Mutation Analysis (MutSigCV v0.9)
Ovarian Serous Cystadenocarcinoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C137780C
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Mutation Analysis (MutSigCV v0.9). Broad Institute of MIT and Harvard. doi:10.7908/C137780C
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: 467

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): 5

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: 5. 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 441315 128892 94348 389 385 177 0 0 4 0.91 0 1500 0.41 0
RB1 1330016 350717 222588 15 15 15 0 0 20 0.49 9.3e-14 78 0.36 8.5e-10
BRCA1 2130454 538451 205642 19 19 19 0 0 14 0.57 6.2e-13 100 0.44 3.8e-09
CDK12 1594338 481477 121828 15 15 15 0 0 20 0.74 3.4e-10 73 0.37 1.5e-06
NF1 4395404 1231946 542272 26 24 26 0 0 0 0 1.1e-06 110 0.44 0.0039
IL21R 579080 174658 71448 8 8 8 0 0 20 1.1 0.000044 35 0.36 0.13
EFEMP1 558999 148973 92058 7 7 7 0 0 20 0.92 0.000064 32 0.51 0.17
GPX6 250779 66781 47632 4 4 4 0 0 20 0.27 0.00014 19 0.34 0.32
NRAS 215287 56974 37556 4 4 3 0 0 20 0.67 0.0003 19 0.53 0.61
PTEN 454858 109278 79692 5 5 5 0 0 20 1.1 0.0004 25 0.35 0.69
TOP2A 1535496 391813 205184 8 8 8 1 0 20 0.51 0.00042 36 0.36 0.69
DUSP19 243774 66314 38014 4 4 4 0 0 20 0.4 0.00059 16 0.32 0.89
DAD1 121887 39228 20152 2 2 2 0 0 20 0.86 0.00081 14 0.33 1
GPR1 387143 110679 10992 4 4 4 0 0 20 0.72 0.0012 18 0.37 1
CREBBP 2516196 721982 282586 11 11 11 1 0 11 0.25 0.0012 48 0.36 1
EMR3 727586 204546 144728 7 7 7 2 0 20 0.65 0.0013 26 0.35 1
GABRA6 505761 143369 83814 7 7 7 1 0 20 1.3 0.0013 24 0.35 1
FAM171B 887767 256850 70990 9 9 9 1 0 20 2.1 0.0017 33 0.38 1
ITGA2B 942406 296545 218008 7 7 7 0 0 20 0.82 0.0018 28 0.35 1
L1CAM 1336554 384341 240450 8 8 8 2 0 20 0.67 0.002 31 0.34 1
PROKR2 417031 121887 21068 6 6 6 1 0 20 1 0.0021 20 0.34 1
DEFB118 135897 39228 20152 4 4 4 0 0 12 2.9 0.0021 19 0.37 1
CACNG6 178394 62111 33892 3 3 3 0 0 20 0.87 0.0021 17 0.35 1
EOMES 497355 141968 52212 5 5 5 0 0 20 1 0.0024 23 0.35 1
PAGE2 112547 31289 24274 2 2 2 0 0 20 1.6 0.0027 14 0.74 1
ATP11C 1281915 344179 321058 7 7 7 0 0 20 0.76 0.0028 28 0.35 1
FAM24A 119552 31756 19236 2 2 2 0 0 20 0.67 0.0028 11 0.28 1
TAS2R1 324098 94801 10992 6 6 6 0 0 9 0.9 0.0029 23 0.34 1
OR5AK2 336240 95268 11908 3 3 3 1 0 17 0.4 0.003 17 0.36 1
PTGDS 187734 54639 46258 2 2 2 0 0 20 0.46 0.0031 14 0.32 1
C9orf171 302149 92466 53128 5 5 5 0 0 20 1.5 0.0032 20 0.34 1
MARCH4 372666 107877 37098 5 5 5 0 0 11 0.79 0.0036 20 0.33 1
NF2 598227 150841 128240 4 4 4 0 0 20 0.36 0.0037 20 0.35 1
PPM1F 366128 116283 47174 5 5 4 0 0 20 0.73 0.0038 17 0.35 1
MYH1 2210778 568806 348538 15 15 15 2 0 19 2.2 0.0038 47 0.44 1
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.

CDK12

Figure S4.  This figure depicts the distribution of mutations and mutation types across the CDK12 significant gene.

NF1

Figure S5.  This figure depicts the distribution of mutations and mutation types across the NF1 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)
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