CHASM 1.0.5 (Cancer-Specific High-throughput Annotation of Somatic Mutations)
Uterine Carcinosarcoma (Primary solid tumor)
02 April 2015 | analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1FX78K0
Maintained by Spring Yingchun Liu (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): CHASM 1.0.5 (Cancer-Specific High-throughput Annotation of Somatic Mutations). Broad Institute of MIT and Harvard. doi:10.7908/C1FX78K0
Overview
Introduction

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 [2].

Summary

There are 5812 mutations identified by MuTect and 401 mutations with significant functional impact at BHFDR <= 0.25.

Results
Top Mutations

Table 1. Get Full Table Top mutations with significant funcational impact.
MutationID: mutation seen in sample.
Mutation: mutation seen in gene and AA variant.
CHASM: CHASM score.
BHFDR: Benjamini-Hochberg False Discovery Rate.

MutationID | Mutation | CHASM | PValue | BHFDR
--- | --- | --- | --- | ---
TCGA-ND-A4WC-01A-21D-A28R-08 | NM_007313.2_Y331H | 0.19 | 0 | 0.05
TCGA-N8-A4PO-01A-11D-A28R-08 | NM_004304.3_P968S | 0.17 | 0 | 0.05
TCGA-ND-A4WC-01A-21D-A28R-08 | NM_000051.3_L2952I | 0.23 | 0.001 | 0.05
TCGA-ND-A4WC-01A-21D-A28R-08 | NM_005881.2_F217V | 0.24 | 0.001 | 0.05
TCGA-N5-A4RN-01A-12D-A28R-08 | NM_000097.5_R391G | 0.22 | 0.001 | 0.05

Methods & Data
Input

UCS-TP.final_analysis_set.maf

About CHASM

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 [1].

Download Results

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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

References
[1] About CHASM (2013)
[2] CHASM wiki (2013)
[3] Wong WC, CHASM and SNVBox: toolkit for detecting biologically important single nucleotide mutations, Bioinformatics 27(15):2147-8 (2011)
[4] Carter H, Cancer-specific high-throughput annotation of somatic mutations: computational prediction of driver missense mutations, Cancer Research 69(16):6660-7 (2009)
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