Mutation Assessor
Cholangiocarcinoma (Primary solid tumor)
21 August 2015 | analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C1M32TXG
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/C1M32TXG
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.

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

Methods & Data
Input

1. CHOL-TP.maf.annotated

2. sig_genes.txt

3. 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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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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