Acute Myeloid Leukemia: Mutation Analysis (MutSig vS2N)
Maintained by Dan DiCara (Broad Institute)
Overview
Introduction

This report serves to describe the mutational landscape and properties of a given individual set, as well as rank genes and genesets according to mutational significance. MutSig vS2N was used to generate the results found in this report.

- Working with individual set: LAML

Input

The input for this pipeline is a set of individuals with the following files associated for each:

1. An annotated .maf file describing the mutations called for the respective individual, and their properties.

2. A .wig file that contains information about the coverage of the sample.

Summary

- MAF used for this analysis:LAML.final_analysis_set.maf

- Significantly mutated genes (q ≤ 0.1): 25

Results
Significantly Mutated Genes

Column Descriptions:

- N = number of sequenced bases in this gene across the individual set

- nnon = number of (nonsilent) mutations in this gene across the individual set

- nnull = number of (nonsilent) null mutations in this gene across the individual set

- nflank = number of noncoding mutations from this gene's flanking region, across the individual set

- nsil = number of silent mutations in this gene across the individual set

- p = p-value (overall)

- q = q-value, False Discovery Rate (Benjamini-Hochberg procedure)

Table 1. Get Full Table A Ranked List of Significantly Mutated Genes. Number of significant genes found: 25. Number of genes displayed: 35. Click on a gene name to display its stick figure depicting the distribution of mutations and mutation types across the chosen gene (this feature may not be available for all significant genes).

gene | N | nflank | nsil | nnon | nnull | p | q
--- | --- | --- | --- | --- | --- | --- | ---
NPM1 | 21733 | 0 | 0 | 47 | 47 | 0 | 0
FLT3 | 79543 | 0 | 0 | 51 | 35 | 0 | 0
DNMT3A | 58018 | 0 | 0 | 51 | 14 | 0 | 0
RUNX1 | 21118 | 0 | 0 | 18 | 11 | 0 | 0
WT1 | 25212 | 0 | 0 | 13 | 12 | 0 | 0
IDH1 | 32800 | 0 | 0 | 19 | 0 | 0 | 0
NRAS | 14968 | 0 | 0 | 13 | 0 | 0 | 0
TET2 | 84665 | 0 | 1 | 21 | 19 | 0 | 0
CEBPA | 5125 | 0 | 0 | 6 | 5 | 0 | 0
U2AF1 | 18242 | 0 | 0 | 10 | 0 | 0 | 0
TP53 | 25830 | 0 | 1 | 14 | 5 | 0 | 0
KRAS | 21118 | 0 | 0 | 6 | 0 | 1.5e-312 | 2.3e-309
PHF6 | 31983 | 0 | 0 | 6 | 4 | 1.3e-99 | 1.9e-96
PTPN11 | 45510 | 0 | 0 | 6 | 0 | 3.1e-70 | 4.2e-67
ASXL1 | 93480 | 0 | 0 | 5 | 5 | 5.4e-69 | 6.8e-66
KIT | 77285 | 0 | 0 | 7 | 2 | 1.1e-62 | 1.3e-59
PRUNE2 | 208853 | 0 | 0 | 7 | 5 | 2.8e-31 | 3.1e-28
OR5H6 | 27678 | 0 | 2 | 4 | 4 | 1.7e-30 | 1.7e-27
FAM5C | 58223 | 0 | 0 | 5 | 0 | 1.1e-28 | 1.1e-25
CYP21A2 | 40537 | 0 | 0 | 4 | 0 | 4.2e-24 | 4e-21
EPPK1 | 110657 | 0 | 0 | 5 | 0 | 1.9e-15 | 1.7e-12
ZAN | 155631 | 0 | 0 | 5 | 4 | 3.4e-11 | 2.9e-08
STAG2 | 101885 | 0 | 0 | 4 | 4 | 7.8e-09 | 6.4e-06
IDH2 | 29523 | 0 | 0 | 4 | 0 | 7.5e-08 | 0.000059
ETV6 | 32803 | 0 | 0 | 4 | 3 | 3.8e-07 | 0.00029
SMC1A | 89769 | 0 | 0 | 4 | 1 | 0.0045 | 1
QRICH2 | 96160 | 0 | 0 | 4 | 3 | 0.0064 | 1
MUC4 | 128173 | 0 | 4 | 8 | 3 | 0.0068 | 1
SMC3 | 98397 | 0 | 0 | 4 | 2 | 0.0072 | 1
MAP3K4 | 115198 | 0 | 0 | 4 | 3 | 0.015 | 1
SDK1 | 135532 | 0 | 0 | 4 | 1 | 0.028 | 1
CSMD1 | 201585 | 0 | 0 | 4 | 1 | 0.09 | 1
APC | 202546 | 0 | 0 | 4 | 0 | 0.091 | 1
TTN | 2613382 | 0 | 1 | 5 | 1 | 0.24 | 1
MUC16 | 946874 | 0 | 1 | 4 | 0 | 0.6 | 1

Methods & Data
Methods

In brief, we tabulate the number of mutations and the number of covered bases for each gene. The counts are broken down by mutation context category: four context categories that are discovered by MutSig, and one for indel and 'null' mutations, which include indels, nonsense mutations, splice-site mutations, and non-stop (read-through) mutations. For each gene, we calculate the probability of seeing the observed constellation of mutations, i.e. the product P1 x P2 x ... x Pm, or a more extreme one, given the background mutation rates calculated across the dataset. [1]

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

References
[1] TCGA, Integrated genomic analyses of ovarian carcinoma, Nature 474:609 - 615 (2011)
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