Mutation Assessor - Head and Neck Squamous Cell Carcinoma (Primary solid tumor)

Mutation Assessor

Head and Neck Squamous Cell Carcinoma (Primary solid tumor)

15 January 2014 | analyses__2014_01_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/C11Z42T4

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

HNSC-TP.maf.annotated
HNSC-TP.sig_genes.txt
Mutation Assessor Scores Release 2:

hg18
hg19

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)

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