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

Mutation Assessor

Head and Neck Squamous 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/C1W37VQK

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: 3731
Medium Functional Impact Missense Mutations: 19951
Low Functional Impact Missense Mutations: 20681
Neutral Functional Impact Mutations: 14595

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	TP53	0	231	12	2	3.0750	medium	medium
2	CDKN2A	1	1	9	1	0.9750	low	low
3	NSD1	7	11	6	5	2.5100	medium	medium
4	CASP8	6	17	1	2	3.2075	medium	medium
5	MLL2	2	10	10	8	1.3350	low	medium/low
6	TGFBR2	4	5	7	1	2.0450	medium	low
7	HLA-B	4	4	3	0	2.9050	medium	high/medium
8	FBXW7	3	10	1	8	2.2850	medium	medium
9	NOTCH1	19	24	8	7	2.8750	medium	medium
10	FAT1	12	10	5	3	2.7275	medium	high

Methods & Data

Input

HNSC-TP.maf.annotated
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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