Abstract
Motivation: The number of missense mutations being identified in cancer genomes has greatly increased as a consequence of technological advances and the reduced cost of whole-genome/whole-exome sequencing methods. However, a high proportion of the amino acid substitutions detected in cancer genomes have little or no effect on tumour progression (passenger mutations). Therefore, accurate automated methods capable of discriminating between driver (cancer-promoting) and passenger mutations are becoming increasingly important. In our previous work, we developed the Functional Analysis through Hidden Markov Models (FATHMM) software and, using a model weighted for inherited disease mutations, observed improved performances over alternative computational prediction algorithms. Here, we describe an adaptation of our original algorithm that incorporates a cancer-specific model to potentiate the functional analysis of driver mutations.Results: The performance of our algorithm was evaluated using two separate benchmarks. In our analysis, we observed improved performances when distinguishing between driver mutations and other germ line variants (both disease-causing and putatively neutral mutations). In addition, when discriminating between somatic driver and passenger mutations, we observed performances comparable with the leading computational prediction algorithms: SPF-Cancer and TransFIC.Availability and implementation: A web-based implementation of our cancer-specific model, including a downloadable stand-alone package, is available at http://fathmm.biocompute.org.uk.Contact: fathmm@biocompute.org.ukSupplementary information: Supplementary data are available at Bioinformatics online.
Highlights
Human cancers are characterized by the accumulation of somatic mutations, e.g. gross insertions and deletions, as well as the more subtle single base pair substitutions (Iengar, 2012), some of which confer a growth advantage on the tumour cells (Hanahan and Weinberg, 2011)
The Capritotti and Altman (2011) benchmark comprises three mutation datasets: the cancer and neutral only (CNO) mutation dataset assesses the performance of computational prediction algorithms when tasked with discriminating between driver mutations and neutral polymorphisms; the cancer, neutral and other disease (CND) mutation dataset is used to evaluate the performance of computational prediction
We described an adaptation to the Functional Analysis through Hidden Markov Models (FATHMM) algorithm (Shihab et al, 2013) in which a cancer-specific weighting scheme was incorporated to potentiate the functional analysis of driver mutations
Summary
Human cancers are characterized by the accumulation of somatic mutations, e.g. gross insertions and deletions, as well as the more subtle single base pair substitutions (Iengar, 2012), some of which confer a growth advantage on the tumour cells (Hanahan and Weinberg, 2011). The identification of cancerpromoting AASs (driver mutations) promises to lead to a better understanding of the molecular mechanisms underlying the disease, as well as providing potential diagnostic and therapeutic markers (Furney et al, 2006). This remains a major challenge, as the majority of AASs detected in cancer genomes do not contribute to carcinogenesis; rather, these ‘passenger mutations’ are a consequence of tumorigenesis rather than a cause (Greenman et al, 2007). Several cancer-specific computational prediction algorithms capable of distinguishing between driver mutations and other germ line variants (both disease-causing and putatively neutral mutations) and/or capable of discriminating between somatic driver and passenger mutations have been developed (Carter et al, 2009; Gonzalez-Perez et al, 2012; Kaminker et al, 2007b; Reva et al, 2011)
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