Mutation Assessor - Colorectal Adenocarcinoma (Primary solid tumor)

Mutation Assessor

Colorectal Adenocarcinoma (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/C1736P9V

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: 3455
Medium Functional Impact Missense Mutations: 20746
Low Functional Impact Missense Mutations: 20504
Neutral Functional Impact Mutations: 13860

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
APC	1	0	5	22	9	1.3725	low	low
FBXW7	2	5	15	10	8	2.0575	medium	medium
NRAS	3	10	10	1	0	3.3250	medium	high/medium
BRAF	4	0	1	22	0	1.3200	low	low
KRAS	5	12	78	4	0	3.2450	medium	medium
TP53	6	0	82	3	0	3.1450	medium	medium
SMAD4	7	2	22	2	0	2.7900	medium	medium
FAM123B	8	0	4	1	3	1.7800	low	medium
PIK3CA	9	0	19	14	7	1.6850	low	medium
SMAD2	10	3	7	2	0	2.9125	medium	medium

Methods & Data

Input

COADREAD-TP.maf.annotated
COADREAD-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 COADREAD-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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