Mutation Assessor
Brain Lower Grade Glioma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_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/C1251GZ4
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: 663

  • Medium Functional Impact Missense Mutations: 2348

  • Low Functional Impact Missense Mutations: 2114

  • Neutral Functional Impact Mutations: 1634

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 1 220 0 0 0 4.5400 high high
TP53 2 0 148 1 1 3.1450 medium medium
CIC 4 6 11 12 1 2.3150 medium low
NOTCH1 5 7 5 0 3 3.1700 medium high
IDH2 6 12 0 0 0 3.9000 high high
FUBP1 7 0 0 0 1 0.7050 neutral neutral
NF1 8 0 3 0 1 2.2950 medium medium
PIK3R1 9 0 5 1 0 2.5925 medium medium
PTEN 11 3 6 0 0 3.1050 medium medium
PIK3CA 12 0 9 5 6 1.5325 low medium
Methods & Data
Input
  1. LGG-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 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)