Mutation Assessor - Breast Invasive Carcinoma (Primary solid tumor)

Mutation Assessor

Breast Invasive Carcinoma (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/C1VX0DTV

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: 1535
Medium Functional Impact Missense Mutations: 8897
Low Functional Impact Missense Mutations: 9018
Neutral Functional Impact Mutations: 6670

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
PTEN	1	6	2	0	0	3.9050	high	high
MLL3	2	2	15	5	5	1.9950	medium	medium
GIGYF2	4	0	0	1	0	1.0650	low	low
PIK3CA	7	0	46	101	124	1.1400	low	neutral
NCOA3	8	0	4	2	2	1.8875	low	medium
RBMX	10	0	0	0	1	-0.9750	neutral	neutral
RUNX1	11	0	6	1	0	3.1850	medium	medium
PIK3R1	12	0	4	2	1	2.3400	medium	medium
MEF2A	13	0	0	1	0	1.7650	low	low
NCOR2	14	0	0	3	0	1.1800	low	low

Methods & Data

Input

BRCA-TP.maf.annotated
BRCA-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 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)

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