Mutation Assessor - Glioblastoma Multiforme (Primary solid tumor)

Mutation Assessor

Glioblastoma Multiforme (Primary solid tumor)

15 January 2014 | analyses__2014_01_15

Maintainer Information

Citation Information

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/C1FB51BB

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: 887
Medium Functional Impact Missense Mutations: 4563
Low Functional Impact Missense Mutations: 4167
Neutral Functional Impact Mutations: 3296

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
EGFR	1	21	54	7	2	3.0600	medium	medium
PIK3R1	2	1	12	1	0	2.7100	medium	medium
BRAF	3	1	0	5	0	1.3200	low	low
IDH1	4	14	0	0	0	4.5400	high	high
TP53	5	0	75	2	0	3.0750	medium	medium
PTEN	6	17	25	5	0	2.9600	medium	medium
PIK3CA	7	0	13	7	4	1.9600	medium	medium
RB1	8	0	0	1	0	0.8300	low	low
NF1	9	0	3	0	1	2.3950	medium	medium
STAG2	10	0	2	0	0	2.2275	medium	medium

Methods & Data

Input

GBM-TP.maf.annotated
GBM-TP.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 GBM-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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