Mutation Analysis (MutSigCV v0.9)
Prostate Adenocarcinoma (Primary solid tumor)
17 October 2014  |  analyses__2014_10_17
Maintainer Information
Citation Information
doi:10.7908/C18G8JNJ
Maintained by David Heiman (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/C18G8JNJ
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: PRAD-TP

  • Number of patients in set: 261

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:PRAD-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: PRAD-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
SPOP 238815 62640 47288 27 26 11 0 0 20 0.26 6.7e-16 93 0.26 1.2e-11
PTEN 254214 61074 44718 13 13 13 0 0 20 0.32 5.7e-15 74 0.26 5.2e-11
TP53 246645 72036 52942 24 23 19 0 0 4 1.6 9.2e-11 76 0.27 5.6e-07
FOXA1 212976 62379 11051 13 12 11 2 0 20 2.5 5.4e-09 49 0.25 0.000025
CDKN1B 121365 33669 44718 4 4 4 0 0 20 0.37 7.7e-06 26 0.24 0.028
SLC10A2 213759 63684 31611 5 5 5 0 0 18 1 4e-05 21 0.25 0.12
NKX3-1 101790 30798 6168 5 5 5 0 0 11 1.1 0.00017 21 0.23 0.45
CTNNB1 479979 143550 72474 9 9 7 0 0 20 1.1 0.00046 27 0.25 1
PBX4 184266 52722 29555 4 4 4 0 0 20 0.84 0.00059 20 0.25 1
CDK12 891054 269091 68362 10 7 10 0 0 20 0 0.00085 27 0.25 1
HDGFL1 59769 15399 3084 2 2 1 0 0 20 1.7 0.0011 14 0.23 1
LMOD2 229941 64989 5397 3 3 1 0 0 20 0.89 0.0012 19 0.24 1
DNASE2B 201492 53766 19275 3 3 3 0 0 20 1.1 0.0014 18 0.24 1
ZCCHC10 105183 27144 20046 2 2 2 0 0 20 0.5 0.0015 13 0.23 1
ZMYM3 684081 196272 87123 7 6 7 0 0 20 1 0.0015 28 0.26 1
BRAF 450747 128673 87123 6 6 5 0 0 19 0.7 0.0017 21 0.24 1
CLEC1A 175653 46197 30583 4 4 4 0 0 20 0.76 0.0019 14 0.22 1
IL27 123453 42804 23130 2 2 1 0 0 20 0 0.0021 14 0.22 1
KCNA3 294147 91872 3084 4 4 4 2 0 20 1 0.0022 19 0.25 1
LMTK2 899145 257085 66306 6 6 6 0 0 20 0 0.0023 24 0.24 1
CASZ1 837027 250299 71703 6 6 6 0 0 20 0.54 0.0024 29 0.25 1
ALG14 131283 41238 21331 2 2 2 0 0 20 0.25 0.0025 13 0.22 1
FAM181B 51939 16965 2570 2 2 2 0 0 20 1.1 0.0025 13 0.21 1
LHX3 135459 38367 16448 3 3 3 0 0 20 0.72 0.0032 14 0.25 1
DLX6 84303 24273 6168 2 2 2 0 0 20 1.5 0.0032 13 0.22 1
RYBP 132066 36540 15677 3 3 3 1 0 20 1.2 0.0037 14 0.23 1
RPTN 499815 114579 11308 6 6 5 0 0 5 0 0.0039 27 0.24 1
BANF2 58986 14616 10023 2 2 2 0 0 20 0.29 0.0041 8.5 0.19 1
PRTFDC1 139113 34191 92263 3 3 3 0 0 19 0.29 0.0043 11 0.21 1
PIK3CA 678339 173565 101772 10 9 10 2 0 20 1.8 0.0044 26 0.28 1
MED15 453879 129717 85581 5 5 5 0 0 20 0.92 0.0045 20 0.28 1
ZNF485 256824 62901 13107 3 3 3 0 0 20 0 0.0049 14 0.22 1
ZFP36L1 199404 63945 11308 4 4 4 0 0 20 0.58 0.0049 16 0.26 1
TAPT1 204885 55854 29298 2 2 1 0 0 20 0.44 0.0049 13 0.22 1
ETV3 91611 22968 16448 3 3 3 0 0 11 1.6 0.005 16 0.24 1
SPOP

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

PTEN

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

TP53

Figure S3.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

FOXA1

Figure S4.  This figure depicts the distribution of mutations and mutation types across the FOXA1 significant gene.

CDKN1B

Figure S5.  This figure depicts the distribution of mutations and mutation types across the CDKN1B 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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