National Cancer Institute Breast Research Articles

Deep learning vs traditional breast cancer risk assessment models: Are we offering supplemental services to those who benefit the most?

1556 Background: Mammography-based deep learning (DL) risk stratification models improve discriminatory accuracy in predicting future breast cancer compared to traditional risk models; however, decisions for and insurance payment of supplemental services such as MRI are driven by traditional scores. The purpose of this study was to compare risk scores and cancer detection rates in patients identified as increased risk by DL vs traditional models in a large screening mammography cohort. Methods: This multisite study included consecutive patients ≥40y undergoing routine bilateral screening mammography from 9/2017 to 1/2022 at five facilities. Tyrer-Cuzick version 8 (TC) and National Cancer Institute Breast Cancer Risk Assessment Tool (BCRAT) 5-year and lifetime models and a DL 5-year model were used to assess risk. The following thresholds were used to define intermediate risk: ≥1.67% for TC and BCRAT 5-year, 2.2 for DL model and high risk: ≥20% for TC and BCRAT lifetime, ≥6.0 for DL model. Patients were included if all risk scores were available. Patient demographics were retrieved from electronic medical records and cancer outcomes through regional tumor registry linkage. The proportion of increased-risk patients and cancer detection rates (CDR [cancers per 1000 women screened]) across models were compared using Pearson’s Chi-square test. Results: 148476 exams in 69464 patients (mean age 58.0y [IQR: 50.0-67.0]) were performed. 81.7% (119386/146075) of exams were in White patients and 18.3% (26689/146075) in races other than White. CDR of those classified as intermediate risk by DL was 7.1 (450/63109) vs 4.7 (320/68545; P<0.001) by TC and 4.2 (300/70831; P<0.001) by BCRAT, and as high risk was 20.6 (305/14772) by DL vs 3.9 (64/16442; P<0.001) by TC and 5.0 (29/5782; P<0.001) by BCRAT. 13.0% (19248/148476) were classified as intermediate risk by DL but not by TC or BCRAT, with a CDR of 6.0 in this cohort. 32.0% (47435/148476) were classified as intermediate risk by TC or BCRAT but not by DL with a CDR of 1.1. 8.5% (12577/148476) were classified as high risk by DL but not by TC or BCRAT with a CDR of 20.8. 11.1% (16451/148476) were classified as high risk by TC or BCRAT but not by DL with a CDR of 1.9. Conclusions: A significant proportion of patients identified as increased risk by DL model are not assessed as increased risk by commonly used risk models, effectively excluding them from accessing potentially life-saving supplemental services. DL scores met the American College of Radiology's acceptable CDR on screening mammography (≥2.5) regardless of traditional scores, however, elevated traditional scores in the absence of elevated DL scores did not meet acceptable standards. Risk assessment guidelines incorporating DL models are necessary to capture patients who benefit the most.

Abstract 5207: Hypermethylation of SOX17 is an important epigenetic mark in Afro-Brazilian breast cancers

Abstract Afro-Brazilian women suffer from higher excess breast cancer mortality after adjusting for age, level of education and stage at diagnosis. Limited studies have researched disease characteristics in this population, which can benefit from the identification of molecular markers that can provide an insight into who is at greater risk of mortality. Epigenetic biomarkers represent the combined effect of environmental and genetic factors. Particularly DNA methylation biomarkers can be valuable as biomarkers of breast cancer risk and prognosis. While studies in other populations have identified DNA methylation biomarkers associated with breast cancer and its clinicopathological outcomes, research on Brazilian cohorts has been limited in scope and focused on women of European descent. The goal of this work was to determine the epigenetic makeup of breast cancer in Brazilian women of African descent. To identify epigenetic biomarkers in Brazilian women who self-identified as Black or Brown, we conducted a case-series analysis investigating DNA methylation patterns in tumor (T) and adjacent non-tumor (NT) tissue. For this, we used the Illumina EPIC (850k) arrays to carry out DNA methylation profiling in 48 frozen tissue paired T and NT samples of breast cancer patients treated at the National Cancer Institute Breast Cancer Hospital in Rio de Janeiro, Brazil. We identified a total of 7,749 probes with Benjamini-Hochberg adjusted p-value < 0.0001 and differences between T and NT tissue above |0.3|. Of these, 5,976 or 77.12% had lower DNA methylation in T when compared to NT tissue; while 1,773 (22.88%) had higher DNA methylation. These were used to identify 21 differentially methylated regions (DMRs) that contained a minimum of seven cg sites, a mean difference larger than |0.2| and a maximum difference above |0.4|. DMRs were mostly (12) hypermethylated in T, and included PCDHGA, NKAPL, ZKSCAN4, SPAG6, CA3, DPP6, RGS22, NXPH1, ANK3, OTX2, SOX17, and CDO1. We used pyrosequencing to validate the levels of SOX17 in eighty-eight tumor and non-adjacent tumor tissue DNAs. We found SOX17 to be hypermethylated in T (44.5±19.1% vs 10.2±10.6, p<0.0001). Differences in DNA methylation in T and NT tissue we still observed by race/ethnicity categories, 43.9±18.6% vs 11.0.±13.2% (p<0.0001) for Brown, and 45.1±20.7% vs 8.8±3.0% for Black women (p<0.0001), respectively. Similarly to previously reported, SOX17 hypermethylation was found to be associated with Luminal A and B molecular subtypes (p<0.0001). To our knowledge, this is the first study to use a discovery approach to identify epigenetic biomarkers in Black and Brown Brazilian women. Our findings confirming previously reported data on SOX17 hypermethylation in breast cancer are encouraging. We expect to validate other epigenetic markers unique to this population that can ultimately aid in the molecular characterization of Brazilian breast cancers. Citation Format: Lissette Delgado-Cruzata, Diego J. Gomes de Paula, Jennifer G. Vieira, André L. Christianes, Tatiana Simao, Leonor B. Gusmao, Luis Felipe Ribeiro Pinto, Sheila Coelho Soares Lima. Hypermethylation of SOX17 is an important epigenetic mark in Afro-Brazilian breast cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5207.

Comparison of the Diagnostic Accuracy of Mammogram-based Deep Learning and Traditional Breast Cancer Risk Models in Patients Who Underwent Supplemental Screening with MRI.

Background Access to supplemental screening breast MRI is determined using traditional risk models, which are limited by modest predictive accuracy. Purpose To compare the diagnostic accuracy of a mammogram-based deep learning (DL) risk assessment model to that of traditional breast cancer risk models in patients who underwent supplemental screening with MRI. Materials and Methods This retrospective study included consecutive patients undergoing breast cancer screening MRI from September 2017 to September 2020 at four facilities. Risk was assessed using the Tyrer-Cuzick (TC) and National Cancer Institute Breast Cancer Risk Assessment Tool (BCRAT) 5-year and lifetime models as well as a DL 5-year model that generated a risk score based on the most recent screening mammogram. A risk score of 1.67% or higher defined increased risk for traditional 5-year models, a risk score of 20% or higher defined high risk for traditional lifetime models, and absolute scores of 2.3 or higher and 6.6 or higher defined increased and high risk, respectively, for the DL model. Model accuracy metrics including cancer detection rate (CDR) and positive predictive values (PPVs) (PPV of abnormal findings at screening [PPV1], PPV of biopsies recommended [PPV2], and PPV of biopsies performed [PPV3]) were compared using logistic regression models. Results This study included 2168 women who underwent 4247 high-risk screening MRI examinations (median age, 54 years [IQR, 48-60 years]). CDR (per 1000 examinations) was higher in patients at high risk according to the DL model (20.6 [95% CI: 11.8, 35.6]) than according to the TC (6.0 [95% CI: 2.9, 12.3]; P < .01) and BCRAT (6.8 [95% CI: 2.9, 15.8]; P = .04) lifetime models. PPV1, PPV2, and PPV3 were higher in patients identified as high risk by the DL model (PPV1, 14.6%; PPV2, 32.4%; PPV3, 36.4%) than those identified as high risk with the TC (PPV1, 5.0%; PPV2, 12.7%; PPV3, 13.5%; P value range, .02-.03) and BCRAT (PPV1, 5.5%; PPV2, 11.1%; PPV3, 12.5%; P value range, .02-.05) lifetime models. Conclusion Patients identified as high risk by a mammogram-based DL risk assessment model showed higher CDR at breast screening MRI than patients identified as high risk with traditional risk models. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Bae in this issue.

Abstract 5516: Identifying DNA methylation biomarkers in Brazilian women of African descent

Abstract Breast cancer is the most common cancer, and the leading cause of cancer-related deaths in Brazilian women. Studies have shown that in Brazil while breast cancer mortality has increased in the last decade for all women, mortality in women of African descent has doubled. A recent study showed that Afro-Brazilian women suffer from higher excess breast cancer mortality even after adjusting for extent of disease, year of diagnosis, age and socioeconomic status, which suggests biological factors also contribute to the differences in the clinicopathological make-up of the disease in women in this group. DNA methylation biomarkers, representing a combination of genetic and non-genetic factors, have the potential to be especially valuable when considering the combined contribution of the environment and genetics in breast cancer etiology. Several groups have found differences in DNA methylation levels associated with race and ethnicity in breast cancer. In addition, research has shown that women of different races/ethnicities with similar breast cancer tumor subtypes have distinct DNA methylation patterns. To explore further the association between race, ancestry and DNA methylation, we conducted a study in paired tumor and non-tumor tissue samples of forty-eight Brazilian women who self-identified as Black or Brown and were treated at National Cancer Institute Breast Cancer Hospital in Rio de Janeiro, Brazil. We carried out a DNA methylation profiling analysis using Illumina EPIC (850k) arrays in bisulfite converted DNA originally extracted from frozen tissue samples. A total of 739,883 differentially methylated probes (DMP) were identified, of which a large proportion 6,241 (78.4%) were hypomethylated. Lower DNA methylation was more common on intragenic regions and gene bodies, while hypermethylation was more commonly found on the promoter regions. DNA methylation was often higher in non-tumor than tumor samples. Among our top hits we found genes previously associated with estrogen receptor negative tumor types, CDH4, CERK, PROX1 and ADHFE1, and with high risk of breast cancer mortality (ST6GAL1, LYN, TFF1 and BMP3). A DPM enrichment pathway analysis predicted effects in known signaling pathways and transcriptional misregulation. We are currently analyzing the associations between the top differentially methylated regions with ancestry informative markers, and the clinicopathological characteristics of disease, to gain a better understanding of the role of DNA methylation marks in this population. This study adds to our current knowledge on epigenetics marks that will ultimately help us address mortality due to this disease in this population. Citation Format: Lissette Delgado-Cruzata, Diego J. Gomes de Paula, Jennifer Vieira Gomes, Tatiana A. Simão, Leonor Gusmão, Luis Felipe Ribeiro Pinto, Sheila C. Soares-Lima. Identifying DNA methylation biomarkers in Brazilian women of African descent. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5516.

Abstract P3-03-07: The Breast Cancer Prevention Clinic: A Single Institution’s Experience

Abstract The Breast Cancer Prevention Clinic: A Single Institution’s Experience Authors: Joe J. Stephenson MD1, LeAnn Perkins, FNP-BC, DipACLM1, Erinn Crowe, RN, BSN, OCN1, Regina Franco, MSN, NP-C, DipACLM1, Pamela Cloys, MSN, ANP-C1, Carmen Hancock, MSN, APRN, FNP-C, AOCNP1, Marie Smith, MS1 1Prisma Health Cancer Institute, Center for Integrative Oncology &amp; Survivorship Background: Roughly 1 in 8 women will be diagnosed with invasive breast cancer in their lifetime1. Some women have higher risk for developing breast cancer due to numerous factors such as elevated risk modeling scores, a history of chest radiation therapy, a history of atypical cells on a breast biopsy or a pathogenic genetic mutation. Enhanced surveillance to detect cancers early and addressing modifiable lifestyle risks are helpful to mitigate risks. Methods: The Prisma Health’s Breast Cancer Prevention Clinic (BCPC) approaches prevention by monitoring non-modifiable risks with regular surveillance and education on optimizing lifestyle habits to decrease modifiable risks. Established in 2017, the BCPC team now includes multiple medical oncologists and nurse practitioners, allowing the clinic to be offered any day of the week. During the visit, patients receive a personal risk assessment. Recommendations are given for surveillance imaging, risk-reducing endocrine therapy, and risk-reducing lifestyle modifications such as diet, alcohol consumption, and exercise routines. Family history is also reviewed to recommend Genetic Counseling if appropriate. In March 2022, a statement was added to patient mammograms, generating referrals for those with calculated elevated risk (a calculated lifetime risk &amp;gt;20% using the Tyrer-Cuzick or International Breast Cancer Intervention Study model and/or a 5-year risk &amp;gt;1.7% using the National Cancer Institute Breast Cancer Risk Assessment Tool or Gail model). Results: In a 12-month period from June 1, 2021 through May 31, 2022, 56.2% of patients were referred based on family history, 26.9% for higher-risk breast pathology, 8.1% for calculated elevated risk, 4.4 % for a pathogenic genetic variant that increases breast cancer risk and 4.1% for mammographic identification risk modeling. Following the BCPC visit, referrals for patients may include imaging such as mammograms, MRIs, genetic counseling or testing, or surgical evaluations. Referrals for lifestyle modifications may include nutrition, exercise navigation, mindfulness classes, or Sexual Health Evaluations. From June 2021 through May 2022, 41.4% of patients were referred for a breast MRI, 26.6% were referred to genetic counselors for genetic testing recommendations, 11.1% were referred to nutrition or exercise services, 8.42% were referred for a Bone Density Scan (DXA), 4% were referred to a Breast Surgeon and 3.7% were referred to the Genetic Management Clinic. Additionally, 79.5% of patients were referred to the High-Risk Breast Lifetime Clinic for continued monitoring and surveillance for early detection of breast cancer, continued reinforcement of lifestyle modifications, and management of risk-reduction endocrine therapy. Conclusion: The unique structure of this model allows for enhanced surveillance through greater use of evidence-based cancer prevention, early detection methods, and increased education for lifestyle medicine practices with the overall goals of reducing breast cancer risk, earlier breast cancer detection, and increasing quality of life. Reference List 1. DeSantis C, Ma J, Gaudet, M, et al. Breast cancer statistics, 2019. CA: A Cancer Journal for Clinicians. 2019;69(6), 438-451. https://doi.org/10.3322/caac.21583. Citation Format: Joe Stephenson, LeAnn Perkins, Erinn Crowe, Regina Franco, Carmen Hancock, Pamela Cloys, Marie Smith. The Breast Cancer Prevention Clinic: A Single Institution’s Experience [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-03-07.

Deep Learning vs Traditional Breast Cancer Risk Models to Support Risk-Based Mammography Screening.

Deep learning breast cancer risk models demonstrate improved accuracy compared with traditional risk models but have not been prospectively tested. We compared the accuracy of a deep learning risk score derived from the patient's prior mammogram to traditional risk scores to prospectively identify patients with cancer in a cohort due for screening. We collected data on 119 139 bilateral screening mammograms in 57 617 consecutive patients screened at 5 facilities between September 18, 2017, and February 1, 2021. Patient demographics were retrieved from electronic medical records, cancer outcomes determined through regional tumor registry linkage, and comparisons made across risk models using Wilcoxon and Pearson χ2 2-sided tests. Deep learning, Tyrer-Cuzick, and National Cancer Institute Breast Cancer Risk Assessment Tool (NCI BCRAT) risk models were compared with respect to performance metrics and area under the receiver operating characteristic curves. Cancers detected per thousand patients screened were higher in patients at increased risk by the deep learning model (8.6, 95% confidence interval [CI] = 7.9 to 9.4) compared with Tyrer-Cuzick (4.4, 95% CI = 3.9 to 4.9) and NCI BCRAT (3.8, 95% CI = 3.3 to 4.3) models (P &lt; .001). Area under the receiver operating characteristic curves of the deep learning model (0.68, 95% CI = 0.66 to 0.70) was higher compared with Tyrer-Cuzick (0.57, 95% CI = 0.54 to 0.60) and NCI BCRAT (0.57, 95% CI = 0.54 to 0.60) models. Simulated screening of the top 50th percentile risk by the deep learning model captured statistically significantly more patients with cancer compared with Tyrer-Cuzick and NCI BCRAT models (P &lt; .001). A deep learning model to assess breast cancer risk can support feasible and effective risk-based screening and is superior to traditional models to identify patients destined to develop cancer in large screening cohorts.

Assessment of the Risk of Breast Cancer Development Applying NCI Tool among Iraqi Women.

Objective:As part of the bioinformatics studies, we utilized National Cancer Institute (NCI)’s Breast Cancer Risk Assessment Tool to estimate the five-year period and lifetime risk of breast cancer development among Iraqi risky women. Methods:Totally, 110 risky women aged 21-67 (mean=36±7.4) years were interviewed by a series of questions regarding the risk of breast cancer development. Moreover, 100 cases with mutation in the BRCA1 or BRCA2 genes were included. Results:Our results demonstrated that the patient’s estimated risk of breast cancer development during the next five years and lifetime (until the age 90 years) included 0.96% (p=0.211) and 9.97% (p=0.002), respectively being relatively low. Accordingly, the lifetime risk for the breast cancer development was significantly higher (10.38%) than that of 5-year. However, the age of patients was not significantly associated to the breast cancer development as there was no significant difference among various age groups. Conclusion: It was concluded that long-term or lifetime period plays as a significant risk factor for developing breast cancer among female patients who had had a screening episode in Iraq.

Abstract NG15: Investigation of relationships between breast cancer risk factors and bilateral mammographic breast density asymmetry among women undergoing diagnostic image-guided breast biopsies

Introduction: The etiology of breast cancer remains an area of ongoing investigation. Improving our understanding of factors associated with breast cancer development will strengthen the utility of risk prediction strategies and improve risk stratification. Mammographic breast density remains one of the most influential breast cancer risk factors, with 4 to 6 fold elevated risk observed among women with the highest compared to the lowest levels. These associations have consistently been observed irrespective of the method (visual or automated), used to quantify breast density, showing the robustness of the associations. An understudied breast cancer risk factor is the bilateral asymmetry of mammographic features, with prior studies suggesting that women with higher levels of breast asymmetry may be at elevated breast cancer risk. Increasingly, studies are recognizing the potential of bilateral breast asymmetry defined by mammographic breast density in determining breast cancer risk. Recently, Eriksson and colleagues highlighted a strong influence of breast asymmetry within a model they developed to predict short-term breast cancer risk among women attending breast screening. They observed that the contribution to the model of asymmetry in mammographic breast density, microcalcifications, and masses between the breasts was as substantial as the total number of microcalcifications and masses found within a mammogram, indicating that differences between the breasts may be an important risk marker. To help further understand associations between breast cancer risk factors and asymmetry of breast density, particularly among women at elevated risk for breast cancer, we evaluated risk factor relationships with the bilateral asymmetry of volumetric measures of global and local breast density across the spectrum of premalignant and invasive breast cancer diagnoses. Methods: This study included 882 women enrolled as part of the National Cancer Institute9s Breast Radiology Evaluation and Study of Tissues (BREAST)-Stamp Project (2007-2010). The BREAST-Stamp Project is a cross-sectional molecular epidemiologic study that aims to understand how novel breast density measures are related to breast cancer etiology. Women were enrolled if they were referred for diagnostic image-guided breast biopsy following an abnormal mammogram at the University of Vermont Medical Center, and had not previously been diagnosed or treated for cancer, undergone breast surgery within one year or received chemoprevention. Risk factor data were collected at study enrolment via interview and self-administered questionnaires. Breast density measures were estimated using Single X-ray Absorptiometry (SXA), a technique in which an SXA breast density phantom was affixed to the compression paddle of the mammography machine during routine mammography so that it was included in the X-ray field. It served as a reference standard to estimate volumetric breast density. Breast density was assessed using pre-biopsy craniocaudal full-field digital mammograms of both the ipsilateral (affected) and contralateral (unaffected) breast. Firstly, global density from each laterality was determined as percent fibroglandular volume (%FGV). Secondly, localized volumetric density measures were estimated following identification of the biopsy site on the ipsilateral pre-biopsy mammogram by the study radiologist and identifying the corresponding site on the contralateral mammogram. The SXA estimated %FGV in a perilesional volume twice the size of, but excluding, the biopsy target, centered at the biopsy site. Previous estimates of reproducibility for the SXA test phantoms demonstrated a repeatability SD of 2%, with a ±2% accuracy for the entire thickness and density ranges. Breast density asymmetry was defined as an absolute bilateral difference in measures when subtracting the breast density measures of the contralateral breast from the ipsilateral measures. Spearman9s correlations (rho) examined associations between breast density measures of the left and right breasts. To determine relationships between breast cancer risk factors (defined as categorical variables) and measures of bilateral breast density asymmetry, analysis of covariance (ANCOVA) models (PROC GLM) were used. Analyses were conducted at the per woman level using SAS. Probability values of Citation Format: Maeve Mullooly, Shaoqi Fan, Ruth M. Pfeiffer, Brian Sprague, Pamela M. Vacek, Donald L. Weaver, John A. Shepherd, Amir Pasha Mahmoudzadeh, Jeff Wang, Serghei Malkov, Jason M. Johnson, Sally D. Herschorn, Gretchen L. Gierach. Investigation of relationships between breast cancer risk factors and bilateral mammographic breast density asymmetry among women undergoing diagnostic image-guided breast biopsies [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr NG15.

Perception of Breast Cancer Risk in Over 11,000 Patients During Routine Mammography Exam.

To assess women's knowledge of personal breast cancer risk. Additionally, we aimed to determine how often physicians are discussing individualized risk assessments of breast cancer with their patients, and if those discussion affected risk perception. Surveys were distributed in radiology centers in New York and made available to patients presenting for routine mammography. As exact number of patients who viewed the survey is unknown, response rate could not be assessed. Data from the survey was used to calculate each patient's personal breast cancer risk using the National Cancer Institute Breast Cancer Risk Assessment Tool. Each patient's actual breast cancer risk was compared to her perceived risk. 15,006 subjects submitted the survey and 11,365 were included in the final analysis. Fourteen percent of women were accurate in their 5-year estimation as compared to calculated risk, and only 7% of respondents were accurate in their estimation of risk by 90years of age. There was no association between time since discussion of breast cancer risk and accuracy of estimating personal breast cancer risk (p = 0.17). There was no difference in risk estimation accuracy between women who had ever or had never discussed risk with her physician (p = 0.1064). Patients' perceived personal risk of developing breast cancer does not match the Gail model calculated risk. These findings hold true whether or not her physician had discussed personal breast cancer risk with her in the past. Further studies should determine effective strategies to educate patients about their breast cancer risk.

Prospective Validation of the NCI Breast Cancer Risk Assessment Tool and the Autodensity Mammographic Density Tool on 40,000 Australian Screening Program Participants

Background: In Australia and elsewhere, there is a growing interest in delivering more personalised, risk-based breast cancer screening protocols. This requires reliable, feasible and accurate estimates of risk. The US National Cancer Institute Breast Cancer Risk Assessment Tool (BCRAT) and the AutoDensity fully automated mammographic density measurement tool have each been shown to stratify women into groups according to their risk of breast cancer; the AutoDensity tool also provides information on the likely sensitivity and specificity of mammographic screening tests. The Australian 'lifepool' cohort of over 53,000 women recruited predominantly through BreastScreen Australia screening program offers an opportunity to validate these tools and examine how they can be combined to estimate various risks. Aim: To validate BCRAT and AutoDensity on a large Australian population, and examine how the tools can be combined to provide information on breast cancer risk and the accuracy of the screening test. Methods: We use lifepool cohort questionnaire data and linked screening records and mammograms, cancer registrations and death records to describe the association between BCRAT and AutoDensity scores assessed at the time of screening and future breast cancer diagnosis. We use hazards models to account for censoring and describe outcomes according to mode of detection (screen-detected, interval cancers or other). Our primary analysis is restricted to women in the historical screening target age range of 50-69 with no prevalent breast cancer diagnosis on entry to the lifepool cohort. Results: The primary analysis included approximately 40,000 women with a median follow-up period of 4.5 years (1.1-6.5 years). The BCRAT tool generated a median 5-year breast cancer risk score of 1.5% (range 0.6%-22.0%). Compared with women in the lowest quintile of this score, women in the highest quintile had a 2.3-fold risk (95% CI 1.7-3.0, P &lt; 0.001) of incident invasive breast cancer. For the approximately 35,000 women with digital screening mammograms on enrolment, women in the highest quintile of AutoDensity values had a 1.5-fold risk (95% CI 1.1-2.0 P = 0.011) of incident invasive breast cancer and a 2.6-fold risk (95% CI 1.1-6.2, P = 0.034) of an interval cancer compared with women in the lowest quintile. With BRCAT and AutoDensity measurements weakly correlated (r2= 0.003, P = 0.05), we demonstrate various approaches to combining this information to stratify women according to breast cancer risk and risk of an interval cancer. Conclusion: The US National Cancer Institute Breast Cancer Risk Assessment Tool and the AutoDensity mammographic density tool can be used to stratify breast cancer screening participants into risk groups according to their future breast cancer risk and the risk of an interval cancer. This is likely to be of interest to screening program managers and policy-makers, and women considering screening participation.

National Cancer Institute Breast Cancer Steering Committee Working Group Report on Meaningful and Appropriate End Points for Clinical Trials in Metastatic Breast Cancer.

To provide evidence-based consensus recommendations on choice of end points for clinical trials in metastatic breast cancer, with a focus on biologic subtype and line of therapy. The National Cancer Institute Breast Cancer Steering Committee convened a working group of breast medical oncologists, patient advocates, biostatisticians, and liaisons from the Food and Drug Administration to conduct a detailed curated systematic review of the literature, including original reports, reviews, and meta-analyses, to determine the current landscape of therapeutic options, recent clinical trial data, and natural history of four biologic subtypes of breast cancer. Ongoing clinical trials for metastatic breast cancer in each subtype also were reviewed from ClinicalTrials.gov for planned primary end points. External input was obtained from the pharmaceutic/biotechnology industry, real-world clinical data specialists, experts in quality of life and patient-reported outcomes, and combined metrics for assessing magnitude of clinical benefit. The literature search yielded 146 publications to inform the recommendations from the working group. Recommendations for appropriate end points for metastatic breast cancer clinical trials focus on biologic subtype and line of therapy and the magnitude of absolute and relative gains that would represent meaningful clinical benefit.

Catalysts towards cancer risk management action: A longitudinal study of reproductive-aged women with BRCA1/2 mutations

ABSTRACTDeleterious mutations in BRCA1 or BRCA2 genes increase a woman's lifetime risk of breast and ovarian cancer. Risk management guidelines endorse early detection and prevention behaviors. Despite expressed intent, uptake of these measures remains low. This longitudinal, qualitative study integrated retrospective and prospective data to distinguish factors shaping intent to act from those that are catalysts to taking action to reduce cancer risk. Twelve BRCA1/2 mutation-positive women participating in the National Cancer Institute's Breast Imaging Study aged 18–35 completed two semi-structured interviews three years apart. Researchers completed focused coding to identify points of behavioral intent and action and contextual factors acting as catalysts upon participant narratives. All women shared only two action steps: seeking information about cancer risk and completing genetic testing. The constellation of action steps created a unique action trajectory that was defined, with precise ideas about risk perception and clear behavioral response, or iterative, in which unanticipated life events shifted the speed, accessibility, or order in which risk management and family planning goals were prioritized, planned, or executed. Factors shifting action steps included salient, unanticipated life events, such as infertility, insurance/financial constraints, birth of the last child, or a relative's cancer diagnosis. Focus on cancer morbidity may obfuscate how women prioritize actions, and ignore varied pragmatic, relational, and social factors affecting how intended actions are completed, particularly during the reproductive years. We recommend providers update patients' risk management plans at each visit to assess readiness for next steps and reduce reluctance to discuss, or guilt associated with, change.

Interactions Between Genome-Wide Significant Genetic Variants and Circulating Concentrations of 25-Hydroxyvitamin D in Relation to Prostate Cancer Risk in the National Cancer Institute BPC3.

Genome-wide association studies (GWAS) have identified over 100 single nucleotide polymorphisms (SNPs) associated with prostate cancer. However, information on the mechanistic basis for some associations is limited. Recent research has been directed towards the potential association of vitamin D concentrations and prostate cancer, but little is known about whether the aforementioned genetic associations are modified by vitamin D. We investigated the associations of 46 GWAS-identified SNPs, circulating concentrations of 25-hydroxyvitamin D (25(OH)D), and prostate cancer (3,811 cases, 511 of whom died from the disease, compared with 2,980 controls-from 5 cohort studies that recruited participants over several periods beginning in the 1980s). We used logistic regression models with data from the National Cancer Institute Breast and Prostate Cancer Cohort Consortium (BPC3) to evaluate interactions on the multiplicative and additive scales. After allowing for multiple testing, none of the SNPs examined was significantly associated with 25(OH)D concentration, and the SNP-prostate cancer associations did not differ by these concentrations. A statistically significant interaction was observed for each of 2 SNPs in the 8q24 region (rs620861 and rs16902094), 25(OH)D concentration, and fatal prostate cancer on both multiplicative and additive scales (P≤0.001). We did not find strong evidence that associations between GWAS-identified SNPs and prostate cancer are modified by circulating concentrations of 25(OH)D. The intriguing interactions between rs620861 and rs16902094, 25(OH)D concentration, and fatal prostate cancer warrant replication.

Projecting Individualized Absolute Invasive Breast Cancer Risk in US Hispanic Women

There is no model to estimate absolute invasive breast cancer risk for Hispanic women. The San Francisco Bay Area Breast Cancer Study (SFBCS) provided data on Hispanic breast cancer case patients (533 US-born, 553 foreign-born) and control participants (464 US-born, 947 foreign-born). These data yielded estimates of relative risk (RR) and attributable risk (AR) separately for US-born and foreign-born women. Nativity-specific absolute risks were estimated by combining RR and AR information with nativity-specific invasive breast cancer incidence and competing mortality rates from the California Cancer Registry and Surveillance, Epidemiology, and End Results program to develop the Hispanic risk model (HRM). In independent data, we assessed model calibration through observed/expected (O/E) ratios, and we estimated discriminatory accuracy with the area under the receiver operating characteristic curve (AUC) statistic. The US-born HRM included age at first full-term pregnancy, biopsy for benign breast disease, and family history of breast cancer; the foreign-born HRM also included age at menarche. The HRM estimated lower risks than the National Cancer Institute's Breast Cancer Risk Assessment Tool (BCRAT) for US-born Hispanic women, but higher risks in foreign-born women. In independent data from the Women's Health Initiative, the HRM was well calibrated for US-born women (observed/expected [O/E] ratio = 1.07, 95% confidence interval [CI] = 0.81 to 1.40), but seemed to overestimate risk in foreign-born women (O/E ratio = 0.66, 95% CI = 0.41 to 1.07). The AUC was 0.564 (95% CI = 0.485 to 0.644) for US-born and 0.625 (95% CI = 0.487 to 0.764) for foreign-born women. The HRM is the first absolute risk model that is based entirely on data specific to Hispanic women by nativity. Further studies in Hispanic women are warranted to evaluate its validity.

Association of breast cancer risk loci with breast cancer survival

The survival of breast cancer patients is largely influenced by tumor characteristics, such as TNM stage, tumor grade and hormone receptor status. However, there is growing evidence that inherited genetic variation might affect the disease prognosis and response to treatment. Several lines of evidence suggest that alleles influencing breast cancer risk might also be associated with breast cancer survival. We examined the associations between 35 breast cancer susceptibility loci and the disease over-all survival (OS) in 10,255 breast cancer patients from the National Cancer Institute Breast and Prostate Cancer Cohort Consortium (BPC3) of which 1,379 died, including 754 of breast cancer. We also conducted a meta-analysis of almost 35,000 patients and 5,000 deaths, combining results from BPC3 and the Breast Cancer Association Consortium (BCAC) and performed in silico analyses of SNPs with significant associations. In BPC3, the C allele of LSP1-rs3817198 was significantly associated with improved OS (HRper-allele =0.70; 95% CI: 0.58-0.85; ptrend = 2.84 × 10(-4) ; HRheterozygotes = 0.71; 95% CI: 0.55-0.92; HRhomozygotes = 0.48; 95% CI: 0.31-0.76; p2DF = 1.45 × 10(-3) ). In silico, the C allele of LSP1-rs3817198 was predicted to increase expression of the tumor suppressor cyclin-dependent kinase inhibitor 1C (CDKN1C). In the meta-analysis, TNRC9-rs3803662 was significantly associated with increased death hazard (HRMETA =1.09; 95% CI: 1.04-1.15; ptrend = 6.6 × 10(-4) ; HRheterozygotes = 0.96 95% CI: 0.90-1.03; HRhomozygotes = 1.21; 95% CI: 1.09-1.35; p2DF =1.25 × 10(-4) ). In conclusion, we show that there is little overlap between the breast cancer risk single nucleotide polymorphisms (SNPs) identified so far and the SNPs associated with breast cancer prognosis, with the possible exceptions of LSP1-rs3817198 and TNRC9-rs3803662.

Genetic risk variants associated with in situ breast cancer.

IntroductionBreast cancer in situ (BCIS) diagnoses, a precursor lesion for invasive breast cancer, comprise about 20 % of all breast cancers (BC) in countries with screening programs. Family history of BC is considered one of the strongest risk factors for BCIS.MethodsTo evaluate the association of BC susceptibility loci with BCIS risk, we genotyped 39 single nucleotide polymorphisms (SNPs), associated with risk of invasive BC, in 1317 BCIS cases, 10,645 invasive BC cases, and 14,006 healthy controls in the National Cancer Institute’s Breast and Prostate Cancer Cohort Consortium (BPC3). Using unconditional logistic regression models adjusted for age and study, we estimated the association of SNPs with BCIS using two different comparison groups: healthy controls and invasive BC subjects to investigate whether BCIS and BC share a common genetic profile.ResultsWe found that five SNPs (CDKN2BAS-rs1011970, FGFR2-rs3750817, FGFR2-rs2981582, TNRC9-rs3803662, 5p12-rs10941679) were significantly associated with BCIS risk (P value adjusted for multiple comparisons <0.0016). Comparing invasive BC and BCIS, the largest difference was for CDKN2BAS-rs1011970, which showed a positive association with BCIS (OR = 1.24, 95 % CI: 1.11–1.38, P = 1.27 x 10−4) and no association with invasive BC (OR = 1.03, 95 % CI: 0.99–1.07, P = 0.06), with a P value for case-case comparison of 0.006. Subgroup analyses investigating associations with ductal carcinoma in situ (DCIS) found similar associations, albeit less significant (OR = 1.25, 95 % CI: 1.09–1.42, P = 1.07 x 10−3). Additional risk analyses showed significant associations with invasive disease at the 0.05 level for 28 of the alleles and the OR estimates were consistent with those reported by other studies.ConclusionsOur study adds to the knowledge that several of the known BC susceptibility loci are risk factors for both BCIS and invasive BC, with the possible exception of rs1011970, a putatively functional SNP situated in the CDKN2BAS gene that may be a specific BCIS susceptibility locus.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-015-0596-x) contains supplementary material, which is available to authorized users.

Additive interactions between susceptibility single-nucleotide polymorphisms identified in genome-wide association studies and breast cancer risk factors in the Breast and Prostate Cancer Cohort Consortium.

Additive interactions can have public health and etiological implications but are infrequently reported. We assessed departures from additivity on the absolute risk scale between 9 established breast cancer risk factors and 23 susceptibility single-nucleotide polymorphisms (SNPs) identified from genome-wide association studies among 10,146 non-Hispanic white breast cancer cases and 12,760 controls within the National Cancer Institute's Breast and Prostate Cancer Cohort Consortium. We estimated the relative excess risk due to interaction and its 95% confidence interval for each pairwise combination of SNPs and nongenetic risk factors using age- and cohort-adjusted logistic regression models. After correction for multiple comparisons, we identified a statistically significant relative excess risk due to interaction (uncorrected P = 4.51 × 10(-5)) between a SNP in the DNA repair protein RAD51 homolog 2 gene (RAD51L1; rs10483813) and body mass index (weight (kg)/height (m)(2)). We also compared additive and multiplicative polygenic risk prediction models using per-allele odds ratio estimates from previous studies for breast-cancer susceptibility SNPs and observed that the multiplicative model had a substantially better goodness of fit than the additive model.

Insulin-like growth factor pathway genetic polymorphisms, circulating IGF1 and IGFBP3, and prostate cancer survival.

The insulin-like growth factor (IGF) signaling pathway has been implicated in prostate cancer (PCa) initiation, but its role in progression remains unknown. Among 5887 PCa patients (704 PCa deaths) of European ancestry from seven cohorts in the National Cancer Institute Breast and Prostate Cancer Cohort Consortium, we conducted Cox kernel machine pathway analysis to evaluate whether 530 tagging single nucleotide polymorphisms (SNPs) in 26 IGF pathway-related genes were collectively associated with PCa mortality. We also conducted SNP-specific analysis using stratified Cox models adjusting for multiple testing. In 2424 patients (313 PCa deaths), we evaluated the association of prediagnostic circulating IGF1 and IGFBP3 levels and PCa mortality. All statistical tests were two-sided. The IGF signaling pathway was associated with PCa mortality (P = .03), and IGF2-AS and SSTR2 were the main contributors (both P = .04). In SNP-specific analysis, 36 SNPs were associated with PCa mortality with P trend less than .05, but only three SNPs in the IGF2-AS remained statistically significant after gene-based corrections. Two were in linkage disequilibrium (r 2 = 1 for rs1004446 and rs3741211), whereas the third, rs4366464, was independent (r 2 = 0.03). The hazard ratios (HRs) per each additional risk allele were 1.19 (95% confidence interval [CI] = 1.06 to 1.34; P trend = .003) for rs3741211 and 1.44 (95% CI = 1.20 to 1.73; P trend < .001) for rs4366464. rs4366464 remained statistically significant after correction for all SNPs (P trend.corr = .04). Prediagnostic IGF1 (HRhighest vs lowest quartile = 0.71; 95% CI = 0.48 to 1.04) and IGFBP3 (HR = 0.93; 95% CI = 0.65 to 1.34) levels were not associated with PCa mortality. The IGF signaling pathway, primarily IGF2-AS and SSTR2 genes, may be important in PCa survival.

Post-GWAS gene-environment interplay in breast cancer: results from the Breast and Prostate Cancer Cohort Consortium and a meta-analysis on 79,000 women.

We studied the interplay between 39 breast cancer (BC) risk SNPs and established BC risk (body mass index, height, age at menarche, parity, age at menopause, smoking, alcohol and family history of BC) and prognostic factors (TNM stage, tumor grade, tumor size, age at diagnosis, estrogen receptor status and progesterone receptor status) as joint determinants of BC risk. We used a nested case-control design within the National Cancer Institute's Breast and Prostate Cancer Cohort Consortium (BPC3), with 16 285 BC cases and 19 376 controls. We performed stratified analyses for both the risk and prognostic factors, testing for heterogeneity for the risk factors, and case-case comparisons for differential associations of polymorphisms by subgroups of the prognostic factors. We analyzed multiplicative interactions between the SNPs and the risk factors. Finally, we also performed a meta-analysis of the interaction ORs from BPC3 and the Breast Cancer Association Consortium. After correction for multiple testing, no significant interaction between the SNPs and the established risk factors in the BPC3 study was found. The meta-analysis showed a suggestive interaction between smoking status and SLC4A7-rs4973768 (Pinteraction = 8.84 × 10(-4)) which, although not significant after considering multiple comparison, has a plausible biological explanation. In conclusion, in this study of up to almost 79 000 women we can conclusively exclude any novel major interactions between genome-wide association studies hits and the epidemiologic risk factors taken into consideration, but we propose a suggestive interaction between smoking status and SLC4A7-rs4973768 that if further replicated could help our understanding in the etiology of BC.

Insulin-like Growth Factor Pathway Genetic Polymorphisms, Circulating IGF1 and IGFBP3, and Prostate Cancer Survival

The insulin-like growth factor (IGF) signaling pathway has been implicated in prostate cancer (PCa) initiation, but its role in progression remains unknown. Among 5887 PCa patients (704 PCa deaths) of European ancestry from seven cohorts in the National Cancer Institute Breast and Prostate Cancer Cohort Consortium, we conducted Cox kernel machine pathway analysis to evaluate whether 530 tagging single nucleotide polymorphisms (SNPs) in 26 IGF pathway-related genes were collectively associated with PCa mortality. We also conducted SNP-specific analysis using stratified Cox models adjusting for multiple testing. In 2424 patients (313 PCa deaths), we evaluated the association of prediagnostic circulating IGF1 and IGFBP3 levels and PCa mortality. All statistical tests were two-sided. The IGF signaling pathway was associated with PCa mortality (P = .03), and IGF2-AS and SSTR2 were the main contributors (both P = .04). In SNP-specific analysis, 36 SNPs were associated with PCa mortality with Ptrend less than .05, but only three SNPs in the IGF2-AS remained statistically significant after gene-based corrections. Two were in linkage disequilibrium (r2 = 1 for rs1004446 and rs3741211), whereas the third, rs4366464, was independent (r2 = 0.03). The hazard ratios (HRs) per each additional risk allele were 1.19 (95% confidence interval [CI] = 1.06 to 1.34; Ptrend = .003) for rs3741211 and 1.44 (95% CI = 1.20 to 1.73; Ptrend < .001) for rs4366464. rs4366464 remained statistically significant after correction for all SNPs (Ptrend.corr = .04). Prediagnostic IGF1 (HRhighest vs lowest quartile = 0.71; 95% CI = 0.48 to 1.04) and IGFBP3 (HR = 0.93; 95% CI = 0.65 to 1.34) levels were not associated with PCa mortality. The IGF signaling pathway, primarily IGF2-AS and SSTR2 genes, may be important in PCa survival.