Mutation Analysis (MutSigCV v0.9)
Testicular Germ Cell Tumors (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C14B30D8
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/C14B30D8
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: TGCT-TP

  • Number of patients in set: 149

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:TGCT-TP.final_analysis_set.maf

  • Blacklist used for this analysis: pancan_mutation_blacklist.v14.hg19.txt

  • Significantly mutated genes (q ≤ 0.1): 8

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: TGCT-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: 8. 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
ANKLE1 148093 49512 13048 12 12 7 0 0 20 1.7 9.5e-15 69 0.11 1.6e-10
KIT 351330 95356 75456 27 26 13 0 0 20 1.1 1.7e-14 72 0.11 1.6e-10
FAM104B 44356 10349 10672 11 7 3 0 0 20 0.35 8.2e-10 32 0.1 5e-06
NRAS 68697 18178 15420 7 7 4 0 0 20 0 3.5e-07 25 0.098 0.0016
ATXN3 133923 30690 38796 4 4 1 0 0 20 1.3 5e-06 25 0.1 0.018
FAM101B 35494 11034 2476 4 4 3 0 0 20 0.71 0.000015 18 0.094 0.045
FANK1 124270 34717 37880 4 4 1 0 0 10 0 0.000035 25 0.1 0.091
PRSS3 87318 25777 22408 7 6 4 1 0 20 2.4 4e-05 22 0.099 0.091
CDC27 290276 78527 62624 6 6 4 1 0 20 1.6 0.000073 29 0.1 0.15
SP8 60409 20433 5804 6 6 1 0 0 20 0.69 0.000092 19 0.096 0.17
ADSS 155862 43210 46072 3 3 3 0 0 20 0.043 0.00012 12 0.084 0.19
MUC2 562546 162967 99424 18 17 13 9 0 20 3.9 0.00013 45 0.11 0.2
GSX2 62359 19491 4340 4 4 4 0 0 20 0.29 0.00016 17 0.093 0.23
RAC1 70779 21307 22716 4 4 3 0 0 20 0.28 0.00027 14 0.15 0.33
KRTAP10-10 85973 25926 4460 5 5 1 2 0 20 1.6 0.00029 17 0.095 0.33
GFRA4 12858 4933 4408 2 2 2 0 0 20 1.5 0.00029 11 0.081 0.33
KRAS 90169 22056 19212 19 19 7 0 0 1 0 0.00053 43 0.11 0.57
MLLT3 205294 49911 39296 4 4 2 1 0 20 1.7 0.00091 21 0.099 0.89
HSF4 151283 43069 36336 6 6 5 0 0 20 0.37 0.00093 18 0.097 0.89
PNPLA4 82921 25161 18040 5 5 1 0 0 9 0.65 0.0011 16 0.093 0.98
AZU1 69924 22861 11932 3 3 3 0 0 20 1.7 0.0013 16 0.095 1
MAFA 20928 6427 0 2 2 2 0 0 20 1.6 0.0016 11 0.082 1
SERINC2 144699 42916 31116 4 4 2 0 0 20 0.47 0.0022 13 0.092 1
GRP 38744 7897 7980 2 2 2 0 0 20 0.31 0.0022 10 0.08 1
NKD2 79679 23965 13844 2 2 1 0 0 20 0.3 0.0023 13 0.089 1
C22orf43 86271 21754 43052 3 3 1 0 0 20 0 0.0026 11 0.084 1
PSCA 19982 6262 1260 2 2 2 0 0 20 0.35 0.0027 8 0.071 1
TMEM86B 42956 13869 5624 2 2 2 0 0 20 0.38 0.0033 10 0.083 1
FAM43B 32474 10277 1620 2 2 2 0 0 20 1 0.0033 10 0.083 1
RHPN2 236004 67940 51584 6 6 3 1 0 20 0.97 0.0036 18 0.098 1
TWIST1 30581 9101 772 2 2 2 0 0 20 0.77 0.0036 8.1 0.074 1
RRAD 73759 23244 12152 3 3 3 0 0 20 0.8 0.0036 13 0.09 1
CCDC64 152395 42606 28440 5 5 5 0 0 20 1.7 0.0037 18 0.097 1
ZNF880 58799 14595 1128 2 2 2 0 0 20 0 0.0038 10 0.081 1
RRBP1 329740 93020 85784 7 7 7 2 0 20 1.4 0.0042 20 0.099 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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