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

Mutation Assessor

Kidney Renal Papillary Cell 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/C1X63MBK

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: 597
Medium Functional Impact Missense Mutations: 3284
Low Functional Impact Missense Mutations: 3303
Neutral Functional Impact Mutations: 2537

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	SETD2	2	1	0	2	1.9350	medium	high/neutral
3	ZNF814	0	4	0	6	-0.6300	neutral	neutral
4	MET	0	13	1	6	2.2600	medium	medium
5	NEFH	0	0	1	0	1.6100	low	low
6	KRAS	0	5	0	0	3.2450	medium	medium
7	CUL3	2	2	0	0	3.3025	medium	high/medium
8	PCF11	0	0	6	6	0.7225	neutral	low/neutral
9	BCLAF1	0	2	0	1	1.9050	medium	medium
10	PAM	0	2	1	0	2.8300	medium	medium
11	SMARCB1	0	2	0	0	2.8425	medium	medium

Methods & Data

Input

KIRP-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 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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