Mutation Assessor - Cholangiocarcinoma (Primary solid tumor)

Mutation Assessor

Cholangiocarcinoma (Primary solid tumor)

28 January 2016 | analyses__2016_01_28

Maintainer Information

Citation Information

Maintained by David Heiman (Broad Institute)

Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Mutation Assessor. Broad Institute of MIT and Harvard. doi:10.7908/C1639P4F

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: 245
Medium Functional Impact Missense Mutations: 1082
Low Functional Impact Missense Mutations: 1129
Neutral Functional Impact Mutations: 878

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.

MutSig Rank	Gene	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
2	HLA-B	1	1	0	3	-1.2400	neutral	neutral
3	MLL3	0	1	0	1	1.3075	low	medium/neutral
4	FTH1	3	0	0	0	3.7750	high	high
5	TP53	0	3	1	0	3.2100	medium	medium
6	ARID1A	0	0	1	0	1.0400	low	low
7	DDHD1	0	0	0	4	0.6900	neutral	neutral
8	MUC2	0	0	1	0	1.6250	low	low
9	IDH1	4	0	0	0	4.5400	high	high
10	MUC21	0	0	0	3	-0.2050	neutral	neutral
11	CLIP4	0	0	1	1	1.0625	low	low/neutral

Methods & Data

Input

CHOL-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 CHOL-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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