Mutation Assessor
Kidney Renal Clear Cell Carcinoma (Primary solid tumor)
17 October 2014  |  analyses__2014_10_17
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/C1NV9H40
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: 950

  • Medium Functional Impact Missense Mutations: 4972

  • Low Functional Impact Missense Mutations: 5141

  • Neutral Functional Impact Mutations: 3759

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
SETD2 1 8 7 3 2 3.0800 medium high
BAP1 2 6 3 2 3 2.7200 medium high
PTEN 3 4 1 0 0 3.8800 high high
PBRM1 4 5 11 7 1 2.4925 medium medium
VHL 6 0 64 27 12 2.1350 medium medium
FAM200A 7 0 4 1 1 2.1650 medium medium
TP53 8 0 8 0 0 3.0375 medium medium
NEFH 9 0 1 0 0 2.2350 medium medium
MTOR 11 6 19 1 0 3.2050 medium medium
GUSB 12 2 1 1 2 1.9900 medium high/neutral
Methods & Data
Input
  1. KIRC-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 KIRC-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)