Mutation Analysis (MutSigCV v0.9)
Pheochromocytoma and Paraganglioma (Primary solid tumor)
17 October 2014  |  analyses__2014_10_17
Maintainer Information
Citation Information
doi:10.7908/C1XD10M1
Maintained by David Heiman (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/C1XD10M1
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: PCPG-TP

  • Number of patients in set: 178

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:PCPG-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 1

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: PCPG-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: 1. 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
HRAS 82414 23852 15308 18 18 3 0 0 20 0 1.2e-14 63 0.099 2.1e-10
EPAS1 349948 97544 53044 8 8 4 0 0 20 0.96 0.000067 25 0.096 0.61
NF1 1675336 469564 210752 16 15 16 0 0 0 0 0.00045 70 0.1 1
NDUFS7 49484 15486 12282 2 2 2 0 0 20 0 0.0016 11 0.075 1
AMMECR1 95764 26878 35956 2 2 1 0 0 20 0 0.0018 13 0.081 1
CSDE1 359382 98078 67818 4 4 4 0 0 9 0 0.0023 23 0.093 1
GYPE 32218 9612 8544 2 2 1 0 0 20 1.6 0.0029 8.8 0.069 1
TRDN 155572 40406 43966 3 3 2 0 0 14 3.9 0.0029 18 0.19 1
SOX4 67996 21182 890 2 2 2 0 0 20 1.7 0.0043 13 0.083 1
VHL 53756 16910 7832 3 3 3 0 0 13 0 0.0053 13 0.082 1
RET 408866 124244 53756 7 6 4 0 0 20 0 0.0087 18 0.089 1
CLEC17A 105020 29370 29014 2 2 1 0 0 16 0 0.011 13 0.085 1
FAM83D 196156 60520 12816 3 3 3 0 0 20 1.1 0.012 13 0.085 1
NKX6-3 21004 6942 1958 1 1 1 0 0 20 0 0.015 7.2 0.059 1
PI3 49128 14952 7476 1 1 1 0 0 20 1.1 0.016 6.9 0.06 1
NKD1 156284 47526 22606 2 2 2 0 0 20 1.4 0.016 13 0.082 1
SLC46A1 166786 53578 15486 2 2 2 0 0 20 0 0.017 9.9 0.076 1
RPS27L 37380 9790 14774 1 1 1 0 0 20 1.4 0.018 6.9 0.059 1
P2RY1 152724 45924 4094 2 2 2 0 0 20 0 0.018 10 0.076 1
ATP6V1G3 61588 11748 14774 2 2 2 0 0 20 1.4 0.02 7.2 0.064 1
NDUFAF2 69954 18512 16732 2 2 1 0 0 20 2 0.022 7.3 0.064 1
ECHDC2 96832 32930 47170 2 2 2 0 0 12 0 0.023 9.9 0.077 1
ANKK1 189926 57138 13706 2 2 2 0 0 20 1.3 0.024 12 0.086 1
RNF149 148452 44144 23496 2 2 2 0 0 20 0 0.032 7 0.065 1
PRMT1 145782 37736 38982 2 2 2 0 0 20 0 0.033 9.7 0.076 1
FAM72B 53222 13528 10324 1 1 1 0 0 20 1.3 0.034 6.7 0.061 1
TCTEX1D4 58384 20114 9790 1 1 1 0 0 20 1.1 0.034 6.8 0.061 1
UBL4A 53222 16732 8900 1 1 1 0 0 20 1.3 0.035 6.8 0.064 1
FAM162B 45212 12994 11036 1 1 1 0 0 20 0 0.035 6.9 0.061 1
TCF23 61232 21360 7654 1 1 1 0 0 20 2 0.036 6.8 0.061 1
LIG1 344608 102528 81168 2 2 2 0 0 20 0 0.036 9.6 0.078 1
UBE2W 47882 12104 11214 1 1 1 0 0 20 0 0.036 6.8 0.13 1
AVP 22962 6942 3916 1 1 1 0 0 20 0 0.038 4.1 0.036 1
DHRS4 106800 34176 25098 1 1 1 0 0 20 0.81 0.039 6.7 0.062 1
PTMA 37202 8544 32040 1 1 1 0 0 7 2.3 0.04 6.7 0.06 1
HRAS

Figure S1.  This figure depicts the distribution of mutations and mutation types across the HRAS 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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