Mutation Assessor - Skin Cutaneous Melanoma (Metastatic)

Mutation Assessor

Skin Cutaneous Melanoma (Metastatic)

15 January 2014 | analyses__2014_01_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/C1B56H7H

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: 8373
Medium Functional Impact Missense Mutations: 42446
Low Functional Impact Missense Mutations: 42680
Neutral Functional Impact Mutations: 29794

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
C15orf23	1	0	0	5	14	0.0000	neutral	neutral
ANKRD20A4	2	0	0	7	0	1.2400	low	low
CDKN2A	3	0	0	4	0	1.3950	low	low
NRAS	4	42	40	0	0	3.5300	high	high
BRAF	5	6	7	105	5	1.3200	low	low
OXA1L	6	0	0	0	8	0.6950	neutral	neutral
TP53	7	0	27	0	1	3.1675	medium	medium
STK19	8	0	1	3	13	0.6950	neutral	neutral
PCDHAC2	10	87	171	105	80	2.1300	medium	medium
PTEN	11	4	5	0	1	3.1800	medium	medium

Methods & Data

Input

SKCM-TM.maf.annotated
SKCM-TM.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 SKCM-TM.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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