Mutation Assessor - Prostate Adenocarcinoma (Primary solid tumor)

Mutation Assessor

Prostate Adenocarcinoma (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/C1348HTC

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: 675
Medium Functional Impact Missense Mutations: 3122
Low Functional Impact Missense Mutations: 2895
Neutral Functional Impact Mutations: 2253

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
CTNNB1	1	0	9	0	0	2.460	medium	medium
SPOP	2	4	17	2	0	2.520	medium	medium
PTEN	3	1	2	0	0	3.400	medium	medium
PCDHAC2	4	4	9	7	7	1.565	low	medium
FOXA1	5	1	3	3	0	2.290	medium	medium/low
TP53	6	0	15	0	0	3.070	medium	medium
IDH1	9	4	0	0	0	4.540	high	high
MED12	11	0	4	1	0	2.195	medium	medium
SLC10A2	12	1	0	2	1	1.110	low	low
BRAF	13	2	0	3	0	1.700	low	low

Methods & Data

Input

PRAD-TP.maf.annotated
PRAD-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 PRAD-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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