Mutation Assessor - Pancreatic Adenocarcinoma (Primary solid tumor)

Mutation Assessor

Pancreatic Adenocarcinoma (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/C14748B1

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: 749
Medium Functional Impact Missense Mutations: 4755
Low Functional Impact Missense Mutations: 4706
Neutral Functional Impact Mutations: 3362

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	Medium Functional Impact Count	Low Functional Impact Count	Neutral Functional Impact Count	Median Functional Impact Score	Median Functional Impact Level	Mode Functional Impact Level
INTS10	6	0	1	0	1.1550	low	low
KRAS	16	33	0	0	3.1800	medium	medium
TP53	17	23	1	0	3.0475	medium	medium
QRICH1	18	1	0	0	1.9550	medium	medium
CDKN2A	21	0	1	0	0.8050	low	low
TMC4	24	0	0	1	0.2550	neutral	neutral
MED15	25	1	1	0	1.8750	low	medium/low
BRDT	27	0	1	0	1.6100	low	low
MBD3	29	0	0	1	0.3450	neutral	neutral
TULP1	30	0	0	1	0.2050	neutral	neutral

Methods & Data

Input

PAAD-TP.maf.annotated
PAAD-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 PAAD-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