial, the incidence rate of breast cancer in America has decreased by approximately 7% due to the reduction in the use of HRT 78 .

Modern lifestyles such as excessive alcohol consumption and too much dietary fat intake can increase the risk of breast cancer. Alcohol consumption can elevate the level of estrogen-related hormones in the blood and trigger the estrogen receptor pathways. A meta-analysis based on 53 epidemiological studies indicated that an intake of 35-44 grams of alcohol per day can increase the risk of breast cancer by 32%, with a 7.1% increase in the RR for each additional 10 grams of alcohol per day 79 , 80 . Modern western diet contains too much fat and excess intake of fat, especially the saturated fat, is associated with mortality (RR=1.3) and poor prognosis in breast cancer patients 81 . Although the relationship between smoking and breast cancer risk remains controversial, mutagens from cigarette smoke have been detected in the breast fluid from non-lactating women. The risk of breast cancer is also elevated in women who both smoke and drink (RR=1.54) 82 . Up to now, accumulating evidences demonstrate that smoking, especially at an early age, has a higher risk on breast cancer occurrence 83 - 86 .

Preventions

Thus far, great advances have been made in clinical and theoretical studies of breast cancer (Figure (Figure2). 2 ). The current prevention methods including screening, chemoprevention and biological prevention are more direct and effective than those in the past (Figure (Figure2). 2 ). The mortality of breast cancer has decreased. However, breast cancer is still the first leading cause of cancer death among females aged 20-59 years.

Not primary tumors but the tumor metastasis causes over 90% of cancer deaths 87 . However, if breast cancer is diagnosed as a primary tumor or at an early stage of metastasis, the breast tumor could be removed by surgery and the chemotherapy could work effectively. Early detection is the cornerstone of breast cancer prevention. Mammography is an effective screening method to use low energy X-rays to obtain high-resolution images of the breast. The entire testing process only lasts for 20 minutes and it does not require any contrast-enhancing agent. Since the first recommendation for breast cancer screening by Professor Forrest, over 70% of women (aged 50-74 years) in America have been undergone breast cancer screening via mammography every 2 years 88 . A meta-analysis of 11 randomized trials showed that women aged 50-70 years had a significant reduction in breast cancer mortality after screening with mammography (RR=0.81) 89 . However, the reduction in mortality rate was not significant in women aged 40-49 years 90 . These results indicate the importance of mammography screening programs. Although the reported percentage of overdiagnosis due to mammography varies across trials, overdiagnosis is undoubtedly a serious problem that cannot be ignored during breast cancer screening.

MRI is another widely used screening tool for breast cancer. It is more sensitive than mammography in high-risk women, especially in detecting the invasive ductal carcinoma 91 . Compared to mammography, MRI is not affected by the breast density and has advantages in detecting occult primary breast cancer, axillary nodal metastasis, residual tumors after neoadjuvant chemotherapy or other small tumors 92 . Advanced MRI scanners can measure tissues as small as 0.5 mm 3 . However, there's no identified benefit of MRI in patient outcomes such as in the rate of detection of ipsilateral breast tumor recurrence and contralateral breast cancer incidence. The specificity of MRI is much poorer than that of mammography, with detection rates ranging from 37% to 100% 93 . Women with a family history of breast cancer have an approximately 20-25% or higher lifetime risk of breast cancer as demonstrated by MRI screening 94 . Each coin has two sides, and we should balance both the goodness and weakness. Considering its sensitivity, MRI may be a useful choice in high risk groups when the mammography results are normal.

Chemoprevention

The classical definition of chemotherapy by Sporn is “the use of pharmacologic or natural agents that inhibit the development of invasive breast cancer either by blocking the DNA damage that initiates carcinogenesis, or by arresting or reversing the progression of premalignant cells in which such damage has already occurred.” 95 . Estrogen receptor is a major target for chemotherapy because more than 70% of breast cancers are ER-positive breast cancers. Selective estrogen receptor modulators (SERMs) and the aromatase inhibitors (AIs) are two major classes of anti-estrogen drugs. SERMs are compounds that act as either agonists or antagonists of estrogen receptors. One of the most famous SERMs is tamoxifen (TAM), which has been used to treat breast cancer for more than 30 years 96 . Regardless of the number, the scale, the related areas or the time lasting in the follow-up visit of TAM's research, this drug has no doubt the most abundant clinical data in SERMs. Meanwhile, TAM is used to treat all stages of breast cancer 97 . Many large-scale trials including the Breast Cancer Prevention Trial (NSABP-1), the Royal Marsden Prevention Trial, the Italian Prevention Trial and the International Breast Cancer Intervention Study (IBIS-I Trial) have shown that TAM could reduce the risk of both invasive and non-invasive breast cancer. Despite the differences in data collection and study design, all these trials have demonstrated greater than 30% reduction in ER-positive breast cancer after 5 years treatment with TAM. However, no significant reduction has been observed in ER-negative tumors 98 , 99 . Nevertheless, there're some side-effects of TAM therapy. The risk of endometrial cancer, stroke, pulmonary embolism, and deep-vein thrombosis is increased in TAM-treated patients, and the risk is especially high among women older than 50 100 . Therefore, TAM should be used individually by balancing between its toxicity and benefits.

Raloxifene, a second generation of SERMs with fewer side-effects than TAM, has been approved for the treatment of invasive breast cancer in postmenopausal women as well as osteoporosis and heart disease 101 . However, raloxifene shows no effect on ductal carcinoma in situ (DCIS) and lobular carcinoma in situ (LCIS). The Study of Tamoxifen and Raloxifene (STAR) trials showed that raloxifene was less effective than TAM 99 . Considering its less risk in endometrial cancers and thromboembolic complications, raloxifene is still a good therapeutic option for the invasive breast cancer. Several third generations of SERMs such as ospemifen, arzoxifene, lasofoxifene (LFX) and bazedoxifene (BZA) have also been discovered, but only BZA has reached the stage of clinical use. BZA has demonstrated potent effects in pre-clinical studies, but its efficacy was limited in pivotal clinical trials 102 .

Recently, AIs are being used instead of TAM as the first line therapy in postmenopausal breast cancer patients. AIs reduce the plasma levels of estrogens by inhibiting aromatase, an enzyme that catalyzes the biosynthesis of estrogen from androgen 103 . There're two classes of AIs: steroidal inhibitors and non-steroidal inhibitors. Compared with exemestane, a steroidal inhibitor, non-steroidal inhibitors such as anastrozole and letrozole can bind to the enzyme's active site reversibly. However, these three third-generation AIs (exemestane, anastrozole and letrozole) have no significant differences in terms of efficacy in preventing breast cancer. Many trials such as the Italian Tamoxifen Anastrozole (ITA) trial, Intergroup Exemestane Study (IES), Austrian Breast and Colorectal Cancer Study Group (ABCSG-8), and the Breast International Group (BIG) 1-98 study have showed that in the adjuvant setting, AIs are more potent than TAM in reducing the incidence of breast cancer both as upfront monotherapy and after 2-3 years treatment with tamoxifen 104 - 110 . A 10-year ATAC trial study indicated fewer serious side-effects for anastrozole than for tamoxifen in postmenopausal women with early ER + breast cancer 111 . However, there're some limitations of AIs. Because AIs inhibit the biosynthesis of estrogen, they are typically used only in postmenopausal women. The main side-effect of AIs is that they can increase the risk of osteoporosis, which is a significant health threat for older women. Other side-effects, such as joint pain and stiffness, incidence of carpal tunnel syndrome or dysregulated lipid metabolism, have also been reported for AIs, but these side-effects have a lower impact on the quality of life of patients than endometrial cancer and thromboembolic complications associated with TAM treatment. Acquired resistance to AIs has been observed after prolonged treatment, which also occurs in the case of SERMs. Crosstalks between estrogen receptor pathway and several signaling pathways such as PI3K/Akt/mTOR and Ras/Raf/MEK/MAPK could lead cancer cells to be resistant to AIs 112 . The combination of AIs and inhibitors of the related signaling pathways may be a promising strategy for AI-resistant patients.

Biological prevention

Recently, biological prevention, mainly known as the monoclonal antibodies for the breast cancer, has been developed to improve the quality of life in breast cancer patients. One of the major targets of these monoclonal antibodies is HER2. About 20-30% of all breast cancer cases exhibit HER2 protein overexpression or HER2 gene amplification 36 . Trastuzumab (Herceptin), a recombinant humanized monoclonal antibody, is the first HER2-targeted drug to be approved by the FDA. It can directly interact with the C-terminal portion of domain IV in the extracellular part of HER2 113 , 114 . Up to now, the anti-tumor mechanism of trastuzumab has not been clearly elucidated. Some potential mechanisms may be that trastuzumab can suppress the growth and proliferation of cancer cells by recruiting ubiquitin to internalize and degrade HER2, by activating the immune system against cancer cells via a mechanism called antibody-dependent cell-mediated cytotoxicity (ADCC) or by inhibiting the MAPK and PI3K/Akt pathways 115 - 117 . Trastuzumab was initially used for treating metastatic breast cancer (MBC) and found to be efficacious as a single agent with an objective response rate (ORR) of 26%. In vitro experiments have shown that trastuzumab has a synergistic effect with other anti-tumor drugs such as nimotuzumab, carboplatin, 4-hydroxycyclophosphamide, docetaxel and vinorelbine 118 , 119 . The HERA and TRAIN trials demonstrated that chemotherapy combined with adjuvant trastuzumab for 1 year could improve the disease-free survival in patients with HER2 + breast cancer (HR=0.76) 120 , 121 . A randomized phase II trial executed by Marty also showed that trastuzumab plus docetaxel was more efficacious than docetaxel alone in treating HER2-positive MBC, with the ORR of 50% versus 32% 122 . However, side-effects such as congestive heart failure and left ventricular ejection fraction (LVEF) decline were found in trastuzumab-treated patients 123 .

Similar to trastuzumab, pertuzumab (Perjeta), another humanized monoclonal antibody, can bind to the extracellular portion of HER2 like trastuzumab. However, the binding domain is different 124 . Pertuzumab combined with trastuzumab and docetaxel has been approved for treating HER2-positive breast cancer. The pathologic complete response (pCR) rate, as well as invasive-disease-free survival rate, significantly increased in HER + tumors than those in HER - tumors (57.8% versus 22.0%) 125 , 126 . However, toxic side-effects like diarrhea and febrile neutropenia were common in pertuzumab-treated groups.

Recently, immunotherapy becomes a hot spot in cancer therapy, and it shows great potential in clinical use. Programmed cell death 1 (PD1) is a membrane protein expressed in various immune cells, including T cells, which can be engaged by its specific ligand to block the immune system. PD1 inhibitor drugs Nivolumab (Opdivo) and Pembrolizumab (Keytruda) were approved for the treatment of several solid tumors such as metastatic melanoma and non-small cell lung cancer. In the KEYNOTE-012 study, pembrolizumab was found to be effective in 27 TNBC patients with a clinical benefit rate of 20% 127 . Programmed cell death receptor ligand 1 (PDL1), a ligand of PD1, is detected in 20% of TNBC and in 50% of all breast cancers 128 . PDL1 inhibitor drug Atezolizumab (Tecentriq) exhibits a 19% of objective response rate in a phase Ⅰ study including 54 TNBC patients 129 . Though TNBC patients typically have poor clinical outcomes, anti-PD1/PDL1 drugs might be promising strategies for treating this subtype of breast cancer.

Conclusion and further directions

Breast cancer is the most frequently diagnosed cancer in women across 140 countries 1 . Approximately 1 in 8 women worldwide have a lifetime risk of developing breast cancer 130 . Breast cancer develops through a multistep process, and the pathogenesis of this disease has not yet been elucidated. In the last decade, the tumor microenvironment and breast CSCs have been identified as contributors to breast tumorigenesis. Breast cancer is also influenced by genetic and environmental factors. Targeted prevention strategies against these risk factors should be taken ahead of time.

Although the incidence rate of breast cancer is high in developed countries, the fact which we can't ignore is that almost half of the breast cancer cases and over half of deaths occur in developing countries. The 5-year relative survival rates of breast cancer varied widely in developed and developing countries. The rate is over 80% in North American and Japan, but below 40% in Africa countries like Algeria. Breast cancer is a preventable disease, and there are adequate medical resources available in developed countries, which can protect against this disease, such as annual mammography screening or the daily use of chemopreventative drugs. These may be attributable for the higher survival rate of breast cancer patients in developed countries than that in middle-income or low-income countries. Considering the financial burden of developing countries, the clinical breast examination is an effective way to diagnose breast cancer in the early stage. Moreover, if women are educated about breast cancer, breast self-examination may be a simple, economical and motivated method to prevent this disease. People know their own bodies more clearly than any doctors. However, most of the women in developing countries don't realize the importance of breast cancer prevention. Therefore, in these countries, more attention should be focused on breast health promotion ahead of clinical treatment.

Nowadays, with the reduction in the cost of DNA sequencing, individual genome sequencing may be affordable by middle-class populations, and this could be a new method in preventing breast cancer. If a woman have a family history of breast cancer, it is wise to do a screen especially on hereditary cancer susceptibility genes such as BRCA1 or BRCA2 . The risk of breast cancer could then be evaluated based on the screening results and prevention advice could be offered personally. Individual genome sequencing may be a mainstream in the future for prevention of breast cancer as well as other hereditary disease. Additionally, risk factors should be taken more seriously either in normal or high-risk women. Environmental factors such as the exogenous estrogen intake, alcohol abuse and excess dietary fat consumption could be avoided to minimize breast cancer risk. Though some risk facts such as aging and reproductive factors are inevitable, measures should be taken ahead of time to reduce the risk. In the modern world, many people spend countless hours sitting at tables. People are more engaged in mental work rather than in physical work. However, physically active women have a 25% lower risk of breast cancer on average than women who are less active 131 . Regular physical exercise may be a convenient and inexpensive way to prevent breast cancer in women from both developed and developing countries.

Although traditional film mammography has limitations in detecting dense breasts, digital mammography can overcome this deficiency. It can capture images directly through an X-ray-sensitive detector and the digital data are analyzed in the computer. The Digital Mammographic Imaging Screening Trial (DMIST) showed that digital mammography had a better diagnostic performance than film mammography in pre- and perimenopausal women with dense breasts who were younger than 50 years of age. With advancements in digital technology, high-resolution digital mammography may replace film mammography in the future 132 . What's more, both mammography and MRI screening for a large population of women are expensive and only countries with good health insurance systems can offer these services. Direct breast ultrasonography, an adjuvant technique method to mammography and MRI, is less expensive and could be used widely in low- and middle-income countries. The ultrasonography is much more accurate if the operator is skilled and experienced.

Although great progress has been made in breast cancer prevention in the last decade, there is still a lack of effective therapies against TNBC. TNBC tends to have a higher relapse risk and is more aggressive than other subtypes, resulting in a poor 5-year survival rate 133 . Due to the absence of ER/PR expression and HER2 amplification, drugs targeted against these three receptors are useless in TNBC. In the last decade, several potential biomarkers in TNBC such as EGFR, androgen receptor (AR), PARP and mTOR, and microRNA-based biomarkers, such as miR-374b-5p and miR-629-3p have been identified and explored for targeted therapies 134 - 136 . The EGFR-inhibitor cetuximab combined with cisplatin increased the progression-free survival from 1.5 to 3.7 months, and the overall survival from 9.4 to 12.9 months in a phase Ⅱ clinical trial 137 . Expressions of AR is observed in 30% of TNBC patients, and the AR inhibitor bicalutaminde showed a clinical benefit rate of 19% in ER/PR negative breast cancer patients in a phase Ⅱ study 138 , 139 . PARP-inhibitor iniparib plus chemotherapy were also tested and showed promising results in phase Ⅰ and Ⅱ clinical trials. However, the phase III clinical trial failed with the lack of improvement in progression-free survival and overall survival 140 . Targeting the PI3K/AKT/mTOR pathway was thought to be an effective strategy to treat TNBC recently, and the mTOR inhibitor everolimus combined with doxorubicin and bevacizumab increased the objective response rate but not the clinical benefit rate 141 . Although many biomarker-based trials have been performed in TNBC, none has been successful finally. One of the main reasons for this failure could be the heterogeneity of TNBC. More work is needed to elucidate tumor heterogeneity, and the discovery of a robust biomarker regardless of tumor heterogeneity may be a breakthrough in TNBC treatment. Immunotherapy agents such as anti-PD1/PDL1 drugs will also shed light on treating TNBC.

In summary, breast cancer is preventable. Reducing risk factors and taking chemoprevention are two main measures to prevent breast cancer. However, there's a long way to go in creating public breast cancer awareness. Only 4.1% of high-risk women are willing to take chemoprevention drugs 142 . The fear of adverse effects and lack of understanding of breast cancer might be attributable for this unwillingness. Although, the Gail model or the IBIS model is widely used for determining the risk of breast cancer based on a woman's age, family history, race and reproductive factors, we still lack a reliable strategy to exactly evaluate the risk ratio of breast cancer. With improvements in sequencing technology, individual genome sequencing may be a powerful method to evaluate the risk of breast cancer. Better medicines with less adverse effects and a favorable risk-benefit ratio need to be developed in the future.

Additional genes associated with breast cancer

Gene	Location	Function	Abnormality in Breast cancer	Ref.
P53	17p13.1	tumor suppressor gene	Mutations in 30% of breast cancers	,
NME1	17q21.3	metastasis-suppressor gene	SNP of gene associated with higher breast cancer-specific mortality (HR=1.4) and patients with an early-stage cancer (HR=1.7)	,
RB1	13q14.2	tumor suppressor gene	Rb1 inactivation in 20-35% of breast cancers	,
PTEN	10q23.3	tumor suppressor gene	Loss of PTEN protein expression in up to 33% of breast cancers	,
ATM	11q22-q23	tumor suppressor gene	Mutation of ATM increases the risk of 2- to 3-fold in general, and 5- to 9-fold in women under age 50
CDH1 (E-cadherin)	16q22.1	tumor suppressor gene	Inactivation of CDH1 in 85% of lobular breast carcinomas
FHIT	3p14.2	putative tumor suppressor gene	The rate of FHIT hypermethylation in breast cancer was 8.4-fold higher than that in normal breast tissues
Maspin	18q21.33	tumor suppressor gene	Expression of Maspin in 20-80% invasive breast cancer	,
PIK3CA	3q26.3	oncogene	Mutations in 37% of the HR /HER2 metastatic and 40% of early breast cancer
CCND1 (Cyclin D1)	11q13	oncogene	Overexpression in 50% of breast tumors

Notes: SNP-Single Nucleotide Polymorphisms; HR-Hazard ratio

Acknowledgments

We would like to thank Dr. Yan-Ling Wu for her advice on this paper. This work is supported by Zhejiang Provincial Natural Science Foundation of China under Grant No. LQ15C040001 and LY18H190003, Social Development Project of Public Welfare Technology Research in Zhejiang Province (LGF18H100002), and Zhejiang Provincial Foundation for Scientific Research in Medicine and Health (2015RCB009).

IMAGES

️ Research paper about breast cancer. Breast cancer research paper
⚡ Sample research paper on breast cancer. Sample Research Paper on
[PDF] Breast Cancer
Examples Of Breast Cancer Research Papers Ln : Breast cancer causes
(PDF) Breast cancer
(PDF) Molecular Classification of Triple-Negative Breast Cancer

COMMENTS

Articles
Breast cancer (BC) is a highly prevalent malignancy worldwide, with complex pathogenesis and treatment challenges. Research reveals that methyltransferase-like 3 (METTL3) is widely involved in the pathogenesis... Dongqiong Xiao, Mingfu Zhang, Yi Qu and Xiaojuan Su. Breast Cancer Research 2024 26 :110.
Home page
Breast Cancer Research is an international, peer-reviewed online journal, publishing original research, reviews, editorials and reports. Open access research articles of exceptional interest are published in all areas of biology and medicine relevant to breast cancer, including normal mammary gland biology, with special emphasis on the genetic, biochemical, and cellular basis of breast cancer.
Breast Cancer—Epidemiology, Risk Factors, Classification, Prognostic
Breast cancer is currently one of the most prevalently diagnosed cancers and the 5th cause of cancer-related deaths with an estimated ... Colombet M., Mery L., Piñeros M., Znaor A., Soerjomataram I., Bray F. International Agency for Research on Cancer; Lyon, France: 2020. [(accessed on 9 July 2021)]. Global Cancer Observatory: Cancer Tomorrow. ...
Breast cancer
Read the latest Research articles in Breast cancer from Nature. ... An analysis of 780 breast cancer genomes shows that focal amplifications are frequently preceded by dicentric chromosome ...
Current State of Breast Cancer Diagnosis, Treatment, and Theranostics
1. Introduction. Breast cancer has a very long history as it was first reported by the ancient Egyptians more than 3500 years ago in about 1500 B.C [].Today, breast cancer is the second most prevalent type of cancer and is a leading cause of most cancer-related deaths in women in the United States [].Around 281,550 women are projected to be diagnosed with breast cancer in 2021, and 43,600 ...
Breast Cancer Treatments: Updates and New Challenges
Go to: 1. Introduction. Breast cancer (BC) is the most frequent cancer and the second cause of death by cancer in women worldwide. According to Cancer Statistics 2020, BC represents 30% of female cancers with 276,480 estimated new cases and more than 42,000 estimated deaths in 2020 [ 1 ]. Invasive BC can be divided into four principal molecular ...
Breast cancer: an up-to-date review and future perspectives
The estimated new breast cancer cases reached 2.3 million in 2020, accounting for 11.7% of all new cancers, and 684,996 cases died of it . In China, breast cancer was the most common malignancy among women, with an estimated number of 306,000 new cases occurring in 2016 . The incidence of breast cancer has increased since the widespread uptake ...
Breast Cancer Research Articles
Posted: January 20, 2023. Many young women who are diagnosed with early-stage breast cancer want to become pregnant in the future. New research suggests that these women may be able to pause their hormone therapy for up to 2 years as they try to get pregnant without raising the risk of a recurrence in the short term.
Global trends and forecasts of breast cancer incidence and deaths
The latest research evidence 1 shown that 44.4% of global cancer deaths and 42.0% of global cancer disability-adjusted life years can be attributed to GBD 2019 estimated risk factors. Our study ...
Home
Breast Cancer Research and Treatment is a comprehensive forum dedicated to all aspects of breast cancer research. The journal's focus spans across various disciplines including surgery, radiotherapy, medical oncology, endocrinology, epidemiology, immunology and cell biology. Provides an international platform for the discussion and resolution ...
Breast Cancer: Basic and Clinical Research: Sage Journals
Breast Cancer: Basic and Clinical Research is an international, peer-reviewed, open access journal that covers all aspects of research and treatment of breast cancer. The journal aims to promote understanding of breast cancer biology and pathogenesis, clinical interventions, and epidemiology and population genetics. ... Submit your paper to one ...
Breast-Cancer Tumor Size, Overdiagnosis, and Mammography Screening
Assuming that the underlying burden of clinically meaningful breast cancer was unchanged, these data suggest that 30 cases of cancer per 100,000 women were destined to become large but were ...
Breast cancer
Breast cancer is caused by the development of malignant cells in the breast. The malignant cells originate in the lining of the milk glands or ducts of the breast (ductal epithelium). Breast ...
(PDF) Breast cancer
PDF | Breast cancer is the most frequent malignancy in women worldwide and is curable in ~70-80% of patients with early-stage, non-metastatic disease.... | Find, read and cite all the research you ...
Advances in Breast Cancer Research
Advances in Breast Cancer Research. A polyploid giant cancer cell (PGCC) from triple-negative breast cancer. NCI-funded researchers are working to advance our understanding of how to prevent, detect, and treat breast cancer. They are also looking at how to address disparities and improve quality of life for survivors of the disease.
Breast cancer
Breast cancer is still the most common cancer worldwide. But the way breast cancer is viewed has changed drastically since its molecular hallmarks were extensively characterised, now including immunohistochemical markers (eg, ER, PR, HER2 [ERBB2], and proliferation marker protein Ki-67 [MKI67]), genomic markers (eg, BRCA1, BRCA2, and PIK3CA), and immunomarkers (eg, tumour-infiltrating ...
Current State of Breast Cancer Diagnosis, Treatment, and ...
In this review, we will highlight the different diagnosis techniques and treatment strategies for breast cancer management and explore recent advances in breast cancer theranostics. Our main focus will be to summarize recent trends and technologies in breast cancer diagnosis and treatment as reported in recent research papers and patents and ...
Breast Cancer—Epidemiology, Classification, Pathogenesis and Treatment
The relationship between hormonal contraceptive use and breast cancer risk has been demonstrated in two important papers—a reanalysis of 54 epidemiological studies by the ... fish, legumes, oils, and vegetable oils reduces the risk of breast cancer . Research suggests that nutrition affects the prognosis of breast cancer. ...
Deep Learning to Improve Breast Cancer Detection on Screening ...
1. Set learning rate to 10 −3 and train the last layer for 3 epochs. 2. Set learning rate to 10 −4, unfreeze the top layers and train for 10 epochs, where the top layer number is set to 46 for ...
Deep learning applications to breast cancer detection by magnetic
Deep learning analysis of radiological images has the potential to improve diagnostic accuracy of breast cancer, ultimately leading to better patient outcomes. This paper systematically reviewed the current literature on deep learning detection of breast cancer based on magnetic resonance imaging (MRI). The literature search was performed from 2015 to Dec 31, 2022, using Pubmed.
Top 100 Cited Classic Articles in Breast Cancer Research
The object of this study was to identify and analyze the qualities of the top 100 cited papers in breast cancer-related studies. Materials and Methods. ... Breast cancer research has progressed historically and gone through milestones in various areas in this process. These areas include breast cancer gene associations, breast cancer treatment ...
Critical research gaps and translational priorities for the successful
Introduction Breast cancer remains a significant scientific, clinical and societal challenge. This gap analysis has reviewed and critically assessed enduring issues and new challenges emerging from recent research, and proposes strategies for translating solutions into practice. Methods More than 100 internationally recognised specialist breast cancer scientists, clinicians and healthcare ...
Risk Factors and Preventions of Breast Cancer
Introduction. Breast cancer is one of the most common cancers in women worldwide, accounting for approximately 570,000 deaths in 2015. Over 1.5 million women (25% of all women with cancer) are diagnosed with breast cancer every year throughout the world 1, 2.In America, it is estimated that 30% of all new cancer cases (252,710) among women are breast cancer in 2017 3.

Breast Cancer Research and Treatment

Latest issue

Latest articles

SGLT2 inhibition improves PI3Kα inhibitor–induced hyperglycemia: findings from preclinical animal models and from patients in the BYLieve and SOLAR-1 trials

Patient characteristics and treatment patterns of patients with locally advanced or metastatic HER2-low breast cancer, a single site descriptive study

Cytokine levels in breast cancer are highly dependent on cytomegalovirus (CMV) status

Polycystic ovary syndrome and risk of breast cancer in premenopausal and postmenopausal women: a nationwide population-based cohort study

Journal information

Breast cancer

Advances in Breast Cancer Research

Early Detection of Breast Cancer

Breast Cancer Treatment

Shortening Radiation Therapy for Some with Early Breast Cancer

HR-Positive Breast Cancer Treatment

Drug Combo Effective for Metastatic Breast Cancer in Younger Women

HER2-Positive Breast Cancer Treatment

HER2-Low Breast Cancer

Immunotherapy Improves Survival in Triple-Negative Breast Cancer

Triple-Negative Breast Cancer Treatment

NCI-Supported Breast Cancer Research Programs

Breast Cancer Survivorship Research

Breast Cancer Clinical Trials

Breast Cancer Research Results

Save citation to file

Add to My Bibliography

Current State of Breast Cancer Diagnosis, Treatment, and Theranostics

Conflict of interest statement

Similar articles

Publication types

Related information

Grants and funding

LinkOut - more resources

Deep Learning to Improve Breast Cancer Detection on Screening Mammography

Similar content being viewed by others

Intelligent breast cancer diagnosis with two-stage using mammogram images

A convolutional deep learning model for improving mammographic breast-microcalcification diagnosis

Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach

Converting a classifier from recognizing patches to whole images

Network design

Computational environment

Developing patch and whole image classifiers on CBIS-DDSM

Network training

Development of patch classifiers

Converting patch to whole image classifiers

Transfer learning for whole image classification on INbreast

Effectiveness and efficiency of transfer learning

Data Availability

Acknowledgements

Author information

Contributions

Corresponding author

Ethics declarations

Additional information

Supplementary information

About this article

Share this article

This article is cited by

Are better AI algorithms for breast cancer detection also better at predicting risk? A paired case–control study

Dual view deep learning for enhanced breast cancer screening using mammography

Deep learning system for true- and pseudo-invasion in colorectal polyps

Machine vision-based detections of transparent chemical vessels toward the safe automation of material synthesis

Quick links

Critical research gaps and translational priorities for the successful prevention and treatment of breast cancer

Introduction

Conclusions

Genetics, epigenetics and epidemiology

BRCA1 and BRCA2

Epigenetics

Psychosocial considerations

What are the key gaps in our knowledge and how might they be filled?

BRCA1 and 2

Risk and prevention

Chemoprevention

Lifestyle changes

Lifestyle change for breast cancer prevention

Molecular pathology

Microenvironmental influences and tumour - host interactions

What are the key gaps in our knowledge and how might these be filled?

Breast cancer subtypes: genomics and bioinformatics

Tumour microenvironment and stromal influences