Mutation Analysis (MutSigCV v0.9)
Thymoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1222T76
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Mutation Analysis (MutSigCV v0.9). Broad Institute of MIT and Harvard. doi:10.7908/C1222T76
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: THYM-TP

  • Number of patients in set: 123

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:THYM-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 2

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: THYM-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: 2. 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
GTF2I 178842 49077 50307 49 49 2 0 0 20 0.86 4.6e-15 150 0.16 8.3e-11
HRAS 56949 16482 10578 10 10 8 0 0 20 0 6e-10 36 0.18 5.5e-06
UNC93B1 51783 14268 7380 5 5 2 0 0 20 0 2e-05 19 0.083 0.12
CEBPA 21771 6642 492 2 2 2 0 0 20 0.94 0.00014 14 0.09 0.63
FOXD1 30627 8979 0 2 2 1 0 0 20 1.6 0.00082 13 0.085 1
IRX3 66174 19680 4920 2 2 2 0 0 20 0 0.0027 13 0.096 1
ATRN 385974 99261 68019 3 3 2 0 0 20 0 0.0068 17 0.095 1
HMX3 35055 11070 738 1 1 1 0 0 20 0.33 0.0073 7.2 0.066 1
NRAS 56703 15006 10086 3 3 3 0 0 20 4.3 0.0084 9.9 0.077 1
CALY 18327 5166 2952 1 1 1 0 0 20 0 0.016 7 0.062 1
COX8A 15621 5781 4182 1 1 1 0 0 20 0 0.017 4.3 0.046 1
TMEM47 23862 7011 3444 1 1 1 0 0 20 1.3 0.024 4.2 0.085 1
WSB2 117096 34194 19803 1 1 1 0 0 17 0.26 0.025 5.9 0.054 1
PTH2 18204 7503 3075 1 1 1 1 0 20 4.3 0.028 6.7 0.072 1
PHF10 128904 33579 22755 2 2 2 0 0 20 0.89 0.029 8.5 0.084 1
FOXE3 25953 7995 0 1 1 1 0 0 20 1.5 0.031 6.7 0.071 1
L3MBTL3 233823 59409 50553 3 3 3 0 0 18 1.5 0.032 13 0.092 1
RFXAP 32718 8364 6027 1 1 1 0 0 20 0 0.032 6.7 0.059 1
PIK3R1 229272 58917 42435 2 2 2 0 0 20 0 0.037 12 0.082 1
CAPNS1 70848 17835 26568 3 3 1 0 0 5 0 0.037 17 0.096 1
LCE3B 15744 4551 1968 1 1 1 0 0 20 0 0.038 4.2 0.043 1
EMP3 46125 13899 9348 1 1 1 0 0 20 0 0.038 3.9 0.041 1
LYPLA1 60885 16728 18327 1 1 1 0 0 20 1.1 0.039 6.5 0.087 1
EVI5L 107994 31242 20910 2 2 2 0 0 20 0 0.039 6.8 0.066 1
PDCL2 52644 11931 7134 1 1 1 0 0 20 0 0.04 6.6 0.061 1
TMEM185A 45879 13284 7380 1 1 1 0 0 20 0 0.041 6.6 0.074 1
CBY1 37392 10824 10332 1 1 1 0 0 20 2.3 0.042 6.5 0.075 1
BCOR 438372 131118 25215 4 3 4 1 0 20 0.98 0.042 14 0.089 1
BHLHE22 31857 10824 1230 1 1 1 0 0 20 0 0.043 6.7 0.068 1
DEFB112 33456 8487 5658 1 1 1 0 0 20 0 0.044 3.9 0.045 1
PLA2G4C 161868 45756 37638 2 2 2 0 0 20 0 0.044 6.6 0.073 1
TCEAL4 57441 13161 2952 1 1 1 0 0 20 0 0.044 6.6 0.064 1
GET4 74907 21771 17589 2 2 2 0 0 20 3.3 0.045 9.1 0.092 1
DNAJC27 79950 20787 16113 2 2 2 0 0 20 0 0.045 6.6 0.081 1
C3orf27 41205 13899 2829 1 1 1 0 0 20 0 0.046 6.6 0.075 1
GTF2I

Figure S1.  This figure depicts the distribution of mutations and mutation types across the GTF2I significant gene.

HRAS

Figure S2.  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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