Mutation Assessor
Cholangiocarcinoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
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/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:

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)