Mutation Assessor - Uterine Carcinosarcoma (Primary solid tumor)

Mutation Assessor

Uterine Carcinosarcoma (Primary solid tumor)

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/C1N87888

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: 201
Medium Functional Impact Missense Mutations: 1079
Low Functional Impact Missense Mutations: 975
Neutral Functional Impact Mutations: 773

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
FBXW7	1	4	10	3	4	2.165	medium	medium
KRAS	2	0	7	0	0	3.135	medium	medium
TP53	3	0	41	0	0	3.145	medium	medium
PPP2R1A	4	10	5	1	0	3.670	high	high
PIK3CA	5	0	7	7	6	1.620	low	medium/low
PTEN	6	5	2	0	0	3.575	high	high
PCDHAC2	7	2	5	2	2	2.285	medium	medium
PIK3R1	8	0	1	0	0	2.475	medium	medium
ZBTB7B	9	0	2	2	0	1.740	low	medium/low
CHD4	10	5	2	1	0	3.950	high	high

Methods & Data

Input

UCS-TP.maf.annotated
UCS-TP.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 UCS-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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