Mutation Analysis (MutSigCV v0.9)
Prostate Adenocarcinoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1BK19SF
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/C1BK19SF
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: 251

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: 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: 6. 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
PTEN 244474 58734 348 13 13 13 0 0 20 0.29 1e-14 71 0.17 1.1e-10
SPOP 229665 60240 368 24 24 10 0 0 20 0.24 1.2e-14 80 0.18 1.1e-10
PCDHAC2 541909 172437 124 27 26 27 19 0 20 1.6 1e-12 70 0.18 6.2e-09
FOXA1 204816 59989 86 12 11 10 2 0 20 1.8 2.9e-07 45 0.17 0.0013
TMEM216 53714 16315 78 4 4 1 0 0 20 0.92 1.3e-06 26 0.16 0.0048
TP53 237195 69276 412 20 20 17 1 0 4 2.7 3.2e-06 64 0.18 0.0097
CDKN1B 116715 32379 348 4 4 4 0 0 20 0.33 0.000062 25 0.16 0.15
SLC10A2 205569 61244 246 5 5 5 0 0 18 0.62 0.000067 21 0.16 0.15
RBPMS2 97639 31124 500 3 3 3 1 0 20 0.83 0.00053 19 0.16 1
LCE2D 64758 18574 48 3 3 3 0 0 20 0.23 0.0006 14 0.14 1
RYBP 127006 35140 122 4 4 4 0 0 20 0.83 0.0017 17 0.16 1
ZMYM3 657871 188752 678 8 7 8 0 0 20 1.1 0.0019 33 0.17 1
C1orf95 59738 18072 158 2 2 2 0 0 20 0.77 0.003 13 0.14 1
PBX4 177206 50702 230 4 4 4 0 0 20 1.2 0.0033 19 0.16 1
NKX3-1 97890 29618 48 4 4 4 0 0 11 1.3 0.0048 16 0.15 1
GJD3 22590 8032 8 2 2 2 0 0 20 1.4 0.0058 10 0.13 1
CLEC1A 168923 44427 238 4 4 4 0 0 20 0.67 0.0064 14 0.15 1
HBG1 47941 14558 66 2 2 2 0 0 20 1.4 0.0074 10 0.13 1
CTNNB1 461589 138050 564 9 9 7 0 0 20 1.3 0.0075 25 0.17 1
LMOD2 221131 62499 42 3 3 1 1 0 20 1.5 0.0076 19 0.16 1
DLX6 81073 23343 48 2 2 2 0 0 20 1.5 0.0077 13 0.15 1
DDX47 269574 79065 472 4 4 4 0 0 20 0.21 0.0081 16 0.15 1
BRAF 433477 123743 678 6 6 5 0 0 19 0.61 0.0094 20 0.16 1
CASZ1 804957 240709 558 6 6 6 0 0 20 0.23 0.0099 29 0.17 1
KCNA3 282877 88352 24 5 5 5 3 0 20 1.2 0.01 20 0.16 1
SPRR2F 43172 11797 48 2 2 2 0 0 20 0.49 0.01 7.7 0.11 1
ZFP36L1 191764 61495 88 4 4 4 0 0 20 0.25 0.011 16 0.15 1
LHX3 130269 36897 128 3 3 3 0 0 20 0.65 0.011 13 0.15 1
BANF2 56726 14056 78 2 2 2 0 0 20 0.56 0.012 8.1 0.12 1
EMG1 170680 51957 246 2 2 1 0 0 20 0.64 0.013 13 0.15 1
ETV3 88101 22088 128 3 3 3 0 0 11 1.3 0.014 16 0.15 1
WSB2 238952 69778 322 2 2 2 1 0 17 0.17 0.015 7.6 0.11 1
OR5AS1 187999 55220 54 4 4 4 0 0 20 1.3 0.017 14 0.15 1
TAPT1 197035 53714 228 2 2 1 0 0 20 0.59 0.017 13 0.14 1
HTR2A 277104 79567 128 3 3 3 0 0 20 0.44 0.017 15 0.15 1
PTEN

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

SPOP

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

PCDHAC2

Figure S3.  This figure depicts the distribution of mutations and mutation types across the PCDHAC2 significant gene.

FOXA1

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

TMEM216

Figure S5.  This figure depicts the distribution of mutations and mutation types across the TMEM216 significant gene.

TP53

Figure S6.  This figure depicts the distribution of mutations and mutation types across the TP53 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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