Mutation Analysis (MutSig 2CV v3.1)
Adrenocortical Carcinoma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C1697283
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Mutation Analysis (MutSig 2CV v3.1). Broad Institute of MIT and Harvard. doi:10.7908/C1697283
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. MutSig 2CV v3.1 was used to generate the results found in this report.

  • Working with individual set: ACC-TP

  • Number of patients in set: 64

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): 130

Results
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 1.  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 2.  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 3.  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

  • 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: 130. 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).

rank gene longname codelen nnei nncd nsil nmis nstp nspl nind nnon npat nsite pCV pCL pFN p q
1 ZFPM1 zinc finger protein, multitype 1 3059 52 0 0 5 0 0 39 44 26 6 1e-16 1e-05 1 1e-16 4.6e-13
2 LACTB lactamase, beta 1668 100 0 0 19 0 0 1 20 20 2 1e-16 1e-05 1 1e-16 4.6e-13
3 CCDC102A coiled-coil domain containing 102A 1681 177 0 0 17 0 0 0 17 17 1 1e-16 1e-05 0.81 1e-16 4.6e-13
4 ZNF517 zinc finger protein 517 1495 326 0 0 14 0 0 0 14 13 2 2.4e-15 1e-05 0.99 1e-16 4.6e-13
5 TOR3A torsin family 3, member A 1214 88 0 0 12 0 0 0 12 12 1 1.7e-12 1e-05 1 6.7e-16 2.4e-12
6 USP42 ubiquitin specific peptidase 42 2985 268 0 1 17 0 0 0 17 16 3 2.1e-15 NaN NaN 2.1e-15 6.5e-12
7 CLDN23 claudin 23 879 40 0 0 10 0 0 0 10 10 1 8e-12 1e-05 0.99 3e-15 7.8e-12
8 TP53 tumor protein p53 1889 7 0 1 5 3 2 5 15 13 15 3.6e-15 0.54 0.17 4.1e-14 9.4e-11
9 KCNK17 potassium channel, subfamily K, member 17 1296 88 0 0 9 0 0 0 9 9 2 1.7e-08 1e-05 0.97 5.2e-12 1.1e-08
10 LZTR1 leucine-zipper-like transcription regulator 1 2621 4 0 0 0 0 0 6 6 6 1 2.2e-08 1e-05 8e-05 6.5e-12 1.1e-08
11 APOE apolipoprotein E 969 129 0 0 8 0 0 0 8 7 2 5.6e-09 1e-05 0.85 6.7e-12 1.1e-08
12 CCDC105 coiled-coil domain containing 105 1526 85 0 0 7 0 0 0 7 7 1 4.6e-08 1e-05 1 1.4e-11 2.1e-08
13 RINL Ras and Rab interactor-like 1569 73 0 0 8 0 0 0 8 8 1 6.4e-08 1e-05 0.45 1.9e-11 2.5e-08
14 MAL2 mal, T-cell differentiation protein 2 546 1 0 1 1 0 0 12 13 12 2 6.5e-08 1e-05 0.21 1.9e-11 2.5e-08
15 LRIG1 leucine-rich repeats and immunoglobulin-like domains 1 3356 33 0 0 28 0 0 0 28 17 2 7.1e-08 1e-05 0.95 2.1e-11 2.5e-08
16 C19orf10 chromosome 19 open reading frame 10 544 52 0 0 8 0 0 0 8 8 1 3.1e-10 0.0018 1 4.1e-11 4.7e-08
17 C10orf95 chromosome 10 open reading frame 95 780 47 0 0 7 0 0 0 7 7 1 2.5e-08 2e-05 0.81 4.9e-11 5.3e-08
18 SYT8 synaptotagmin VIII 1240 49 0 0 9 0 0 0 9 9 3 8.1e-10 0.0031 0.074 6.9e-11 7e-08
19 IDUA iduronidase, alpha-L- 2072 214 0 1 9 0 0 0 9 9 2 3.1e-07 2e-05 2e-05 8.5e-11 8.2e-08
20 HHIPL1 HHIP-like 1 2482 42 0 0 8 0 0 0 8 8 1 5e-07 1e-05 1 1.3e-10 1.2e-07
21 ASPDH aspartate dehydrogenase domain containing 913 40 0 0 8 0 0 0 8 8 2 2e-08 0.00025 1 4.6e-10 3.9e-07
22 C1orf106 chromosome 1 open reading frame 106 2030 27 0 0 9 0 0 0 9 9 2 5e-08 0.00014 1 4.7e-10 3.9e-07
23 THEM4 thioesterase superfamily member 4 743 237 0 0 6 0 0 0 6 6 1 2.4e-07 1e-05 0.96 6.1e-10 4.9e-07
24 CTNNB1 catenin (cadherin-associated protein), beta 1, 88kDa 2406 76 0 0 6 0 0 1 7 7 4 5.4e-06 1e-05 0.68 1.3e-09 9.9e-07
25 GDF1 growth differentiation factor 1 1146 29 0 0 5 0 0 0 5 5 1 6.1e-07 1e-05 0.85 1.4e-09 9.9e-07
26 TSC22D2 TSC22 domain family, member 2 2355 54 0 0 7 1 0 0 8 8 3 1.9e-06 1e-05 1 2.2e-09 1.6e-06
27 ZAR1 zygote arrest 1 1378 5 0 0 11 0 0 0 11 11 2 0.000011 1e-05 1 2.7e-09 1.8e-06
28 RGS9BP regulator of G protein signaling 9 binding protein 708 124 0 0 8 0 0 0 8 8 1 1.1e-06 6e-05 0.79 4e-09 2.6e-06
29 OPRD1 opioid receptor, delta 1 1127 2 0 1 12 0 0 0 12 12 1 3e-05 1e-05 1 7e-09 4.4e-06
30 C16orf3 chromosome 16 open reading frame 3 549 66 0 0 6 0 0 0 6 6 2 1.4e-07 0.00063 0.93 7.7e-09 4.7e-06
31 FPGS folylpolyglutamate synthase 1822 35 0 0 6 0 0 0 6 6 2 4.1e-06 1e-05 1 8.4e-09 4.9e-06
32 PLIN5 perilipin 5 1424 36 0 0 5 0 0 0 5 5 1 0.00013 0.00025 1e-05 2.8e-08 0.000016
33 IRX3 iroquois homeobox 3 1518 16 0 0 5 0 0 0 5 5 1 0.000031 5e-05 1 3.3e-08 0.000018
34 TRIOBP TRIO and F-actin binding protein 7486 7 0 0 12 1 0 0 13 12 4 0.00019 1e-05 0.87 4.1e-08 0.000022
35 KRTAP4-5 keratin associated protein 4-5 548 70 0 0 4 0 0 3 7 5 4 3e-05 1e-05 1 5.7e-08 3e-05
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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