Mutation Assessor
Rectum Adenocarcinoma (Primary solid tumor)
17 October 2014 | analyses__2014_10_17
Maintainer Information
Citation Information
doi:10.7908/C1ZW1JVX
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Mutation Assessor. Broad Institute of MIT and Harvard. doi:10.7908/C1ZW1JVX
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: 588

- Medium Functional Impact Missense Mutations: 3650

- Low Functional Impact Missense Mutations: 3698

- Neutral Functional Impact Mutations: 2298

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 | 0 | 8 | 1 | 1.3900 | low | low
TP53 | 2 | 0 | 31 | 1 | 0 | 3.1300 | medium | medium
KRAS | 3 | 5 | 29 | 2 | 0 | 3.2450 | medium | medium
SMAD4 | 4 | 0 | 8 | 0 | 0 | 3.0800 | medium | medium
FBXW7 | 5 | 1 | 3 | 2 | 2 | 1.8525 | low | medium
NRAS | 6 | 3 | 2 | 0 | 0 | 3.5300 | high | high
TCF7L2 | 9 | 0 | 3 | 0 | 0 | 2.6400 | medium | medium
ERBB2 | 10 | 0 | 0 | 1 | 3 | 0.6350 | neutral | neutral
KIAA1804 | 11 | 0 | 6 | 3 | 2 | 2.0150 | medium | medium
CCDC88C | 12 | 0 | 0 | 1 | 0 | 1.6100 | low | low

Methods & Data
Input

1. READ-TP.maf.annotated

2. sig_genes.txt

3. Mutation Assessor Scores Release 2:

- hg18

- hg19

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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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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