Klein, Robert J.

Abstract Inherited susceptibility plays a critical role in prostate cancer (PCa) risk. Using large biobank and case-control datasets, we evaluated the contribution of both common variants and rare germline pathogenic variants (PVs) to overall PCa risk. The Prostate Cancer Exome Sequencing Consortium currently includes 427,388 male participants (51,452 PCa cases and 375,936 controls) with whole-exome sequencing data from ten biobanks and studies: UK Biobank (14,669 cases/195,600 controls), All of Us Research Program (7,577/75,226), African Ancestry Prostate Cancer Consortium (7,176/4,675), Mayo Clinic Biobank (6,031/15,084), Mass General Brigham Biobank (3,393/14,095), Geisinger’s MyCode Community Health Initiative (3,026/15,130), UCLA ATLAS Precision Medicine Biobank (2,850/16,904), Penn Medicine Biobank (2,598/16,255), Colorado Center for Personalized Medicine (2,269/13,399), and Malmo Diet and Cancer (1,863/9,568). Based on self-reported race/ethnicity and estimated genetic ancestry, the cases comprise approximately 79% European, 18% African, and 3% other ancestry populations. Single-variant association analyses tested all variants on chromosomes 1-22 and X with a minor allele count ≥ 5. In gene-based analyses, PVs were defined as rare variants (minor allele frequency [MAF] < 1% in controls) that had either a Variant Effect Predictor (VEP) impact score of “high” or a pathogenic or likely pathogenic ClinVar classification. Associations were estimated using Firth logistic regression, adjusting for age and the top ten genetic principal components. Results from individual studies were combined using fixed-effect meta-analysis. In single-variant association analyses, 496 variants reached genome-wide significance (p<5×10-8; MAF>0.02%). Among these, 458 (92%) variants mapped to previously known risk regions, including three rare PVs in HOXB13 (rs138213197), CHEK2 (rs555607708), and FAM111A (rs533676902). Characterization of the remaining 38 variants is ongoing. Gene-based analyses identified significant associations (p<2.4×10-6) for eight genes: HOXB13 (OR=3.7, 95% CI=3.3-4.2), BRCA2 (OR=2.0, 95% CI=1.7-2.3), CHEK2 (OR=1.6, 95% CI=1.5-1.8), ATM (OR=1.6, 95% CI=1.4-1.9), FAM111A (OR=1.4, 95% CI=1.3-1.5), BIK (OR=1.4, 95% CI=1.2-1.6), SAMHD1 (OR=2.1, 95% CI=1.6-2.7), and SMOC2 (OR=3.2, 95% CI=2.0-5.1). All genes except SMOC2 have been previously implicated in PCa susceptibility. Among cancer predisposition and DNA repair genes, nominal associations were also observed for XRCC2 (OR=1.6, 95% CIs=1.2-2.3) and BRCA1 (OR=1.2, 95% CI=1.0-1.4), whereas the association was not significant for PALB2 (OR=1.2, 95% CI=0.9-1.5). These findings reinforce the role of rare germline PVs, particularly in cancer predisposition and DNA repair genes, in PCa susceptibility. As additional studies are incorporated into the Consortium, we expect this work to provide a more comprehensive characterization of the genetic architecture of PCa. Citation Format: Yifan Zhang, Shuyan Cheng, Nicholas Boddicker, Matthew Lebo, Alexander S. Berry, Roni Haas, Ryan Hausler, Tokhir Dadaev, Heena Desai, Alex A. Rodriguez, Ravi K. Madduri, Andrew Hill, Xin Sheng, Susan M. Gundell, Mine Cicek, Penn Medicine Biobank, Olle Melander, Chris R. Gignoux, Isla P. Garraway, Bogdan Pasaniuc, Paul C. Boutros, Matt Oetjens, Adam S. Kibel, Robert J. Klein, Zsofia Kote-Jarai, Fergus J. Couch, Kara N. Maxwell, Burcu F. Darst, David V. Conti, Christopher A. Haiman, Fei Chen. Genetic risk of prostate cancer: Insights from the Prostate Cancer Sequencing Consortium [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(8_Suppl):Abstract nr LB390.

Background/Objectives: Family history is a known risk factor for multiple myeloma (MM) and its precursor condition, monoclonal gammopathy of undetermined significance (MGUS). Previous genome-wide association studies (GWASs) have identified 35 common loci associated with MM risk and 21 associated with MGUS. The objective of this study was to identify less common and rare genetic loci predisposing to MM/MGUS through whole-exome sequencing (WES)-based linkage analysis. Methods:Multipoint linkage analysis was conducted using the Multipoint Engine for Rapid Likelihood Inference (MERLIN) with the Lander–Green algorithm on germline WES data from 79 pedigrees with 2 or more affected relatives (120 MM, 86 MGUS, and 21 unaffected). Genome-wide linkage was evaluated using 12,946 independent single-nucleotide variants (linkage disequilibrium r2 < 0.05). Results: Significant linkage was observed at chromosome 6q22.33–q24.2 by the non-parametric model (logarithm-of-odds (LOD) = 3.3) and suggestive linkage by the dominant parametric model (heterogeneity LOD (HLOD) = 2.5). Fourteen rare variants within this region were prioritized using family-specific partial LOD scores and in silico functional prediction tools. Nine of these variants, REPS1, THEMIS, TAAR6, AHI1, VNN1, VNN3, MTFR2/FAM54A, LAMA2, and PHACTR2, overlapped immune-regulatory regions in blood cell lines and were not previously identified in GWASs. Conclusions: This study demonstrates the utility of applying a linkage analysis framework to familial WES data for identifying genomic regions and candidate genes that may contribute to MM/MGUS predisposition. These findings provide new insight into the inherited risk and etiology of familial MM and MGUS.

Abstract Inherited genetic risk factors play a role in multiple myeloma (MM), yet considerable missing heritability exists. Rare risk variants at genome-wide association study (GWAS) loci are a new avenue to explore. Pleiotropy between lymphoid neoplasms (LNs) has been suggested in family history and genetic studies, but no studies have interrogated sequencing for pleiotropic genes or rare risk variants. Sequencing genetically enriched cases can help discover rarer variants. We analyzed exome sequencing in familial or early-onset MM cases to identify rare, functionally relevant variants near GWAS loci for a range of LNs. A total of 149 distinct and significant LN GWAS loci have been published. We identified six recurrent, rare, potentially deleterious variants within 5 kb of significant GWAS single nucleotide polymorphisms in 75 MM cases. Mutations were observed in BTNL2, EOMES, TNFRSF13B, IRF8, ACOXL and TSPAN32. All six genes replicated in an independent set of 255 early-onset MM or familial MM or precursor cases. Expansion of our analyses to the full length of these six genes resulted in a list of 39 rare and deleterious variants, seven of which segregated in MM families. Three genes also had significant rare variant burden in 733 sporadic MM cases compared with 935 control individuals: IRF8 (P = 1.0 × 10−6), EOMES (P = 6.0 × 10−6) and BTNL2 (P = 2.1 × 10−3). Together, our results implicate six genes in MM risk, provide support for genetic pleiotropy between LN subtypes and demonstrate the utility of sequencing genetically enriched cases to identify functionally relevant variants near GWAS loci.

Abstract Humans vary considerably both in their baseline and activated immune phenotypes. We developed a user-friendly open-access web portal, ImmuneRegulation, that enables users to interactively explore immune regulatory elements that drive cell-type or cohort-specific gene expression levels. ImmuneRegulation currently provides the largest centrally integrated resource on human transcriptome regulation across whole blood and blood cell types, including (i) ∼43,000 genotyped individuals with associated gene expression data from ∼51,000 experiments, yielding genetic variant-gene expression associations on ∼220 million eQTLs; (ii) 14 million transcription factor (TF)-binding region hits extracted from 1945 ChIP-seq studies; and (iii) the latest GWAS catalog with 67,230 published variant-trait associations. Users can interactively explore associations between queried gene(s) and their regulators (cis-eQTLs, trans-eQTLs or TFs) across multiple cohorts and studies. These regulators may explain genotype-dependent gene expression variations and be critical in selecting the ideal cohorts or cell types for follow-up studies or in developing predictive models. Overall, ImmuneRegulation significantly lowers the barriers between complex immune regulation data and researchers who want rapid, intuitive and high-quality access to the effects of regulatory elements on gene expression in multiple studies to empower investigators in translating these rich data into biological insights and clinical applications, and is freely available at https://immuneregulation.mssm.edu.

Abstract Given the frequent and largely incurable occurrence of multiple myeloma, identification of germline genetic mutations that predispose cells to multiple myeloma may provide insight into disease etiology and the developmental mechanisms of its cell of origin, the plasma cell (PC). Here, we identified familial and early-onset multiple myeloma kindreds with truncating mutations in lysine-specific demethylase 1 (LSD1/KDM1A), an epigenetic transcriptional repressor that primarily demethylates histone H3 on lysine 4 and regulates hematopoietic stem cell self-renewal. In addition, we found higher rates of germline truncating and predicted deleterious missense KDM1A mutations in patients with multiple myeloma unselected for family history compared with controls. Both monoclonal gammopathy of undetermined significance (MGUS) and multiple myeloma cells have significantly lower KDM1A transcript levels compared with normal PCs. Transcriptome analysis of multiple myeloma cells from KDM1A mutation carriers shows enrichment of pathways and MYC target genes previously associated with myeloma pathogenesis. In mice, antigen challenge followed by pharmacologic inhibition of KDM1A promoted PC expansion, enhanced secondary immune response, elicited appearance of serum paraprotein, and mediated upregulation of MYC transcriptional targets. These changes are consistent with the development of MGUS. Collectively, our findings show that KDM1A is the first autosomal-dominant multiple myeloma germline predisposition gene providing new insights into its mechanistic roles as a tumor suppressor during post-germinal center B-cell differentiation. Significance: KDM1A is the first germline autosomal dominant predisposition gene identified in multiple myeloma and provides new insights into multiple myeloma etiology and the mechanistic role of KDM1A as a tumor suppressor during post-germinal center B-cell differentiation. Cancer Res; 78(10); 2747–59. ©2018 AACR.

Objective Determinants of the increased risk of diffuse large B-cell lymphoma (DLBCL) in SLE are unclear. Using data from a recent lymphoma genome-wide association study (GWAS), we assessed whether certain lupus-related single nucleotide polymorphisms (SNPs) were also associated with DLBCL. Methods GWAS data on European Caucasians from the International Lymphoma Epidemiology Consortium (InterLymph) provided a total of 3857 DLBCL cases and 7666 general-population controls. Data were pooled in a random-effects meta-analysis. Results Among the 28 SLE-related SNPs investigated, the two most convincingly associated with risk of DLBCL included the CD40 SLE risk allele rs4810485 on chromosome 20q13 (OR per risk allele=1.09, 95% CI 1.02 to 1.16, p=0.0134), and the HLA SLE risk allele rs1270942 on chromosome 6p21.33 (OR per risk allele=1.17, 95% CI 1.01 to 1.36, p=0.0362). Of additional possible interest were rs2205960 and rs12537284. The rs2205960 SNP, related to a cytokine of the tumour necrosis factor superfamily TNFSF4, was associated with an OR per risk allele of 1.07, 95% CI 1.00 to 1.16, p=0.0549. The OR for the rs12537284 (chromosome 7q32, IRF5 gene) risk allele was 1.08, 95% CI 0.99 to 1.18, p=0.0765. Conclusions These data suggest several plausible genetic links between DLBCL and SLE.

Abstract Germline mutations of BRCA1 and BRCA2 underlie joint susceptibility to breast and ovarian cancers, but do not account for all cases. Genome wide association studies show little overlap between common variants associated with breast and ovarian cancer susceptibility, whereas rare variants in genes initially identified as breast cancer susceptibility genes also confer susceptibility to ovarian cancer cases, unselected for breast cancer. Ovarian and breast tumors also are observed in Peutz-Jeghers and possibly in Lynch syndrome. To further elucidate rare germline susceptibilities for breast and ovarian cancer, we have ascertained a unique cohort of 90 BRCA1/2-negative probands with a personal history of breast cancer and ovarian/fallopian tube/peritoneal cancer, unselected for family history. Cases are from the clinical genetics services of Memorial Sloan-Kettering Cancer Center and University of Pennsylvania. Analysis is ongoing using both targeted and exome sequencing approaches. Germline DNA of 69 cases will be subjected to whole exome sequencing. A total of 21 additional cases will undergo targeted sequencing for 28 genes (8 cases) and an extended panel of 54 genes (13 cases). The majority of ovarian cancers are high-grade serous epithelial ovarian carcinoma (n=51), in combination with invasive ductal or DCIS (n=36), invasive lobular (n=4), or unspecified (n=11) breast cancers. The remaining ovarian subgroups comprise other epithelial subtypes; endometrioid (n=9), clear cell (n=3), mucinous and low-grade serous (n=3) and non-epithelial (n=4) or unclassified (n=20), in combination with invasive ductal or DCIS (n=21), invasive lobular (n=5) or unspecified (n=13) breast cancers. Preliminary analysis of the targeted set of genes in 69 cases, reveals 43 predicted pathogenic coding or splice site variants; 17 of which are within PALB2, MSH2, MSH6, BARD1, FANCE, CDKN2A, TP53, SETD2, SIRT1, BRIP1, RAD50, and RAD51D and are not seen in dbSNP138, 1000 Genomes or Exome Variant Server ESP6500 and 26 variants with reported frequencies in MUTYH, MSH6, MLH1, FAM175A, RAD50, UIMC1, JARID2, PHF3, SIRT1, MRE11A, ATM, BRCA2, TP53BP1, CDH1, RAD51D, BRCA1, BABAM1 of less than 1% allele frequency in public databases. Analysis of these variants across and within histologic subtypes is being undertaken to look for specific genotype-phenotype correlations. Analysis of novel candidate genes and pathways by co-segregation, functional analysis, as well as external replication is underway to determine whether shared rare variants, mutated genes, or altered pathways confer cancer susceptibility in this cohort. Germline susceptibility to breast and ovarian cancer is heterogenous and remains a critical research and clinical question. Improved understanding of the susceptibility genetics will allow further examination of the utility of targeted cancer prevention strategies in those who are found to be at risk. Citation Format: Kasmintan A. Schrader, Kara N. Maxwell, Joseph Vijai, Steven Hart, Tinu Thomas, Bradley Wubbenhorst, Lucia Guidugli, Robert Klein, Marina Corines, Liying Zhang, Susan Neuhausen, Jeffrey Weitzel, Namrata Gupta, Larry Norton, Clifford Hudis, Gad Getz, Mark Daly, Steven Lipkin, David Altshuler, Fergus Couch, Katherine Nathanson, Kenneth Offit. Determination of cancer susceptibility in probands with breast and ovarian cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3282. doi:10.1158/1538-7445.AM2014-3282

Abstract Massively parallel next generation sequencing data (NGS) has proved to be useful in identification of rare Mendelian disorders. Efforts are on-going to use NGS for discovery of novel rare variants in common disease etiology. Distinguishing rare private mutations from causal variants remains a major challenge in complex disorders. With decreasing cost, investigators are generating NGS data for common cancers. However, power and other considerations suggest that a consortia approach of pooling data is more likely to succeed where individual efforts fail. Combining data within such consortia brings its own level of challenges. We enumerate these challenges and suggest recommendations based on our experience of combining and harmonizing NGS data on 358 breast cancer samples from four centers; (City of Hope n=8, Mayo Clinic n=221, MSKCC n=96 and University of Pennsylvania n=33). Major concerns were ability of individual datasets to discover known and novel variations, depth of coverage across the exome, and quality of variants. Using pre- and post-processed data, we demonstrate an increased power for detecting variants when using a joint-calling method, quality control (QC) and quality assurance pre- and post-merge, as well as the advantages of combined annotations and filtering procedures. To generate a filtered list of high confident calls, we subset the data on Depth per Allele (DP), Genotyping Quality (GQ), Allele balance, Phred likelihood score (PL) as well as using frequency filters on the alternate allele both within the data and comparing against convenience control summaries. We describe general principles and QC measures to be adopted when NGS data is used from diverse sources, specifically applicable to large consortia. Note: This abstract was not presented at the meeting. Citation Format: Joseph Vijai, Steven Hart, Tinu Thomas, Bradley Wubbenhorst, Lucia Guidugli, Kasmintan Schrader, Kara Maxwell, Lauren Jacobs, Danylo Villano, Robert Klein, Steven Lipkin, Susan Neuhausen, Jeffrey Weitzel, David Altshuler, Fergus Couch, Kenneth Offit, Katherine Nathanson. Harmonization of next generation sequencing data within consortia for gene discovery in familial breast cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2378. doi:10.1158/1538-7445.AM2014-2378

Abstract BRCA1 and BRCA2 (BRCA) testing is uninformative in approximately 80% of families with clinical features of inherited susceptibility to breast cancer. Early identification of the individuals at risk in BRCAx families may lead to enhanced screening and prevention strategies, and potentially improved overall survival as has been seen for carriers of BRCA1/2 mutations. A proportion of these families are likely explained by mutations in other high penetrance and moderate penetrance breast cancer susceptibility genes. Although multi-gene panels are now commercially available for clinical testing, only limited information is available on the spectrum of mutations in breast cancer susceptibility genes in specific subgroups of high-risk breast cancer patients. We performed whole exome sequencing in 278 BRCA1/2 negative individuals with high-risk familial breast cancer, defined as a proband and at least two first or second degree relatives with breast cancer. Samples included 232 individuals from 97 families and 49 unrelated individuals, for a total of 146 independent cases. Data were analyzed for identification of all variants in 18 known high and moderate penetrance breast cancer susceptibility genes, in addition to BRCA1 and BRCA2. Two of the 97 families and one of the 49 singletons (2% of independent cases) were found to have deleterious mutations in high penetrance genes, namely TP53 (2) and CDH1 (1). In addition, nine of 146 independent cases (6%) were found to have deleterious mutations in the other predisposition genes. Mutations in ATM and CHEK2 accounted for seven of the ten moderate penetrance mutations identified; additionally one family had a mutation in BLM, one in MRE11A, and one in PALB2. Furthermore, 15 of 146 independent cases (10%) were found to have variants of unknown significance (VUS) in one of eight genes. There were no deleterious mutations or VUS in eight of the 18 genes. Our data show that significant genetic heterogeneity exists in BRCAx families. Large-scale collaborative efforts will be required to attain sufficient power to understand how to appropriately apply these results clinically in cancer risk evaluation. Citation Format: Kara N. Maxwell, Lucia Guidugli, Kasmintan Schrader, Steven Hart, Vijai Joseph, Tinu Thomas, Xianshu Wang, Bradley Wubbenhorst, Robert Klein, Susan M. Domchek, Csilla Szabo, Susan Neuhausen, Jeffrey Weitzel, Katherine L. Nathanson, Kenneth Offit, Fergus Couch. High and moderate penetrance germline mutations in a number of genes are responsible for a small proportion of familial breast cancer risk in BRCAx families. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1291. doi:10.1158/1538-7445.AM2014-1291

Abstract Purpose: To explore the effects of single-nucleotide polymorphisms (SNP) on pancreatic cancer risk and overall survival (OS). Experimental Design: The germ line DNA of 531 pancreatic cancer cases and 305 healthy controls from a hospital-based study was genotyped at SNPs previously reported to be associated with pancreatic cancer risk or clinical outcome. We analyzed putative risk SNPs for replication of their reported effects on risk and tested for novel effects on OS. Similarly, we analyzed putative survival-associated SNPs for replication of their reported effects on OS and tested for novel effects on risk. Finally, we conducted a genome-wide association study (GWAS) of OS using a subset of 252 cases, with two subsequent validation sets of 261 and 572 patients, respectively. Results: Among seven risk SNPs analyzed, two (rs505922 and rs9543325) were associated with risk (P &lt; 0.05). Among 24 survival-associated SNPs analyzed, one (rs9350) was associated with OS (P &lt; 0.05). No putative risk SNPs or putative survival-associated SNPs were found to be associated with OS or risk, respectively. Furthermore, our GWAS identified a novel SNP [rs1482426, combined stage I and II, P = 1.7 × 10−6, per-allele HR, 1.74; 95% confidence interval (CI), 1.38–2.18] to be putatively associated with OS. Conclusions: The effects of SNPs on pancreatic cancer risk and OS were replicated in our study, although further work is necessary to understand the functional mechanisms underlying these effects. More importantly, the putative association with OS identified by GWAS suggests that GWAS may be useful in identifying SNPs associated with clinical outcome in pancreatic cancer. Clin Cancer Res; 18(14); 3942–51. ©2012 AACR.