Mutation Assessor - Brain Lower Grade Glioma (Primary solid tumor)

Mutation Assessor

Brain Lower Grade Glioma (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/C1TT4Q6P

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: 1819
Medium Functional Impact Missense Mutations: 8046
Low Functional Impact Missense Mutations: 7558
Neutral Functional Impact Mutations: 5532

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
IDH1	1	398	0	0	0	4.5400	high	high
TP53	2	0	244	2	1	3.1450	medium	medium
CIC	4	16	29	17	4	3.3200	medium	medium
NOTCH1	5	10	10	0	5	3.1550	medium	high/medium
IDH2	6	20	0	0	0	3.9000	high	high
PIK3R1	7	0	6	1	0	2.7100	medium	medium
FUBP1	8	0	0	0	1	0.7050	neutral	neutral
NF1	9	0	7	2	2	2.2500	medium	medium
ARID1A	10	0	4	2	2	1.9375	medium	medium
PTEN	11	7	6	2	0	3.4600	medium	high

Methods & Data

Input

LGG-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 LGG-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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