Mutation Analysis (MutSig 2CV v3.1)
Ovarian Serous Cystadenocarcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C11G0KBQ
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Mutation Analysis (MutSig 2CV v3.1). Broad Institute of MIT and Harvard. doi:10.7908/C11G0KBQ
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: OV-TP

  • Number of patients in set: 466

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:OV-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 11

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: 11. 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 1889 21 0 2 254 39 41 53 387 383 175 3.9e-15 1e-05 1e-05 1e-16 1.8e-12
2 BRCA1 breast cancer 1, early onset 5750 8 0 0 2 7 1 9 19 19 19 1e-16 1 0.18 1.2e-15 1.1e-11
3 NF1 neurofibromin 1 (neurofibromatosis, von Recklinghausen disease, Watson disease) 12120 0 0 0 7 6 2 11 26 24 26 4.3e-14 1 0.77 1.4e-12 8.3e-09
4 RB1 retinoblastoma 1 (including osteosarcoma) 3704 0 0 0 5 4 2 4 15 15 15 2.4e-09 1 0.19 2.6e-08 0.00012
5 BRCA2 breast cancer 2, early onset 10361 9 0 0 4 3 0 6 13 13 13 3.1e-08 1 0.96 5.7e-07 0.0021
6 IL21R interleukin 21 receptor 1649 75 0 0 5 1 0 2 8 8 8 2e-07 1 0.74 3.3e-06 0.01
7 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 709 1000 0 0 5 0 0 0 5 5 2 0.024 1e-05 0.11 4e-06 0.01
8 YSK4 yeast Sps1/Ste20-related kinase 4 (S. cerevisiae) 4025 89 0 0 9 1 0 0 10 10 10 6.8e-07 1 0.9 1e-05 0.024
9 ANKRD35 ankyrin repeat domain 35 3058 32 0 0 8 1 0 0 9 9 9 1.9e-06 1 0.32 0.000027 0.055
10 C9orf171 chromosome 9 open reading frame 171 989 137 0 0 4 0 0 1 5 5 5 0.00067 1 0.0016 0.000041 0.075
11 MTA2 metastasis associated 1 family, member 2 2581 34 0 0 3 0 1 0 4 4 3 0.00039 0.0087 0.87 0.000046 0.077
12 CYP11B1 cytochrome P450, family 11, subfamily B, polypeptide 1 1546 63 0 0 8 0 0 0 8 8 8 5.1e-06 1 0.72 0.000067 0.1
13 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 941 58 0 0 3 1 0 0 4 4 3 0.00034 0.021 0.95 0.000092 0.12
14 ACBD4 acyl-Coenzyme A binding domain containing 4 1092 18 0 0 0 1 0 2 3 3 3 8.2e-06 1 0.61 0.0001 0.12
15 EFEMP1 EGF-containing fibulin-like extracellular matrix protein 1 1522 325 0 0 5 1 0 1 7 7 7 0.000027 1 0.22 0.00011 0.12
16 PODN podocan 2028 92 0 0 5 0 0 1 6 6 6 8.7e-06 1 0.81 0.00011 0.12
17 TOP2A topoisomerase (DNA) II alpha 170kDa 4732 111 0 1 4 0 0 4 8 8 8 9.1e-06 1 0.63 0.00011 0.12
18 NCOA3 nuclear receptor coactivator 3 4389 3 0 1 3 0 0 2 5 5 5 0.013 0.008 0.11 0.00012 0.12
19 TP53TG5 TP53 target 5 893 54 0 0 2 1 0 0 3 3 2 0.002 0.0084 0.55 0.00015 0.14
20 RPGRIP1 retinitis pigmentosa GTPase regulator interacting protein 1 3955 70 0 1 6 0 1 0 7 7 7 0.000078 1 0.12 0.00016 0.15
21 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 1244 124 0 0 2 0 1 2 5 5 5 0.000016 1 0.5 0.00019 0.17
22 RB1CC1 RB1-inducible coiled-coil 1 4873 6 0 1 4 3 0 2 9 9 9 0.000046 1 0.32 0.00021 0.17
23 LPAR3 lysophosphatidic acid receptor 3 1070 6 0 1 2 0 0 2 4 4 4 0.000022 1 0.49 0.00025 0.2
24 ADAMTS4 ADAM metallopeptidase with thrombospondin type 1 motif, 4 2546 127 0 0 7 0 0 0 7 7 6 0.00064 0.041 0.92 0.00034 0.26
25 NCF2 neutrophil cytosolic factor 2 (65kDa, chronic granulomatous disease, autosomal 2) 1641 116 0 0 6 0 0 0 6 6 6 8e-05 1 0.34 0.00036 0.26
26 PGAP1 post-GPI attachment to proteins 1 2873 42 0 1 6 0 0 1 7 7 6 0.00076 0.036 0.7 0.00043 0.3
27 NADK NAD kinase 1385 19 0 0 0 0 0 2 2 2 1 0.0054 0.01 0.9 0.00059 0.39
28 HYDIN hydrocephalus inducing homolog (mouse) 15717 48 0 5 17 0 1 0 18 18 18 0.00039 1 0.072 0.00062 0.39
29 SAMD9L sterile alpha motif domain containing 9-like 4759 30 0 2 6 2 0 1 9 9 9 0.0003 1 0.19 0.00064 0.39
30 CCDC104 coiled-coil domain containing 104 1067 141 0 0 2 0 0 1 3 3 3 0.0022 1 0.0081 0.00065 0.39
31 PKD1L1 polycystic kidney disease 1 like 1 8862 5 0 1 5 1 0 1 7 6 7 0.046 1 0.00025 0.00067 0.39
32 ACTN2 actinin, alpha 2 2765 217 0 1 6 1 0 0 7 7 6 0.0038 0.047 0.21 0.00068 0.39
33 ATP6V1C2 ATPase, H+ transporting, lysosomal 42kDa, V1 subunit C2 1434 170 0 0 3 0 0 0 3 3 2 0.0095 0.0076 0.93 0.00076 0.42
34 SMG6 Smg-6 homolog, nonsense mediated mRNA decay factor (C. elegans) 4334 11 0 0 2 2 0 0 4 4 3 0.0061 0.016 0.59 0.0012 0.63
35 CLEC4F C-type lectin domain family 4, member F 1796 159 0 0 7 0 0 0 7 7 7 0.00014 1 0.84 0.0013 0.7
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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