Mutation Analysis (MutSig 2CV v3.1)
Lung Adenocarcinoma (Primary solid tumor)
17 October 2014  |  analyses__2014_10_17
Maintainer Information
Citation Information
doi:10.7908/C108647W
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Mutation Analysis (MutSig 2CV v3.1). Broad Institute of MIT and Harvard. doi:10.7908/C108647W
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 2CV v3.1 was used to generate the results found in this report.

  • Working with individual set: LUAD-TP

  • Number of patients in set: 229

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-TP.final_analysis_set.maf

  • Blacklist used for this analysis: pancan_mutation_blacklist.v14.hg19.txt

  • Significantly mutated genes (q ≤ 0.1): 18

Results
Lego Plots

The mutation spectrum is depicted in the lego plots below in which the 96 possible mutation types are subdivided into six large blocks, color-coded to reflect the base substitution type. Each large block is further subdivided into the 16 possible pairs of 5' and 3' neighbors, as listed in the 4x4 trinucleotide context legend. The height of each block corresponds to the mutation frequency for that kind of mutation (counts of mutations normalized by the base coverage in a given bin). The shape of the spectrum is a signature for dominant mutational mechanisms in different tumor types.

Figure 1.  Get High-res Image SNV Mutation rate lego plot for entire set. Each bin is normalized by base coverage for that bin. Colors represent the six SNV types on the upper right. The three-base context for each mutation is labeled in the 4x4 legend on the lower right. The fractional breakdown of SNV counts is shown in the pie chart on the upper left. If this figure is blank, not enough information was provided in the MAF to generate it.

Figure 2.  Get High-res Image SNV Mutation rate lego plots for 4 slices of mutation allele fraction (0<=AF<0.1, 0.1<=AF<0.25, 0.25<=AF<0.5, & 0.5<=AF) . The color code and three-base context legends are the same as the previous figure. If this figure is blank, not enough information was provided in the MAF to generate it.

CoMut Plot

Figure 3.  Get High-res Image The matrix in the center of the figure represents individual mutations in patient samples, color-coded by type of mutation, for the significantly mutated genes. The rate of synonymous and non-synonymous mutations is displayed at the top of the matrix. The barplot on the left of the matrix shows the number of mutations in each gene. The percentages represent the fraction of tumors with at least one mutation in the specified gene. The barplot to the right of the matrix displays the q-values for the most significantly mutated genes. The purple boxplots below the matrix (only displayed if required columns are present in the provided MAF) represent the distributions of allelic fractions observed in each sample. The plot at the bottom represents the base substitution distribution of individual samples, using the same categories that were used to calculate significance.

Significantly Mutated Genes

Column Descriptions:

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

  • npat = number of patients (individuals) with at least one nonsilent mutation

  • nsite = number of unique sites having a non-silent mutation

  • 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: 18. 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).

rank gene longname codelen nnei nncd nsil nmis nstp nspl nind nnon npat nsite pCV pCL pFN p q
1 TP53 tumor protein p53 1900 1 3 2 79 21 10 17 127 118 106 2.5e-15 0.0011 1e-05 1e-16 1.8e-12
2 STK11 serine/threonine kinase 11 1338 5 2 0 10 5 4 2 21 20 20 1e-16 0.37 0.056 7.8e-16 7.1e-12
3 KEAP1 kelch-like ECH-associated protein 1 1895 21 2 0 34 3 0 1 38 38 36 1.3e-15 0.6 0.01 1.2e-15 7.5e-12
4 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 709 0 1 0 60 0 0 0 60 60 6 9.4e-11 1e-05 1e-05 3.3e-14 1.5e-10
5 SMARCA4 SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 4 5189 21 4 2 7 9 0 2 18 17 18 1.6e-10 1 0.63 3.9e-09 0.000014
6 ARID1A AT rich interactive domain 1A (SWI-like) 6934 1 3 1 5 8 1 2 16 14 16 2.9e-10 1 0.53 6.7e-09 2e-05
7 NF1 neurofibromin 1 (neurofibromatosis, von Recklinghausen disease, Watson disease) 12127 4 9 4 13 6 4 9 32 27 31 1.3e-08 0.26 0.3 3.7e-08 0.000097
8 U2AF1 U2 small nuclear RNA auxiliary factor 1 824 22 2 0 6 0 0 0 6 6 2 0.00047 1e-05 0.00085 9.4e-08 0.00022
9 EGFR epidermal growth factor receptor (erythroblastic leukemia viral (v-erb-b) oncogene homolog, avian) 3999 0 21 6 24 0 0 9 33 27 19 0.0006 1e-05 0.012 1.2e-07 0.00025
10 SETD2 SET domain containing 2 7777 0 2 1 11 8 0 3 22 18 22 3.9e-08 1 0.21 2.7e-07 0.0005
11 CDKN2A cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4) 1002 0 4 1 9 1 1 3 14 14 14 0.0083 0.47 1e-05 1.4e-06 0.0024
12 MET met proto-oncogene (hepatocyte growth factor receptor) 4307 1 9 4 7 1 3 1 12 12 12 0.0072 0.0012 0.0072 5.7e-06 0.0088
13 ERBB2 v-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma derived oncogene homolog (avian) 3893 2 10 2 5 0 1 4 10 10 7 0.013 1e-05 0.32 6.2e-06 0.0088
14 CHRND cholinergic receptor, nicotinic, delta 1600 8 5 1 8 2 1 0 11 10 11 4.6e-06 1 0.11 0.000017 0.022
15 BRAF v-raf murine sarcoma viral oncogene homolog B1 2371 0 9 1 15 1 1 0 17 17 12 0.022 1e-05 0.34 3e-05 0.037
16 GEN1 Gen homolog 1, endonuclease (Drosophila) 2779 2 0 0 5 0 0 0 5 5 5 6.1e-06 1 0.63 0.000061 0.07
17 STX2 syntaxin 2 987 2 2 0 6 0 2 0 8 6 7 0.00025 0.24 0.029 9e-05 0.093
18 MGA MAX gene associated 9290 8 5 3 10 10 2 2 24 17 24 7.1e-06 1 0.9 0.000091 0.093
19 GALC galactosylceramidase 2185 0 19 0 4 1 0 0 5 5 4 0.83 0.00024 0.097 0.00011 0.1
20 CTNNB1 catenin (cadherin-associated protein), beta 1, 88kDa 2406 1 2 0 11 0 0 0 11 9 9 0.0045 0.0025 0.52 0.00014 0.13
21 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 945 5 3 1 4 0 0 0 4 4 2 0.2 0.0001 0.2 0.00023 0.2
22 RIT1 Ras-like without CAAX 1 882 38 2 1 8 0 0 2 10 9 9 0.00016 0.18 0.42 0.00026 0.22
23 ATM ataxia telangiectasia mutated 9440 0 10 3 16 6 0 0 22 19 21 0.0011 0.11 0.059 0.00029 0.24
24 APC adenomatous polyposis coli 8592 7 3 1 14 3 1 5 23 21 23 0.00023 0.11 0.97 0.00034 0.26
25 IL12RB2 interleukin 12 receptor, beta 2 2649 3 2 3 9 1 2 0 12 11 12 0.000078 1 0.34 0.00037 0.26
26 RB1 retinoblastoma 1 (including osteosarcoma) 3704 8 14 1 4 4 1 4 13 13 13 0.000033 1 0.49 0.00037 0.26
27 PKLR pyruvate kinase, liver and RBC 1776 8 5 0 10 0 0 1 11 10 10 0.0013 0.18 0.061 0.00048 0.33
28 ANKRD44 ankyrin repeat domain 44 2860 2 10 1 9 1 2 0 12 10 11 0.0015 0.13 0.082 0.00051 0.34
29 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 3287 1 4 1 11 0 0 0 11 11 8 0.066 0.0021 0.17 0.00057 0.36
30 MBD1 methyl-CpG binding domain protein 1 3058 7 6 1 5 1 1 0 7 7 7 0.031 1 0.0008 0.00071 0.44
31 CGRRF1 cell growth regulator with ring finger domain 1 1021 3 1 0 0 2 1 0 3 3 3 0.0037 1 0.0068 0.00075 0.45
32 UBE2O ubiquitin-conjugating enzyme E2O 3949 2 4 1 6 0 0 0 6 6 5 0.11 0.021 0.02 0.00084 0.47
33 FAT1 FAT tumor suppressor homolog 1 (Drosophila) 13871 0 6 10 41 5 1 3 50 31 48 0.0037 0.089 0.054 0.00085 0.47
34 ADCY6 adenylate cyclase 6 3591 11 5 2 9 0 1 0 10 10 10 0.00042 1 0.13 0.00092 0.5
35 RUFY2 RUN and FYVE domain containing 2 2111 0 1 0 5 1 0 0 6 6 6 0.018 0.038 0.1 0.00099 0.52
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

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