Mutation Assessor - Prostate Adenocarcinoma (Primary solid tumor)

Mutation Assessor

Prostate Adenocarcinoma (Primary solid tumor)

23 September 2013 | analyses__2013_09_23

Maintainer Information

Citation Information

Maintained by David Heiman (Broad Institute)

Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Mutation Assessor. Broad Institute of MIT and Harvard. doi:10.7908/C1S75DQW

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: 181
Medium Functional Impact Missense Mutations: 1065
Low Functional Impact Missense Mutations: 978
Neutral Functional Impact Mutations: 999

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
ZNF285	1	0	4	0	0	2.7575	medium	medium
NKX3-1	2	1	2	0	1	2.4775	medium	medium
SLC2A6	3	2	0	0	0	3.9350	high	high
CLSTN1	4	0	3	0	0	2.7550	medium	medium
YBX1	5	0	0	4	0	1.4175	low	low
CCNF	6	0	3	0	0	2.0850	medium	medium
SPOP	7	1	2	1	0	2.7050	medium	medium
NDUFS4	8	0	3	0	0	2.2600	medium	medium
AGT	9	0	0	0	3	0.3450	neutral	neutral
SETD5	10	0	1	3	0	1.5450	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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