Mutation Assessor - Lung Adenocarcinoma (Primary solid tumor)

Mutation Assessor

Lung Adenocarcinoma (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/C1028PZM

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: 5040
Medium Functional Impact Missense Mutations: 21588
Low Functional Impact Missense Mutations: 20554
Neutral Functional Impact Mutations: 14485

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
CDKN2A	1	0	0	6	0	0.9350	low	low
KRAS	2	4	58	0	0	3.1350	medium	medium
TP53	3	0	76	4	2	3.1575	medium	medium
EGFR	4	1	15	3	6	2.7900	medium	medium
STK11	5	8	2	0	0	4.6175	high	high
KEAP1	6	8	18	10	0	2.5850	medium	medium
RBM10	7	2	0	0	0	3.8750	high	high
NF1	8	0	4	5	6	1.4300	low	neutral
GPR112	9	0	2	26	24	0.8050	low	low
SMARCA4	10	2	1	2	3	1.4625	low	neutral

Methods & Data

Input

LUAD-TP.maf.annotated
LUAD-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 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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