Mutation Assessor - Liver Hepatocellular Carcinoma (Primary solid tumor)

Mutation Assessor

Liver Hepatocellular 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/C14Q7SS1

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: 2655
Medium Functional Impact Missense Mutations: 14450
Low Functional Impact Missense Mutations: 15166
Neutral Functional Impact Mutations: 13615

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
TP53	1	0	41	1	1	3.1400	medium	medium
CTNNB1	2	0	54	2	0	2.4600	medium	medium
ARID1A	3	0	5	1	2	1.9525	medium	medium
RB1	4	0	1	2	2	1.8700	low	low/neutral
AXIN1	5	1	2	0	1	2.8425	medium	medium
KRTAP5-11	6	0	1	0	0	2.1750	medium	medium
AHCTF1	7	0	0	3	3	0.5500	neutral	low/neutral
GPATCH4	8	0	0	1	0	0.8950	low	low
CD207	9	0	0	4	0	0.9850	low	low
EEF1A1	10	3	2	2	0	2.1350	medium	high

Methods & Data

Input

LIHC-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 LIHC-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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