Lung Squamous Cell Carcinoma: 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: LUSC-TP

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
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: 8. 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 22428 0 5 146 47 4.8e-145 9.1e-141
CDKN2A 10020 3 1 26 12 9e-62 8.5e-58
PIK3CA 74998 1 1 29 0 4.2e-29 2.6e-25
HLA-A 19194 0 0 7 6 7.4e-16 3.5e-12
KEAP1 32842 0 0 23 2 1.8e-13 6.9e-10
PTEN 28478 1 0 16 8 1.6e-12 5e-09
NFE2L2 38270 1 0 28 1 3e-12 8e-09
MLL2 221958 3 6 40 18 1.1e-11 2.7e-08
PRR23B 9552 0 2 9 0 0.000087 0.18
FSCB 40910 0 2 19 1 0.00012 0.23
SCN1A 141098 2 9 32 3 0.00013 0.23
WHSC1L1 93628 2 1 16 2 0.00025 0.39
COL19A1 56664 6 4 19 2 0.00027 0.39
AHNAK 358404 0 8 25 1 0.00034 0.45
ASCL4 3234 0 0 6 2 0.00036 0.45
MAGEB2 15514 0 1 9 0 0.00044 0.51
OR2G6 20648 0 3 15 0 0.00047 0.52
ZNF208 84566 1 6 27 1 0.00059 0.57
TRIOBP 96658 2 1 20 1 0.00059 0.57
AKAP13 169872 3 4 19 0 0.0006 0.57
TAS2R60 21004 0 0 8 0 0.00071 0.61
SPHKAP 103032 2 3 34 1 0.00072 0.61
TPTE 40970 10 2 30 5 0.00087 0.69
CPS1 99740 10 3 26 2 0.00091 0.69
REG3A 10858 2 2 13 1 0.00095 0.69
CYP11B1 29548 1 0 15 0 0.00096 0.69
MYPN 78468 1 4 15 0 0.0011 0.76
MYH2 131572 2 6 33 3 0.0013 0.82
FAM47C 50224 0 5 21 1 0.0013 0.82
DNAH6 28928 1 1 5 1 0.0014 0.87
EBF1 33494 1 1 8 1 0.0016 0.96
CD109 99058 3 1 8 0 0.0019 1
ZIC1 23436 0 4 20 2 0.0019 1
PKHD1L1 250712 6 11 42 2 0.002 1
OR4M2 22072 0 3 15 1 0.0027 1
TP53

Figure S1.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

CDKN2A

Figure S2.  This figure depicts the distribution of mutations and mutation types across the CDKN2A significant gene.

PIK3CA

Figure S3.  This figure depicts the distribution of mutations and mutation types across the PIK3CA significant gene.

HLA-A

Figure S4.  This figure depicts the distribution of mutations and mutation types across the HLA-A significant gene.

KEAP1

Figure S5.  This figure depicts the distribution of mutations and mutation types across the KEAP1 significant gene.

PTEN

Figure S6.  This figure depicts the distribution of mutations and mutation types across the PTEN significant gene.

NFE2L2

Figure S7.  This figure depicts the distribution of mutations and mutation types across the NFE2L2 significant gene.

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.

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