Mutation Assessor - Adrenocortical Carcinoma (Primary solid tumor)

Mutation Assessor

Adrenocortical Carcinoma (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/C1VD6WZJ

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: 298
Medium Functional Impact Missense Mutations: 1534
Low Functional Impact Missense Mutations: 1530
Neutral Functional Impact Mutations: 1256

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	Medium Functional Impact Count	Low Functional Impact Count	Neutral Functional Impact Count	Median Functional Impact Score	Median Functional Impact Level	Mode Functional Impact Level
CTNNB1	1	9	0	0	2.0100	medium	medium
MUC5B	2	0	2	14	-0.4550	neutral	neutral
CRIPAK	3	0	0	11	0.0000	neutral	neutral
TP53	4	5	0	0	3.2400	medium	medium
KRTAP4-5	7	2	0	2	1.3100	low	medium/neutral
GPR111	8	3	0	0	2.2050	medium	medium
ATN1	9	0	0	4	0.6900	neutral	neutral
DMKN	10	0	3	2	0.9750	low	low
MSH3	11	0	0	4	-0.1725	neutral	neutral
AIM1	12	0	6	2	1.2800	low	low

Methods & Data

Input

ACC-TP.maf.annotated
ACC-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 ACC-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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