To elucidate the role of metabolic dysregulation and associated DNA methylation changes on breast cancer risk and aggressive subtypes among Nigerian women. We describe the design and methods of a collaborative molecular epidemiology study of breast cancer in Nigerian hospitals.The Mechanisms for Novel and Established Risk Factors for Breast Cancer in Women of Nigerian Descent (MEND) study was designed as a matched case-control study of 350 patients, age 18 to 75 years, with newly diagnosed, treatment-naïve breast cancer and 350 age-matched healthy controls from surrounding geographic areas. Patients with breast cancer seen for initial diagnosis at four large tertiary hospitals in southwest Nigeria and one affiliated private hospital were recruited. Healthy female controls were selected from a cohort of 4,000 healthy women recruited as part of the Human Heredity and Health (H3) in Africa Chronic Kidney Disease Case-Control Study in Nigeria. Tumor and adjacent normal tissue, and blood and saliva samples were collected for molecular and epigenetic assays.Although recruitment is ongoing, a total of 416 patients have been recruited to date, with tumor and blood samples obtained from at least 310 patients. Data on age-matched (± 6 months) controls have also been obtained and harmonized. Lipid assays for 350 pathologically verified cases and 350 age-matched controls is underway, and pathologic characterization of tumors (including immunohistochemistry for subtyping) is ongoing. Data on DNA methylation for tumors and adjacent normal tissue are expected by the end of the study period.The MEND study will provide a unique, high-quality source of data to evaluate the contribution of metabolic dysregulation such as obesity, diabetes, hypertension, and metabolic syndrome to the biology of breast cancer among Nigerian women and foster collaborative studies relevant for women of African descent globally. This is evidenced by an increase in the prevalence of so-called diseases of affluence that result in higher rates of metabolic dysregulation, characterized by obesity, hypertension, diabetes, and metabolic syndrome (MetS).5 These cardiometabolic risk factors are associated with substantially increased risk of coronary heart disease, stroke, and type-2 diabetes,11 and prevalence of MetS is estimated to be 35% to 43% in urban areas of Nigeria.13 Given a consistent association with increased risk of breast cancer in other settings,15 the coinciding epidemics of MetS and breast cancer in Nigeria are cause for concern.Developing countries accounted for 45% of new breast cancer cases globally in 2009 and 57% in 2012, with these proportions projected to increase to 70% by 2030.17 Breast cancer in Nigeria also exhibits several epidemiologic features that are striking when compared with the disease in developed countries. First, the majority of cases are premenopausal, with more than 70% of patients diagnosed between ages 20 and 50 years18; this is similar to patterns observed among women of African descent in the United States and the United Kingdom but in contrast with predominantly postmenopausal breast cancer observed among white women.21 Many established risk factors for breast cancer, such as low parity and lack of breastfeeding, are less common in Nigerian women and typically are associated with postmenopausal breast cancer24; however, risk factors for premenopausal breast cancer are not well understood. Second, 60% to 80% of patients with breast cancer in Nigeria are diagnosed at stages III or IV with high-grade disease,28 because of a combination of infrequent screening and fast-growing tumors. Comparably aggressive phenotypes have also been documented among women of African descent in the United States and the United Kingdom, where better screening strategies exist, whereas less aggressive phenotypes are observed in white women.31 Third, several studies have reported that, similar to women of African descent in the United States and United Kingdom, but in contrast with white women, breast tumors in Nigerian women are more likely to be receptor negative for estrogen, progesterone, and human epidermal growth factor 2 .32 Fourth, differential DNA methylation of genes involved in tumor suppression and DNA repair has been implicated in young onset, aggressive, triple-negative breast cancer subtypes among black women versus with white women,33 although this pattern has not been well characterized in Nigerian women.In Nigeria, breast cancer incidence increased three-fold from 15 cases per 100,000 in 1973 to 52 cases per 100,000 in 2012.34 However, no study, to our knowledge, has evaluated whether DNA methylation, given its sensitivity to environmental and genetic cues, may provide additional insights into unique biologic mechanisms through which metabolic dysregulation or MetS increases risk of breast cancer and aggressive subtypes in Nigerian women  study.Despite the striking similarities in breast cancer phenotypes among Nigerian women and other women of African descent and in contrast with white women, molecular epidemiology studies characterizing the biologic mechanisms associated with the aggressive subtypes frequently observed among women of African descent remain scarce. Metabolic dysregulation may influence breast cancer etiology, aggressiveness, and outcomes via pathways that involve (1) higher circulating insulin levels leading to mitogenic, antiapoptotic, and angiogenic properties; (2) chronic inflammation; and (3) increased visceral fat stores and other cancer-associated adipokines that may promote tumor growth.an women . We hypoThe MEND study was designed to generate high-quality epidemiologic, molecular, and epigenetic data regarding the role of metabolic dysregulation in breast cancer risk and subtypes in Nigeria. The primary aims are as follows:To recruit 350 patients (18 to 75 years of age) with newly diagnosed, treatment-naïve breast cancer from tertiary hospitals in Nigeria, and 350 age-matched healthy controls;To determine the prevalence of metabolic dysregulation  among cases and controls, and estimate the association between MetS and components with breast cancer subtype; andTo characterize genome-wide DNA methylation levels in tumor and adjacent normal tissue from incident breast cancer cases and identify differentially methylated genes by MetS status.The study was originally designed as a matched case-control study of 350 patients, 18 to 75 years of age, with newly diagnosed, treatment-naïve incident breast cancer, and 350 age-matched healthy controls from surrounding geographic areas , and blood pressure  using equipment standardized across all study sites. In addition, two 8-mL venous blood samples were obtained , and stored in purple top  and plain vacutainer blood collection tubes, and saliva samples were stored directly in an Oragene Saliva Collection Kit . After data and sample collection, a routine biopsy was performed. Sterile tissue containers , a biopsy needle , and ancillary materials  were provided by the study for routine pathologic evaluation, as well as tumor and adjacent normal tissue collection (from the affected breast) for research purposes. Finally, the patient was provided with an incentive of a N500  telephone recharge card for their participation, and the biopsy needle  was provided by the study for free . The research samples were immediately (within 30 minutes) delivered to the laboratory technician for processing and storage in −80°C freezers. Routine treatment and follow-up continued for patients as usual.Next, the research nurse obtained physical measurements, including height , weight , waist circumference (Lufkin W606 Biologic specimens were labeled with unique patient identifying numbers (PIDs), data of collection and sample type, and then stored in −80°C freezers in institutional laboratories. Laboratory freezers are powered year-round using a combination of national electricity supply, back-up power generators, and inverters. Every 3 to 4 months, samples were packaged in freezer boxes, arranged in dry shippers charged with liquid nitrogen , and shipped to the United States. Liquid nitrogen was available for purchase in Lagos , and dry shippers were initially charged, transported back to the laboratory to be stocked and recharged, and then picked up by a World Courier representative (World Courier Inc). The dry shipper with samples was delivered to the Pathology Shared Resource Facility at the University of Kentucky Markey Cancer Center within 5 to 7 days, and samples were immediately transferred into −80°C freezers.Research nurses at each institution maintained a patient-tracking database that was continuously updated with information on date of recruitment, sample or data collected , incentive received, contact information, next of kin, and notes on recruitment process. De-identified recruitment and clinical data from each site, labeled with a unique PID, were sent to the MEND data manager in the United States for integration into a single data management system. Survey data from Qualtrics were downloaded and checked for completeness and consistency. A US pathologist reviewed each tissue sample for pathologic verification of tumor status and immunohistochemistry (IHC) staining for receptors of estrogen, progesterone, human epidermal growth receptor 2, androgen, and Ki-67 was performed. Blood samples were analyzed at a clinical research laboratory to assess for cholesterol, triglyceride, and blood glucose levels. In addition, pathologically verified tumor and adjacent normal samples were sent to a genomics laboratory for epigenome-wide DNA methylation analysis. Survey and clinical data, as well as results from the pathology, biomarker, and methylation assays, were cleaned and integrated into the final study analytical data set. The MEND data manger conducted detailed quality checks on all data items and requested clarification from local interviewers and research coordinators for missing or inconsistent variables when needed. All corrections were carefully annotated and integrated into the main study database for additional analysis. Each institution received a data set with data on patients recruited at their institutions only identified with the PID (to be decoded by site research nurses) as well as laboratory results from US pathology, IHC, and clinical assays for comparative purposes.Research nurses were carefully selected by site collaborators and trained extensively to ensure that study protocols were followed closely, errors in data collection were minimized, and biologic samples were handled safely and properly to ensure quality. Research nurses were trained to emphasize the importance of patient privacy and confidentiality, and were monitored for sensitivity to patients who were receiving a new cancer diagnosis—a devastating experience in any environment, but especially in Nigeria, where cancer stigma still exists. Research nurses completed human subjects’ protection and extensive patient-interview training before working on the project.38 Important study covariates such as sociodemographics, physical measurements, reproductive factors, and family and personal health for the CKD controls were obtained, as well as 1 mL of serum for biomarker analysis of MetS components.Rather than recruit controls de novo as previously described, healthy controls were selected from a cohort of 4,000 women recruited as part of the Human Heredity and Health (H3) Africa Chronic Kidney Disease (CKD) Case-Control Study in Nigeria, predominantly from Lagos, Ogun, Oyo, and Osun states (the same geographic region as the MEND study), and age matched (± 6 months) with breast cancer cases at enrollment. Sample and data collection protocols were similar between the CKD and MEND studies. Specifically, each breast cancer case was matched on age with one healthy control woman without replacement, so that once a control was matched, she was ineligible for matching with other cases. A nearest-matching approach was used such that if there was no control with age ± 6 months of the case, then the control closest in age was chosen. This approach has been used routinely in matched case-control studies.Active and passive surveillance of recruited patients was conducted to ascertain fatal and nonfatal cancer outcomes. Patients were contacted by telephone every 12 months around the time of their recruitment anniversary, and additional data were collected on vital status, self-reported physician diagnosis of recurrence, metastasis or remission, adverse effects of treatment, and quality of life. If the patient was unable to respond to follow-up calls or the patient could not be reached, the next of kin was contacted to complete the interview. Working closely with institutional collaborators, research nurses requested access to patient medical records 6 to 12 months after diagnosis and recruitment, and resident doctors assisted with abstracting relevant information on pathology, surgery, chemotherapy, radiation, recurrence, or metastasis.A total of 416 patients with breast cancer have been recruited to date , 372 of v 15.37 years) and menopause (48.15 v 47.88 years), had higher diastolic blood pressure (80.17 v 79.13 mm Hg), were less likely to self-report a history of hypertension (17.54% v 45.79%) or diabetes (2% v 19%), and had slightly fewer pregnancies (4.36 v 4.77).As expected, there was no statistically significant difference in age between cases and controls, as a result of matching . HoweverThe MEND study will generate and integrate patient data at the epigenetic , molecular (blood and tissue samples), pathologic (IHC and tumor makers), individual (questionnaire data), and clinical  levels to improve understanding of the unique features of breast cancer in Nigeria and to assess the contribution of metabolic dysregulation on breast cancer outcomes. The first MEND scientific papers will focus on the topics highlighted in the introduction—the role of obesity, diabetes, and MetS, and the epigenetic mechanisms linking these exposures with breast cancer incidence and subtypes. We will explore these topics further by examining other novel risk factors for breast cancer in this setting, including metabolic and inflammatory biomarkers, changes in body size over the life course, social determinants, and other genomic markers, and we will re-examine the role of established risk factors for breast cancer subtypes in this setting .The study design for MEND is motivated primarily by the desire to improve understanding and meaningfully affect the prevention and treatment of breast cancer in Nigeria. Although there are several inherent limitations to the case-control study design, such as the potential for differential misclassification of exposures, recall bias, and limitations to external generalizability, this design was the most efficient for the study setting and can serve as a basis for larger prospective studies that may address these limitations.There are many important scientific questions relevant to improving health and reducing fatal and nonfatal disease outcomes in lower- and middle-income countries, which require better understanding of etiology and biologic mechanisms involved. This information may also enhance treatment strategies in high-income countries. However, there are significant challenges associated with developing high-quality molecular epidemiology studies in resource-limited settings. These may include logistical challenges, difficulty identifying and cultivating scientific collaborations, cultural and language barriers, cost, time frame, and administrative hurdles. Important scientific questions are likely to remain unanswered if these challenges or barriers are not addressed, and, as the MEND study shows, the challenges are surmountable.Studies using the extensive MEND data and infrastructure will focus on characterizing other genomic features of Nigerian patients with breast cancer and incorporate a prospective assessment of survival. MEND data will foster collaborative studies to better understand breast cancer risk patterns among Africans (Nigerian) and women of the African diaspora, including women of African descent in the United States, United Kingdom, and the Caribbean. Given their shared genetic ancestry, these studies may help elucidate key genetic risk factors that may be conserved across generations, and the impact of distinct environmental and/or lifestyle conditions on breast cancer risk. Findings from these studies can enhance the genetic diversity of pooled studies, provide empirical data that can be used to tailor breast cancer prevention strategies to specific risk factors relevant for different racial groups, and reduce the burden of breast cancer globally.


We applied machine learning to find a novel breast cancer predictor based on information in a mammogram.Using image-processing techniques, we automatically processed 46 158 analog mammograms for 1345 cases and 4235 controls from a cohort and case–control study of Australian women, and a cohort study of Japanese American women, extracting 20 textural features not based on pixel brightness threshold. We used Bayesian lasso regression to create individual- and mammogram-specific measures of breast cancer risk, Cirrus. We trained and tested measures across studies. We fitted Cirrus with conventional mammographic density measures using logistic regression, and computed odds ratios (OR) per standard deviation adjusted for age and body mass index.P < 10−6). For the Cirrus measure derived from combining studies, the OR was 1.90 , equivalent to a fourfold interquartile risk ratio, and was little attenuated after adjusting for conventional measures. In contrast, the OR for the conventional measure was 1.34 (95% CI = 1.25 to 1.43), and after adjusting for Cirrus it became 1.16 .Combining studies, almost all textural features were associated with case–control status. The ORs for Cirrus measures trained on one study and tested on another study ranged from 1.56 to 1.78 (all A fully automated personal risk measure created from combining textural image features performs better at predicting breast cancer risk than conventional mammographic density risk measures, capturing half the risk-predicting ability of the latter measures. In terms of differentiating affected and unaffected women on a population basis, Cirrus could be one of the strongest known risk factors for breast cancer. It is well established that there is information in a mammogram that predicts a woman’s risk of a future breast cancer. Mammographic density has conventionally been defined as the white or bright regions on a mammographic image. Considerable research has shown that, after adjusting for age and body mass index (BMI), the residuals of the absolute and percentage values of conventional mammographic density are highly correlated with one another, and both sets of residuals have been found by many studies to be associated with breast cancer risk . ResiduaFor the conventional measures of mammographic density, once adjusted for age and BMI, the risk increases by about 1.4-fold per adjusted standard deviation ,5,6, equThe bench mark for measuring mammographic density has been a computer-assisted thresholding technique using Cumulus software  . A digitAlthough highly repeatable across trained measurers, the semiautomated Cumulus measurements involve subjective judgment and are too labor intensive for clinical use. Automated measures of conventionally defined mammographic density, such as AutoDensity , LIBRA , and VolAll the risk-predicting measures above summarize a mammographic image by a single quantity: an estimate of the area or volume of dense tissue. This is equivalent to counting the number of pixels above a brightness threshold, and uses first-order statistical information only. Even the volumetric Volpara measure reduces to the same process, in that it defines mammographic density as the number of voxels (3 D pixels) deemed to be dense.In this article, we describe an alternative and agnostic approach to discovering risk-predicting information in a mammographic image. We used machine learning to capture the combination of textural and spatial information, not necessarily observable by a human, that best predicts breast cancer risk. In contrast to conventional approaches, we used second-order statistical information .We tested the risk predicting capability of each measure, trained on a specific study, on the other studies. We then combined these features and studies to produce a fully automated mammography-based risk measure that we named Cirrus. Although we are not the first to take such an approach [see Gastounioti et al.  for a suCaucasian cohort : a nested case−control study of 669 cases and 2629 controls from the Melbourne Collaborative Cohort Study for whom we obtained 36 897 mammograms (average six visits per woman) .Caucasian case−control : a case−control study of 384 case patients with invasive disease and 1314 control subjects from the Australian Breast Cancer Family Study and the Australian Mammographic Density Twins and Sisters Study for whom we obtained 6243 mammograms (average two visits per woman) .Japanese cohort (Hawaii): a nested case−control study within the Multiethnic Cohort Study of Japanese American women living in Hawaii, USA, consisting of 292 cases  and 292 controls, for whom we obtained 3018 mammograms (average three visits per woman) .We analyzed digitized film mammograms of women with breast cancer (cases) and women without a diagnosis of breast cancer (controls) from three studies:All film mammograms were digitizsed. Because our software automatically handled removal of pectoral muscles from craniocaudal (CC) views, but not from mediolateral oblique (MLO) mammograms, analysis was restricted to CC view mammograms. (Our pilot study of around 600 MLO views found that the risk prediction was no different between CC and MLO views.) We used both left and right mammograms at the same visits, and multiple vists when available. The median number of mammograms per woman was 10 , 2 , and 5 (Japanese American study).All datasets underwent quality control to remove inappropriate mammograms  as well as mammograms that failed the automatic preprocessing stage by being incorrectly segmented . After quality control, less than 3% of mammograms in any dataset were removed.We developed and applied an automatic preprocessing algorithm that uses image-processing techniques to segment the breast from the background noise, and to remove artefacts and labels before feature extraction  class of features , based oA total of 20 GLCM features common in the literature were extracted from all mammograms see . ImportaFor each feature, the information for a given woman was taken to be the median of her features across all her mammograms because this increased the amount of information for risk prediction and produces a more stable predictor. Marginal (one-at-a-time) estimation of the association between the 20 GLCM features and breast cancer risk, unadjusted for covariates, was first performed using logistic regression and presented as the odds ratio (OR) per standard deviation of the unadjusted cross-sectional measure.A risk measure, Cirrus, was computed from features by applying the Bayesian lasso regression procedure (21), which is based on a Bayesian interpretation of the lasso  penalty A separate Cirrus measure was constructed from each dataset. Each trained measure was tested on the given dataset, to assess its maximum risk prediction, and then tested on each of the other two independent datasets, to see the extent to which the discovery process could be externally validated.P values were determined using the Wald test. As shown in the We assessed risk-predicting performance using logistic regression and adjusting for age and BMI. Risk gradients were presented as the change in the age- and BMI-adjusted OR per unit change in the standard deviation of the residual of the measure after adjusting for age and BMI using the controls, following the OPERA concept . The nomWe also created a Cirrus measure trained on the combined data, and fitted it to the combined data using logistic regression adjusting for age and BMI, with and without the conventional risk measures of absolute and percentage mammographic density power transformed and adjusted for age and BMI. Based on the Box–Cox transformation, we used the fourth root of absolute density and the cube root of percentage density. We used the conventional mammographic density measures, created using the semiautomated computer software Cumulus , from thP < 10–6). All the replication log(OR)s were within 20% of their in-sample training log(OR)s. The strongest cross-validation was for the Cirrus measure trained on the Australian cohort study and replicated on the study of Japanese American women living in Hawaii.r > 0.9). These features also had similar absolute log(OR)s in r < 0.4). When we repeated the analyses using measures based on a single mammogram (the earliest) we found the general findings of r = −0.1).For the Cirrus risk measure (Cirrus adjusted for age and BMI) constructed from combining all datasets and all features and adjusted for age and BMI see , the OR P = 4 × 10−5). This was nearly a halving in log(OR), and a similar result applied to percentage density. The correlations between the risk estimates for Cirrus and the risk estimates for the absolute and percentage measures of conventional mammographic density were −0.3 and −0.4, respectively. When the two conventional mammographic density measures were modeled together, the percentage measure was not significant, consistent with risk being best captured by the absolute measure.In contrast, the OR for the conventional measure was 1.34 (95% CI = 1.25 to 1.43), and after adjusting for Cirrus it became 1.16 (95% CI = 1.08 to 1.24)  scale, and this was from training on the smallest sample (Japanese American women living in Hawaii) and testing on Australian women. The strong consistency across studies suggests that the identified risk-predicting features and their risk gradients are reliable predictors of breast cancer. It also suggests that our modification to the GLMC technique to make it resistant to regularity and collinearity effects across manufacturers and digitizers was successful.Altocumulus and Cirrocumulus, defined at higher pixel brightness thresholds. Cirrus uses only relative brightness between pixels, yet achieves superior performance than the conventional measure across all studies, and has similar risk-predicting performance as do Altocumulus and Cirrocumulus for the Australian case–control study . However, considering that feature alone gave poorer internal and external risk prediction than using the combined measure, Cirrus, as is illustrated in Some strengths of our study are our consistent findings related to textural features and strengths of association within and between study findings despite the variation in 1) ethnic origin of women, 2) the machines used to produce the mammograms, and 3) the digitization of mammograms. The strong cross-predictive performance suggests that we selected important aspects of a mammogram that are robust to differences in image acquisition. Cirrus appears to be essentially uncorrelated with age, and only weakly correlated with BMI and with both the absolute and percentage measures of conventional density. Further analyses (data not shown) suggest Cirrus is not highly correlated with family history and weakly corelated with number of live births and menopause status similar to Cumulus, and we are conducting detailed analyses of these issues as we did for Cumulus ,6.The two main weaknesses of our study are 1) we combined interval and screen-detected cancers, which could weaken the ability of our measure to predict specifically risk or specifically masking and 2) our study was composed entirely of digitized film mammograms. Although the GLCM features account for some differences between images and machines, there is no guarantee that our current Cirrus measure will perform as well when ported to digital mammography; it could require further modification. It might also be that different features, or different weightings of features, provide better predictors of risk from digital mammograms. Not being able to readily identify and “see” the Cirrus measure and the individual features that underlie this phenomenon might be considered another weakness, and we have tried to address this. However, given that the consequence of applying machine learning is an automated mammography-based risk measure that does not require visual measurement, this might not be an impediment to future clinical use of measures like Cirrus when based on machine learning applied to digital mammograms.In conclusion, we have used machine learning to create a new and fully automated mammography-based risk measure that has reliability within and across studies. Note that although we averaged mammograms to discover the predictor, we have created a risk predictor applicable to a single mammogram, thus allowing future studies of changes in time for a particular woman. One of the powerful features of using machine learning to create an automated measure of risk is that is it not necessary to have a human interpretation of the features—the human perception of the features will not be used on their own in practice. Our risk measure performs better at predicting breast cancer risk than the conventional mammographic density risk measures and captures half the risk-predicting ability of those measures. In terms of differentiating affected and unaffected women on a population basis, Cirrus could be one of the strongest known risk factors for breast cancer.Affiliations of authors: Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia ; Faculty of Information Technology, Monash University, Clayton, Victoria, Australia (DFS); IBM Australia - Research, Southbank, Victoria, Australia (BG); Curtin UWA Centre for Genetic Origins of Health and Disease, Curtin University, and the University of Western Australia, Perth, Western Australia, Australia (JS); Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy (LB); Department of Pathology, University of Melbourne, Carlton, Victoria, Australia (MCS); Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia (MCS); University of Hawaii Cancer Center, Honolulu, HI (GM); Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria, Australia (GGG). The authors declare no conflicts of interest.Supplementary DataClick here for additional data file.


BRCA1 and BRCA2 mutations confer a substantial breast risk of developing breast cancer to those who carry them. For this reason, the United States Preventative Services Task Force (USPSTF) has recommended that all women be screened in the primary care setting for a family history indicative of a mutation, and women with strong family histories of breast or ovarian cancer be referred to genetic counseling. However, few high-risk women are being routinely screened and fewer are referred to genetic counseling. To address this need we have developed two decision support tools that are integrated into clinical care.RealRisks, and the provider-facing Breast Cancer Risk Navigation Toolbox (BNAV) or standard education alone. We will assess these tools’ effectiveness in promoting genetic counseling uptake and informed and shared decision making about genetic testing.This study is a cluster randomized controlled trial of high-risk patients and their health care providers. Patient-provider dyads will be randomized to receive either standard education that is supplemented with the patient-facing decision aid, If found to be effective, these tools can help integrate genomic risk assessment into primary care and, ultimately, help expand access to risk-appropriate breast cancer prevention options to a broader population of high-risk women.NCT03470402: 20 March 2018.This trial is retrospectively registered with ClinicalTrials.gov Identifier:  BRCA1 and BRCA2 (BRCA1/2) genes [BRCA1/2 mutations account for an estimated 2–7% of breast cancers and 10–15% of ovarian cancers [BRCA1/2 mutation [Breast and ovarian cancers confer significant morbidity and mortality to women in the United States, and their development is strongly influenced by genetic predisposition , 2. Here2) genes . Carrier2) genes . Inherit cancers , 5, 6. ABRCA1/2 mutations is further associated with a reduction in cancer-specific and all-cause mortality [While mutation carriers are at a substantially higher risk for developing breast and ovarian cancers than the general population, preventative actions can reduce a carrier’s cancer risk by up to 90% once she is identified . Such riortality , 18.BRCA1/2 mutations, the United States Preventive Services Task Force (USPSTF) recommends that primary care providers screen asymptomatic women for an increased BRCA1/2 mutation risk [BRCA1/2 testing if further indicated and desired after counseling [In order to promote the identification of women carrying ion risk . Women wunseling .BRCA1/2 founder mutations), or a previously identified BRCA1/2 mutation in the family [The identification of women whose family history indicates an increased risk for carrying a mutation is based upon a set of “red flags” in the woman’s medical history and that of her family. These red flags include early onset of breast or ovarian cancer, multiple cases of breast or ovarian cancer in the family, bilateral breast cancer, male breast cancer, Ashkenazi Jewish decent  of these women met USPSTF guidelines for BRCA1/2 genetic testing. Nevertheless, only 4.6% (n = 17) of those eligible women had received testing or counseling. Bellcross et al. have reported similar under-utilization of genetic testing services in another health system [BRCA1/2 genetic testing.Despite the USPSTF recommendation and the increasing availability of genetic testing for hereditary cancer syndromes, many women who are at an increased risk of carrying services –25. In 2services . Based oh system . In a laBRCA1/2 mutations is similar across most racial and ethnic groups (except Ashkenazi Jews), women from racial/ethnic minority groups and lower education and income levels are less likely to be referred for genetic testing [While the prevalence of  testing , 27, 28. testing .Providers find it difficult to assess breast cancer risk and communicate probabilistic risk information to their patients during the primary care encounter . BarrierFurther research is needed to determine how breast cancer knowledge, the concept of risk—including both general and personalized risk information—and the pros and cons associated with genetic testing are best communicated to women and their providers in order to inform genetic testing decisions and promote risk-appropriate prevention strategies. Improving a patient’s knowledge of breast cancer and genetic testing and the accuracy of her risk perceptions may help her make a more informed choice about pursuing HBOC genetic counseling and testing. Integrating specific and actionable risk information into the clinical encounter may help enable providers to conduct appropriate risk assessments and refer high-risk patients to further testing and downstream services.BNAV) tool [RealRisks, [RealRisks and BNAV compared to patient education alone on promoting appropriate uptake of BRCA1/2 genetic counseling. Secondarily, we will assess the efficacy if the RealRisks/BNAV intervention with measures of decision process and quality as endpoints.The study goal is to expand genetic testing for HBOC to a broader population of high-risk women by prompting appropriate referrals from the primary care setting with the use of an electronic health record-embedded breast cancer risk navigation (AV) tool , 41. To alRisks, , 43 whicBRCA1/2 genetic testing, less than 5% have undergone HBOC genetic testing due to the lack of systemic family history screening [This study is being conducted in the outpatient clinics of Columbia University/New York Presbyterian Hospital in New York, NY. These clinics serve about 42,000 adults each year . About 7creening .The study clinics provide a range of services, including internal medicine, family medicine, gynecology, and family planning. We will engage providers and patients from each of these specialties.BRCA1/2 genetic testing based upon a validated family history screener, [To be eligible, a provider must be a physician, nurse practitioner, physician assistant, or nurse midwife who sees patients in the study clinics and is willing and able to provide informed consent. Patients must see a provider enrolled in the study, be 21 to 75 years of age, have no personal history of breast or ovarian cancer, have never received genetic counseling or testing for HBOC, meet family history criteria for creener,  and be aBecause this study focuses on patient-provider dyads, we will cluster randomize the patients at the provider letter. We will first recruit health care providers, and, upon consenting, these providers will be given a baseline survey and then randomized into either the intervention or control group.BRCA1/2 testing. Those who meet eligibility criteria will be recruited and assigned to the same randomization group as their provider. In this way, the patients grouped by provider will form the clusters in this randomized controlled trial.These providers’ patients will then be screened to determine eligibility for the study and for RealRisks decision aid coupled with the provider-facing BNAV toolbox. These user-centered tools are integrated into clinical workflow, were designed with consistent feedback from members of our target populations, and have been demonstrated to be useable and appropriate [The study intervention includes the patient-facing ropriate , 45.RealRisks incorporates constructs from shared decision making and self-determination theory to model patient-provider dialog, communicate numeric concepts central to risk, and engage women in deciding upon a preference-sensitive course of action regarding genetic testing [RealRisks was designed to incorporate a patient’s preferences and values in order to improve accuracy of risk perceptions and promote self-efficacy in a way that will support autonomous decision making. Specifically, RealRisks was designed to improve genetic testing knowledge, accuracy of breast cancer risk perceptions, and self-efficacy to engage in a collaborative dialogue about breast cancer risk and genetic testing. testing , 47. ReaRealRisks first provides general education using a narrative in which a fictitious character named Rose discusses breast cancer risk, family history, and cancer prevention options with friends, family, and health care providers. Patients can choose to read this narrative in English or Spanish and in graphic novel format (narrative light) or slide presentations (narrative dense). The patient reinforces this information by playing experience-based, pictographic risk games that are embedded into the education modules  is judged clinically important because effect sizes observed between those who make and those who delay decisions have ranged between 0.43 and 0.82 [Our goal is to recruit 76 providers and a total of 190 women. With a total sample size of 190 women (95 per arm), assuming a two-sided Type 1 error of 5%, a 20% drop-out rate (effective sample size of 76 per arm), and an intracluster correlation of less than 0.2, we will have greater than 90% power to detect a difference of a 5% rate of appropriate genetic counseling in the control arm (based upon our pilot data) and 30% in the and 0.82 . AgreemeComparisons between the intervention and control groups will be conducted using Chi-square tests for categorical variables and Student’s t-tests for continuous variables. After generating descriptive statistics, we will conduct bivariate analyses using Chi-Square tests, t-tests, and Pearson correlation coefficients to determine associations between study variables and genetic counseling uptake rate. Depending on the scale of each outcome variable (continuous or binary) and the scale of the independent variable , we will use ANOVAs, linear regression, logistic regression, variance components analysis, mixed models, and generalized estimating equations (GEE) to identify variables that are associated with each outcome.We have begun recruiting and enrolling both patients and providers into the trial. After 5 months of recruitment and engagement, we have enrolled over 50 health care providers and 20 patients.clinicaltrials.gov under trial number NCT03470402: 20 March 2018.This trial is registered retrospectively in BRCA1/2 mutations are the strongest known breast cancer risk factors, conferring up to a 60–80% lifetime risk of breast cancer among female carriers [BRCA1 and BRCA2 genes are actionable once identified. Cancer prevention options can reduce breast cancer risk by up to 90% [BRCA1/2 mutation [carriers . While tp to 90% , 18. Idemutation .BRCA1/2 testing, collecting detailed family history is time consuming and difficult. We have found that providers at our institution often report insufficient time to complete a comprehensive family history intake or risk assessment during the clinical encounter, particularly when the patient presents with ongoing comorbidities that need to be managed [Nevertheless, while incorporating efficient family history screening into clinic workflow is critical to identifying patients eligible for  managed . While r managed .RealRisks and BNAV have the potential to addresses this issue by aiding a patient in collecting her family history data, clarifying her values, and deliberating her choice to seek genetic testing before she sees her provider during her clinical visit. By doing so, RealRisks and BNAV can help to ensure that both the patient and her provider are better prepared to make a shared and informed decision about BRCA1/2 genetic testing while reducing the time burden this conversation would normally bestow upon the rest of the clinical encounter.BRCA1/2 mutations is comparable among most racial/ethnic groups except Ashkenazi Jews, racial/ethnic minorities and women from lower education or income levels are less likely to be referred to and receive genetic testing for these mutations [This intervention may also have the potential to alleviate some of the disparities posed by the proliferation of precision prevention and genomic medicine. Although the prevalence of utations , 34, 68.RealRisks and BNAV can help to promote such practices in primary care by communicating risk information in a way that is integrated into clinic workflow. Furthermore, this study’s focus on patient-provider dyads in a diverse primary care setting—including various types of physicians and physician extenders practicing in internal medicine, family medicine, and gynecology—can help expand our understanding of how best to promote appropriate referrals for BRCA1/2 genetic counseling from the primary care setting.The American Society for Clinical Oncology recommends that all oncology providers use family history information to inform clinical decision making . While tRealRisks may allow for the more efficient use of genetic counseling and testing services by providing genetic counselors with a pre-collected and detailed pedigree.Additionally, as genetic testing services are becoming increasingly accessible and affordable, the patient preparation provided by RealRisks with existing EHRs by developing a Fast Healthcare Interoperability Resources-standard application programing interface (FHIR API) to prepopulate risk information in RealRisks and return patient-derived data to the medical record.Finally, while DAs are often shown to be efficacious in improving knowledge, risk perceptions, and decision confidence, they are insufficient in and of themselves in incorporating decision-making about genetic testing in the clinic . In ordeOur future and related work focuses on ensuring access to proper downstream, risk management services, including psychosocial support, for patients with positive genetic test results.In conclusion, we have developed informatics-based tools which collect, analyze, and communicate patient risk information in order to provide tailored educational resources to patients and their providers. These tools have the potential to mitigate common patient and provider barriers to breast cancer risk assessment and prevention. We are currently evaluating these tools’ effectiveness among patient and provider clusters in various primary care practices that serve a large and diverse patient population. We hope the results from this trial will help to inform the future implementation of personalized breast cancer prevention efforts.


Bilateral nipple‐sparing mastectomy (NSM) is a technically feasible operation and is associated with excellent cosmetic outcomes. The aim of this study was to evaluate trends in patient characteristics, indications for surgery and long‐term outcomes of bilateral NSM for breast cancer risk reduction over time.2 tests.A review of a single‐centre experience with bilateral NSM performed between 2001 and 2017 for breast cancer risk reduction in patients without breast cancer was performed. Trends in patient characteristics and indications for surgery were evaluated over four time intervals: 2001–2005, 2006–2009, 2010–2013 and 2014–2017. Statistical analysis was performed using χBRCA1 (62 patients), BRCA2 (35), PTEN (2), TP53 (3) and ATM (2). Other indications were family history of breast cancer , lobular carcinoma in situ  and a history of mantle irradiation . The proportion of patients having a bilateral NSM for mutation in a breast cancer‐associated gene increased over time . Mean follow‐up was 53 months; no breast cancers were found during follow‐up.Over the study period, 272 NSMs were performed in 136 patients; their median age was 41 years. The number of bilateral NSMs performed increased over time. The most common indication was a mutation in breast cancer‐associated genes , which included The use of bilateral NSM for breast cancer risk reduction is increasing and the indications have evolved over the past 16 years. These excellent long‐term oncological results suggest that bilateral NSM is a good option for surgical breast cancer risk reduction. BRCA1 and BRCA2 genesBRCA1 and BRCA2. In addition, many of these series report only short‐term oncological follow‐up. The limited published experience, relatively short‐term reported follow‐up and presence of terminal ductal lobular units in the nipple–areolar complex have led some groupsRisk‐reducing simple mastectomy, subcutaneous mastectomy and skin‐sparing mastectomy have all been demonstrated to be associated with breast cancer risk reduction in patients with a strong family history of breast cancer and in patients with mutations in Since 2001, bilateral NSM for breast cancer risk reduction in high‐risk patients with a genetic predisposition to breast cancer, a history of high‐risk or atypical breast lesions, a strong family history of breast cancer, and history of mantle irradiation has been offered at Cleveland Clinic. The aims of this study were to determine whether the performance of risk‐reducing NSM is increasing over time and whether the indications for risk‐reducing bilateral NSM have changed over the past 17 years in the context of the increasing incorporation of more extensive germline genomic testing in clinical practice. It also aimed to ascertain whether bilateral NSM for risk reduction is oncologically safe and associated with low rates of subsequent breast cancer in high‐risk patients with long‐term follow‐up.Approval for this study was obtained by the Cleveland Clinic Institutional Review Board. Local databases were reviewed to identify patients who underwent risk‐reducing bilateral NSM at Cleveland Clinic between 2001 and 2017. Patients found to have occult cancer at the time of surgery, those who underwent contralateral risk‐reducing mastectomy, and patients with breast cancer or a history of breast cancer were excluded from the analysis. The decision to perform a risk‐reducing bilateral NSM was made by the patient and surgeon. Technical aspects of the procedures have been described previouslyThe electronic medical record was reviewed to confirm and update data relevant to the study. Temporal trends were evaluated over four time intervals  to facilitate analysis of the data over time. The short‐term technical outcomes of bilateral NSM have been well documented2 test using StatView® 4 . P < 0·050 was considered statistically significant.Statistical analysis was performed with the χFig. A total of 136 patients (135 women and 1 man) underwent risk‐reducing bilateral NSM between October 2001 and May 2017. Their median age was 41 (range 20–67) years. The number of patients having risk‐reducing bilateral NSM increased over the study interval ; BRCA2, 35 (25·7 per cent); PTEN, two (1·5 per cent); TP53, three (2·2 per cent); and ATM, two (1·5 per cent)  and a history of mantle field irradiation . The proportion of patients who had a risk‐reducing bilateral NSM for mutation in a breast cancer‐associated gene increased significantly over the study interval  (P < 0·001) : BRCA1, BRCA2, PTEN and TP53) are shown in Table Mean and median duration of follow‐up for all patients in the series was 53 and 38 (range 0·5–326) months respectively; 61 patients had follow‐up for more than 4 years. Follow‐up for patients in the series with mutations in breast cancer predisposition genes , a strong family history and/or a personal history of high‐risk breast lesions such as lobular carcinoma BRCA1 and BRCA2 mutations have up to an 87 per cent lifetime risk of developing breast cancer. Most other series have focused on risk‐reducing NSM only for patients with BRCA1 and BRCA2 mutations, which did represent the most common indication for risk‐reducing bilateral NSM in the present series. It is noteworthy that patients with other genetic mutations associated with increased breast cancer risk  underwent risk‐reducing bilateral NSM in this series from 2013. Patients with PTEN, TP53 and ATM have a significantly increased breast cancer risk of up to 85, 90 and 60 per cent respectivelyPTEN, TP53 or ATM mutation developed breast cancer following risk‐reducing bilateral NSM. As multigene panel testing continues in practice, and penetrance estimates become more precise, it is anticipated that bilateral NSM for other genetic indications will become more commonPatients with Three patients in the present series had risk‐reducing bilateral mastectomy for a previous history of mantle irradiation. Patients with a history such as this for childhood cancer are known to be at increased risk of breast cancerBRCA1/2 and other breast cancer‐associated genes that are increasingly being recognized.Risk‐reducing bilateral NSM has increased over time and indications have shifted increasingly towards patients with documented genetic mutations. The present large single‐institution series supports the oncological efficacy of risk‐reducing bilateral NSM for a variety of indications, including The authors declare no conflict of interest.


GSTM1 and GSTT1 genes are associated with breast cancer among selected Filipinos. The association of genetic polymorphisms with cancer development has been shown to be race- and tumor site-specific. Thus, this study aimed to determine whether polymorphisms in the GSTM1 and GSTT1 genetic polymorphisms by multiplex PCR. A total of 136 histologically confirmed breast cancer cases were age- and sex-matched with 136 clinically healthy controls. Genomic DNA extracted from blood samples of participants were screened for GSTM1: n=78; 57.4%; GSTT1: n=61; 44.9%) was not significantly different (p>0.05) from the controls . It was also demonstrated that risk for breast cancer was increased in passive smokers carrying the GSTM1 null  or GSTT1 positive  genotypes. Moreover, risk was decreased in alcohol users carrying the GSTT1 null  genotypeThe frequency of null genotypes among the cases (GSTM1 and GSTT1 may not be risk factors for breast cancer development among Filipinos. However, the risk may be increased when these genotypes were combined with lifestyle or environmental factors.This study suggests that variants of  Polymorphism is a genetic variation resulting in the occurrence of several different forms or types of individuals among the members of a single species. One of the most widely known types of genetic polymorphisms among people is the single nucleotide polymorphism (SNP), which involves a variation at a single position in a DNA sequence and occurs in more than one percent of the general population. SNPs have been found to help predict an individual’s response to certain drugs, susceptibility to environmental factors, and risk of developing diseases including cancer . Genes that encode xenobiotic metabolizing enzymes (XMEs) are being reviewed continuously due to their essential functions in the detoxification of toxic compounds implicated in carcinogenesis . The reactions catalyzed by these enzymes are classified into phase I and phase II reactions. Phase I system utilizes enzymes that initiate the detoxification process by transforming lipid-soluble substances into water-soluble compounds through the introduction of polar and reactive groups into the incoming substrate . Meanwhile, phase II system is responsible for conjugating the compounds modified and activated in phase I to specific substrates, yielding more polar and less toxic groups .GSTM1) and glutathione S-transferase θ1 (GSTT1). These genes are implicated in the conjugation of numerous carcinogenic compounds to excretable hydrophilic compounds . However, null alleles of these genes corresponding to the deletion of whole protein-coding region have been identified to exist and are common across major human populations . Individuals who completely lack GSTM1 and GSTT1 enzyme activity due to the inherited homozygous loss of these genes are more likely to have a higher risk of acquiring cancer .Among the most widely reviewed genes encoding the phase II detoxifying enzymes are glutathione S-transferase μ1 . Numerous studies have also reported the possible association of deletions in GSTM1 and GSTT1 with the risk of developing other cancer types, including colorectal cancer in Caucasians , lung cancer in African-Americans, Japanese, Swedes, and South Indians , acute lymphoblastic leukemia in Thai , and myeloid leukemia in non-Hispanic Caucasians . Meta-analyses have shown the association of polymorphisms in GSTM1 and GSTT1 polymorphisms with malignancies of the breast, colon, lung, prostate, and chronic myelogenous leukemia among Icelanders , Chinese , Greeks , and Japanese , respectively. Acute lymphoblastic leukemia has been associated with GSTM1 null but not GSTT1 null genotypes in Filipino children .On the contrary, several studies have reported the absence of association of GSTM1 and GSTT1 genes with cancer is tumor site- and race-dependent. Hence, this study analyzed the association of polymorphisms in the GSTM1 and GSTT1 genes in a set of Filipino breast cancer cases and matched clinically healthy controls. The results of the molecular analyses were also associated with other risk factors for breast cancer.Those studies indicate that the association of polymorphisms in the Study SubjectsPrior to the execution of this study, ethical clearances were secured from the Institutional Review Board (IRB) of the University of Santo Tomas Hospital (USTH) in Manila (Protocol Reference No. IRB-MD-09-2015-133) and the Mariano Marcos Memorial Hospital and Medical Center (MMMH-MC) in Ilocos Norte (RERC Protocol Nos. MMMH-RERC-15-005 and MMMH-RERC-15-006), Philippines. A total of 136 Filipino patients with histologically confirmed breast cancer, seen at the USTH and MMMH-MC from December 2015 to July 2016 were recruited for this study. The cancer cases were age- (±2 years) and sex-matched with clinically healthy controls who were not suspected to have any type of malignancy. All participants gave their written informed consent and were asked to accomplish through interview by a member of the research team a standardized questionnaire inquiring on their risk factors. Clinical data of the cases were retrieved from medical records and histopathologic reports.Genotyping of GSTM1 and GSTT1 Genetic PolymorphismsGSTM1 and GSTT1 was carried out by multiplex polymerase chain reaction (PCR) as described  but with minor modifications. The primer sequences used for the detection of GSTM1 and GSTT1 genotypes were as follows: GSTM1 (219 bp): 5’-GAACTCCCTGAAAAGCTAAAGC-3’ (forward) and 5’-GTTGGGCTCAAATATACGGTGG-3’ (reverse); GSTT1 (459 bp): 5’-TTCCTTACTGGTCCTCACATCTC-3’ (forward) and 5’-TCACCGGATCATGGCCAGCA-3’ (reverse); and albumin (350 bp): 5’-GCCCTCTGCTAACAAGTCCTAC-3’ (forward) and 5’-GCCCTAAAAAGAAAATCGCCAATC-3’ (reverse). A total PCR reaction volume of 20 μL containing 25 units/mL Taq DNA polymerase, 200 μM dNTPs, 1.5 mM MgCl2, 0.25 μM of each primer, nuclease-free water, and 89 ng of genomic DNA was prepared. The desired genes were amplified under the following PCR conditions: 95°C (1 min), 60°C (1 min), 72°C (1 min) for 40 cycles, with a final extension step at 72°C for 10 min in a PTC-200 thermal cycler . All PCR products were separated by electrophoresis on a 1% agarose gel containing SYBR™ Safe DNA gel stain .Genomic DNA was isolated from peripheral blood leukocytes of cancer cases and controls using ReliaPrep™ Blood gDNA Miniprep System , following the manufacturer’s protocol. Genotyping for Statistical AnalysisGSTM1 and GSTT1 genotypes, genotype frequencies were computed, including deviations from the Hardy–Weinberg Equilibrium (HWE). The Pearson χ2 test was used to compare the differences in genotype frequencies between breast cancer cases and controls, while the relative risks of genotypes for breast cancer cases were determined by computing for the crude odds ratio (OR) with a 95% confidence interval (CI). GSTM1 and GSTT1 genotypes were further correlated with other well-established risk factors for breast cancer.To determine the prevalence of GSTM1 and GSTT1 genotyping in 136 breast cancer cases and sex- and age-matched 136 clinically healthy controls by multiplex PCR. The presence of a 219 bp GSTM1  in the frequency of genotype  between genotype  was not GSTM1 null genotype, the passive smokers had higher risk  compared to active smokers . In the Philippines, 14,955 newly diagnosed cases were reported in Metro Manila and Rizal Province between 2008 to 2012 . GSTM1 and GSTT1, which belong to the glutathione S-transferase (GST) supergene family. GSTs primarily catalyze the oxidation of glutathione, which subsequently detoxifies xenobiotics and carcinogens to make them easily excretable . The GSTM1 and GSTT1 genes encode the GST μ (mu) and θ (theta) isoenzymes, GSTM1 and GSTT1, respectively. The GSTM1 isoenzyme is implicated in the cleansing of activated carcinogens such as hydroxylated metabolites of benzo[a]pyrene, epoxides, and polycyclic aromatic hydrocarbons (PAHs) , rendering these compounds more water-soluble for easy excretion outside the cell. The GSTT1 isoenzyme is involved in the detoxification of numerous low molecular weight toxins such as ethylene oxides and butadienes, and haloalkanes including dichloromethanes . Several studies have shown the potential association of genetic polymorphisms with susceptibility to various types of cancer. Among the genes being extensively studied are GSTM1 and GSTT1 genes result in the loss of catalytic activity of the enzymes they encode correspondingly, leading to the decreased ability of the cells to detoxify various xenobiotic or genotoxic agents . Exhaustion of glutathione to ~20-30% of the total glutathione level can weaken defense against xenobiotics, causing harm to several cellular processes . Hence, depletion of glutathione, which may be caused by the combined actions of all the GSTs, can expose the cells to damaging effects of oxidative stress and related mutagenic lesions. It has been proven that homozygous deletions of the polymorphic GSTM1 and GSTT1 null genotypes between Filipino breast cancer cases and clinically healthy controls. A slightly higher prevalence of the GSTM1 null genotype in controls (68.4%) compared to the cases (57.4%) . While studies have demonstrated that complete absence of GSTT1 results in diminished detoxification capabilities , this study shows lack of association between the GSTT1 null genotype and the risk of developing breast cancer (Results of this study show no significant difference in the frequencies of  (57.4%)  was seent cancer .GSTM1 null genotype with breast cancer has also been seen in Iranian , Lebanese , Pakistani , Taiwanese , Icelandic , Mexican , and Caucasian and African-American  populations. The lack of association between GSTT1 null genotype and breast cancer has been seen in Dutch , Lebanese , Pakistani , Icelandic , Mexican , Caucasian and African-American  and Iranian  populations. Association of GSTT1 null genotype with breast cancer have been observed in studies from Northeast India , Iowa , Southern Taiwan , and China . Findings of this study are contrary to what have been seen among Indians , Brazilians , Caucasians , Dutch , and French . The lack of association of GSTM1 and GSTT1 null genotypes with breast cancer, it should be noted that an interplay of genetic predisposition with environmental and lifestyle factors plays a role in increased susceptibility to breast cancer. Reproductive factors, which include nulliparity, family history of breast cancer, use of oral contraceptives, and age at menopause and menarche, are well-established risk factors for breast cancer. Lifestyle practices such as tobacco smoking, alcohol consumption, sedentary existence, and fatty diet are also among the predisposing factors .Although this study shows lack of association of the GSTM1 null .Passive smoking appears to increase the risk of developing breast cancer among Filipinos carrying the TM1 null  and GSTTpositive  genotypeGSTM1 and GSTT1 genetic polymorphisms and the risk of developing breast cancer among Filipinos. However, this risk was modified when individual genotypes of GSTM1 and GSTT1 were combined with risk factors such as alcohol use and passive smoking. Nevertheless, it is recommended that the number of participants is increased to further validate the results of this study. It is further suggested to recruit participants from other parts of the country in order to accurately represent the entire Filipino population. In conclusion, no association was found between The authors declare no conflict of interest.


Receptor activator of nuclear factor kappa-B (RANK)-signaling is involved in tumor growth and spread in experimental models. Binding of RANK ligand (RANKL) to RANK activates signaling, which is inhibited by osteoprotegerin (OPG). We have previously shown that circulating soluble RANKL (sRANKL) and OPG are associated with breast cancer risk. Here we extend these findings to provide the first data on pre-diagnosis concentrations of sRANKL and OPG and risk of breast cancer-specific and overall mortality after a breast cancer diagnosis.Two thousand six pre- and postmenopausal women with incident invasive breast cancer (1620 (81%) with ER+ disease) participating in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort were followed-up for mortality. Pre-diagnosis concentrations of sRANKL and OPG were quantified in baseline serum samples using an enzyme-linked immunosorbent assay and electrochemiluminescent assay, respectively. Hazard ratios (HRs) and 95% confidence intervals (CIs) for breast cancer-specific and overall mortality were calculated using Cox proportional hazards regression models.trend 0.10) and overall mortality . sRANKL and the sRANKL/OPG ratio were not associated with mortality following a breast cancer diagnosis.Especially in women with ER+ disease, higher circulating OPG concentrations were associated with higher risk of breast cancer-specific  contains supplementary material, which is available to authorized users. The RANK-axis consists of three tumor necrosis superfamily (TNF) members; receptor activator of nuclear factor kappa-B (RANK), its ligand (RANKL), and osteoprotegerin (OPG). Binding of RANKL to RANK promotes cell proliferation and, in experimental models, promotes primary mammary tumorigenesis and mammary stem cell expansion –4. RANK-There has been increasing interest in the RANK-axis with respect to breast cancer risk and prognosis given the availability of a RANKL inhibitor, denosumab, which has been shown to reduce skeletal-related events in breast cancer patients with bone metastases  and may This study provides the first data on circulating RANK-axis members and breast cancer-specific mortality risk and the first data on differences in mortality risk by tumor hormone receptor status.The European Prospective Investigation into Cancer and Nutrition (EPIC) recruited more than 520,000 participants , aged predominantly 35–75 years, between 1992 and 2000 in ten European countries . Detailed dietary, reproductive, lifestyle, anthropometric, and medical history data were collected using standardized methods . Incidenn = 235,607) of women provided a blood sample at baseline. Blood samples were collected according to standardized protocols. As independent studies on breast cancer were conducted by the Swedish centers, participants from these centers were not included in the current study. For all countries included in this study, except Denmark, half of the aliquots were stored locally and the other half centrally at the International Agency for Research on Cancer (IARC). The samples used in this study were stored at IARC under liquid nitrogen at − 196 °C, or locally at − 150 °C for Danish participants.A total of 64%  and the University of Heidelberg (project no. S311/2014).The breast cancer cases in this study were part of a case-control study nested within the EPIC cohort. The study design and methods have been described previously , 12, 15.All cases with available serum sample and information on ER status of the tumor were eligible for the nested case-control study Fig. . From 20Pre-diagnosis sRANKL and OPG concentrations were analyzed at the Laboratory of the Division of Cancer Epidemiology at the German Cancer Research Center (DKFZ). Free serum sRANKL was quantified using an enzyme-linked immunosorbent assay , total serum OPG using an electrochemiluminescence assay . All batches included the same serum quality control samples in duplicate to monitor inter-batch variation. Measurements and standard curves were done on a Victor system using Workout 2.5 software (Perkin Elmer) for sRANKL. The Quickplex SQ 120 Reader and Workbench 4.0.12 software  were used to measure OPG and create standard curves.Of the 2013 breast cancer cases with complete follow-up data, four were missing OPG and 44 were missing sRANKL concentrations  variables. The proportional hazards assumption was assessed using Schoenfeld residuals ; p0) Table . Additioint ≤ 0.04 in models for OPG among all cases and ER+ cases) showed that pre-diagnosis OPG concentrations were not associated with risk of death after a breast cancer diagnosis in those with a high BMI (≥ 25 kg/m2) at blood collection , high pre-diagnosis concentrations of OPG were strongly associated with an increased risk of both breast cancer-specific and all-cause mortality, especially among those with ER+ disease .Stratifying by BMI at blood collection . There was no heterogeneity in associations by breast cancer stage at diagnosis , and we observed no heterogeneity in associations by menopausal status at blood collection for sRANKL and sRANKL/OPG (phet ≥ 0.44). For OPG, we observed no heterogeneity in associations by menopausal status at blood collection for breast-cancer specific mortality (Phet ≥ 0.14), and suggestive heterogeneity in models for all-cause mortality (phet = 0.05 in the full population). In analyses stratified by menopausal status at blood collection associations between OPG and all-cause mortality among postmenopausal women were similar to those in the full population . In the smaller group of women premenopausal at blood collection (n = 75 deaths and 60 breast cancer deaths), log2 OPG concentrations were not associated with breast cancer risk.Information on PR status was available for 1478 (74%) of cases; stratification by both ER and PR status (i.e. ER + PR+ and ER-PR-) did not materially affect results (data not shown). Excluding those diagnosed with breast cancer within two years of blood collection did not impact results for pre-diagnosis sRANKL or the pre-diagnosis sRANKL/OPG ratio, but did attenuate associations between pre-diagnosis OPG concentrations and risk death  concentrations, though only weakly with sRANKL concentrations (r = − 0.15). We observed no difference in log-transformed sRANKL and OPG concentrations, or the sRANKL/OPG ratio by ER and/or PR status  or by breast cancer stage at diagnosis , nor did we observe any interaction by cancer stage at diagnosis in our Cox regression models  breast cancer-specific mortality phet ≥ 0.18; all-cause mortality phet ≥ 0.43). In a sensitivity analysis excluding 15 cases with distant metastases, associations with mortality were somewhat weakened.Few prior studies have evaluated circulating concentrations of sRANKL, OPG, and prognosis-related factors or mortality in breast cancer patients. Vik et al. evaluated cancer risk and mortality in the Trømso study and found no association between serum OPG concentrations measured a median of 13.5 years before diagnosis and cancer-related mortality in women . HoweverThe RANK-axis may be particularly relevant in BRCA mutation carriers . In the r = 0.02) or by menopausal status (r = <− 0.03), nor a correlation between OPG and estradiol in all cases (r = − 0.05) or by menopausal status (r = <− 0.06) in all cases. OPG concentrations did not differ by ever use of OCs in premenopausal women (pdif 0.95) or PMH use at blood collection in postmenopausal women (pdif 0.86).Our analyses showed significant interaction between pre-diagnosis OPG and BMI, with an increased risk of death among ER+ breast cancer cases with a normal or underweight BMI at blood collection. We saw no apparent association in those with an overweight or obese baseline BMI. Experimental studies have shown that both ER activation and 17beta-estradiol treatment may downregulate OPG expression in the tumor . It is pr = 0.85 and r = 0.75 respectively) [r = 0.60 and r = 0.38 over one and 14-years, respectively) [We measured pre-diagnosis concentrations of sRANKL and OPG a median of 4.7 years before diagnosis. We have previously shown that OPG concentrations are reproducible over one and 14 years (ctively) . Reproductively) . This inctively) –42. Furttrend = 0.03) [trend 0.20) [Pre-diagnosis concentrations of OPG and sRANKL may influence survival at a number of different stages in the disease process—for example, impacting initiation of a more vs. less aggressive tumor subtype and/or impacting tumor progression and/or influencing survival post-diagnosis (e.g. by interacting with treatment). It is plausible that concentrations at diagnosis are a more informative measure for breast cancer mortality. Literature on circulating concentrations of RANKL and OPG before and after onset of breast cancer is limited. In one study comparing serum samples taken before and after breast cancer diagnosis in 19 women, sRANKL concentrations were lower and OPG concentrations were higher after breast cancer diagnosis . It woulnd 0.20) . ResultsHigher pre-diagnosis endogenous concentrations of the decoy receptor for RANKL, OPG, appear to increase risk of death after a breast cancer diagnosis especially in those diagnosed with ER+ disease. Further investigations in well-defined patient cohorts are needed to confirm these results, and to clarify whether circulating OPG may be relevant for breast cancer prognosis.Additional file 1:Table S1. Circulating concentrations of OPG and risk of death following a breast cancer diagnosis, by ER subtype and stratified by BMI at blood collection. Table S2 The sRANKL/OPG ratio and risk of death following a breast cancer diagnosis, by ER subtype. Table S3 sRANKL/OPG cross-classification and risk of death following a breast cancer diagnosis, by ER subtype. (DOCX 43 kb)


Texture also improved baseline models . Our findings suggest that incorporating breast anatomy information could augment imaging markers of breast cancer risk with the potential to improve personalized breast cancer risk assessment.We retrospectively analyzed negative screening digital mammograms from 115 women who developed unilateral breast cancer at least one year later and 460 matched controls. Texture features were estimated in multiple breast regions defined by an anatomically-oriented polar grid, and were weighted by their position and underlying dense versus fatty tissue composition. Elastic net regression with cross-validation was performed and area under the curve (AUC) of the receiver operating characteristic (ROC) was used to evaluate ability to predict breast cancer. We also compared our anatomy-augmented features to current state-of-the-art in which parenchymal texture was assessed without considering breast anatomy and evaluated the added value of the extracted features to breast density, body-mass-index (BMI) and age as baseline predictors. Our anatomy-augmented texture features resulted in higher discriminatory capacity (AUC = 0.63 vs. AUC = 0.59) when breast anatomy was not considered ( Full-field digital mammography (FFDM), routinely used for breast cancer screening3, has demonstrated substantial potential in providing quantitative image-derivable measures which relate to breast cancer risk7. Mammographic density has been the most established such measure and has been shown to be a strong independent risk factor for breast cancer9.Breast cancer risk assessment has become increasingly important for forming tailored breast cancer screening and prevention strategies10. This research field, aiming to translate mammographic images to computational imaging phenotypes of breast cancer risk, is rapidly evolving and several computerized methodologies have been developed to quantify the properties of the breast parenchymal pattern using texture descriptors10.Going beyond breast density, recent studies also suggest that parenchymal texture, which reflects the heterogeneity of the breast parenchymal pattern, plays a complementary role in breast cancer risk assessment by capturing information that is not reflected by mammographic density alone or other established risk factorsi.e., the shape, size, and tissue structure of the specific breast analyzed. Breast anatomy may, however, be an important component in quantification of breast parenchymal patterns. For instance, texture characterization adapted to the largely variable breast morphology would allow establishing anatomical correspondences across mammograms and, therefore, generate more comparable textural measurements across subjects. In addition, and perhaps most importantly, parenchymal characteristics from different areas of the breast may contribute differently towards the risk for developing breast cancer. For example, a recent study11 showed significant associations between breast cancer and the relative spatial distribution of dense versus fatty regions within the breast, with fatty tissue in the lower quadrants and the absence of density in the retromammary space being protective against breast cancer. Moreover, the central breast area (CBA) and the upper-outer area (UOA) of the breast have been associated with a higher frequency by location of breast cancers12 and with early changes in texture due to breast cancer development13. These findings suggest that inherent breast tissue properties predisposing women to higher risk of breast cancer may not be uniformly expressed in the breast parenchyma and, therefore, allowing breast morphology and anatomy to drive texture measurements could augment their predictive capacity.Although useful in risk prediction, parenchymal texture measurements do not generally incorporate breast morphology and anatomy — The purpose of our study was to evaluate whether incorporating breast anatomy information can strengthen the associations of mammographic parenchymal texture phenotypes with breast cancer risk. Towards this end, we introduce a computational breast-anatomy-driven approach for extracting parenchymal texture features and evaluate their predictive value in a case-control study with full-field digital mammograms.i.e., ‘FOR PROCESSING’) bilateral digital mammograms. Briefly, cases (n = 116) included all women diagnosed with unilateral breast cancer between July 2012 and December 2014 (biopsy and state registry confirmed cancer diagnosis) who also had negative screening mammograms available at our institution at least one year prior to their diagnosis. One case was excluded due to the presence of bilateral breast implants. For all the remaining 115 cases, we retrieved the earliest negative screening study with raw mammograms available (average time from screening to diagnosis: 1.9 years ± 0.7) which was used for our analysis. Eligible controls were women who had a negative routine screening exam during the same period and confirmed negative one-year follow-up. Following case-control matching without replacement, controls were matched to cases on age at screening , ethnicity and screening exam date (within 1 year) at a 4:1 ratio (n = 460), yielding a total sample of 575 women. Briefly, cases and controls were randomly ordered. Then, for each case, eligible controls were identified as controls matching to cases on the specified matching criteria and four of them were randomly selected. Since we were matching without replacement, matched controls were not available to serve as matches for subsequent cases. This process was repeated until four matches were found for each cancer case.In this IRB-approved , HIPAA-compliant study under a waiver of consent, we retrospectively analyzed a case-control sample of raw (14 which provides area-based breast PD (APD) measurements, and the commercially available software package Quantra TM , which allows for APD and volumetric breast PD (VPD) assessment.For the purposes of our study, we used the mediolateral-oblique (MLO) view of the available digital mammograms as it visualizes the maximum amount of breast tissue and can potentially better define anatomically the breast quadrants. All images were acquired using Selenia Dimensions  units. Breast Imaging-Reporting and Data System (BI-RADS) density classification and body-mass index (BMI) were extracted from the archived clinical records and the radiologists’ screening reports for all women. Fully-automated quantitative measurements of breast percent density (PD) were also obtained for all images in this study, using the publicly available “Laboratory for Individualized Breast Radiodensity Assessment”  softwareTo incorporate breast anatomy in quantitative computational metrics, first several landmarks and sub-regions of the breast parenchyma were defined in each 2D FFDM image: (a) the air-breast boundary, (b) the pectoralis muscle, (c) regions of predominantly dense versus predominantly fatty tissue, (d) the nipple, and (e) the UOA and CBA Fig. .Figure 115. Specifically, LIBRA delineates the air-breast boundary using automated gray-level thresholding, and extracts the pectoralis muscle region by applying a straight-line Hough transform16. To identify dense versus fatty tissue areas, LIBRA uses adaptive fuzzy c-means clustering to partition the breast into density clusters (DCs) of similar gray-level intensity, which are then aggregated into the final dense tissue segmentation.The anatomical components (a)–(c) were identified using LIBRArP equal to the perpendicular distance from the pectoralis muscle to the nipple, the CBA was defined as that part of the breast at a radial distance less than f *rP from the nipple, and the UOA as that part of the breast lying above the nipple and further than f *rP from the nipple. The parameter f, was implemented to range within  so that CBA adequately covers the central part of the breast, defining the relative sizes of the CBA and the UOA.For landmarks (d)–(e), the breast image was first rotated, and then flipped for right breasts, so that the pectoralis muscle was vertically aligned on the left side of all images. Then, the nipple was approximated as the rightmost circular region of the breast bounded by a rectangle of maximum size 1.5 cm in both dimensions. Lastly, two breast sub-regions were identified: for D, centered on the nipple, was then overlaid on the mammographic image , including gray-level histogram, co-occurrence, run-length, and structural features, all of which have been previously used for mammographic pattern analysis and breast cancer risk assessment10. Briefly, gray-level histogram features are common first-order statistics which describe the distribution of gray-level intensity. The co-occurrence features also consider the spatial relationships of pixel intensities in specified directions and are based on the gray-level co-occurrence matrix (GLCM) which encodes the relative frequency of neighboring intensity values. Run-length features capture the coarseness of texture in specified directions by measuring strings of consecutive pixels which have the same gray-level intensity along specific linear orientations. Using the polar grid, the axes for calculating the co-occurrence and run-length features were aligned with the structure of the parenchymal tissue and ductal structures  was designed as a combination of weights due to the region’s anatomical position (S), and weights due to the underlying tissue composition (T) of each region As the final step, we generated a weight map which assigned a weight to each region; this weight map was, then, region-wise multiplied to the 34 original texture feature maps to generate a set of 34 weighted texture feature maps. Our design of the weight map was motivated by studies associating the CBA and UOA with potential specific roles in breast cancer developmentFor each weighted texture feature map we estimated the mean value of the corresponding weighted feature across all regions (labeled “TF#_mean”), and the standard deviation (labeled “TF#_std”). These constituted the breast’s texture signature: a 68-element feature vector corresponding to the average and the variation of each of the weighted texture feature maps.D, f, a, b, and c) and determining the most discriminatory subset of covariates out of the 68 available texture features. To this end, for different parameter combinations  of the receiver operating characteristic (ROC) (see Supplementary Note N3 for feature optimization details).The optimization of our anatomy-driven approach for parenchymal texture analysis and breast cancer prediction involved two inter-related tasks: automatically configuring the tunable texture analysis parameters 25.The model corresponding to maximum cross-validated AUC  was compared with a model built using the same statistical approach as described above but where textural features were generated using a current state-of-the-art algorithm based on a simpler lattice approach to sample the breast without incorporating breast anatomy23 were also evaluated in terms of their ability to augment established risk factors in breast cancer risk prediction.Finally, we assessed the potential of augmenting established risk factors by evaluating four baseline models including breast density  adjusted by BMI and age at screening, and tested the added value of incorporating our breast-anatomy-driven texture features. Baseline and augmented logistic regression models were fitted to obtain estimates of AUC, odds ratios (ORs), and statistical significance of predictor variables. For comparison, conventional lattice-based texture measurementsp = 0.05 level. The breast-anatomy-driven approach was developed in Matlab R2014b  and statistical analysis was performed in Stata 13 .All tests of statistical significance were at the standard There was a significant difference in Quantra VPD and a marginally significant difference in age between cases and controls in our sample Table .Table 2SD = 6.3 mm) and for a weight map in which dense tissue regions were assigned higher weights than fatty regions (b = 1 in weights T), and regions in the CBA were weighted more heavily than in the UOA (a = 0.8 in weights S). Each region’s location (weights S) was slightly less important than the region’s underlying tissue composition (weights T) in calculating the final weight (c = 0.4 in weights W). After excluding 17 features with low IQR, elastic net regression selected 30 of the 51 textural features for inclusion in this optimal model, including nine gray-level histogram, nine co-occurrence, eight run-length, and four structural features; among these, 14 represented mean values and 16 the variation  in texture feature maps (Supplementary Table The best model performance for the anatomy-driven features was AUC = 0.63 (95% CI [0.59 0.69]). This was achieved for a spatially dense polar grid (p = 0.021 by DeLong’s test)   (p = 0.p = 0.029 by DeLong’s test)  Table . The latt) Table .Table 3AOur findings suggest that incorporating breast anatomy information in mammographic phenotypes of parenchymal pattern could augment imaging markers of breast cancer risk with the potential to improve personalized breast cancer risk assessment. Interestingly, the configuration of the weight map in our breast-anatomy-driven approach as indicated by the optimization experiments also suggests that the textural properties of different regions in the breast may contribute differently towards breast cancer risk, with dense tissue regions and the central breast quadrant having potentially a more important role. Further, the heterogeneity in textural measurements within the breast may also be important, as more than half of the features selected as strongest covariates in our model represent the variation  of the corresponding texture feature distribution within the breast.without the incorporation of factors describing breast anatomy structure and variability23. We postulate that the improvement observed in this preliminary comparison is due to including factors that capture the wide variety of breast morphology and anatomy, found not only across the screening population but also within a single woman due to differences in positioning for FFDM. Incorporating such information, therefore, allows for also establishing anatomical correspondences across breasts of the same or different women, which in turn results in standardized imaging features and more comparable texture measurements. The observed improvement in discriminatory capacity may also be due to our polar grid, which allows for more granular texture measurements to be obtained in the retroareolar breast area where some of the most complex parenchymal tissue patterns usually appear17, and the ability to consider different contributions of the different sub-regions within the breast in the overall parenchymal texture signature. With this improvement, our anatomy-driven approach achieved a promising performance in this challenging task of breast cancer risk prediction with prior screening mammograms.In addition, our anatomy-driven approach to breast parenchymal texture analysis outperformed parenchymal texture features assessed 26, where case-control classification models considering parenchymal textural features in addition to established risk factors and breast density achieved AUC values of 0.62 up to 0.7810. Together, these findings consistently support independent associations of parenchymal texture with breast cancer and, therefore, create a strong argument for incorporating quantitative breast textural features in models estimating breast cancer risk. The improvement of breast cancer risk estimation models can have substantial clinical implications, as it would allow for more informed recommendations for supplementary breast cancer screening  and prevention tailored by individual risk profiling. Hence, the potential of parenchymal texture to leverage breast cancer risk assessment might ultimately affect the chance of early cancer detection or prevention in women categorized as being at low breast cancer risk based on conventional risk factorsOverall, adding the breast-anatomy-driven features to baseline models with established breast cancer risk factors led to a significant increase in discriminatory capacity, suggesting a promising role in augmenting breast cancer risk assessment models. Similar conclusions have been reported in related studies27, different FFDM representations, and vendors28, in our future studies we will plan to more thoroughly test the robustness of our method by incorporating multiple FFDM vendors from larger populations. In addition, larger studies will allow us to more rigorously evaluate the added discriminatory capacity of such imaging biomarkers when considering additional demographic and clinical risk factors , potentially also expanding our feature set into higher phenotypic representations, including deep learning. Finally, while 2D FFDM images were analyzed as a first step for the purposes of this proof-of-concept study, we ultimately envision extending our algorithm to volumetric texture analysis for digital breast tomosynthesis images , as tomosynthesis is increasingly being clinically implemented due to its reported improvements in sensitivity and specificity as compared to conventional 2D FFDM29. This new pseudo-3D imaging technology may, therefore, also result in superior imaging phenotypes of breast cancer risk.Important limitations of our study must also be noted. To avoid potential confounding effects of proprietary FFDM post-processing algorithms in textural measurements, our study focused only on “For Processing”  FFDM images from a single vendor. In addition, our analysis was confined to a fixed feature set and, although elastic-net regression was used to alleviate model over-fitting, our reported model performance may be over-estimated due to the relatively small sample size as a single-institution evaluation. Considering the reported substantial differences in textural measurements across image acquisition settingsIn conclusion, our study provides evidence that incorporating breast anatomy strengthens the associations of mammographic parenchymal phenotypes with breast cancer risk and suggests that anatomy-driven measurements of parenchymal texture could complement current established risk factors and quantitative breast density measures. This additional information reflecting breast anatomy structure has the potential to further refine individualized risk assessment and, therefore, advance tailored screening and prevention strategies for breast cancer.Supplementary Information