Mutation Assessor
Pheochromocytoma and Paraganglioma (Primary solid tumor)
17 October 2014  |  analyses__2014_10_17
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/C1NV9H5F
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: 108

  • Medium Functional Impact Missense Mutations: 585

  • Low Functional Impact Missense Mutations: 539

  • Neutral Functional Impact Mutations: 439

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
HRAS 1 3 15 0 0 3.155 medium medium
EPAS1 2 0 8 0 0 2.140 medium medium
NF1 3 0 0 1 0 1.665 low low
RET 4 0 3 4 0 1.630 low low
GPR128 6 0 0 1 3 0.620 neutral neutral
FAM83D 9 0 0 1 1 0.795 neutral low/neutral
LILRB5 12 0 1 0 0 1.955 medium medium
OSBPL6 13 1 0 0 0 4.130 high high
MAP3K4 14 0 0 0 3 0.550 neutral neutral
PRMT1 15 0 1 0 0 3.430 medium medium
Methods & Data
Input
  1. PCPG-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 PCPG-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)