Mutation Assessor - Breast Invasive Carcinoma (Primary solid tumor)

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

BRCA-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 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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