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
  1. OV-TP.maf.annotated

  2. OV-TP.sig_genes.txt

  3. Mutation Assessor Scores Release 2:

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)