Mutation Assessor - Stomach Adenocarcinoma (Primary solid tumor)

Mutation Assessor

Stomach Adenocarcinoma (Primary solid tumor)

21 August 2015 | analyses__2015_08_21

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/C13R0S5B

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: 4922
Medium Functional Impact Missense Mutations: 27758
Low Functional Impact Missense Mutations: 27205
Neutral Functional Impact Mutations: 20198

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
TP53	1	0	80	5	2	3.0200	medium	medium
ARID1A	2	0	7	9	2	1.6800	low	low
RNF43	3	0	1	2	3	0.8675	low	neutral
MLL4	4	1	2	6	7	0.8575	low	neutral
XYLT2	5	0	5	2	1	2.2075	medium	medium
LARP4B	6	1	1	1	2	1.3850	low	neutral
BZRAP1	7	0	2	3	8	0.6900	neutral	neutral
B2M	8	2	1	0	0	4.1650	high	high
KRAS	9	2	22	4	0	3.2450	medium	medium
HLA-B	10	5	3	0	0	4.2975	high	high

Methods & Data

Input

STAD-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 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)

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