Mutation Assessor
Kidney Chromophobe (Primary solid tumor)
15 January 2014  |  analyses__2014_01_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/C1JS9NXB
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: 175

  • Medium Functional Impact Missense Mutations: 1122

  • Low Functional Impact Missense Mutations: 1168

  • Neutral Functional Impact Mutations: 1315

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
MUC6 2 0 6 18 11 1.2400 low low
TP53 3 0 15 2 0 3.0200 medium medium
PTEN 4 0 0 2 0 1.4550 low low
PRSS3 5 0 0 2 2 0.4275 neutral low/neutral
HLA-C 7 0 2 3 2 1.5950 low low
TAS2R43 8 0 0 2 3 -1.0550 neutral neutral
TAS2R30 9 0 1 1 2 0.8000 neutral neutral
FAM86B1 12 0 1 1 0 1.5200 low medium/low
HLA-DRB5 13 0 0 2 5 -1.0150 neutral neutral
ADAM21 14 0 1 0 1 -0.4350 neutral medium/neutral
Methods & Data
Input
  1. KICH-TP.maf.annotated

  2. KICH-TP.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 KICH-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)