Mutation Assessor - Pan-kidney cohort (KICH+KIRC+KIRP) (Primary solid tumor)

Mutation Assessor

Pan-kidney cohort (KICH+KIRC+KIRP) (Primary solid tumor)

21 August 2015 | analyses__2015_08_21

Maintainer Information

Citation Information

Maintained by David Heiman (Broad Institute)

Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Mutation Assessor. Broad Institute of MIT and Harvard. doi:10.7908/C11C1W3N

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: 1548
Medium Functional Impact Missense Mutations: 8363
Low Functional Impact Missense Mutations: 8617
Neutral Functional Impact Mutations: 6811

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
FAM18B2	1	0	0	0	35	-3.3350	neutral	neutral
TP53	2	0	25	2	0	3.0200	medium	medium
PTEN	3	3	2	2	2	2.2500	medium	high
SETD2	5	8	5	1	2	3.5850	high	high
PBRM1	6	5	13	8	2	2.4675	medium	medium
NEFH	7	0	2	7	1	1.8950	low	low
VHL	8	0	62	25	13	2.1350	medium	medium
MET	9	0	12	2	4	2.0850	medium	medium
HNRNPM	10	0	1	2	0	1.4450	low	low
NF2	11	0	0	1	0	0.9550	low	low

Methods & Data

Input

KIPAN-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 KIPAN-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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