Mutation Assessor - Ovarian Serous Cystadenocarcinoma (Primary solid tumor)

Mutation Assessor

Ovarian Serous Cystadenocarcinoma (Primary solid tumor)

28 January 2016 | analyses__2016_01_28

Maintainer Information

Citation Information

Maintained by David Heiman (Broad Institute)

Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Mutation Assessor. Broad Institute of MIT and Harvard. doi:10.7908/C1TT4QCX

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: 1017
Medium Functional Impact Missense Mutations: 5406
Low Functional Impact Missense Mutations: 5358
Neutral Functional Impact Mutations: 3823

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.

MutSig Rank	Gene	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
1	TP53	0	248	6	1	3.1050	medium	medium
2	RB1	0	2	1	2	1.0850	low	medium/neutral
3	BRCA1	1	0	0	0	4.3700	high	high
4	NF1	1	5	1	0	2.2500	medium	medium
5	CDK12	1	2	3	0	1.6500	low	low
6	KRAS	0	5	0	0	3.1350	medium	medium
7	HNF1B	0	2	1	0	2.3600	medium	medium
8	PTEN	1	0	1	0	2.8325	medium	high/low
10	BRCA2	0	0	2	2	1.1800	low	low/neutral
11	EFEMP1	1	1	0	2	1.1100	low	neutral

Methods & Data

Input

OV-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 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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