Mutation Assessor - Thymoma (Primary solid tumor)

Mutation Assessor

Thymoma (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/C1SN08GB

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: 104
Medium Functional Impact Missense Mutations: 415
Low Functional Impact Missense Mutations: 416
Neutral Functional Impact Mutations: 376

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	GTF2I	0	0	0	48	0.345	neutral	neutral
2	HRAS	7	2	0	0	4.610	high	high
4	TP53	0	3	0	0	3.145	medium	medium
7	NRAS	2	1	0	0	3.730	high	high
9	PLEKHG4B	0	0	0	1	0.550	neutral	neutral
10	UNC93B1	0	0	0	5	0.550	neutral	neutral
13	MUC21	0	0	0	2	0.460	neutral	neutral
14	JPH3	0	0	2	0	0.805	low	low
15	TMEM63B	0	1	0	0	2.790	medium	medium
16	MYH14	0	0	1	0	1.115	low	low

Methods & Data

Input

THYM-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 THYM-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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