Mutation Analysis (MutSigCV v0.9)
Adrenocortical Carcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1DF6Q5G
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/C1DF6Q5G
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: ACC-TP

  • Number of patients in set: 90

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:ACC-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 65

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: ACC-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: 65. 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
OPRD1 51840 16650 99 26 26 1 1 0 20 0.28 1.1e-15 78 0.12 1.5e-11
ZFPM1 57420 17010 255 112 47 6 0 0 20 1 3e-15 260 0.098 1.5e-11
TOR3A 69750 19800 288 25 25 1 0 0 20 0.85 3.9e-15 70 0.073 1.5e-11
ASPDH 25200 8640 186 19 19 2 0 0 20 2.3 4.7e-15 57 0.094 1.5e-11
MAL2 27180 8280 135 23 22 2 1 0 20 1.1 4.8e-15 140 0.084 1.5e-11
GLTPD2 23310 8190 147 19 19 1 2 0 20 0.68 6.6e-15 55 0.067 1.5e-11
IRX3 48420 14400 120 21 21 1 0 0 20 1.4 7.1e-15 61 0.11 1.5e-11
GARS 148770 40410 966 35 34 3 6 0 18 1.2 7.4e-15 80 0.16 1.5e-11
ZAR1 24750 5670 186 19 19 2 1 0 20 0.44 7.9e-15 62 0.072 1.5e-11
ZNF517 61830 18540 210 34 33 2 0 0 20 1.2 8.4e-15 95 0.084 1.5e-11
KBTBD13 16380 5220 0 15 15 5 6 0 20 1.5 1.2e-14 51 0.15 2e-11
CCDC102A 73890 21510 339 26 26 1 0 0 20 0.35 1.5e-14 64 0.085 2.2e-11
LACTB 93060 24480 294 27 27 3 0 0 20 1 1.6e-14 70 0.069 2.2e-11
KCNK17 64620 19530 285 19 19 2 0 0 20 0.48 6.7e-14 55 0.069 8.7e-11
CLDN23 21150 7650 18 13 13 1 0 0 20 0.42 5.9e-13 44 0.065 7.2e-10
RINL 79470 25020 459 19 19 1 0 0 20 0 9.9e-13 53 0.085 1.1e-09
ATXN1 156060 51120 129 15 14 9 1 0 20 1 2.7e-11 62 0.076 2.9e-08
FPGS 92070 29430 639 14 14 2 0 0 20 0 8.1e-11 42 0.095 8.2e-08
C16orf3 12150 4140 36 8 8 3 0 0 20 0.75 3e-10 32 0.17 2.9e-07
BHLHE22 23310 7920 30 11 10 3 0 0 20 0.52 2.4e-09 34 0.083 2.2e-06
ERCC2 166950 49770 1503 19 19 1 0 0 20 0 3.5e-09 46 0.069 3.1e-06
NMU 29610 7560 426 10 10 2 0 0 20 0.56 6.1e-09 33 0.064 5e-06
TP53 85050 24840 618 19 17 19 1 0 4 1.9 6.6e-09 66 0.066 5.2e-06
MEN1 121410 38250 561 8 8 7 0 0 20 0.81 7.7e-09 44 0.086 5.9e-06
PANK2 87660 26190 675 15 15 2 0 0 20 0.5 8.8e-09 40 0.068 6.3e-06
C4orf32 17460 4860 66 9 9 1 0 0 20 0.85 8.9e-09 30 0.073 6.3e-06
KRTAP5-5 47880 14040 72 8 6 5 2 0 20 1 1.2e-08 35 0.13 8.4e-06
C14orf180 8550 2520 93 4 4 1 2 0 20 0.88 1.4e-08 27 0.064 9.2e-06
TMEM189 48150 14400 282 12 12 1 0 0 20 0.74 1.5e-08 35 0.28 9.5e-06
UQCRFS1 42480 12600 66 12 12 1 10 0 14 2.3 8e-08 35 0.069 0.000049
SYT8 60480 20610 369 14 14 3 0 0 20 1.3 1.1e-07 35 0.069 0.000065
C19orf10 25110 6840 264 10 10 1 0 0 10 0.27 1.1e-07 30 0.074 0.000065
CTNNB1 165510 49500 846 14 14 9 0 0 20 0.87 1.2e-07 45 0.074 0.000066
CRIPAK 88560 29430 69 22 17 9 41 0 20 4.2 2.5e-07 47 0.066 0.00013
SPERT 65700 18900 168 5 5 1 0 0 20 0.15 2.6e-07 30 0.071 0.00013
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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