Mutation Assessor - Kidney Chromophobe (Primary solid tumor)

Mutation Assessor

Kidney Chromophobe (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/C19Z943X

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: 163
Medium Functional Impact Missense Mutations: 1079
Low Functional Impact Missense Mutations: 1105
Neutral Functional Impact Mutations: 1112

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
TP53	1	0	15	2	0	3.0200	medium	medium
PTEN	2	0	0	2	0	1.4550	low	low
ZNF814	3	0	1	1	2	0.1900	neutral	neutral
CDC27	4	1	0	1	4	0.6225	neutral	neutral
PABPC1	5	0	2	2	0	1.4400	low	medium/low
AMAC1L3	6	0	0	1	5	0.6950	neutral	neutral
FAM26F	9	0	2	0	0	2.2875	medium	medium
GFM1	10	0	0	0	1	0.0000	neutral	neutral
PABPC3	11	0	3	2	4	1.0500	low	neutral
URGCP	12	0	1	0	0	2.6750	medium	medium

Methods & Data

Input

KICH-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 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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