Mutation Assessor
Stomach Adenocarcinoma (Primary solid tumor)
23 September 2013  |  analyses__2013_09_23
Maintainer Information
Citation Information
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Mutation Assessor. Broad Institute of MIT and Harvard. doi:10.7908/C13X851V
Overview
Introduction

This report serves to summarize the functional impact of missense mutations in each gene as determined by Mutation Assessor[1].

Summary
  • High Functional Impact Missense Mutations: 4260

  • Medium Functional Impact Missense Mutations: 24056

  • Low Functional Impact Missense Mutations: 23657

  • Neutral Functional Impact Mutations: 17665

Results
Functional Impact by Gene

Table 1.  Get Full Table A gene-level breakdown of missense mutation functional impact, ordered by MutSig rank. Includes missense mutation counts broken down by level of functional impact (high, medium, low, neutral), median functional impact score and level, and most common level(s) of functional impact (mode) per gene.

Gene MutSig
Rank
High
Functional Impact
Count
Medium
Functional Impact
Count
Low
Functional Impact
Count
Neutral
Functional Impact
Count
Median
Functional Impact
Score
Median
Functional Impact
Level
Mode
Functional Impact
Level
TRIM48 1 0 0 1 13 -0.6650 neutral neutral
PIK3CA 2 0 24 26 11 1.6200 low low
PGM5 3 0 19 4 0 2.9450 medium medium
KRAS 4 2 20 3 0 3.2450 medium medium
CBWD1 5 0 1 1 0 1.7400 low medium/low
TP53 6 0 60 5 0 3.0000 medium medium
ARID1A 7 0 7 7 2 1.7625 low medium/low
SMAD4 8 6 10 0 0 3.4850 medium medium
RHOA 9 4 9 1 0 3.1100 medium medium
MXRA8 10 0 1 3 0 1.7475 low low
Methods & Data
Input
  1. STAD-TP.maf.annotated

  2. STAD-TP.sig_genes.txt

  3. Mutation Assessor Scores Release 2:

A lookup is done against the relevant Mutation Assessor Scores table for each missense mutation in a given MAF file, and available functional impact score and level are appended as two new columns to generate STAD-TP.maf.annotated. These are summarized in Table 1, sorted by MutSig rank.

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.

References
[1] Boris Reva, Yevgeniy Antipin, and Chris Sander, Predicting the functional impact of protein mutations: application to cancer genomics, Nucl. Acids Res. 39(17):e118 (2011)