Mutation Assessor - Pheochromocytoma and Paraganglioma (Primary solid tumor)

Mutation Assessor

Pheochromocytoma and Paraganglioma (Primary solid tumor)

15 July 2014 | analyses__2014_07_15

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/C1W66JKQ

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

PCPG-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 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)

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