Mutation Assessor - Kidney Chromophobe (Primary solid tumor)

Mutation Assessor

Kidney Chromophobe (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/C1JS9NXB

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: 175
Medium Functional Impact Missense Mutations: 1122
Low Functional Impact Missense Mutations: 1168
Neutral Functional Impact Mutations: 1315

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	Medium Functional Impact Count	Low Functional Impact Count	Neutral Functional Impact Count	Median Functional Impact Score	Median Functional Impact Level	Mode Functional Impact Level
MUC6	2	6	18	11	1.2400	low	low
TP53	3	15	2	0	3.0200	medium	medium
PTEN	4	0	2	0	1.4550	low	low
PRSS3	5	0	2	2	0.4275	neutral	low/neutral
HLA-C	7	2	3	2	1.5950	low	low
TAS2R43	8	0	2	3	-1.0550	neutral	neutral
TAS2R30	9	1	1	2	0.8000	neutral	neutral
FAM86B1	12	1	1	0	1.5200	low	medium/low
HLA-DRB5	13	0	2	5	-1.0150	neutral	neutral
ADAM21	14	1	0	1	-0.4350	neutral	medium/neutral

Methods & Data

Input

KICH-TP.maf.annotated
KICH-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 KICH-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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