Haiman, Christopher A.

Abstract Clinical germline genetic testing of cancer predisposition gene panels is used to identify women at increased risk for breast cancer. Pathogenic mutations in cancer predisposition genes are associated with established risks of breast cancer and are actively used for risk management of breast cancer for women qualifying for genetic testing because of a family history of breast and/or ovarian cancer or young age of diagnosis. However, the risks of breast cancer associated with mutations in these genes have likely been overestimated for many women in the general population. The goal of the “CAnceR RIsk Estimates Related to Susceptibility” (CARRIERS) study was to estimate breast cancer risks associated with mutations in hereditary cancer panel genes. Germline DNA from 32,298 breast cancer patients and 31,869 matched unaffected controls enrolled in cohorts and population-based case-control studies were sequenced to identify pathogenic mutations in 37 cancer predisposition genes.The average age at diagnosis was 61.2 years and the average age of last follow-up was 62.1 years. DNA was subjected to dual bar-coded QIAseq multiplex PCR-based amplification of 1733 target regions in 37 predisposition genes. Products from 768 samples were pooled and sequenced in a single lane of a HiSeq 4000 system. Mutations were called by GATK Haplotype Caller and Vardict.High quality sequence data was obtained for 99.3% of target regions. Pathogenic mutations in 12 known breast cancer predisposition genes were identified in 6.2% of all breast cancer cases and 2.1% of controls; and in 6.7% of African American breast cancer cases and 1.8% of controls. In case-control association analyses adjusted for age at diagnosis or last follow up and family history of breast and ovarian cancer, mutations in BRCA1, BRCA2, and PALB2 were associated with high risks of breast cancer (odds ratio (OR)>4.0). Mutations in CHEK2 and BARD1 were associated with moderate risks of breast cancer (OR>2.0), whereas mutations in ATM had lower clinical relevance (OR=1.6). Stratification by ER, PR, and HER2 status of tumors found that BRCA1, BRCA2, PALB2 and TP53 were associated with high risks of studies of triple negative breast cancer (TNBC), and that BARD1, BRIP1, FANCM, and RAD51C were associated with moderate risks of TNBC. Overall, results from the CARRIERS study establish that mutations in predisposition genes are associated with lower risks of breast cancer in the general population than in high-risk families. The age-related estimates of breast cancer risk for each of the hereditary cancer panel genes in this study may inform on the use of cancer screening and other risk management strategies for women in the general population with mutations in predisposition genes. Citation Format: Fergus J. Couch, Chunling Hu, Steven N. Hart, Rohan Gnanaolivu, Jenna Lilyquist, Kun Y. Lee, Chi Gao, Bruce Eckloff, Hoda Anton-Culver, Paul Auer, Leslie Bernstein, Mia Gaudet, Christopher Haiman, Julie Palmer, Amy Tentham-Dietz, Song Yao, Susan Domchek, Jeffrey N. Weitzel, David E. Goldgar, Katherine L. Nathanson, Peter Kraft, Eric C. Polley. Population-based breast cancer risk estimates associated with cancer predisposition gene mutations from 32,298 breast cancer patients and 31,869 matched unaffected controls from the CARRIERS study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4169.

104 Background: The identification of pathogenic mutations in breast cancer susceptibility genes through clinical genetic testing leads to focused screening and prevention strategies for women at increased risk of cancer. However, the frequency of mutations and the risks of cancer associated with breast cancer predisposition genes has not been established for the African American population. Methods: Germline DNA samples from African American women (5,054 breast cancer cases and 4,993 age-matched unaffected controls) from 10 U.S. studies were tested for mutations in 20 established breast cancer predisposition genes using a QIAseq multiplex amplicon panel as part of the “CAnceR RIsk Estimates Related to Susceptibility” (CARRIERS) study. The frequency of mutations in each gene and associations between mutations and breast cancer risk, adjusted for study design, age, and first-degree family history of breast cancer, were evaluated. Results: The mean age at diagnosis of breast cancer cases was 54.4 years and the mean age of controls was 55.2 years. 18.2% of cases and 10.8% of controls reported a first-degree family history of breast cancer. Pathogenic mutations in any of the 20 breast cancer predisposition genes were identified in 7.6% of breast cancer cases and 2.4% of controls. In multivariable analyses, mutations in BRCA1, BRCA2, and PALB2 were associated with high risks of breast cancer (odds ratio (OR) > 5.0). Mutations in CHEK2 were associated with moderate risks of breast cancer (OR > 2.0), whereas mutations in ATM had lower clinical relevance (OR = 1.8). Mutations in BRCA1, BRCA2, PALB2, and RAD51D, but not CHEK2 or ATM, were associated with increased risks of estrogen receptor negative breast cancer. Conclusions: Cancer predisposition genes confer similar risks of breast cancer in the African American population as in non-Hispanic Whites. These studies provide important insights into the risks of breast cancer associated with predisposition gene mutations in the African American population.

Abstract Background: Clinical germline genetic testing of cancer predisposition gene panels is used to identify women at increased risk for breast cancer. The identification of pathogenic mutations in established high and moderate predisposition genes may result in improved risk management of breast cancer for tested patients and their family members through tailored screening, prophylactic surgeries, or chemoprevention. However, the risks of breast cancer associated with mutations in these genes have likely been overestimated for many women in the general population because previous studies have focused on individuals with a family history of breast and/or ovarian cancer, early onset disease, or triple negative breast cancer. The goal of the “CAnceR RIsk Estimates Related to Susceptibility” (CARRIERS) study is to estimate breast cancer risks associated with mutations in hereditary cancer panel genes in the general population. Methods: Germline DNA samples from blood or saliva were obtained from 39,439 breast cancer patients and matched unaffected controls from six US-based cohorts (BWHS, CPSII, CTS, MEC, NHS1, NHS2, WHI). DNA was subjected to dual bar-coded QIAseq multiplex PCR-based amplification of 1733 target regions covering all coding regions of 37 cancer predisposition genes and sequenced. Mutation calling was conducted with Haplotype Caller and Vardict. Results: High quality sequence data was obtained for 38,990 of 39,439 samples (98.9%) and for 99.3% of target regions. Pathogenic mutations in 12 known breast cancer predisposition genes were identified 4.5% of all breast cancer cases and 2.1% of controls; and in 6.7% of African American breast cancer cases and 1.8% of controls. Differences in mutation frequencies were observed by age with mutations in 7.8% of cases diagnosed £50 years of age and 4.0% of cases diagnosed over age 50. Mutations in ATM, BRCA1, BRCA2, and PALB2 were enriched 2 to 3-fold in cases diagnosed under age 50 relative to older cases. No change in frequency of CHEK2 mutations by age was observed. In case-control analyses mutations in BRCA1, BRCA2 and PALB2 were significantly associated with a high risk of breast cancer (odds ratio (OR)>4.0). Of these, BRCA1 and BRCA2 displayed ORs of 13.5 and 16.6 in the £50 age group, but only 5.7 and 3.2 in the >50 age group. Only minor age-specific effects were observed for PALB2. Mutations in ATM and CHEK2 were associated with moderate risks of breast cancer (OR=2.0 to 4.0) in the younger age group, but not in the older age group. Conclusions: Results from the CARRIERS cohort-based study establish that mutations in known breast cancer predisposition genes are associated with only moderate risks of breast cancer in the general population. However, risks are substantially increased for BRCA1 and BRCA2 but not ATM, CHEK2 or PALB2 mutations in those £50 years of age. The age-related estimates of breast cancer risk for each of the hereditary cancer panel genes in this study may inform selection of individuals in the general population who may benefit from genetic testing and associated risk management strategies. Citation Format: Couch FJ, Hu C, Hart SN, Gnanaolivu RD, Lilyquist J, Lee KY, Gao C, Eckloff B, Samara R, Klebba J, Auer P, Bernstein L, Gaudet M, Haiman C, Palmer JR, Yao S, Domchek SM, Weitzel JN, Goldgar DE, Nathanson KL, Kraft P, Polley EC. Age-related breast cancer risk estimates for the general population based on sequencing of cancer predisposition genes in 19,228 breast cancer patients and 20,211 matched unaffected controls from US based cohorts in the CARRIERS study [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr GS2-01.

AbstractAn association between genetic variants in the vitamin D receptor (VDR) gene and epithelial ovarian cancer (EOC) was previously reported in women of African ancestry (AA). We sought to examine associations between genetic variants in VDR and additional genes from vitamin D biosynthesis and pathway targets (EGFR, UGT1A, UGT2A1/2, UGT2B, CYP3A4/5, CYP2R1, CYP27B1, CYP24A1, CYP11A1, and GC). Genotyping was performed using the custom‐designed 533,631 SNP Illumina OncoArray with imputation to the 1,000 Genomes Phase 3 v5 reference set in 755 EOC cases, including 537 high‐grade serous (HGSOC), and 1,235 controls. All subjects are of African ancestry (AA). Logistic regression was performed to estimate odds ratios (OR) and 95% confidence intervals (CI). We further evaluated statistical significance of selected SNPs using the Bayesian False Discovery Probability (BFDP). A significant association with EOC was identified in the UGT2A1/2 region for the SNP rs10017134 (per allele OR = 1.4, 95% CI = 1.2‐1.7, P = 1.2 × 10−6, BFDP = 0.02); and an association with HGSOC was identified in the EGFR region for the SNP rs114972508 (per allele OR = 2.3, 95% CI = 1.6‐3.4, P = 1.6 × 10−5, BFDP = 0.29) and in the UGT2A1/2 region again for rs1017134 (per allele OR = 1.4, 95% CI = 1.2‐1.7, P = 2.3 × 10−5, BFDP = 0.23). Genetic variants in the EGFR and UGT2A1/2 may increase susceptibility of EOC in AA women. Future studies to validate these findings are warranted. Alterations in EGFR and UGT2A1/2 could perturb enzyme efficacy, proliferation in ovaries, impact and mark susceptibility to EOC.

Abstract Background: Whether associations between circulating metabolites and prostate cancer are causal is unknown. We report on the largest study of metabolites and prostate cancer (2,291 cases and 2,661 controls) and appraise causality for a subset of the prostate cancer–metabolite associations using two-sample Mendelian randomization (MR). Methods: The case–control portion of the study was conducted in nine UK centers with men ages 50–69 years who underwent prostate-specific antigen screening for prostate cancer within the Prostate Testing for Cancer and Treatment (ProtecT) trial. Two data sources were used to appraise causality: a genome-wide association study (GWAS) of metabolites in 24,925 participants and a GWAS of prostate cancer in 44,825 cases and 27,904 controls within the Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium. Results: Thirty-five metabolites were strongly associated with prostate cancer (P < 0.0014, multiple-testing threshold). These fell into four classes: (i) lipids and lipoprotein subclass characteristics (total cholesterol and ratios, cholesterol esters and ratios, free cholesterol and ratios, phospholipids and ratios, and triglyceride ratios); (ii) fatty acids and ratios; (iii) amino acids; (iv) and fluid balance. Fourteen top metabolites were proxied by genetic variables, but MR indicated these were not causal. Conclusions: We identified 35 circulating metabolites associated with prostate cancer presence, but found no evidence of causality for those 14 testable with MR. Thus, the 14 MR-tested metabolites are unlikely to be mechanistically important in prostate cancer risk. Impact: The metabolome provides a promising set of biomarkers that may aid prostate cancer classification.

Abstract Multiple myeloma (MM) is twice as common in African Americans (AA) compared to European Americans (EA). The reported familial clustering and the elevated MM risk among first-degree relatives of cases implicate genetic susceptibility. Previous genome-wide association studies (GWAS) in EA have identified 16 novel risk loci. In this study, we tested the generalizability of the established risk alleles to AA and conducted a meta-GWAS analysis using two sets of AA to identify additional novel common MM risk variants. In the first study, we genotyped 1,305 incident AA MM cases from the African American Multiple Myeloma Study (AAMMS) using the Illumina HumanCore GWAS array and compared them to 7,078 AA controls from the African Ancestry Prostate Cancer Consortium (AAPC) and African Ancestry Breast Cancer Consortium (AABC) using the Illumina 1M-Duo. In the second study, 95 additional AAMMS cases and 435 AA MM cases from the University of Arkansas for Medical Sciences (UAMS) were genotyped using the Illumina MegaBead Chip and compared to 2,390 AA controls from the Multiethnic Cohort. The Haplotype Reference Consortium (HRC) was used to impute the overlapping typed SNPS from each GWAS case and control set together. Per-allele risk associations were tested for 8,715,278 overlapping genotyped and imputed variants with >1% frequency and >0.8 imputation score using unconditional logistic regression in both sets, and the combined effects were estimated using a fixed-effect meta-analysis. Of the 16 reported risk loci discovered in EA, directional consistency was present for 15 variants; eight of these replicated at nominal significance p<0.05, with the most statistically significant variant being rs4487645 at 7p15.3 (OR=1.38, p=3.56×10-6). AA individuals with polygenic risk scores from these 16 variants (PRS) in the top 10% stratum had a 1.44-fold increased MM risk compared to those with a PRS in the 25th -75th percentiles. Additionally, we identified three suggestive novel loci located at 12q12, 9p24.3 and 9p13.1 at p<1×10-6, with ORs ranging from 1.25-1.55, but none reached genome-wide significance. The variant at 9p24.3 is located in an intron in the KANK1 gene and a correlated SNP in EAs (r2=0.5) is strongly associated with gene expression in neoplastic plasma cells (unpublished, Weinhold and Morgan). Our study replicated most of the reported risk loci discovered among EA, demonstrated that a PRS constructed using the 16 reported risk alleles was associated with MM risk, and provides suggestive evidence for additional loci associated with MM risk in AAs. Citation Format: Zhaohui Du, Chi Song, Kristin Rand, Niels Weinhold, David Van Den Berg, Amie Hwang, Xin Sheng, Victor Hom, Sikander Ailawadhi, Ajay K. Nooka, Seema Singhal, Karen Pawlish, Edward S. Peters, Cathryn Bock, Ann Mohrbacher, Alexander Stram, Sonja I. Berndt, William Blot, John David Carpten, Antoinette Stroup, Andrew Olshan, Wei Zhang, African Ancestry Breast & Prostate Consortium, Stephen Chanock, Jayesh Mehta, Graham A. Colditz, Jeffrey Wolf, Thomas G. Martin, Michael Tomasson, Mark A. Fiala, Howard Terebelo, Nalini Janakiraman, Laurence Kolonel, Loic LeMarchand, Elad Ziv, Daniel Stram, Ravi Vij, Leon Bernal-Mizrachi, Gareth J. Morgan, Jeffrey A. Zonder, Carol Ann Huff, Sagar Lonial, Robert Z. Orlowski, David V. Conti, Christopher A. Haiman, Wendy Cozen. A meta-analysis of genome-wide association studies of multiple myeloma among African Americans [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 223.

AbstractMotivationThe use of single nucleotide polymorphism (SNP) interactions to predict complex diseases is getting more attention during the past decade, but related statistical methods are still immature. We previously proposed the SNP Interaction Pattern Identifier (SIPI) approach to evaluate 45 SNP interaction patterns/patterns. SIPI is statistically powerful but suffers from a large computation burden. For large-scale studies, it is necessary to use a powerful and computation-efficient method. The objective of this study is to develop an evidence-based mini-version of SIPI as the screening tool or solitary use and to evaluate the impact of inheritance mode and model structure on detecting SNP–SNP interactions.ResultsWe tested two candidate approaches: the ‘Five-Full’ and ‘AA9int’ method. The Five-Full approach is composed of the five full interaction models considering three inheritance modes (additive, dominant and recessive). The AA9int approach is composed of nine interaction models by considering non-hierarchical model structure and the additive mode. Our simulation results show that AA9int has similar statistical power compared to SIPI and is superior to the Five-Full approach, and the impact of the non-hierarchical model structure is greater than that of the inheritance mode in detecting SNP–SNP interactions. In summary, it is recommended that AA9int is a powerful tool to be used either alone or as the screening stage of a two-stage approach (AA9int+SIPI) for detecting SNP–SNP interactions in large-scale studies.Availability and implementationThe ‘AA9int’ and ‘parAA9int’ functions (standard and parallel computing version) are added in the SIPI R package, which is freely available at https://linhuiyi.github.io/LinHY_Software/.Supplementary informationSupplementary data are available at Bioinformatics online.

Abstract Background Previous genome-wide association studies (GWAS) have identified 42 loci (P < 5 × 10−8) associated with risk of colorectal cancer (CRC). Expanded consortium efforts facilitating the discovery of additional susceptibility loci may capture unexplained familial risk. Methods We conducted a GWAS in European descent CRC cases and control subjects using a discovery–replication design, followed by examination of novel findings in a multiethnic sample (cumulative n = 163 315). In the discovery stage (36 948 case subjects/30 864 control subjects), we identified genetic variants with a minor allele frequency of 1% or greater associated with risk of CRC using logistic regression followed by a fixed-effects inverse variance weighted meta-analysis. All novel independent variants reaching genome-wide statistical significance (two-sided P < 5 × 10−8) were tested for replication in separate European ancestry samples (12 952 case subjects/48 383 control subjects). Next, we examined the generalizability of discovered variants in East Asians, African Americans, and Hispanics (12 085 case subjects/22 083 control subjects). Finally, we examined the contributions of novel risk variants to familial relative risk and examined the prediction capabilities of a polygenic risk score. All statistical tests were two-sided. Results The discovery GWAS identified 11 variants associated with CRC at P < 5 × 10−8, of which nine (at 4q22.2/5p15.33/5p13.1/6p21.31/6p12.1/10q11.23/12q24.21/16q24.1/20q13.13) independently replicated at a P value of less than .05. Multiethnic follow-up supported the generalizability of discovery findings. These results demonstrated a 14.7% increase in familial relative risk explained by common risk alleles from 10.3% (95% confidence interval [CI] = 7.9% to 13.7%; known variants) to 11.9% (95% CI = 9.2% to 15.5%; known and novel variants). A polygenic risk score identified 4.3% of the population at an odds ratio for developing CRC of at least 2.0. Conclusions This study provides insight into the architecture of common genetic variation contributing to CRC etiology and improves risk prediction for individualized screening.