Mutation Assessor
Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood)
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/C12R3Q03
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: 155

  • Medium Functional Impact Missense Mutations: 469

  • Low Functional Impact Missense Mutations: 412

  • Neutral Functional Impact Mutations: 326

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
DNMT3A 1 0 39 2 2 2.520 medium medium
U2AF1 2 7 1 0 0 3.820 high high
FLT3 3 0 0 13 4 1.040 low low
IDH2 4 20 0 0 0 3.890 high high
IDH1 5 19 0 0 0 4.540 high high
NPM1 6 0 1 0 0 2.175 medium medium
NRAS 7 5 10 0 0 3.325 medium medium
WT1 8 0 1 1 0 1.740 low medium/low
RUNX1 9 0 9 0 0 3.085 medium medium
SMG1 10 1 1 1 0 2.200 medium high/medium/low
Methods & Data
Input
  1. LAML-TB.maf.annotated

  2. LAML-TB.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 LAML-TB.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)