Mutation Assessor - Ovarian Serous Cystadenocarcinoma (Primary solid tumor)

Mutation Assessor

Ovarian Serous Cystadenocarcinoma (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/C11J9869

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: 775
Medium Functional Impact Missense Mutations: 4355
Low Functional Impact Missense Mutations: 4456
Neutral Functional Impact Mutations: 3201

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	173	5	0	3.0825	medium	medium
SRC	3	3	0	0	1	4.4800	high	high
RB1	5	0	1	1	2	0.9200	low	neutral
POTED	6	0	1	1	1	1.5000	low	medium/low/neutral
BRCA1	7	1	0	0	0	4.3700	high	high
C9orf171	8	0	0	3	1	1.7975	low	low
CDK12	9	1	2	1	0	2.4675	medium	medium
LGR6	10	0	0	1	0	1.6400	low	low
GABRA6	11	0	5	0	1	2.1750	medium	medium
SLC4A9	12	0	3	0	0	3.4550	medium	medium

Methods & Data

Input

OV-TP.maf.annotated
OV-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 OV-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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