Mutation Assessor - Bladder Urothelial Carcinoma (Primary solid tumor)

Mutation Assessor

Bladder Urothelial Carcinoma (Primary solid tumor)

28 January 2016 | analyses__2016_01_28

Maintainer Information

Citation Information

Maintained by David Heiman (Broad Institute)

Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Mutation Assessor. Broad Institute of MIT and Harvard. doi:10.7908/C17P8XQR

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: 3935
Medium Functional Impact Missense Mutations: 25598
Low Functional Impact Missense Mutations: 27637
Neutral Functional Impact Mutations: 18127

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.

MutSig Rank	Gene	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
1	TP53	0	145	9	3	3.0700	medium	medium
2	RB1	0	3	3	2	1.7425	low	medium/low
3	ELF3	4	20	8	3	2.0750	medium	medium
4	TSC1	0	7	1	2	2.0700	medium	medium
5	PIK3CA	0	17	62	14	1.6200	low	low
6	RHOB	3	12	14	1	1.8350	low	low
8	CDKN2A	1	2	10	3	0.8950	low	low
9	ARID1A	0	24	11	6	1.9350	medium	medium
11	ZFP36L1	1	1	4	2	1.8450	low	low
12	CDKN1A	0	3	2	3	1.5200	low	medium/neutral

Methods & Data

Input

BLCA-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 BLCA-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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