Vachon, Celine M.

PURPOSETo determine the contribution of germline pathogenic variants (PVs) in hereditary cancer testing panel genes to invasive lobular carcinoma (ILC) of the breast.MATERIALS AND METHODSThe study included 2,999 women with ILC from a population-based cohort and 3,796 women with ILC undergoing clinical multigene panel testing (clinical cohort). Frequencies of germline PVs in breast cancer predisposition genes ( ATM, BARD1, BRCA1, BRCA2, BRIP1, CDH1, CHEK2, PALB2, PTEN, RAD51C, RAD51D, and TP53) were compared between women with ILC and unaffected female controls and between women with ILC and infiltrating ductal carcinoma (IDC).RESULTSThe frequency of PVs in breast cancer predisposition genes among women with ILC was 6.5% in the clinical cohort and 5.2% in the population-based cohort. In case-control analysis, CDH1 and BRCA2 PVs were associated with high risks of ILC (odds ratio [OR] > 4) and CHEK2, ATM, and PALB2 PVs were associated with moderate (OR = 2-4) risks. BRCA1 PVs and CHEK2 p.Ile157Thr were not associated with clinically relevant risks (OR < 2) of ILC. Compared with IDC, CDH1 PVs were > 10-fold enriched, whereas PVs in BRCA1 were substantially reduced in ILC.CONCLUSIONThe study establishes that PVs in ATM, BRCA2, CDH1, CHEK2, and PALB2 are associated with an increased risk of ILC, whereas BRCA1 PVs are not. The similar overall PV frequencies for ILC and IDC suggest that cancer histology should not influence the decision to proceed with genetic testing. Similar to IDC, multigene panel testing may be appropriate for women with ILC, but CDH1 should be specifically discussed because of low prevalence and gastric cancer risk.

PURPOSE The prevalence of germline pathogenic variants (PVs) in established breast cancer predisposition genes in women in the general population over age 65 years is not well-defined. However, testing guidelines suggest that women diagnosed with breast cancer over age 65 years might have < 2.5% likelihood of a PV in a high-penetrance gene. This study aimed to establish the frequency of PVs and remaining risks of breast cancer for each gene in women over age 65 years. METHODS A total of 26,707 women over age 65 years from population-based studies (51.5% with breast cancer and 48.5% unaffected) were tested for PVs in germline predisposition gene. Frequencies of PVs and associations between PVs in each gene and breast cancer were assessed, and remaining lifetime breast cancer risks were estimated for non-Hispanic White women with PVs. RESULTS The frequency of PVs in predisposition genes was 3.18% for women with breast cancer and 1.48% for unaffected women over age 65 years. PVs in BRCA1, BRCA2, and PALB2 were found in 3.42% of women diagnosed with estrogen receptor (ER)–negative, 1.0% with ER-positive, and 3.01% with triple-negative breast cancer. Frequencies of PVs were lower among women with no first-degree relatives with breast cancer. PVs in CHEK2, PALB2, BRCA2, and BRCA1 were associated with increased risks (odds ratio = 2.9-4.0) of breast cancer. Remaining lifetime risks of breast cancer were ≥ 15% for those with PVs in BRCA1, BRCA2, and PALB2. CONCLUSION This study suggests that all women diagnosed with triple-negative breast cancer or ER-negative breast cancer should receive genetic testing and that women over age 65 years with BRCA1 and BRCA2 PVs and perhaps with PALB2 and CHEK2 PVs should be considered for magnetic resonance imaging screening.

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 Clinical practice guidelines recommend that individuals with HC MBL are evaluated annually for evidence of progression as it has been shown that they progress to develop CLL requiring therapy at a rate of 1-5%/year. Next-generation sequencing identified 59 genes recurrently mutated in CLL, and we previously showed that the tumor mutational load (TML), i.e., the total number of these genes mutated, is associated with a shorter time to first treatment (TTT) among 112 HC MBLs independent of the CLL international prognostic index (CLL-IPI), an established prognostic score. Herein, we continue to support our finding in additional HC MBLs and report the pooled results across the two cohorts. From the Mayo Clinic CLL Resource, we sequenced a total 167 HC MBL (112 from the initial round and 55 from additional sequencing). We extracted DNA from CD5+/CD19+ B-cells and sequenced the coding regions of 59 driver genes. Somatic mutations were called using MuTect2 in tumor-only mode. Germline variants were eliminated based on minor allele frequencies &gt;0.01% and by those identified in public databases, unless present in known CLL mutation hotspots. After filtering, we computed the TML based on the number of genes with high impact (nonsense, frameshift and splicing) and hotspot mutations. Cox regression was used to estimate hazard ratios (HR) and 95% confidence intervals (CI), adjusted for sex and CLL-IPI. Stratified analyses were conducted by low/intermediate risk and high/very high CLL-IPI. Of the 167 HC MBL, low risk CLL-IPI comprised 60%, intermediate risk 28%, high risk 12%; and none were very-high risk. The most commonly mutated genes were SF3B1 (11%), NOTCH1 (9%), TP53 (7%), BIRC3 (7%), and MYD88 (6%); and 52 (31%) individuals had mutations in at least one gene. The median follow-up was 4.4 years, and 26 of the 167 individuals progressed to CLL requiring therapy. HC MBL with ≥2 mutated genes had a 28% 5-year risk of needing therapy compared to 6% among those with no mutated genes. Similar to our initial finding (HR=1.53, CI:1.12-2.07; P=0.007), we found a shorter TTT in the additional HC MBLs (HR=4.63, CI:1.57-13.7, P=0.006), and in the pooled cohort (HR=1.72; CI:1.29-2.28, P=0.0002). When we stratified by CLL-IPI risk, TML remained associated with TTT among individuals with low/intermediate CLL-IPI risk in the additional HC MBLs (HR=3.52, CI:1.11-11.2, P=0.03) and in the pooled cohort (HR=2.25, CI:1.34-3.77, P=0.002). A non-significant 2.8 fold association was observed with high risk CLL-IPI in the pooled cohort. In conclusion, the TML along with the CLL-IPI provide clinicians with a more accurate prognostic information regarding disease progression requiring therapy, especially for those MBLs with low to intermediate risk based on CLL-IPI. If validated, these findings may modify current practice guidelines for individuals with HC MBL. Citation Format: Geffen Kleinstern, Cecilia Bonolo de Campos, Nicholas J. Boddicker, Daniel R. O'Brien, Shulan Tian, Chantel Barney, Kari G. Rabe, Cristine Allmer, Laura Bruins, Aaron D. Norman, Timothy G. Call, Sameer A. Parikh, Jose F. Leis, Wei Ding, Huihuang Yan, James R. Cerhan, Neil E. Kay, Celine Vachon, Esteban Braggio, Susan L. Slager. Tumor mutational load derived from recurrently mutated genes in chronic lymphocytic leukemia (CLL) predicts time-to-first treatment in high-count monoclonal B-cell lymphocytosis (HC MBL) [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 884.

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 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&lt;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&lt;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.