Mutation Assessor
Adrenocortical 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/C14X56RV
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: 402

  • Medium Functional Impact Missense Mutations: 2086

  • Low Functional Impact Missense Mutations: 2336

  • Neutral Functional Impact Mutations: 2353

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
ZFPM1 1 0 0 4 0 1.870 low low
LACTB 2 0 0 0 18 -0.550 neutral neutral
CCDC102A 3 0 0 0 17 -2.415 neutral neutral
ZNF517 4 0 0 13 1 1.080 low low
CLDN23 6 0 0 10 0 0.805 low low
TOR3A 7 0 0 0 12 -1.590 neutral neutral
USP42 8 0 0 0 16 0.000 neutral neutral
TP53 9 0 5 0 0 3.145 medium medium
APOE 10 0 2 0 6 -1.700 neutral neutral
ZAR1 11 0 0 0 11 -0.345 neutral neutral
Methods & Data
Input
  1. ACC-TP.maf.annotated

  2. sig_genes.txt

  3. Mutation Assessor Scores Release 2:

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)