Mutation Analysis (MutSigCV v0.9)
Ovarian Serous Cystadenocarcinoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1930RM2
Maintained by Dan DiCara (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Mutation Analysis (MutSigCV v0.9). Broad Institute of MIT and Harvard. doi:10.7908/C1930RM2
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. MutSigCV v0.9 was used to generate the results found in this report.

  • Working with individual set: OV-TP

  • Number of patients in set: 316

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
Target Coverage for Each Individual

The x axis represents the samples. The y axis represents the exons, one row per exon, and they are sorted by average coverage across samples. For exons with exactly the same average coverage, they are sorted next by the %GC of the exon. (The secondary sort is especially useful for the zero-coverage exons at the bottom). If the figure is unpopulated, then full coverage is assumed (e.g. MutSig CV doesn't use WIGs and assumes full coverage).

Figure 1. 

Distribution of Mutation Counts, Coverage, and Mutation Rates Across Samples

Figure 2.  Patients counts and rates file used to generate this plot: OV-TP.patients.counts_and_rates.txt

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 3.  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 4.  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 5.  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

  • 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: 1. 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 Nnon Nsil Nflank nnon npat nsite nsil nflank nnei fMLE p score time q
TP53 298620 87216 0 279 276 143 3 0 4 3.7 3.4e-15 990 0.21 6.3e-11
OR4F21 60988 18644 0 3 3 1 0 0 20 0.47 0.000014 21 0.16 0.1
PECAM1 6952 2528 0 2 2 2 0 0 20 0 0.000021 13 0.14 0.1
RB1 899968 237316 0 9 9 9 0 0 20 0.64 0.000022 41 0.2 0.1
BRCA1 1441592 364348 0 12 12 12 0 0 14 0.82 0.00021 61 0.18 0.75
POTED 101752 23384 0 4 4 4 0 0 16 0.6 0.00062 18 0.16 1
TBP 249640 70468 0 4 4 2 0 0 20 2 0.0016 25 0.17 1
C1orf95 75208 22752 0 3 3 3 0 0 20 0.46 0.0017 14 0.15 1
DAD1 82476 26544 0 2 2 2 0 0 20 0.94 0.0018 14 0.15 1
DUSP19 164952 44872 0 4 4 4 0 0 20 0.38 0.0019 14 0.15 1
ZNF706 60040 14536 0 2 2 2 0 0 20 1.5 0.0024 12 0.14 1
GPX6 169692 45188 0 3 3 3 0 0 20 0.18 0.0032 14 0.17 1
CDK12 1078824 325796 0 9 9 9 0 0 20 1.2 0.0032 39 0.17 1
FAM200A 47716 12640 0 2 2 2 0 0 20 0 0.0046 8.6 0.12 1
C10orf140 406060 119448 0 5 5 3 1 0 20 1.2 0.0055 24 0.16 1
UTP14A 582704 159264 0 4 4 4 0 0 20 0.58 0.0057 21 0.16 1
C9orf171 204452 62568 0 5 5 5 0 0 20 1.4 0.0067 18 0.16 1
FRG2 21172 4424 0 2 2 2 0 0 20 2 0.0068 7.8 0.11 1
BDKRB1 253748 81212 0 4 4 4 0 0 20 0.18 0.0072 14 0.15 1
PAK3 417120 113760 0 5 5 5 0 0 11 0.24 0.0075 19 0.16 1
GABRA6 342228 97012 0 6 6 6 1 0 20 0.97 0.0083 19 0.19 1
SLC10A2 258804 77104 0 2 2 2 1 0 18 0.81 0.0092 13 0.15 1
GPHB5 59092 20540 0 2 2 2 0 0 20 0.9 0.0099 8.1 0.13 1
NXPH2 184228 50560 0 3 2 3 0 0 20 0.39 0.011 13 0.16 1
OR6A2 236052 74260 0 4 4 4 0 0 20 0.74 0.011 13 0.15 1
PAOX 338120 83740 0 4 4 4 0 0 20 0.46 0.011 19 0.16 1
LCOR 321372 91640 0 4 4 4 0 0 3 0.3 0.012 19 0.18 1
C10orf113 116604 29388 0 3 3 3 0 0 20 0.97 0.013 11 0.14 1
FAM167B 72048 23068 0 2 2 2 0 0 20 1.2 0.015 10 0.13 1
CHST2 243636 81212 0 5 5 5 1 0 20 1.3 0.016 16 0.15 1
CBLN4 135564 42344 0 3 3 3 0 0 20 1 0.016 11 0.14 1
UIMC1 551104 146308 0 4 4 4 0 0 20 0.29 0.017 16 0.15 1
ACBD4 253748 36656 0 3 3 3 0 0 20 2 0.018 18 0.16 1
CACNG6 120712 42028 0 2 2 2 0 0 20 0.62 0.018 13 0.15 1
FAM24A 80896 21488 0 2 2 2 0 0 20 0.89 0.018 7.7 0.11 1
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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