Mutation Assessor
Skin Cutaneous Melanoma (Metastatic)
15 July 2014  |  analyses__2014_07_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/C1PV6J6G
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: 8113

  • Medium Functional Impact Missense Mutations: 41097

  • Low Functional Impact Missense Mutations: 41139

  • Neutral Functional Impact Mutations: 28381

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
NRAS 1 42 40 0 0 3.5300 high high
TP53 2 0 27 0 1 3.1675 medium medium
CDKN2A 3 0 0 4 0 1.3950 low low
MRPS31 5 0 1 1 0 2.1075 medium medium/low
NF1 6 2 8 5 3 2.2025 medium medium
RAC1 7 1 8 9 2 1.7700 low low
ARID2 8 0 9 6 5 1.6425 low medium
C15orf23 9 0 0 5 14 0.0000 neutral neutral
PTEN 10 4 5 0 1 3.1800 medium medium
NOTCH2NL 11 0 2 1 5 0.2125 neutral neutral
Methods & Data
Input
  1. SKCM-TM.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 SKCM-TM.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)