We conducted the largest investigation of predisposition variants in cancer to date, discovering 853 pathogenic or likely pathogenic variants in 8% of 10,389 cases from 33 cancer types. Twenty-one genes showed single or cross-cancer associations, including novel associations of SDHA in melanoma and PALB2 in stomach adenocarcinoma. The 659 predisposition variants and 18 additional large deletions in tumor suppressors, including ATM, BRCA1, and NF1, showed low gene expression and frequent (43%) loss of heterozygosity or biallelic two-hit events. We also discovered 33 such variants in oncogenes, including missenses in MET, RET, and PTPN11 associated with high gene expression. We nominated 47 additional predisposition variants from prioritized VUSs supported by multiple evidences involving case-control frequency, loss of heterozygosity, expression effect, and co-localization with mutations and modified residues. Our integrative approach links rare predisposition variants to functional consequences, informing future guidelines of variant classification and germline genetic testing in cancer.

Highlights d This large analysis identified ancestry correlates in cancer d Ancestry-associated artifacts and confounders were identified d Ancestry effects are profoundly tissue specific d Rates of FBXW7, VHL, and PBRM1 mutations and immune activity vary by ancestry

BackgroundThe American College of Medical Genetics and American College of Pathologists (ACMG/AMP) variant classification guidelines for clinical reporting are widely used in diagnostic laboratories for variant interpretation. The ACMG/AMP guidelines recommend complete concordance of predictions among all in silico algorithms used without specifying the number or types of algorithms. The subjective nature of this recommendation contributes to discordance of variant classification among clinical laboratories and prevents definitive classification of variants.ResultsUsing 14,819 benign or pathogenic missense variants from the ClinVar database, we compared performance of 25 algorithms across datasets differing in distinct biological and technical variables. There was wide variability in concordance among different combinations of algorithms with particularly low concordance for benign variants. We also identify a previously unreported source of error in variant interpretation (false concordance) where concordant in silico predictions are opposite to the evidence provided by other sources. We identified recently developed algorithms with high predictive power and robust to variables such as disease mechanism, gene constraint, and mode of inheritance, although poorer performing algorithms are more frequently used based on review of the clinical genetics literature (2011–2017).ConclusionsOur analyses identify algorithms with high performance characteristics independent of underlying disease mechanisms. We describe combinations of algorithms with increased concordance that should improve in silico algorithm usage during assessment of clinically relevant variants using the ACMG/AMP guidelines.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-017-1353-5) contains supplementary material, which is available to authorized users.

Previous studies of rare germline variants in cancer has largely been limited to the coding regions of known predisposition genes. The TCGA PanCanAtlas Germline Working Group is analyzing germline predisposing variants of 10,389 cases in 33 cancer types. We deployed more than 121,000 virtual machines running for over 600,000 hours on the ISB Cancer Genome Cloud to conduct massively parallel variant calling and analyses, and the resulting data are shared with scientists across institutions worldwide. Carriers of the functional regulatory variants add on to the 8.9% of cases carrying known pathogenic variants. Burden analyses reveal enrichment of rare variants in the 3'UTR region of NHP2 and POLH. Further, we observed variants aggregating in conserved regions of selected microRNA families that are also affected by somatic mutations, including mir-17 and mir-29. We nominate regulatory variants by using GWAVA and FunSeq2 corroborated with their enrichment in cancer. The prioritized variants are then further evaluated by further co-occurrence of two-hit events and expression changes in their respective tumor samples. Finally, we examine ancestries, familial history and age at onset for carriers of these variants. Overall, we aim to discover and establish the role of regulatory germline variants in oncogenesis. Citation Format: Kuan-lin Huang, Amila Weerasinghe, Yige Wu, Wen-wei Liang, R. Jay Mashl, Sheila Reynolds, Kathleen E. Houlahan, Ninad Oak, The Cancer Genome Atlas, Alexander J. Lazar, Michael C. Wendel, Ekta Khurana, Sharon Plon, Feng Chen, Mark Gerstein, Ilya Shmulevich, Li Ding. Regulatory germline variants in 10,389 adult cancers [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 5359.