Mutation Assessor
Thymoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
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/C1SN08GB
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: 104

  • Medium Functional Impact Missense Mutations: 415

  • Low Functional Impact Missense Mutations: 416

  • Neutral Functional Impact Mutations: 376

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 GTF2I 0 0 0 48 0.345 neutral neutral
2 HRAS 7 2 0 0 4.610 high high
4 TP53 0 3 0 0 3.145 medium medium
7 NRAS 2 1 0 0 3.730 high high
9 PLEKHG4B 0 0 0 1 0.550 neutral neutral
10 UNC93B1 0 0 0 5 0.550 neutral neutral
13 MUC21 0 0 0 2 0.460 neutral neutral
14 JPH3 0 0 2 0 0.805 low low
15 TMEM63B 0 1 0 0 2.790 medium medium
16 MYH14 0 0 1 0 1.115 low low
Methods & Data
Input
  1. THYM-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 THYM-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)