Cancer-specific High-throughput Annotation of Somatic Mutations: computational prediction of driver missense mutations

By Hannah Carter, Sining Chen, Leyla Isik, Svitlana Tyekucheva, Victor E. Velculescu, Kenneth W. Kinzler, Bert Vogelstein, Rachel Karchin. Cancer Research, 2009

457 citations recorded by the source corpus.

Large-scale sequencing of cancer genomes has uncovered thousands of DNA alterations, but the functional relevance of the majority of these mutations to tumorigenesis is unknown. We have developed a computational method, called CHASM (Cancer-specific High-throughput Annotation of Somatic Mutations), to identify and prioritize those missense mutations most likely to generate functional changes that enhance tumor cell proliferation. The method has high sensitivity and specificity when discriminating between known driver missense mutations and randomly generated missense mutations (area under ROC curve > 0.91, area under Precision-Recall curve > 0.79). CHASM substantially outperformed previously described missense mutation function prediction methods at discriminating known oncogenic mutations in TP53 and the tyrosine kinase EGFR. We applied the method to 607 missense mutations found in a recent glioblastoma multiforme sequencing (GBM) study. Based on a model that assumed the GBM mutations are a mixture of drivers and passengers, we estimate that 8% of these mutations are drivers, causally contributing to tumorigenesis.

Subfields: Cancer Research, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/28f713c02af0effe8dcbd06bee82f5dc1ab6405e

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Citations in the corpus

Mutual exclusivity analysis identifies oncogenic network modules. (2012, background): “…methods explicitly do not take recurrence into account—for example, machine learning methods based on prior known cancer-causing mutations have been successfully trained to classify and predict the functional consequences of somatic missense mutations (Kaminker et al. 2007; Carter et al. 2009).”
Integrative and comparative genomic analysis of HPV-positive and HPV-negative head and neck squamous cell carcinomas (2014, methodology): “We used an established machine learning–based approach— Cancer-Specific High-Throughput Annotation of Somatic Mutations (CHASM)—to predict and prioritize missense mutations leading to functional changes and, thus, likely driving tumorigenesis (24).”
Comprehensive Characterization of Cancer Driver Genes and Mutations (2018, methodology): “CHASM scores (precompute version 3.0) were retrieved from the CRAVAT web server (v4.”
WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation (2013, methodology): “These methods are mainly based on statistical and/or machine learning approaches that take as input information from protein sequence [18,19,23,24,26,28,29], structure [20,25] and function [19,21,27].”
Network-based stratification of tumor mutations (2013, methodology): “Missense mutations were scored using three methods: CHASM 13 , VEST 44 and MutationAssessor 43 .”
Identifying Mendelian disease genes with the Variant Effect Scoring Tool (2013, methodology): “We use a statistical hypothesis testing framework to assess the significance of each score and adjust for multiple testing as described previously [28].”
Ranking non-synonymous single nucleotide polymorphisms based on disease concepts (2014, background): “The sole context in which disease-specific predictions have been developed is in the prediction of cancer-associated mutations [8-11].”
Assessment of computational methods for predicting the effects of missense mutations in human cancers (2013, methodology): “CHASM (cancer-specific high-throughput annotation of somatic mutations) [31] is a major machine learning approach that uses a random forest approach and is trained on cancer mutations from COSMIC and other cancer-related resources.”