Mutation Assessor
Kidney Renal Clear Cell Carcinoma (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/C1M044TK
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.

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 SETD2 8 4 1 1 3.9225 high high
2 PBRM1 5 12 7 1 2.4900 medium medium
4 VHL 0 61 25 12 2.1350 medium medium
5 MTOR 6 21 2 1 3.0725 medium medium
6 TP53 0 7 0 0 3.1050 medium medium
7 PTEN 3 2 0 0 3.8800 high high
8 NEFH 0 1 0 0 2.2350 medium medium
9 NF2 0 0 1 0 0.9550 low low
10 ATM 1 4 2 0 2.4200 medium medium
12 PIK3CA 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)