Mutation Assessor - Uterine Carcinosarcoma (Primary solid tumor)

Mutation Assessor

Uterine Carcinosarcoma (Primary solid tumor)

28 January 2016 | analyses__2016_01_28

Maintainer Information

Citation Information

doi:10.7908/C1930SPC

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/C1930SPC

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: 342
Medium Functional Impact Missense Mutations: 2051
Low Functional Impact Missense Mutations: 1944
Neutral Functional Impact Mutations: 1448

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	42	0	0	3.1125	medium	medium
2	FBXW7	4	10	3	5	2.1650	medium	medium
3	PPP2R1A	11	5	1	0	3.5500	high	high
4	KRAS	0	7	0	0	3.1350	medium	medium
5	PTEN	6	3	0	0	3.5750	high	high
6	RB1	0	1	0	0	3.1650	medium	medium
7	ZBTB7B	0	2	2	0	1.7400	low	medium/low
8	PIK3R1	0	1	0	0	2.4750	medium	medium
9	ARHGAP35	0	1	0	0	2.1200	medium	medium
10	PIK3CA	0	9	7	6	1.6200	low	medium

Methods & Data

Input

UCS-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 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)

Made with Nozzle