Acute Myeloid Leukemia: Mutation Analysis (MutSig vS2N)
(primary blood tumor (peripheral) cohort)
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-TB

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-TB.final_analysis_set.maf

- Significantly mutated genes (q ≤ 0.1): 23

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: 23. 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 | 21309 | 0 | 0 | 47 | 47 | 0 | 0
FLT3 | 77991 | 0 | 0 | 51 | 35 | 0 | 0
DNMT3A | 56886 | 0 | 0 | 50 | 14 | 0 | 0
RUNX1 | 20706 | 0 | 0 | 18 | 11 | 0 | 0
IDH1 | 32160 | 0 | 0 | 19 | 0 | 0 | 0
WT1 | 24720 | 0 | 0 | 12 | 11 | 0 | 0
NRAS | 14676 | 0 | 0 | 13 | 0 | 0 | 0
TET2 | 83013 | 0 | 1 | 21 | 19 | 0 | 0
CEBPA | 5025 | 0 | 0 | 6 | 5 | 0 | 0
U2AF1 | 17886 | 0 | 0 | 10 | 0 | 0 | 0
TP53 | 25326 | 0 | 1 | 14 | 5 | 0 | 0
KRAS | 20706 | 0 | 0 | 5 | 0 | 7.800002e-318 | 1.2e-314
PHF6 | 31359 | 0 | 0 | 6 | 4 | 1.5e-101 | 2.2e-98
PTPN11 | 44622 | 0 | 0 | 6 | 0 | 1.4e-71 | 1.9e-68
KIT | 75777 | 0 | 0 | 7 | 2 | 7.1e-64 | 8.9e-61
FAM5C | 57087 | 0 | 0 | 5 | 0 | 3.1e-29 | 3.6e-26
CYP21A2 | 39753 | 0 | 0 | 4 | 0 | 1.9e-24 | 2.1e-21
PRUNE2 | 204777 | 0 | 0 | 6 | 4 | 3.6e-16 | 3.8e-13
EPPK1 | 108501 | 0 | 0 | 5 | 0 | 9.9e-16 | 9.8e-13
ZAN | 152595 | 0 | 0 | 5 | 4 | 2.1e-11 | 2e-08
ASXL1 | 91656 | 0 | 0 | 4 | 4 | 8.3e-10 | 7.4e-07
STAG2 | 99897 | 0 | 0 | 4 | 4 | 4.6e-09 | 4e-06
ETV6 | 32163 | 0 | 0 | 4 | 3 | 2.9e-07 | 0.00024
SMC1A | 88017 | 0 | 0 | 4 | 1 | 0.004 | 1
QRICH2 | 94284 | 0 | 0 | 4 | 3 | 0.0058 | 1
SMC3 | 96477 | 0 | 0 | 4 | 2 | 0.0065 | 1
MUC4 | 125673 | 0 | 4 | 8 | 3 | 0.0068 | 1
MAP3K4 | 112950 | 0 | 0 | 4 | 3 | 0.014 | 1
SDK1 | 132888 | 0 | 0 | 4 | 1 | 0.026 | 1
APC | 198594 | 0 | 0 | 4 | 0 | 0.087 | 1
TTN | 2562390 | 0 | 1 | 5 | 1 | 0.23 | 1
MUC16 | 928398 | 0 | 1 | 4 | 0 | 0.59 | 1
AKAP12 | 108321 | 0 | 0 | 3 | 0 | 1 | 1
ANKRD24 | 26499 | 0 | 0 | 3 | 1 | 1 | 1
AP3S1 | 17487 | 0 | 0 | 3 | 3 | 1 | 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)
Copyright © 2013 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle