Mutation Assessor - Lung Adenocarcinoma (Primary solid tumor)

Mutation Assessor

Lung Adenocarcinoma (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/C1VD6XVM

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: 8489
Medium Functional Impact Missense Mutations: 38146
Low Functional Impact Missense Mutations: 37594
Neutral Functional Impact Mutations: 27102

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	169	12	3	3.1450	medium	medium
2	KRAS	4	155	5	0	3.1350	medium	medium
3	KEAP1	25	33	10	3	3.0150	medium	medium
4	EGFR	2	32	5	12	2.9550	medium	medium
5	STK11	17	4	1	4	4.2450	high	high
7	RB1	0	3	2	0	2.0150	medium	medium
8	ARID1A	0	8	7	1	1.7625	low	medium
9	NF1	0	13	10	7	1.5550	low	medium
10	SMARCA4	7	12	5	1	2.4900	medium	medium
11	BRAF	14	5	11	4	2.7925	medium	high

Methods & Data

Input

LUAD-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 LUAD-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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