Mutation Assessor - Colorectal Adenocarcinoma (Primary solid tumor)

Mutation Assessor

Colorectal Adenocarcinoma (Primary solid tumor)

02 April 2015 | analyses__2015_04_02

Maintainer Information

Citation Information

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/C1NK3D1C

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: 7667
Medium Functional Impact Missense Mutations: 43846
Low Functional Impact Missense Mutations: 42042
Neutral Functional Impact Mutations: 29942

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	51	183	90	1.0600	low	low
TP53	2	0	291	35	16	2.9675	medium	medium
ARID1A	3	0	21	22	9	1.6000	low	low
RNF43	4	0	1	8	8	0.8950	low	low/neutral
CRIPAK	5	0	0	0	13	0.0000	neutral	neutral
SOX9	6	4	2	1	1	3.4450	medium	high
MUC4	7	0	6	3	10	0.5800	neutral	neutral
B2M	8	1	4	2	0	2.5050	medium	medium
ZFP36L2	9	0	0	1	0	1.8300	low	low
ACVR1B	11	6	4	9	4	1.7650	low	low

Methods & Data

Input

COADREAD-TP.maf.annotated
sig_genes.txt
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 COADREAD-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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