Mutation Analysis (MutSigCV v0.9)
Prostate Adenocarcinoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C1T72GQW
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/C1T72GQW
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: 332

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): 6

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
SPOP 303780 79680 59800 38 37 14 0 0 20 0.32 1.8e-15 140 0.26 3.2e-11
TP53 313740 91632 66950 23 23 21 0 0 4 0 2.8e-14 96 0.29 2.5e-10
FOXA1 270912 79348 13975 14 13 11 0 0 20 0.87 8.6e-14 63 0.26 5.2e-10
PTEN 323368 77688 56550 9 9 9 0 0 20 0 2.1e-11 50 0.28 9.7e-08
CDKN1B 154380 42828 56550 4 4 4 0 0 20 0.44 6.6e-06 27 0.27 0.024
BRAF 573364 163676 110175 8 8 8 0 0 19 0.84 0.000029 30 0.26 0.09
HRAS 153716 44488 27950 4 4 2 0 0 20 0 0.001 15 0.23 1
PCDH18 881460 251656 27300 8 8 8 1 0 17 1.1 0.0013 27 0.28 1
NKX3-1 129480 39176 7800 4 4 4 0 0 11 0 0.0017 15 0.24 1
CTNNB1 610548 182600 91650 8 8 7 0 0 20 1 0.002 23 0.31 1
RYBP 167992 46480 19825 3 3 3 1 0 20 0.7 0.0023 14 0.22 1
WSB2 316064 92296 52325 3 3 3 0 0 17 0.32 0.0023 11 0.23 1
ZMYM3 870172 249664 110175 6 6 6 0 0 20 2.2 0.0031 29 0.49 1
ETV3 116532 29216 20800 3 3 3 0 0 11 0.95 0.0033 17 0.24 1
TPPP3 140768 38512 20150 2 2 2 0 0 20 1.3 0.0034 12 0.23 1
NUP37 260620 68724 56550 3 3 3 0 0 20 0 0.0049 14 0.23 1
CD48 192560 53784 27300 4 4 4 0 0 20 1.2 0.0058 14 0.23 1
SPANXD 77688 20252 13975 2 2 2 0 0 20 0.87 0.0077 8.3 0.29 1
RPL10A 164672 44488 35750 2 2 2 0 0 20 1 0.0077 11 0.21 1
MRPS25 117196 29216 19500 2 2 2 0 0 20 0.66 0.0084 11 0.21 1
SDHC 263608 69056 53625 2 2 2 0 0 20 0.35 0.0092 13 0.24 1
IFNA16 148072 40504 7800 2 2 2 0 0 20 0.44 0.01 10 0.2 1
DDX47 356568 104580 76700 3 3 3 0 0 20 0.29 0.011 14 0.31 1
ZFP28 648064 168324 45825 4 4 4 1 0 15 0.67 0.011 18 0.5 1
OR5L2 235720 74036 7475 5 5 5 0 0 20 3.2 0.012 16 0.24 1
ATP6V1C2 361880 100264 90350 3 3 3 0 0 20 0.3 0.012 13 0.22 1
ORMDL1 120516 35524 19825 2 2 2 0 0 20 1.3 0.013 7.9 0.18 1
GPR37L1 352916 115204 12675 3 3 3 0 0 20 0.28 0.014 13 0.22 1
METTL3 456832 132468 71825 3 3 3 0 0 20 0 0.014 13 0.22 1
KLK14 144752 46148 29575 2 2 2 0 0 20 0.34 0.015 10 0.2 1
LCE2D 85656 24568 7800 2 2 2 0 0 20 1.3 0.015 7.9 0.2 1
BCL6 549128 156040 50375 4 4 4 1 0 20 1.2 0.016 16 0.23 1
IFI16 533524 141764 118625 4 4 4 1 0 18 1 0.016 16 0.24 1
CDK12 1133448 342292 86450 7 6 7 3 0 20 1.3 0.017 25 0.29 1
PFN2 102256 29216 19500 2 2 2 0 0 20 0.92 0.017 7.9 0.2 1
SPOP

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

TP53

Figure S2.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

FOXA1

Figure S3.  This figure depicts the distribution of mutations and mutation types across the FOXA1 significant gene.

PTEN

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

CDKN1B

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

BRAF

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