Mutation Assessor
Kidney Renal Clear Cell Carcinoma (Primary solid tumor)
23 September 2013  |  analyses__2013_09_23
Maintainer Information
Citation Information
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Mutation Assessor. Broad Institute of MIT and Harvard. doi:10.7908/C1VH5M44
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: 1001

  • Medium Functional Impact Missense Mutations: 5314

  • Low Functional Impact Missense Mutations: 5507

  • Neutral Functional Impact Mutations: 4312

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
TPSD1 1 0 0 0 7 0.5550 neutral neutral
NEFH 2 0 1 0 0 2.2350 medium medium
VHL 3 0 64 27 14 2.1350 medium medium
SETD2 4 8 7 3 2 3.0800 medium high
PBRM1 5 5 11 7 1 2.4925 medium medium
BAP1 6 6 3 2 3 2.7200 medium high
KDM5C 7 3 6 1 0 3.3275 medium medium
PTEN 8 4 1 0 0 3.8800 high high
TSPAN19 9 0 0 1 2 0.6950 neutral neutral
TCEB1 10 1 2 0 0 3.1700 medium medium
Methods & Data
Input
  1. KIRC-TP.maf.annotated

  2. KIRC-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 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)