Citations

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 < 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 Chronic lymphocytic leukemia (CLL) is a neoplastic disease of mature B-cells with a highly heterogeneous clinical course. While European ancestry (EA) populations present an increased incidence of CLL, African ancestry (AA) populations have a younger median age of onset, higher frequency of adverse prognostic factors, and inferior clinical outcomes. Despite the considerable effort to characterize the genetic landscape of CLL, AA are overwhelmingly underrepresented. Our hypothesis is that the clinical differences observed between AA and EA populations are, in part, explained by underlying genetic features. To address this imbalance, we identified 90 AA patients diagnosed with CLL, 64% of which were untreated at sample collection. RNA and DNA were extracted from CD5+/CD19+ clonal B-cells. We performed mRNA-seq and targeted sequencing in 59 recurrently mutated somatic CLL driver genes. Differentially expressed genes were identified using edgeR. Data was compared to our previously analyzed EA CLL cohort (N=445). The median age at diagnosis was 59 years for AA and 66 for EA and 74% of AA and 50% of EA had unmutated IGHV (u-IGHV) status. When evaluating the entire AA and EA cohorts, there was a significant increased frequency of mutations in TP53, SF3B1, and NFKBIE, identified in 29%, 24%, and 20% of AA CLLs, compared to 5%, 9%, and 9%, respectively, in EA CLLs (p<0.01). When exclusively evaluating the untreated and u-IGHV cases, AA CLLs showed greater proportion of TP53 (30% vs 12%; p=0.0145), NFKBIE (30% vs 15%; p=0.0492), BIRC3 (21% vs 10%; p=0.0697), and KRAS (15% vs 7%; p=0.1683). Furthermore, there was an increase in mutations targeting relevant molecular pathways, such as NF-κB (42% vs 15%) and MAPK (18% vs 8%). Upregulation MAPK pathway was also confirmed by mRNA-seq analysis in the AA u-IGHV CLLs. Because of the high prevalence of TP53 mutations in the AA cohort, we further evaluated differential gene expression in the DNA Damage/Telomere Stress-Induced Senescence pathway. AA CLLs presented a significant downregulation of multiple genes associated with genome stability and cellular DNA damage response - DDR (including TP53 and ATM), double strand break repair (H2AFX and RAD50), telomere maintenance (POT1 and ACD), and cell cycle regulation (RB1, CCNA1, and CCNE2) (FDR<0.05). DDR is responsible for DNA repair or induction of apoptosis, with its deficiency resulting in the accumulation of chromosomal aberrations, negatively impacting clinical outcome in CLL. Disparities in cancer are influenced by numerous factors that affect disease risk, screening and diagnosis, access to treatment, and survival. We identified an increased number of genomic alterations in the AA CLL cohort, primarily inducing activation of NF-κB and MAPK pathways and DDR impairment, with the increased frequency of mutations, notably in TP53 and BIRC3, expected to negatively impact prognosis. Citation Format: Cecilia Bonolo de Campos, Daniel R. O'Brien, Chantal E. McCabe, Huihuang Yan, Geffen Kleinstern, Zhiquan Wang, Laura A. Bruins, Cristine Allmer, Nicholas J. Boddicker, Charla R. Secreto, Aaron D. Norman, Shulan Tian, Kari G. Rabe, Timothy G. Call, Sameer A. Parikh, Jose F. Leis, Wei Ding, Richard Furman, J Brice Weinberg, James R. Cerhan, Celine M. Vachon, Neil E. Kay, Susan L. Slager, Esteban Braggio. Characterization of underlying genomic features among African ancestry populations diagnosed with chronic lymphocytic leukemia [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 2209.

Abstract BackgroundCopy number variants (CNVs) are pervasive in the human genome but potential disease associations with rare CNVs have not been comprehensively assessed in large datasets. We analysed rare CNVs in genes and non-coding regions for 86,788 breast cancer cases and 76,122 controls of European ancestry with genome-wide array data.ResultsGene burden tests detected the strongest association for deletions in BRCA1 (P= 3.7E-18). Nine other genes were associated with a p-value < 0.01 including known susceptibility genes CHEK2 (P= 0.0008), ATM (P= 0.002) and BRCA2 (P= 0.008). Outside the known genes we detected associations with p-values < 0.001 for either overall or subtype-specific breast cancer at nine deletion regions and four duplication regions. Three of the deletion regions were in established common susceptibility loci.ConclusionsThis is the first genome-wide analysis of rare CNVs in a large breast cancer case-control dataset. We detected associations with exonic deletions in established breast cancer susceptibility genes. We also detected suggestive associations with non-coding CNVs in known and novel loci with large effects sizes. Larger sample sizes will be required to reach robust levels of statistical significance.

PURPOSE This study assessed the joint association of pathogenic variants (PVs) in breast cancer (BC) predisposition genes and polygenic risk scores (PRS) with BC in the general population. METHODS A total of 26,798 non-Hispanic white BC cases and 26,127 controls from predominately population-based studies in the Cancer Risk Estimates Related to Susceptibility consortium were evaluated for PVs in BRCA1, BRCA2, ATM, CHEK2, PALB2, BARD1, BRIP1, CDH1, and NF1. PRS based on 105 common variants were created using effect estimates from BC genome-wide association studies; the performance of an overall BC PRS and estrogen receptor–specific PRS were evaluated. The odds of BC based on the PVs and PRS were estimated using penalized logistic regression. The results were combined with age-specific incidence rates to estimate 5-year and lifetime absolute risks of BC across percentiles of PRS by PV status and first-degree family history of BC. RESULTS The estimated lifetime risks of BC among general-population noncarriers, based on 10th and 90th percentiles of PRS, were 9.1%-23.9% and 6.7%-18.2% for women with or without first-degree relatives with BC, respectively. Taking PRS into account, more than 95% of BRCA1, BRCA2, and PALB2 carriers had > 20% lifetime risks of BC, whereas, respectively, 52.5% and 69.7% of ATM and CHEK2 carriers without first-degree relatives with BC, and 78.8% and 89.9% of those with a first-degree relative with BC had > 20% risk. CONCLUSION PRS facilitates personalization of BC risk among carriers of PVs in predisposition genes. Incorporating PRS into BC risk estimation may help identify > 30% of CHEK2 and nearly half of ATM carriers below the 20% lifetime risk threshold, suggesting the addition of PRS may prevent overscreening and enable more personalized risk management approaches.

10541 Background: Biomechanical tissue properties may vary in the breasts of patients at elevated risk for breast cancer. We aim to quantify in vivo biomechanical tissue properties in various breast densities and in both normal risk and high risk women using Magnetic Resonance Imaging (MRI)/MRE and examine the association of biomechanical properties of the breast with cancer risk. Methods: In this IRB–approved prospective single-institution study, we recruited two groups of women differing by breast cancer risk to undergo a 3.0 T dynamic contrast enhanced MRI/MRE of the breast. Low-average risk women were defined as having no personal or significant family history of breast cancer, no prior high risk breast biopsies and a negative mammography within 12 months. High-risk breast cancer patients were recruited from those patients who underwent standard of care breast MR. Within each breast density group (non-dense versus dense), two-sample t-tests were used to compare breast stiffness, elasticity, and viscosity across risk groups (low-average vs high). Results: There were 50 low-average risk and 86 high-risk patients recruited to the study. The risk groups were similar on age (mean age = 55.6 and 53.6 years), density (68% vs. 64% dense breasts) and menopausal status (66.0% vs. 69.8%). Among patients with dense breasts, mean stiffness, elasticity, and viscosity were significantly higher in high risk patients ( N = 55) compared to low-average risk patients ( N = 34; all p < 0.001). In the multivariate logistic regression model, breast stiffness remained a significant predictor of risk status (OR=4.26, 95% CI [1.96, 9.25]) even after controlling for breast density, MRI BPE, age, and menopausal status. Similar results were seen for breast elasticity (OR=4.88, 95% CI [2.08, 11.43]) and viscosity (OR=11.49, 95% CI [1.15, 114.89]). Conclusions: Structurally-based, quantitative biomarker of tissue stiffness obtained from global 3D breast MRE is associated with differences in breast cancer risk in dense breasts. As such, tissue stiffness could provide a novel prognostic marker to help identify the subset of high-risk women with dense breasts who would benefit from increased surveillance.[Table: see text]

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.

Objective: The primary aim of this study was to evaluate the feasibility of collecting risk factor information and accessing digitized mammographic data in a medically marginalized population. A secondary aim was to examine the association between vitamin D status and mammographic density. Methods: Breast-screening examinations were provided for age-appropriate patients, and a referral for no-cost screening mammography was offered. Study participants were asked to undergo 25-hydroxyvitamin D testing at mammography and 1-year follow-up. Results: Of 62 women approached, 35 (56%) consented to participate. Of 32 participants who had baseline mammography, the median mammographic density measured by VolparaDensity (Volpara Solutions Limited) was 5.7%. After 1 year, 9 women obtained follow-up mammograms, with a median density of 5.7%. Vitamin D status was measured for 31 participants at baseline and 13 participants in the following year. Insufficient vitamin D status (<30 ng/mL) was noted in 77% at each time point. Mammographic density was not significantly correlated with vitamin D status ( P = .06). Conclusions: On the basis of this small pilot study, vitamin D insufficiency is common in this study population. Owing to the small sample size, an association between vitamin D insufficiency and breast density was not clear. Additional unexpected findings included substantial barriers in initial access to care and longitudinal follow-up in this population. Further study of these issues is needed.

AbstractGene expression profiling can be used for predicting survival in multiple myeloma (MM) and identifying patients who will benefit from particular types of therapy. Some germline single nucleotide polymorphisms (SNPs) act as expression quantitative trait loci (eQTLs) showing strong associations with gene expression levels. We performed an association study to test whether eQTLs of genes reported to be associated with prognosis of MM patients are directly associated with measures of adverse outcome. Using the genotype‐tissue expression portal, we identified a total of 16 candidate genes with at least one eQTL SNP associated with their expression with P < 10−7 either in EBV‐transformed B‐lymphocytes or whole blood. We genotyped the resulting 22 SNPs in 1327 MM cases from the International Multiple Myeloma rESEarch (IMMEnSE) consortium and examined their association with overall survival (OS) and progression‐free survival (PFS), adjusting for age, sex, country of origin and disease stage. Three polymorphisms in two genes (TBRG4‐rs1992292, TBRG4‐rs2287535 and ENTPD1‐rs2153913) showed associations with OS at P < .05, with the former two also associated with PFS. The associations of two polymorphisms in TBRG4 with OS were replicated in 1277 MM cases from the International Lymphoma Epidemiology (InterLymph) Consortium. A meta‐analysis of the data from IMMEnSE and InterLymph (2579 cases) showed that TBRG4‐rs1992292 is associated with OS (hazard ratio = 1.14, 95% confidence interval 1.04‐1.26, P = .007). In conclusion, we found biologically a plausible association between a SNP in TBRG4 and OS of MM patients.