Abstract 4258: Benchmarking algorithms for mutation impact prediction using functionally validated missense mutations

Background: Massively parallel sequencing studies have demonstrated that there are few highly recurrently mutated genes in tumors and that there are vast numbers of mutations found in a minority of cancers from a given anatomical site. Numerous bioinformatics tools to predict the effect of a given amino acid substitution on protein function are available, and there is no consensus as to which prediction algorithms are the most appropriate for the identification of driver mutations. Here we sought to compare the performance, as standalone or in combination, of nine algorithms predictive of the impact of a mutation on protein function, when challenged against mutations whose pathogenic role is supported or refuted by in vitro or in vivo experiments, or have been shown to be pathogenic in the context of familial cancer syndromes. Methods: We mined the literature and databases and compiled known mutations for six oncogenes (BRAF, KIT, PIK3CA, KRAS, EGFR, ERRB2) and three tumor suppressors (TP53, BRCA1, BRCA2). We applied Boolean logic to identify experimental and clinical evidence of functional effects for each mutation. A total of 3072 missense mutations were subjected to analysis using nine mutation impact predictor algorithms, namely CHASM lung/breast/melanoma, FATHMM cancer/missense, Mutation Assessor, MutationTaster, PolyPhen-2, Condel, Provean, VEST and SIFT. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated and the agreement between predictors evaluated. Furthermore, predictions made by combinations of algorithms were assessed. Results: The PPV of all predictors was relatively high (median 0.941, range 0.909-0.989). Their sensitivity, specificity and NPV, however, varied widely. The median sensitivity was 0.854 (range 0.632-0.987), with the best performers being FATHMM cancer, CHASM lung and melanoma. The median specificity was 0.667 (range 0.482-0.939), with CHASM breast performing best. Inter-predictor agreement was only fair-to-moderate for most pair-wise comparisons (kappa scores ranging from 0.187 to 0.826). Finally, 358/4653 combinations performed better than each of the algorithms tested alone; 163/358 displayed higher NPVs and 131/358 improved sensitivity than the highest single algorithm9s NPV and sensitivity, i.e. FATHMM cancer. The combination CHASM/FATHMM cancer/MutationTaster showed excellent performance, with sensitivity, specificity, PPV and NPV of 0.988, 0.824, 0.974 and 0.913, respectively. Conclusions: Our results revealed discrepancies in performance of mutation impact predictors. No algorithm, on its own, was able to distinguish accurately driver from passenger events. Combining algorithms that provide orthogonal information likely results in substantial improvements in the functional predictions. Citation Format: Luciano G. Martelotto, Yan Zhang, Charlotte K.Y. Ng, Salvatore Piscuoglio, Jorge S. Reis-Filho, Britta Weigelt. Benchmarking algorithms for mutation impact prediction using functionally validated missense mutations. [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 4258. doi:10.1158/1538-7445.AM2014-4258