Mutation Analysis (MutSigCV v0.9)
Uveal Melanoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1668C9M
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/C1668C9M
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: UVM-TP

  • Number of patients in set: 80

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:UVM-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 4

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: UVM-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: 4. 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
GNA11 60720 16720 8374 36 36 3 0 0 20 3.1 8.7e-15 110 0.059 8.8e-11
BAP1 126240 37920 23305 16 16 16 0 0 20 0 1.3e-14 78 0.059 8.8e-11
GNAQ 64960 16800 10270 41 40 4 0 0 12 0 1.5e-14 110 0.06 8.8e-11
EIF1AX 28320 6640 9480 10 10 6 0 0 20 1.9 5.8e-14 43 0.058 2.7e-10
TMEM216 17120 5200 3081 2 2 1 0 0 20 2.5 0.00051 13 0.085 1
TRDN 69920 18160 19513 3 3 1 0 0 14 0 0.00055 17 0.053 1
EIF1B 22560 5680 6557 2 2 2 0 0 20 1.7 0.0011 10 0.046 1
GFRA4 6880 2640 2133 1 1 1 0 0 20 0 0.0094 7 0.059 1
MAOB 94800 27280 21330 2 2 2 0 0 20 0 0.022 9 0.046 1
HHEX 29520 7200 4819 1 1 1 0 0 20 0 0.029 6.6 0.035 1
FCGR2A 60880 17200 11139 2 2 2 0 0 20 3.1 0.031 6.4 0.038 1
ARL6IP4 25440 7840 6715 1 1 1 0 0 20 3.4 0.035 6.4 0.037 1
SOX3 27440 8720 316 1 1 1 0 0 20 1.6 0.035 6.5 0.063 1
GMEB2 82720 25760 13114 2 2 2 0 0 20 2.1 0.036 8.8 0.047 1
COX16 19680 5040 5767 1 1 1 0 0 20 0 0.037 6.4 0.035 1
TMEM35 30080 10080 3318 1 1 1 0 0 20 0 0.038 6.5 0.062 1
C4orf32 15520 4320 1738 1 1 1 0 0 20 0 0.038 4 0.024 1
PURA 40960 12000 79 1 1 1 0 0 20 1.5 0.043 6.3 0.037 1
H2AFZ 24240 7760 7742 1 1 1 0 0 20 1.2 0.043 3.7 0.022 1
FOXD3 26000 7280 0 1 1 1 0 0 20 0 0.043 6.4 0.037 1
HSD17B7 64960 18480 14457 2 2 2 0 0 20 0 0.044 6.2 0.037 1
MMD2 38400 10720 5609 1 1 1 0 0 20 5.2 0.045 6.3 0.037 1
MC2R 55040 16240 1896 2 2 2 0 0 20 0 0.048 6.2 0.037 1
IGLL1 29360 7360 3397 1 1 1 0 0 20 0 0.048 6.4 0.037 1
MAPKAPK5 73520 19360 12482 2 2 2 0 0 7 0 0.049 11 0.051 1
DNAJB6 49680 12960 11455 1 1 1 0 0 20 1.2 0.052 6.3 0.036 1
USP49 111680 33920 6636 2 2 2 0 0 20 0 0.053 8.6 0.046 1
SLC25A6 50320 15360 5056 1 1 1 0 0 20 1.7 0.055 6.2 0.038 1
CDR2L 18560 5440 1422 1 1 1 0 0 20 2 0.056 3.6 0.022 1
KRTAP5-2 33120 9520 1896 1 1 1 0 0 20 1.7 0.057 3.9 0.024 1
CD160 34640 9920 6636 1 1 1 0 0 20 1.7 0.057 3.9 0.024 1
C16orf11 29440 10080 790 1 1 1 0 0 20 3 0.057 6.2 0.043 1
UPK2 33040 12320 7979 1 1 1 0 0 20 2.1 0.058 6.3 0.037 1
UBE2N 29200 8240 6478 1 1 1 0 0 20 1.6 0.059 3.5 0.021 1
SHF 61040 18400 6320 1 1 1 0 0 20 2.8 0.06 6.2 0.037 1
GNA11

Figure S1.  This figure depicts the distribution of mutations and mutation types across the GNA11 significant gene.

BAP1

Figure S2.  This figure depicts the distribution of mutations and mutation types across the BAP1 significant gene.

GNAQ

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

EIF1AX

Figure S4.  This figure depicts the distribution of mutations and mutation types across the EIF1AX 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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