Mutation Analysis (MutSigCV v0.9)
Adrenocortical Carcinoma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C12J69JC
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/C12J69JC
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: 91

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): 88

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: 88. 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
ASPDH 25480 8736 5518 19 19 2 0 0 20 2.2 0 61 0.086 0
CCDC102A 74711 21749 10057 26 26 1 0 0 20 0.34 0 68 0.087 0
ZFPM1 58058 17199 7565 113 48 6 0 0 20 0.97 0 270 0.14 0
GARS 150423 40859 28658 36 35 3 6 0 18 1 1.1e-15 89 0.086 5.1e-12
RINL 80353 25298 13617 19 19 1 0 0 20 0 1.6e-15 57 0.088 5.7e-12
MAL2 27482 8372 4005 24 23 2 1 0 20 1 2.1e-15 150 0.088 6.4e-12
ZAR1 25025 5733 5518 19 19 2 1 0 20 0.43 4.9e-15 65 0.19 1.3e-11
ZNF517 62517 18746 6230 34 33 2 0 0 20 1.1 6.2e-15 100 0.089 1.4e-11
OPRD1 52416 16835 2937 26 26 1 1 0 20 0.27 7.4e-15 83 0.088 1.5e-11
CLDN23 21385 7735 534 13 13 1 0 0 20 0.4 8.1e-15 47 0.085 1.5e-11
KCNK17 65338 19747 8455 19 19 2 0 0 20 0.43 8.8e-15 59 0.087 1.5e-11
IRX3 48958 14560 3560 21 21 1 0 0 20 1.4 1e-14 65 0.087 1.5e-11
TOR3A 70525 20020 8544 25 25 1 0 0 20 0.95 1.3e-14 74 0.094 1.8e-11
GLTPD2 23569 8281 4361 19 19 1 2 0 20 0.66 1.4e-14 58 0.085 1.8e-11
LACTB 94094 24752 8722 27 27 3 0 0 20 0.9 1.5e-14 76 0.09 1.9e-11
KBTBD13 16562 5278 0 15 15 5 6 0 20 1.4 2.1e-14 53 0.086 2.4e-11
ATXN1 157794 51688 3827 15 14 9 1 0 20 1.1 2.8e-13 64 0.087 3e-10
FPGS 93093 29757 18957 14 14 2 0 0 20 0 2.3e-12 45 0.085 2.4e-09
KRTAP5-5 48412 14196 2136 9 7 5 2 0 20 0.98 1e-11 43 0.086 9.9e-09
ERCC2 168805 50323 44589 19 19 1 0 0 20 0 1.6e-11 49 0.086 1.5e-08
C16orf3 12285 4186 1068 8 8 3 0 0 20 0.71 2.3e-11 33 0.082 2e-08
TP53 85995 25116 18334 19 17 19 1 0 4 1.9 3.2e-11 68 0.094 2.7e-08
PANK2 88634 26481 20025 15 15 2 0 0 20 0.47 1.6e-10 43 0.086 1.2e-07
TMEM189 48685 14560 8366 12 12 1 0 0 20 0.7 1.7e-10 38 0.093 1.3e-07
BHLHE22 23569 8008 890 11 10 3 0 0 20 0.56 2e-10 35 0.084 1.4e-07
MEN1 122759 38675 16643 8 8 7 0 0 20 0.78 2.1e-10 46 0.086 1.4e-07
NMU 29939 7644 12638 10 10 2 0 0 20 0.58 5.6e-10 35 0.096 3.8e-07
C4orf32 17654 4914 1958 9 9 1 0 0 20 0.84 7.9e-10 31 0.084 5.2e-07
CTNNB1 167349 50050 25098 14 14 9 0 0 20 0.83 1.6e-09 48 0.087 9.9e-07
C14orf180 8645 2548 2759 4 4 1 2 0 20 0.85 2.3e-09 27 0.084 1.3e-06
CRIPAK 89544 29757 2047 22 17 9 41 0 20 4.1 2.3e-09 50 0.086 1.3e-06
HHIPL1 89726 25844 10680 14 14 1 1 0 20 0.96 5.2e-09 40 0.086 3e-06
AMDHD1 81809 23296 13795 18 18 1 15 0 20 3.1 6e-09 45 0.085 3.3e-06
IDUA 113750 37310 12994 15 15 4 1 0 20 0.86 6.2e-09 42 0.087 3.3e-06
UQCRFS1 42952 12740 1958 12 12 1 10 0 14 2.5 6.3e-09 36 0.083 3.3e-06
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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