Mutation Assessor - Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)

Mutation Assessor

Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)

Due to issues with GAF 3.0, we strongly advise caution in the use of these results.

23 September 2013 | analyses__2013_09_23

Maintainer Information

Citation Information

Maintained by David Heiman (Broad Institute)

Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Mutation Assessor. Broad Institute of MIT and Harvard. doi:10.7908/C16Q1VJ2

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: 327
Medium Functional Impact Missense Mutations: 1507
Low Functional Impact Missense Mutations: 1537
Neutral Functional Impact Mutations: 1239

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
PCDHGC5	6	3	4	2	3	1.9950	medium	medium
MET	7	0	5	0	4	1.9350	medium	medium
PLAC4	8	0	0	0	1	0.0000	neutral	neutral
PCF11	9	0	0	6	2	0.9350	low	low
LGI4	10	0	2	2	0	1.8925	low	medium/low
ELF3	11	0	1	0	0	2.8200	medium	medium
PCDHAC2	12	0	4	0	1	2.0300	medium	medium
ACSBG2	15	0	0	0	3	-0.4050	neutral	neutral
BHMT	17	0	2	0	0	2.7450	medium	medium
NFE2L2	22	0	3	0	0	2.8650	medium	medium

Methods & Data

Input

KIRP-TP.maf.annotated
KIRP-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 KIRP-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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