Mutation Analysis (MutSigCV v0.9)
Testicular Germ Cell Tumors (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C1571B9K
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/C1571B9K
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): 7

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: 7. 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
KIT 351330 95356 75875 27 26 13 0 0 20 1 5.1e-15 78 0.12 9.3e-11
FAM104B 44356 10349 10730 11 7 3 0 0 20 0.95 2.7e-09 31 0.11 0.000025
NRAS 68697 18178 15502 7 7 4 0 0 20 0.32 6.2e-07 25 0.1 0.0038
ATXN3 133923 30690 39010 4 4 1 0 0 20 1.3 8.3e-06 25 0.21 0.038
FAM101B 35494 11034 2490 4 4 3 0 0 20 0.64 0.000015 18 0.17 0.056
RAC1 70779 21307 22834 4 4 3 0 0 20 0 3e-05 18 0.098 0.092
FANK1 124270 34717 38090 4 4 1 0 0 10 0 0.000035 25 0.11 0.092
CDC27 290276 78527 62975 6 6 4 1 0 20 1.5 0.000081 29 0.16 0.19
SP8 60409 20433 5835 6 6 1 0 0 20 0.92 0.00014 19 0.1 0.27
MUC2 562546 162967 100006 18 17 13 9 0 20 3.5 0.00016 45 0.12 0.27
GSX2 62359 19491 4367 4 4 4 0 0 20 0.26 0.00016 17 0.27 0.27
ADSS 155862 43210 46321 3 3 3 0 0 20 0.11 0.0002 12 0.091 0.3
TPTE2 190953 49633 91899 5 5 1 2 0 14 2.7 0.00023 28 0.11 0.32
GFRA4 12858 4933 4435 2 2 2 0 0 20 1.3 0.00029 11 0.41 0.36
KRTAP10-10 85973 25926 4484 5 5 1 2 0 20 1.5 0.0003 17 0.1 0.36
SERINC2 144699 42916 31279 4 4 2 0 0 20 0.42 0.00043 16 0.1 0.5
KRAS 90169 22056 19318 19 19 7 0 0 1 0 0.00053 43 0.11 0.56
PNPLA4 82921 25161 18144 5 5 1 0 0 9 0 0.00069 16 0.1 0.7
HSF4 151283 43069 36542 6 6 5 0 0 20 0.34 0.00086 18 0.1 0.83
MLLT3 205294 49911 39514 4 4 2 1 0 20 1.5 0.001 21 0.11 0.94
MAFA 20928 6427 0 2 2 2 0 0 20 1.4 0.0017 11 0.091 1
AZU1 69924 22861 11998 3 3 3 0 0 20 2.3 0.0021 16 0.12 1
GRP 38744 7897 8022 2 2 2 0 0 20 0.28 0.0022 10 0.097 1
PDLIM1 114074 32164 23665 3 3 2 0 0 20 0.63 0.0023 16 0.1 1
NKD2 79679 23965 13922 2 2 1 0 0 20 0.27 0.0023 13 0.095 1
PSCA 19982 6262 1268 2 2 2 0 0 20 0.32 0.0027 8 0.081 1
TMEM86B 42956 13869 5658 2 2 2 0 0 20 0.34 0.0033 10 0.092 1
FAM43B 32474 10277 1630 2 2 2 0 0 20 0.92 0.0033 10 0.094 1
RHPN2 236004 67940 51865 6 6 3 1 0 20 0.87 0.0035 18 0.13 1
TWIST1 30581 9101 777 2 2 2 0 0 20 0.69 0.0036 8.1 0.081 1
RRAD 73759 23244 12221 3 3 3 0 0 20 0.72 0.0037 13 0.093 1
CCDC64 152395 42606 28590 5 5 5 0 0 20 1.5 0.0038 18 0.11 1
IL34 79119 23840 18293 3 3 3 0 0 20 1.4 0.0046 13 0.1 1
BCL11B 177518 52320 9394 5 5 3 0 0 20 0.28 0.0052 15 0.11 1
UBD 58114 16539 8464 2 2 2 0 0 20 0 0.0052 10 0.091 1
KIT

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

FAM104B

Figure S2.  This figure depicts the distribution of mutations and mutation types across the FAM104B significant gene.

NRAS

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

ATXN3

Figure S4.  This figure depicts the distribution of mutations and mutation types across the ATXN3 significant gene.

FAM101B

Figure S5.  This figure depicts the distribution of mutations and mutation types across the FAM101B significant gene.

RAC1

Figure S6.  This figure depicts the distribution of mutations and mutation types across the RAC1 significant gene.

FANK1

Figure S7.  This figure depicts the distribution of mutations and mutation types across the FANK1 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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