Mutation Assessor
Thyroid 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/C1XK8D1R
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: 266

  • Medium Functional Impact Missense Mutations: 1337

  • Low Functional Impact Missense Mutations: 1599

  • Neutral Functional Impact Mutations: 1012

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
NRAS 1 7 27 0 0 3.325 medium medium
BRAF 2 0 0 234 1 1.320 low low
HRAS 3 3 11 0 0 3.155 medium medium
EIF1AX 4 3 1 0 0 3.800 high high
NUP93 5 0 0 1 0 1.240 low low
NLRP6 6 0 0 3 0 1.790 low low
PPM1D 7 0 0 1 0 0.975 low low
MUC7 8 0 0 0 1 0.695 neutral neutral
OR56A1 9 1 1 0 0 3.745 high high/medium
S100A7 11 0 0 2 1 1.075 low low
Methods & Data
Input
  1. THCA-TP.maf.annotated

  2. THCA-TP.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)