Citations

Abstract Background: Use of menopausal hormone therapy (MHT) is associated with increased risk for breast cancer. However, the relevant mechanisms and its interaction with genetic variants are not fully understood. Methods: We conducted a genome-wide interaction analysis between MHT use and genetic variants for breast cancer risk in 27,585 cases and 34,785 controls from 26 observational studies. All women were post-menopausal and of European ancestry. Multivariable logistic regression models were used to test for multiplicative interactions between genetic variants and current MHT use. We considered interaction p-values<5x10-8 as genome-wide significant, and p-values<1x10-5 as suggestive. Linkage disequilibrium (LD)-based clumping was performed to identify independent candidate variants.Results: None of the 9.7 million genetic variants tested for interactions with MHT use reached genome-wide significance. Only 213 variants, representing 18 independent loci, had p-values<1x105. The strongest evidence was found for rs4674019 (p-value=2.27x10-7), which showed genome-wide significant interaction (p-value=3.8x10-8) with current MHT use when analysis was restricted to population-based studies only. Limiting the analyses to combined estrogen-progesterone MHT use only or to estrogen receptor (ER) positive cases did not identify any genome-wide significant evidence of interactions. Conclusions: In this large genome-wide SNP-MHT interaction study of breast cancer, we found no strong support for common genetic variants modifying the effect of MHT on breast cancer risk. These results suggest that common genetic variation has limited impact on the observed MHT–breast cancer risk association.

Abstract Accurate estimation of mammographic breast density could aid in augmenting breast cancer risk assessment for women undergoing breast screening with full-field digital mammography (FFDM). Breast density can be estimated from FFDM and is most commonly assessed in the clinic by visual grading into one of the four categories defined by the American College of Radiology BI-RADS. However, BI-RADS density assessment is highly subjective and does not provide a quantitative, continuous measure of percent density (PD), which would allow for more refined risk stratification and assessment of density changes. Here, we introduce Deep-LIBRA, an artificial intelligence (AI) tool for fully-automated assessment of breast PD from FFDM images. Two key modules form the core of Deep-LIBRA: 1) an implementation of a modified U-Net architecture for breast segmentation and 2) a radiomic machine learning module that performs PD estimation within the segmented breast region. To develop and validate Deep-LIBRA, raw (i.e., "For Processing") FFDM images (Selenia Dimensions, Hologic Inc.) acquired at two breast cancer screening practices were retrospectively analyzed. For the breast segmentation module, we used a total of 12,100 FFDM studies from 2,200 individual women and a 90%-10% split-sample training-validation approach, using the Dice coefficient to evaluate the accuracy of Deep-LIBRA versus ground-truth manual breast segmentation. For the PD estimation module we used a total of 3,304 FFDM images from 1,652 individual women; manual PD scores obtained with the widely used Cumulus software were used as the "gold standard" in a three-fold cross-validation setting to assess the accuracy of Deep-LIBRA in PD estimation. PD estimates from Deep-LIBRA were also compared with breast density estimates from the commercially available Volpara software. Breast segmentation had a Dice coefficient of 95.31% when compared to ground-truth manual breast segmentation in the validation set. Deep-LIBRA average differences from ground-truth PD scores in the three cross-validation folds were 4.91%, 4.65%, and 4.22%, while Volpara had corresponding average differences of 6.20%, 6.01%, and 5.94%. Deep-LIBRA PD scores were also significantly different from Volpara PD (t-test p-value &lt; 0.001 in all three folds). Preliminary evaluation results show that Deep-LIBRA is a promising AI approach for accurately assessing PD from FFDM images. Citation Format: Omid Haji Maghsoudi, Scott Christopher, Aimilia Gastounioti, Lauren Pantalone, Fang-Fang Wu, Eric A. Cohen, Winham Stacey, Emily F. Conant, Celine Vachon, Despina Kontos. Deep-LIBRA: An artificial intelligence approach for fully-automated assessment of breast density in digital mammography [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2600.

Abstract Benign breast disease (BBD) is an established risk factor for development of breast cancer (BC). However, little is known about the prevalence of somatic genetic alterations at the time of BBD diagnosis and relationship to subsequent BC. To characterize DNA variants in BBD and investigate association with BC risk, we isolated DNA from formalin-fixed paraffin-embedded (FFPE) BBD biopsies and assessed mutations using a panel of 93 breast cancer predisposition genes (Qiagen GeneRead). The study set consisted of 120 BBD patients from three age-matched groups defined based on 16 years of BC follow-up: BBD-controls (cancer-free after 16 years), BBD-ER+, or BBD-ER- breast cancer (within 16 years). Bioinformatics quality checks included comparisons of paired fresh-frozen and FFPE replicates to reduce false variant calls induced by FFPE artifacts. Gene-level variant burden differences among study groups were assessed using 12 statistical approaches, adjusted for patient age, year of biopsy, and histology. To investigate possible immune implications of increased variant burden, extent of leukocytes in the BBD biopsies was assessed using CD45 immunohistochemistry. To assess variant profile changes during the progression from BBD to BC, we also performed targeted sequencing of 12 BBD-matched subsequent tumor samples (six ER+ and six ER-) developed within 5 years of BBD diagnosis. Gene-level association results showed consistently skewed distributions suggesting a higher variant burden in BBD controls: among the top 10 genes associated with case-status (p&lt;0.05), all had BC-risk odds ratio estimates less than 1.0. Immune infiltrates, assessed by CD45 H-scores, were positively associated with overall variant burden (r=0.42, p=0.0031), and noticeably higher in cancer-free controls (r=0.50, p=0.005). Comparison of mutation variants in 12 BBD cases that developed cancer within 5 years of diagnosis and their subsequent tumors revealed that more than half of the non-SNP variants were shared between the BBD and the subsequent tumor (54.6%), whereas less than one quarter were unique to the tumor (20.6%) or unique to the BBD (24.9%). Through BBD-BC paired variant analysis, three subjects were found with newly emergent clones uniquely appearing in the tumors and four subjects had loss of clones in the tumor that were present in the BBD. These results show that somatic variant burden is lower in patients with BBD that progress to BC, mutation profiles of BC progression are heterogeneous, and BBD tissues with reduced variant burden also show reduced immune infiltrate. This implicates a potential protective role for immune activation in BBD. Future directions will define the characteristics of the potentially protective immune response and its role in variant heterogeneity in a larger sample set, integrated with paired RNA sequencing to better understand the pathways involved and the relationship to immune infiltration. Citation Format: Stacey J. Winham, Julie M. Cunningham, Yuanhang Liu, Aditya V. Bhagwate, Ethan P. Heinzen, Samantha J. McDonough, Melody L. Stallings-Mann, Robert A. Vierkant, Jodi M. Carter, Mark E. Sherman, Derek C. Radisky, Amy C. Degnim, Chen Wang. Somatic variant burdenin benign breast disease and association with risk of subsequent breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2274.

Abstract Background: Many loci have been found to be associated with risk of epithelial ovarian cancer (EOC). However, although there is considerable variation in progression-free survival (PFS), no loci have been found to be associated with outcome at genome-wide levels of significance. Methods: We carried out a genome-wide association study (GWAS) of PFS in 2,352 women with EOC who had undergone cytoreductive surgery and standard carboplatin/paclitaxel chemotherapy. Results: We found seven SNPs at 12q24.33 associated with PFS (P &lt; 5 × 10–8), the top SNP being rs10794418 (HR = 1.24; 95% CI, 1.15–1.34; P = 1.47 × 10–8). High expression of a nearby gene, ULK1, is associated with shorter PFS in EOC, and with poor prognosis in other cancers. SNP rs10794418 is also associated with expression of ULK1 in ovarian tumors, with the allele associated with shorter PFS being associated with higher expression, and chromatin interactions were detected between the ULK1 promoter and associated SNPs in serous and endometrioid EOC cell lines. ULK1 knockout ovarian cancer cell lines showed significantly increased sensitivity to carboplatin in vitro. Conclusions: The locus at 12q24.33 represents one of the first genome-wide significant loci for survival for any cancer. ULK1 is a plausible candidate for the target of this association. Impact: This finding provides insight into genetic markers associated with EOC outcome and potential treatment options. See related commentary by Peres and Monteiro, p. 1604

PURPOSE:This randomized, double-blind study sought to understand whether cancer clinical trial consent form verbosity detracts from patients' decision making on trial enrollment.METHODS:This trial tested mock consent forms of 2,000, 4,000, and 6,000 words. The first two comprised the two experimental arms and the third the control arm. Phase II was conducted to identify the promising arm, which, in phase III, was compared with the control arm. Each consent form described the same trial. Eligible adult patients reported a cancer history and English literacy. The primary end point used a patient-reported Likert scale to assess the relationship between information in the consent form and trial decision making.RESULTS:In phase II, 93 patients were accrued and prompted the selection of the 2,000-word consent form for phase III. In phase III, 182 patients were recruited, resulting in 240 total evaluable patients to compare the 2,000-word versus the 6,000-word arm (control). For the primary end point, 103 (84%) and 107 (91%) patients in the 2,000- and 6,000-word arms, respectively, strongly agreed or agreed with the following: “The information in this consent form helped me make a decision about whether or not to enroll in the trial” (two-sided, P = .14). Median time to read each consent form was 8 and 12 minutes, respectively (two-sided, P < .0001). Among those assigned these consent forms, 84% and 73%, respectively (two-sided, P = .04) signed or expressed a willingness to sign.CONCLUSION:This study's primary end point was not met. However, secondary outcomes suggest a need to further study the efficiency and efficacy of shorter consent forms for cancer clinical trial enrollment.

AbstractInterest in analyzing X chromosome single nucleotide polymorphisms (SNPs) is growing and several approaches have been proposed. Prior studies have compared power of different approaches, but bias and interpretation of coefficients have received less attention. We performed simulations to demonstrate the impact of X chromosome model assumptions on effect estimates. We investigated the coefficient biases of SNP and sex effects with commonly used models for X chromosome SNPs, including models with and without assumptions of X chromosome inactivation (XCI), and with and without SNP–sex interaction terms. Sex and SNP coefficient biases were observed when assumptions made about XCI and sex differences in SNP effect in the analysis model were inconsistent with the data‐generating model. However, including a SNP–sex interaction term often eliminated these biases. To illustrate these findings, estimates under different genetic model assumptions are compared and interpreted in a real data example. Models to analyze X chromosome SNPs make assumptions beyond those made in autosomal variant analysis. Assumptions made about X chromosome SNP effects should be stated clearly when reporting and interpreting X chromosome associations. Fitting models with SNP × Sex interaction terms can avoid reliance on assumptions, eliminating coefficient bias even in the absence of sex differences in SNP effect.

Abstract Background: Use of menopausal hormone therapy (MHT) is associated with increased risk for breast cancer. However, the relevant mechanisms and its interaction with genetic variants are not fully understood. Methods: We conducted a genome-wide interaction analysis between MHT use and genetic variants for breast cancer risk in 27,585 cases and 34,785 controls from 26 observational studies. All women were post-menopausal and of European ancestry. Multivariable logistic regression models were used to test for multiplicative interactions between genetic variants and current MHT use. We considered interaction p-values<5x10-8 as genome-wide significant, and p-values<1x10-5 as suggestive. Linkage disequilibrium (LD)-based clumping was performed to identify independent candidate variants.Results: None of the 9.7 million genetic variants tested for interactions with MHT use reached genome-wide significance. Only 213 variants, representing 18 independent loci, had p-values<1x105. The strongest evidence was found for rs4674019 (p-value=2.27x10-7), which showed genome-wide significant interaction (p-value=3.8x10-8) with current MHT use when analysis was restricted to population-based studies only. Limiting the analyses to combined estrogen-progesterone MHT use only or to estrogen receptor (ER) positive cases did not identify any genome-wide significant evidence of interactions. Conclusions: In this large genome-wide SNP-MHT interaction study of breast cancer, we found no strong support for common genetic variants modifying the effect of MHT on breast cancer risk. These results suggest that common genetic variation has limited impact on the observed MHT–breast cancer risk association.

Abstract Background: Benign breast disease (BBD) is a risk factor for breast cancer (BC); however, little is known about the genetic alterations present at the time of BBD diagnosis, and how these relate to risk of incident BC. Methods: A subset of a long-term BBD cohort was selected to examine DNA variation across three BBD groups (42 future estrogen receptor-positive (ER+) BC, 36 future estrogen receptor-negative (ER-) BC and 42 controls cancer-free for at least 16 years post-BBD). DNA extracted from archival formalin fixed, paraffin-embedded (FFPE) tissue blocks was tested and analyzed for presence of DNA alterations using a targeted panel of 93 BC-associated genes. To address artifacts frequently observed in FFPE tissues (e.g., C>T changes), we applied three filtering strategies based on alternative allele frequencies and nucleotide substitution context. Gene-level associations were performed using two burden tests, SKAT-O and logistic regression, and adjusted for clinical and technical covariates including epithelial percentage, histologic impression, patient age and year of BBD biopsy. Results: After filtering, the variant frequency of SNPs in our samples was highly consistent with population allele frequencies reported in 1KG/ExAC (0.986, p <1e-16). Ten genes were found to be associated with later cancer status by four of 12 association methods, with nominal p-values <0.05: MED12, MSH2, BRIP1, PMS1, GATA3, MUC16, FAM175A, EXT2, MLH1 and TGFB1. Additionally, analysis revealed 11 and 19 gene-level associations with p-values <0.05 and OR<1 for all cancer cases and ER- cases, respectively. Comparing between the three case groups, BBD ER+ cases were closer to controls in mutation profile, while BBD ER- cases were distinct. Notably, the variant burden was significantly higher in controls than in either ER+ or ER- cases. CD45 expression was associated with mutational burden (p=0.003).Conclusions: Somatic mutations were more frequent in benign breast tissue from women who did not develop cancer, opening questions of clonal diversity or immune-mediated restraint on future cancer development. CD45 expression was positively associated with mutational burden, most strongly in controls. Further studies in both normal and premalignant tissues are needed to better understand the role of somatic gene mutations and their contribution to future cancer development.

Abstract Background: Women with benign breast disease (BBD) are at increased risk of breast cancer (BC). Classic studies based on film-based mammographic screening and pathology diagnosis of surgical biopsies conducted in the 1980s established a hierarchy of increasing BC risk: non-proliferative (NP) BBD, proliferative BBD without atypia (PDWA) and atypical hyperplasia (AH). Given changes in epidemiological BC risk factors and introduction of percutaneous core needle biopsy (CNB) in mid-1990s, and later, digital mammography, we hypothesized that the patient characteristics and relative frequency of BBD diagnoses have changed over time. Accordingly, we performed a longitudinal analysis of the frequency of patient characteristics and BBD diagnoses in the Mayo BBD cohort. Methods: Utilizing the Mayo Clinic Surgical and Pathology Indices, women ages 18 to 85 who had a BBD biopsy between 1/1/67 and 12/31/13 were identified. Breast pathologists reviewed biopsies masked to diagnoses of incident BC diagnosed in follow-up. Demographic characteristics and BC events were obtained by query of institutional data sources and participant surveys. Trends were evaluated for the following eras: 1: pre-mammogram (1967-1981), 2: pre-CNB (1982-1992), 3: CNB Transition (1993-2001), and 4: CNB (2002-2013). Demographics were formally compared across eras using chi-square tests for categorical variables and analyses of variance (ANOVAs) for continuous variables. Results: From 1967-2013, the cohort includes 19,582 unique women with BBD. The frequency of CNB increased from eras 1-4: 0.04%, 0.6%, 51.3 %, and 88.9%, respectively. Mean age at BBD diagnosis was younger in era 1 (48.0 years) vs eras 2-4 (53.2, 52.0, and 51.8, respectively, p&lt;0.001). The percentage of biopsies diagnosed as PDWA increased from era 1-4 (25.7%, 34.3%, 35.2%, 46.2%, p&lt;0.001), as did the percentage with AH (2.4%, 5.1%, 8.6%, 12.3%, p&lt;0.001). Over eras 1-4, the percentages of women with a strong family history of BC increased (9.9%, 12.7%, 17.1%, and 29.0%, p&lt;0.001) as did mean BMI (24.8, 26.4, 27.4, and 28.6, p&lt;0.001). With a median follow-up of 10.9 years, 1,719 breast cancers have developed, with increasing proportion of noninvasive (DCIS-only) disease across eras 1-4: 15.0%, 21.1%, 21.2%, and 33.2%, p&lt; 0.001. Conclusions: Analysis of this large, single institution BBD cohort for the 46 year period 1967-2013 demonstrates that BC risk factors among BBD patients has changed over time, with subjects demonstrating increasing age, BMI, and family history, and that the percentages of BBD classified as PDWA and AH have increased. Impact on BC risk will be further investigated. Citation Format: Amy Degnim, Karthik Ghosh, Jodi M Carter, Robert A Vierkant, Matthew R Jensen, Stacey J Winham, Tanya L Hoskin, Marlene Frost, Teresa M Allers, Denise L Gehling, Mindy J Kern, Laura M Pacheco-Spann, Celine M Vachon, Derek C Radisky, Daniel W Visscher, Mark E Sherman. Benign breast disease: Temporal trends from 1967 to 2013 [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS7-11.