Mutation Assessor
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/C1Z60N88
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: 2630

  • Medium Functional Impact Missense Mutations: 12257

  • Low Functional Impact Missense Mutations: 11402

  • Neutral Functional Impact Mutations: 8555

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 1 412 0 0 0 4.540 high high
TP53 2 0 319 4 1 3.145 medium medium
CIC 4 16 29 17 4 3.320 medium medium
NF1 5 0 10 2 3 2.340 medium medium
PIK3R1 6 1 18 2 0 2.710 medium medium
NOTCH1 7 10 10 0 5 3.155 medium high/medium
IDH2 8 20 0 0 0 3.900 high high
FUBP1 9 0 0 0 1 0.705 neutral neutral
RB1 10 0 0 1 0 1.750 low low
TCF12 11 0 1 0 0 1.995 medium medium
Methods & Data
Input
  1. GBMLGG-TP.maf.annotated

  2. sig_genes.txt

  3. Mutation Assessor Scores Release 2:

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 GBMLGG-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)