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

Mutation Assessor

Kidney Renal Clear Cell Carcinoma (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/C1M044TK

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: 890
Medium Functional Impact Missense Mutations: 4649
Low Functional Impact Missense Mutations: 4797
Neutral Functional Impact Mutations: 3505

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	SETD2	8	4	1	1	3.9225	high	high
2	PBRM1	5	12	7	1	2.4900	medium	medium
4	VHL	0	61	25	12	2.1350	medium	medium
5	MTOR	6	21	2	1	3.0725	medium	medium
6	TP53	0	7	0	0	3.1050	medium	medium
7	PTEN	3	2	0	0	3.8800	high	high
8	NEFH	0	1	0	0	2.2350	medium	medium
9	NF2	0	0	1	0	0.9550	low	low
10	ATM	1	4	2	0	2.4200	medium	medium
12	PIK3CA	0	1	4	4	1.5250	low	low/neutral

Methods & Data

Input

KIRC-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 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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