Mutation Analysis (MutSig 2CV v3.1)
Ovarian Serous Cystadenocarcinoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
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/C17080P7
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): 14

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: 14. 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 173 0 0 255 39 44 51 389 385 177 1e-16 1e-05 1e-05 1e-16 1.8e-12
2 RB1 retinoblastoma 1 (including osteosarcoma) 3704 12 0 0 5 4 2 4 15 15 15 1e-16 1 0.2 2e-15 1.3e-11
3 BRCA1 breast cancer 1, early onset 5750 8 0 0 1 7 1 9 18 18 18 1e-16 1 0.27 2.1e-15 1.3e-11
4 NF1 neurofibromin 1 (neurofibromatosis, von Recklinghausen disease, Watson disease) 12120 0 0 0 7 6 2 11 26 24 26 9.4e-14 1 0.77 2.9e-12 1.3e-08
5 CDK12 cyclin-dependent kinase 12 4525 11 0 0 6 3 0 6 15 15 15 8.1e-09 1 0.17 4.9e-08 0.00018
6 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 709 1000 0 0 5 0 0 0 5 5 2 0.032 1e-05 0.11 5.1e-06 0.016
7 HNF1B HNF1 homeobox B 1706 16 0 0 3 0 1 1 5 5 5 3e-06 1 0.35 0.000021 0.055
8 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 1244 121 0 0 2 0 1 2 5 5 5 4.9e-06 1 0.5 0.000039 0.077
9 LARP1 La ribonucleoprotein domain family, member 1 3134 8 0 0 0 0 2 2 4 4 4 2.9e-06 1 0.48 4e-05 0.077
10 BRCA2 breast cancer 2, early onset 10361 1 0 0 4 3 0 5 12 12 12 3.1e-06 1 0.96 0.000042 0.077
11 EFEMP1 EGF-containing fibulin-like extracellular matrix protein 1 1522 261 0 0 4 1 1 1 7 7 7 0.000015 1 0.23 0.000061 0.088
12 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 941 34 0 0 3 1 0 0 4 4 3 0.00024 0.02 0.95 0.000064 0.088
13 MTA2 metastasis associated 1 family, member 2 2581 34 0 0 3 0 1 0 4 4 3 0.00064 0.0079 0.86 0.000067 0.088
14 ERCC6 excision repair cross-complementing rodent repair deficiency, complementation group 6 6344 15 0 0 1 2 1 0 4 4 4 0.000018 1 0.24 0.000068 0.088
15 IL21R interleukin 21 receptor 1649 129 0 0 5 1 0 2 8 8 8 6.5e-06 1 0.74 0.000084 0.1
16 PKD1L1 polycystic kidney disease 1 like 1 8862 5 0 1 5 1 0 1 7 6 7 0.013 1 0.00019 0.00012 0.14
17 SAMD9L sterile alpha motif domain containing 9-like 4759 30 0 2 6 2 0 1 9 9 9 0.000048 1 0.19 0.00015 0.16
18 AQP2 aquaporin 2 (collecting duct) 830 9 0 1 2 1 0 0 3 3 3 0.00046 1 0.026 0.00019 0.19
19 CREBBP CREB binding protein (Rubinstein-Taybi syndrome) 7449 13 0 1 5 1 2 3 11 11 11 0.000017 1 0.5 0.0002 0.19
20 C9orf171 chromosome 9 open reading frame 171 989 134 0 0 4 0 0 1 5 5 5 0.0021 1 0.0028 0.00021 0.19
21 NCOA3 nuclear receptor coactivator 3 4389 3 0 1 3 0 0 2 5 5 5 0.019 0.0075 0.11 0.00033 0.27
22 RB1CC1 RB1-inducible coiled-coil 1 4873 7 0 1 4 3 0 2 9 9 9 0.000072 1 0.34 0.00033 0.27
23 PPM1F protein phosphatase 1F (PP2C domain containing) 1393 87 0 0 5 0 0 0 5 5 4 0.0011 0.055 0.41 0.00036 0.28
24 RPGRIP1 retinitis pigmentosa GTPase regulator interacting protein 1 3955 160 0 1 5 0 2 0 7 7 7 0.00017 1 0.11 0.00037 0.28
25 KDM5C lysine (K)-specific demethylase 5C 4879 10 0 1 5 0 0 3 8 8 8 0.000037 1 0.61 0.00041 0.3
26 COL5A3 collagen, type V, alpha 3 5504 25 0 2 9 0 1 0 10 10 10 0.00018 1 0.15 0.00054 0.38
27 SP100 SP100 nuclear antigen 3318 144 0 1 5 1 1 1 8 8 8 0.000065 1 0.98 0.00069 0.47
28 TM9SF1 transmembrane 9 superfamily member 1 1882 113 0 0 3 0 0 2 5 3 5 0.013 0.018 0.18 0.0008 0.52
29 CDC20 cell division cycle 20 homolog (S. cerevisiae) 1542 87 0 0 2 0 1 1 4 4 4 0.0001 1 0.7 0.0011 0.64
30 ZNF232 zinc finger protein 232 1349 134 0 0 1 2 0 0 3 3 3 0.0069 1 0.006 0.0011 0.64
31 CTSL1 cathepsin L1 1030 91 0 0 3 0 0 1 4 4 4 0.00016 1 0.63 0.0011 0.64
32 TP53TG5 TP53 target 5 893 54 0 0 2 1 0 0 3 3 2 0.011 0.0094 0.55 0.0012 0.67
33 KDM2A lysine (K)-specific demethylase 2A 3569 29 0 0 5 0 0 0 5 5 5 0.00047 1 0.24 0.0013 0.71
34 BCORL1 BCL6 co-repressor-like 1 5410 15 0 0 8 0 0 1 9 9 9 0.0004 1 0.28 0.0013 0.72
35 TOP2A topoisomerase (DNA) II alpha 170kDa 4732 56 0 1 4 0 0 4 8 8 8 0.00014 1 0.63 0.0014 0.73
TP53

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

RB1

Figure S2.  This figure depicts the distribution of mutations and mutation types across the RB1 significant gene.

BRCA1

Figure S3.  This figure depicts the distribution of mutations and mutation types across the BRCA1 significant gene.

NF1

Figure S4.  This figure depicts the distribution of mutations and mutation types across the NF1 significant gene.

CDK12

Figure S5.  This figure depicts the distribution of mutations and mutation types across the CDK12 significant gene.

KRAS

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

HNF1B

Figure S7.  This figure depicts the distribution of mutations and mutation types across the HNF1B significant gene.

PTEN

Figure S8.  This figure depicts the distribution of mutations and mutation types across the PTEN significant gene.

LARP1

Figure S9.  This figure depicts the distribution of mutations and mutation types across the LARP1 significant gene.

BRCA2

Figure S10.  This figure depicts the distribution of mutations and mutation types across the BRCA2 significant gene.

EFEMP1

Figure S11.  This figure depicts the distribution of mutations and mutation types across the EFEMP1 significant gene.

NRAS

Figure S12.  This figure depicts the distribution of mutations and mutation types across the NRAS significant gene.

MTA2

Figure S13.  This figure depicts the distribution of mutations and mutation types across the MTA2 significant gene.

ERCC6

Figure S14.  This figure depicts the distribution of mutations and mutation types across the ERCC6 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

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)