CHASM is a method that predicts the functional significance of somatic missense mutations observed in the genomes of cancer cells, allowing mutations to be prioritized in subsequent functional studies, based on the probability that they give the cells a selective survival advantage .
There are 8140 mutations identified by MuTect and 410 mutations with significant functional impact at BHFDR <= 0.25.
CHASM uses a machine learning method called Random Forest that learns to distinguish between driver and passenger somatic missense mutations, based on a training set of labeled positive (driver) and negative (passenger) examples. The positive class of driver mutations was curated from the COSMIC database and the negative class is composed of synthetic passenger mutations. CHASM annotates mutations with amino acid substitution properties, alignment-based estimates of conservation at the mutated position. The user provides a list of mutations for prediction as well as a table describing the passenger mutation spectrum for the tumor type in which the mutations were observed .
In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.