Mutation Assessor - Adrenocortical Carcinoma (Primary solid tumor)

Mutation Assessor

Adrenocortical Carcinoma (Primary solid tumor)

15 July 2014 | analyses__2014_07_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/C1XS5T3J

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: 421
Medium Functional Impact Missense Mutations: 2159
Low Functional Impact Missense Mutations: 2428
Neutral Functional Impact Mutations: 2457

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
ZFPM1	1	0	5	0	1.870	low	low
LACTB	2	0	0	19	-0.550	neutral	neutral
CCDC102A	3	0	0	17	-2.415	neutral	neutral
ZNF517	4	0	13	1	1.080	low	low
TOR3A	5	0	0	12	-1.590	neutral	neutral
USP42	6	0	0	17	0.000	neutral	neutral
CLDN23	7	0	10	0	0.805	low	low
TP53	8	5	0	0	3.145	medium	medium
KCNK17	9	0	0	9	0.460	neutral	neutral
APOE	11	2	0	6	-1.700	neutral	neutral

Methods & Data

Input

ACC-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 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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