Karchin, Rachel

Background Missense (amino-acid changing) variants found in cancer predisposition genes often create difficulties when clinically interpreting genetic testing results. Although bioinformatics has developed approaches to predicting the impact of these variants, many of these approaches have not been readily applicable in the clinical setting. Bioinformatics approaches for predicting the impact of these variants have not yet found their footing in clinical practice because 1) interpreting the medical relevance of predictive scores is difficult; 2) the relationship between bioinformatics “predictors” (sequence conservation, protein structure) and cancer susceptibility is not understood. Methodology/Principal Findings We present a computational method that produces a probabilistic likelihood ratio predictive of whether a missense variant impairs protein function. We apply the method to a tumor suppressor gene, BRCA2, whose loss of function is important to cancer susceptibility. Protein likelihood ratios are computed for 229 unclassified variants found in individuals from high-risk breast/ovarian cancer families. We map the variants onto a protein structure model, and suggest that a cluster of predicted deleterious variants in the BRCA2 OB1 domain may destabilize BRCA2 and a protein binding partner, the small acidic protein DSS1. We compare our predictions with variant “re-classifications” provided by Myriad Genetics, a biotechnology company that holds the patent on BRCA2 genetic testing in the U.S., and with classifications made by an established medical genetics model [ 1 ]. Our approach uses bioinformatics data that is independent of these genetics-based classifications and yet shows significant agreement with them. Preliminary results indicate that our method is less likely to make false positive errors than other bioinformatics methods, which were designed to predict the impact of missense mutations in general. Conclusions/Significance Missense mutations are the most common disease-producing genetic variants. We present a fast, scalable bioinformatics method that integrates information about protein sequence, conservation, and structure in a likelihood ratio that can be integrated with medical genetics likelihood ratios. The protein likelihood ratio, together with medical genetics likelihood ratios, can be used by clinicians and counselors to communicate the relevance of a VUS to the individual who has that VUS. The approach described here is generalizable to regions of any tumor suppressor gene that have been structurally determined by X-ray crystallography or for which a protein homology model can be built.

Abstract Pancreatic cancer is projected to become the second leading cause of cancer-related death in the United States by 2020. A familial aggregation of pancreatic cancer has been established, but the cause of this aggregation in most families is unknown. To determine the genetic basis of susceptibility in these families, we sequenced the germline genomes of 638 patients with familial pancreatic cancer and the tumor exomes of 39 familial pancreatic adenocarcinomas. Our analyses support the role of previously identified familial pancreatic cancer susceptibility genes such as BRCA2, CDKN2A, and ATM, and identify novel candidate genes harboring rare, deleterious germline variants for further characterization. We also show how somatic point mutations that occur during hematopoiesis can affect the interpretation of genome-wide studies of hereditary traits. Our observations have important implications for the etiology of pancreatic cancer and for the identification of susceptibility genes in other common cancer types. Significance: The genetic basis of disease susceptibility in the majority of patients with familial pancreatic cancer is unknown. We whole genome sequenced 638 patients with familial pancreatic cancer and demonstrate that the genetic underpinning of inherited pancreatic cancer is highly heterogeneous. This has significant implications for the management of patients with familial pancreatic cancer. Cancer Discov; 6(2); 166–75. ©2015 AACR. This article is highlighted in the In This Issue feature, p. 109

Abstract Germ line inactivating mutations in BRCA1 confer susceptibility for breast and ovarian cancer. However, the relevance of the many missense changes in the gene for which the effect on protein function is unknown remains unclear. Determination of which variants are causally associated with cancer is important for assessment of individual risk. We used a functional assay that measures the transactivation activity of BRCA1 in combination with analysis of protein modeling based on the structure of BRCA1 BRCT domains. In addition, the information generated was interpreted in light of genetic data. We determined the predicted cancer association of 22 BRCA1 variants and verified that the common polymorphism S1613G has no effect on BRCA1 function, even when combined with other rare variants. We estimated the specificity and sensitivity of the assay, and by meta-analysis of 47 variants, we show that variants with <45% of wild-type activity can be classified as deleterious whereas variants with >50% can be classified as neutral. In conclusion, we did functional and structure-based analyses on a large series of BRCA1 missense variants and defined a tentative threshold activity for the classification missense variants. By interpreting the validated functional data in light of additional clinical and structural evidence, we conclude that it is possible to classify all missense variants in the BRCA1 COOH-terminal region. These results bring functional assays for BRCA1 closer to clinical applicability. [Cancer Res 2007;67(4):1494–501]