Mutation Assessor
Liver Hepatocellular Carcinoma
14 July 2016  |  awg_lihc__2016_07_14
Maintainer Information
Citation Information
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Mutation Assessor. Broad Institute of MIT and Harvard. doi:10.7908/C1M32V8C
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: 1840

  • Medium Functional Impact Missense Mutations: 9545

  • Low Functional Impact Missense Mutations: 9642

  • Neutral Functional Impact Mutations: 7339

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.

MutSig
Rank
Gene 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
1 TP53 0 65 2 0 3.1400 medium medium
2 CTNNB1 0 96 1 1 2.4600 medium medium
3 AXIN1 2 1 0 1 3.0700 medium high
4 RB1 0 1 2 2 1.2450 low low/neutral
5 ARID1A 0 4 1 3 1.8975 low medium
6 BAP1 2 2 1 0 3.0700 medium high/medium
7 CDC27 0 0 3 2 1.5850 low low
8 CDKN2A 0 0 3 1 0.8500 low low
9 KRT2 0 1 0 1 1.5100 low medium/neutral
10 NFE2L2 0 12 0 0 2.8650 medium medium
Methods & Data
Input
  1. LIHC.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 LIHC.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)