Lung Adenocarcinoma: 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: LUAD

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:LUAD.final_analysis_set.maf

- Significantly mutated genes (q ≤ 0.1): 28

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: 28. 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
--- | --- | --- | --- | --- | --- | --- | ---
TP53 | 31248 | 3 | 2 | 137 | 54 | 9.4e-168 | 1.8e-163
KRAS | 25787 | 1 | 0 | 64 | 0 | 1.5e-37 | 1.4e-33
STK11 | 21045 | 2 | 0 | 21 | 11 | 2.1e-28 | 1.3e-24
RBM10 | 49888 | 4 | 1 | 14 | 11 | 5.6e-18 | 2.6e-14
FAM75C1 | 81405 | 1 | 4 | 22 | 1 | 1.5e-11 | 5.6e-08
U2AF1 | 21829 | 2 | 0 | 7 | 0 | 1.8e-11 | 5.7e-08
KEAP1 | 46361 | 2 | 0 | 42 | 5 | 1.2e-09 | 3.2e-06
LOC440563 | 25272 | 1 | 1 | 7 | 1 | 2.2e-09 | 5.3e-06
DCAF8L2 | 25758 | 0 | 2 | 7 | 0 | 3.3e-09 | 6.9e-06
NFATC3 | 88764 | 4 | 2 | 11 | 0 | 2.5e-08 | 0.000048
CDKN2A | 9534 | 4 | 1 | 15 | 5 | 1.6e-07 | 0.00027
KRTAP4-11 | 11178 | 1 | 0 | 5 | 0 | 2.3e-07 | 0.00036
ARID1A | 144336 | 4 | 1 | 17 | 12 | 1.8e-06 | 0.0026
CRIPAK | 26030 | 0 | 4 | 16 | 10 | 2.4e-06 | 0.0032
AMY1A | 13893 | 1 | 1 | 5 | 2 | 4.5e-06 | 0.0056
MAP2K1 | 33480 | 2 | 0 | 7 | 1 | 4.8e-06 | 0.0057
SMARCA4 | 117477 | 7 | 2 | 20 | 12 | 0.000011 | 0.013
OR2T27 | 24810 | 0 | 2 | 15 | 0 | 0.000014 | 0.014
MAGEC1 | 90024 | 5 | 3 | 41 | 3 | 0.000026 | 0.024
CENPF | 295854 | 15 | 7 | 32 | 3 | 0.000026 | 0.024
HRNR | 135606 | 3 | 11 | 55 | 14 | 0.000044 | 0.039
MGA | 242113 | 5 | 3 | 25 | 14 | 0.000049 | 0.042
EGFR | 113093 | 26 | 7 | 34 | 9 | 0.000063 | 0.05
SPRR3 | 12643 | 1 | 0 | 7 | 5 | 0.000063 | 0.05
AGAP6 | 56619 | 3 | 2 | 7 | 1 | 0.000066 | 0.05
SETD2 | 185697 | 2 | 1 | 23 | 12 | 0.000074 | 0.053
FAM55B | 18225 | 0 | 3 | 5 | 0 | 0.000084 | 0.058
BRAF | 64728 | 11 | 1 | 19 | 2 | 0.00011 | 0.073
RIT1 | 19592 | 2 | 1 | 11 | 2 | 0.00019 | 0.12
OR14I1 | 29264 | 0 | 2 | 14 | 0 | 0.00024 | 0.15
ANKRD55 | 52080 | 8 | 2 | 12 | 1 | 0.00028 | 0.17
ZNF770 | 67178 | 0 | 3 | 6 | 0 | 0.00037 | 0.22
POLR3B | 107136 | 5 | 0 | 15 | 2 | 0.00039 | 0.22
MUC17 | 336774 | 16 | 22 | 90 | 10 | 0.00051 | 0.28
RIF1 | 234588 | 11 | 3 | 15 | 2 | 0.00057 | 0.31

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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