Mutation Analysis (MutSigCV v0.9)
Adrenocortical Carcinoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C13T9FP1
Maintained by Dan DiCara (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/C13T9FP1
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
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: 5. 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
MEN1 121410 38250 0 7 7 7 0 0 20 1.4 4.3e-08 39 0.07 0.00068
TP53 85050 24840 0 17 15 17 1 0 4 5.9 7.4e-08 61 0.07 0.00068
CTNNB1 165510 49500 0 13 13 9 0 0 20 2.2 2.3e-07 43 0.064 0.0014
PRKAR1A 82980 23220 0 6 6 5 0 0 12 0 1.3e-06 34 0.073 0.0061
CRIPAK 88560 29430 0 11 11 3 18 0 20 5.6 5.2e-06 33 0.065 0.019
GADD45G 22230 6480 0 2 2 2 0 0 20 0.84 0.00099 13 0.06 1
KRTAP4-5 36900 9360 0 5 4 3 0 0 20 2.8 0.0058 14 0.067 1
DAXX 157770 48330 0 4 4 4 0 0 20 1.4 0.0066 19 0.066 1
LCE1F 24660 7110 0 2 2 2 0 0 20 0.83 0.0067 9.6 0.057 1
TWISTNB 72630 19620 0 4 3 4 0 0 20 1.1 0.0098 14 0.061 1
UCN3 31320 9810 0 1 1 1 0 0 20 0.23 0.011 6.3 0.042 1
IRF2 76500 20430 0 3 3 3 0 0 20 0.47 0.014 12 0.073 1
CLDN5 27270 9180 0 2 2 2 0 0 20 0.36 0.016 6.7 0.045 1
ZNRF3 161550 49320 0 5 4 5 0 0 20 0.64 0.019 18 0.07 1
SAT1 37620 9000 0 2 2 2 0 0 20 2.1 0.023 9.2 0.051 1
TMEM40 46080 12960 0 2 2 2 0 0 20 0.52 0.025 9.2 0.067 1
CEBPB 14670 4320 0 1 1 1 0 0 20 1.1 0.026 6.6 0.042 1
LGALS7 3690 1080 0 2 1 2 0 0 20 0.53 0.027 3.9 0.027 1
MUC5B 753120 270630 0 16 16 3 2 0 17 1.1 0.027 32 0.094 1
FAM9A 55890 13860 0 3 3 3 0 0 14 1.3 0.029 11 0.059 1
NUDT4 24390 7110 0 2 2 2 0 0 20 0 0.03 6.5 0.048 1
KIAA1191 83700 24660 0 2 2 2 0 0 20 0.56 0.031 11 0.072 1
LCE1B 24930 7200 0 1 1 1 0 0 20 0.83 0.033 6.4 0.048 1
FAM200A 13590 3600 0 1 1 1 0 0 20 0 0.034 6.5 0.047 1
PPP1CA 82800 24210 0 2 2 2 0 0 20 0.63 0.034 11 0.06 1
TTC1 64710 15300 0 2 2 2 0 0 20 0.41 0.035 8.6 0.058 1
C11orf57 76680 18270 0 2 2 2 0 0 20 0.6 0.036 11 0.063 1
CLTB 46350 12420 0 4 3 4 0 0 20 0.86 0.038 8.5 0.049 1
RAMP3 27540 7920 0 2 2 2 1 0 20 2.7 0.039 7.1 0.049 1
NDUFS7 25020 7830 0 1 1 1 0 0 20 1.6 0.04 6.4 0.043 1
SUDS3 54270 13410 0 2 2 2 0 0 20 0.52 0.041 8.8 0.053 1
FANK1 75060 20970 0 2 2 1 0 0 10 0.79 0.042 11 0.061 1
CAPRIN1 150840 40950 0 2 2 2 0 0 20 0.79 0.042 11 0.062 1
BEGAIN 74340 22590 0 2 2 2 0 0 20 0.39 0.048 8.7 0.051 1
CXCL1 18360 5670 0 1 1 1 0 0 20 0.57 0.05 6.3 0.04 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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