Mutation Assessor
Kidney Renal Clear Cell Carcinoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Mutation Assessor. Broad Institute of MIT and Harvard. doi:10.7908/C16Q1WGK
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: 890

  • Medium Functional Impact Missense Mutations: 4649

  • Low Functional Impact Missense Mutations: 4797

  • Neutral Functional Impact Mutations: 3505

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 4 1 1 3.9225 high high
PBRM1 2 5 12 7 1 2.4900 medium medium
VHL 4 0 61 25 12 2.1350 medium medium
MTOR 5 6 21 2 1 3.0725 medium medium
TP53 6 0 7 0 0 3.1050 medium medium
PTEN 7 3 2 0 0 3.8800 high high
NEFH 8 0 1 0 0 2.2350 medium medium
NF2 9 0 0 1 0 0.9550 low low
ATM 10 1 4 2 0 2.4200 medium medium
PIK3CA 12 0 1 4 4 1.5250 low low/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)