Mutation Assessor
Rectum Adenocarcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C12F7MH0
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Mutation Assessor. Broad Institute of MIT and Harvard. doi:10.7908/C12F7MH0
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: 980

  • Medium Functional Impact Missense Mutations: 5783

  • Low Functional Impact Missense Mutations: 5693

  • Neutral Functional Impact Mutations: 3853

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
APC 1 0 6 33 12 1.2450 low low
CRIPAK 2 0 0 0 6 0.0000 neutral neutral
TP53 3 0 73 7 1 3.0750 medium medium
MUC4 4 0 0 0 2 -0.6625 neutral neutral
TCF7L2 5 0 5 0 1 2.4575 medium medium
FMN2 6 0 1 2 1 1.5025 low low
NRAS 9 5 6 0 0 3.3250 medium medium
CDH1 10 2 5 2 1 2.5950 medium medium
ARID1A 11 0 1 4 1 1.3150 low low
SMAD2 13 1 5 0 0 2.7250 medium medium
Methods & Data
Input

  1. READ-TP.maf.annotated

  2. 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 READ-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)
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