Mutation Assessor
Head and Neck Squamous Cell Carcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
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/C1JQ1013
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: 3701

  • Medium Functional Impact Missense Mutations: 19395

  • Low Functional Impact Missense Mutations: 20020

  • Neutral Functional Impact Mutations: 14283

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
TP53 1 0 231 11 2 3.0750 medium medium
CDKN2A 2 2 1 10 1 0.9750 low low
PIK3CA 3 0 15 55 23 1.4450 low low
NSD1 4 7 11 6 5 2.5100 medium medium
CASP8 5 6 17 1 3 3.1850 medium medium
HRAS 6 11 12 7 1 3.2550 medium medium
TGFBR2 7 4 4 7 1 1.7400 low low
FAT1 8 12 11 5 4 2.6425 medium high
NOTCH1 9 18 21 6 7 3.0100 medium medium
EPHA2 10 1 4 4 0 2.4250 medium medium/low
Methods & Data
Input
  1. HNSC-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 HNSC-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)