Mutation Assessor
Thyroid Adenocarcinoma (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/C1TQ611W
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: 359

  • Medium Functional Impact Missense Mutations: 1839

  • Low Functional Impact Missense Mutations: 2114

  • Neutral Functional Impact Mutations: 1447

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 BRAF 0 0 288 2 1.3200 low low
2 NRAS 9 31 0 0 3.3250 medium medium
3 HRAS 3 14 0 0 3.1550 medium medium
5 RPTN 0 2 0 0 2.3925 medium medium
12 NUP93 0 0 1 0 1.2400 low low
14 ABL1 0 0 1 0 1.0100 low low
15 LMTK2 0 0 1 0 1.7950 low low
17 PPM1D 0 0 1 0 0.9750 low low
18 TG 2 2 3 1 2.0100 medium low
20 TSC22D1 0 1 0 0 2.3300 medium medium
Methods & Data
Input
  1. THCA-TP.maf.annotated

  2. sig_genes.txt

  3. Mutation Assessor Scores Release 2:

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 THCA-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)