Mutation Assessor - Kidney Renal Clear Cell Carcinoma (Primary solid tumor)

Mutation Assessor

Kidney Renal Clear Cell Carcinoma (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/C1T43RHV

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: 979
Medium Functional Impact Missense Mutations: 5198
Low Functional Impact Missense Mutations: 5383
Neutral Functional Impact Mutations: 3983

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
BAP1	1	6	3	2	3	2.7200	medium	high
VHL	2	0	64	27	12	2.1350	medium	medium
SETD2	3	8	7	3	2	3.0800	medium	high
PBRM1	4	5	11	7	1	2.4925	medium	medium
KDM5C	5	3	6	1	0	3.3275	medium	medium
PTEN	6	4	1	0	0	3.8800	high	high
TSPAN19	7	0	0	1	2	0.6950	neutral	neutral
TCEB1	8	1	2	0	0	3.1700	medium	medium
NEFH	9	0	1	0	0	2.2350	medium	medium
FAM200A	10	0	4	1	1	2.1650	medium	medium

Methods & Data

Input

KIRC-TP.maf.annotated
KIRC-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 KIRC-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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