Abstract 537: Information-driven approaches to predicting familial risk for prostate cancer (2015)

Abstract Given the known genetic heterogeneity of prostate cancer, risk assessment for this disease is a field that is likely to be positively impacted by improved interpretation of genetic variants identified through exome sequencing. At present, there is insufficient evidence to base decisions regarding screening, diagnosis and management of prostate cancer on genetic tests. While a few genes (such as BRCA2 and HOXB13) are definitively linked to prostate cancer risk, they are not found in sizeable patient populations. However, a growing number of candidates for prostate cancer risk have been proposed. As information resources including the TCGA and other consortium databases expand, clinicians and patients will face issues with how to interpret genetic data variant regarding cancer risk. The goal of the present work is to develop a paradigm that addresses the plausible scenario of a clinician seeking to provide prostate cancer genetic risk assessment for patients with personal and/or a family history of one or more types of cancer, but for whom access to relative specimens for sequencing analysis is highly limited. In particular, we focus on the development of an operational framework through which to select and evaluate germ-line variants which may contribute to familial cancer risk. Our study evaluates the current potential of integrated informatics resources to help identify clinically informative variants following exome sequencing of germ-line DNA from 12 prostate cancer patients with elevated risk of prostate or other cancers based on family history. We explored variants in genes meeting specific criteria: 1) genes thought to be relevant to hereditary prostate cancer, or experimentally validated as regulating the growth of prostate tumors in cell or animal models; 2) genes affected by somatic changes in cancer; 3) genes involved in DNA damage repair (DDR) including recently described androgen-regulated DDR genes; and 4) genes involved in disorders of glycosylation. We first filtered exome data based on the observed frequency of gene variants in the general population. We then utilized five independent in silico predictors and expert analysis of protein structure and interactions to identify function-disrupting rare variants. We described an average of 4 potentially disruptive variants in each individual and annotated them in the context of the human variation data accumulated over the last few years and represented in various public databases. Novel variants were found in PALB2, RAD54L2, HSD3B1, NRIP1, SCN11A, CYPBP1, SULT1E1 and UBE2D3. Molecular modeling of a p.(S221N) mutation in the steroid binding site of the aldo-keto reductase AKR1C1 showed it would be predicted to disrupt the active site, influencing androgen metabolism. Our study highlights the need and potential for well-curated databases of variants with clinical relevance that will ultimately facilitate germ-line exome testing in the clinical setting. Citation Format: Emmanuelle Nicolas, Yan Zhou, Ilya G. Serebriiskii, Mark D. Andrake, Elizabeth A. Handorf, Roland L. Dunbrack, Veda N. Giri, Eric A. Ross, Erica A. Golemis, Michael J. Hall, Mary B. Daly. Information-driven approaches to predicting familial risk for prostate cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 537. doi:10.1158/1538-7445.AM2015-537