Lebo, Matthew

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.

Testing asymptomatic individuals for unsuspected conditions is not new to the medical and public health communities and protocols to develop screening tests are well-established. However, the application of screening principles to inherited diseases presents unique challenges. Unlike most screening tests, the natural history and disease prevalence of most rare inherited diseases in an unselected population are unknown. It is difficult or impossible to obtain a “truth set” cohort for clinical validation studies. As a result, it is not possible to accurately calculate clinical positive and negative predictive values for “likely pathogenic” genetic variants, which are commonly returned in genetic screening assays. In addition, many of the genetic conditions included in screening panels do not have clinical confirmatory tests. All of these elements are typically required to justify the development of a screening test, according to the World Health Organization screening principles. Nevertheless, as the cost of DNA sequencing continues to fall, more individuals are opting to undergo genomic testing in the absence of a clinical indication. Despite the challenges, reasonable estimates can be deduced and used to inform test design strategies. Here, we review test design principles and apply them to genetic screening.

Testing asymptomatic individuals for unsuspected conditions is not new to the medical and public health communities and protocols to develop screening tests are well-established. However, the application of screening principles to inherited diseases presents unique challenges. Unlike most screening tests, the natural history and disease prevalence of most rare inherited diseases in an unselected population are unknown. It is difficult or impossible to obtain a “truth set” cohort for clinical validation studies. As a result, it is not possible to accurately calculate clinical positive and negative predictive values for “likely pathogenic” genetic variants, which are commonly returned in genetic screening assays. In addition, many of the genetic conditions included in screening panels do not have clinical confirmatory tests. All of these elements are typically required to justify the development of a screening test, according to the World Health Organization screening principles. Nevertheless, as the cost of DNA sequencing continues to fall, more individuals are opting to undergo genomic testing in the absence of a clinical indication. Despite the challenges, reasonable estimates can be deduced and used to inform test design strategies. Here, we review test design principles and apply them to genetic screening.

Testing asymptomatic individuals for unsuspected conditions is not new to the medical and public health communities and protocols to develop screening tests are well-established. However, the application of screening principles to inherited diseases presents unique challenges. Unlike most screening tests, the natural history and disease prevalence of most rare inherited diseases in an unselected population are unknown. It is difficult or impossible to obtain a “truth set” cohort for clinical validation studies. As a result, it is not possible to accurately calculate clinical positive and negative predictive values for “likely pathogenic” genetic variants, which are commonly returned in genetic screening assays. In addition, many of the genetic conditions included in screening panels do not have clinical confirmatory tests. All of these elements are typically required to justify the development of a screening test, according to the World Health Organization screening principles. Nevertheless, as the cost of DNA sequencing continues to fall, more individuals are opting to undergo genomic testing in the absence of a clinical indication. Despite the challenges, reasonable estimates can be deduced and used to inform test design strategies. Here, we review test design principles and apply them to genetic screening.

As the cost of DNA sequencing continues to fall, more individuals are opting to undergo genomic testing in the absence of a clinical indication. Testing asymptomatic individuals for unsuspected conditions is not new to the medical and public health communities. However, the application of screening principles to inherited diseases with unknown prevalence in an unselected (low-risk) test population raises unique challenges. This paper examines the effect of disease prevalence on the positive predictive value of a test result. Many inherited conditions have very low prevalence in an unselected population, which increases the probability that some likely pathogenic variants may be false positives. In situations where the adverse impact of a false positive result is significant, laboratories should address this issue by either increasing the interpretive specificity of the test, performing a clinical confirmatory test to establish the presence of disease, or restricting the test to a population with increased disease prevalence. Here, we review the statistical concepts relevant to screening tests, apply these concepts to genetic disease screening, create a model to estimate prevalence and positive predictive value, and provide a framework for further discussion.