Mutation Assessor
Head and Neck Squamous Cell Carcinoma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
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/C1WH2NM0
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: 2172

  • Medium Functional Impact Missense Mutations: 11880

  • Low Functional Impact Missense Mutations: 12293

  • Neutral Functional Impact Mutations: 8640

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
PIK3CA 1 0 11 40 13 1.6200 low low
CDKN2A 2 0 1 4 1 1.2825 low low
HRAS 3 3 5 2 1 2.8300 medium medium
TP53 4 0 136 6 1 3.1050 medium medium
NFE2L2 5 0 18 0 0 2.8650 medium medium
NOTCH1 6 14 13 4 5 3.0275 medium high
NSD1 7 4 4 2 4 2.7175 medium high/medium/neutral
FAT1 8 8 5 3 3 2.6050 medium high
CASP8 9 3 6 1 0 3.2625 medium medium
JUB 10 4 1 0 0 4.5500 high high
Methods & Data
Input
  1. HNSC-TP.maf.annotated

  2. HNSC-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 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)