Mutation Assessor
Breast Invasive Carcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Mutation Assessor. Broad Institute of MIT and Harvard. doi:10.7908/C1HQ3XWX
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: 2643

  • Medium Functional Impact Missense Mutations: 15064

  • Low Functional Impact Missense Mutations: 15196

  • Neutral Functional Impact Mutations: 10471

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
PIK3CA 1 0 62 133 150 1.2700 low neutral
TP53 2 0 169 4 3 3.1150 medium medium
CDH1 3 7 6 2 3 3.1550 medium high
GATA3 4 1 2 3 3 1.3900 low low/neutral
MAP3K1 5 4 1 10 2 1.6100 low low
RUNX1 6 0 7 4 0 3.1250 medium medium
PTEN 7 7 6 2 0 3.4600 medium high
ARID1A 8 0 4 3 2 1.7800 low medium
CBFB 9 0 9 2 1 2.2625 medium medium
MLL3 10 3 8 11 6 1.6550 low low
Methods & Data
Input
  1. BRCA-TP.maf.annotated

  2. 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 BRCA-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)