Mutation Assessor
Pheochromocytoma and Paraganglioma (Primary solid tumor)
17 October 2014 | analyses__2014_10_17
Maintainer Information
Citation Information
doi:10.7908/C1NV9H5F
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:

- 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 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.

- 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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