Mutation Analysis (MutSigCV v0.9)
Rectum Adenocarcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1668C85
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/C1668C85
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: READ-TP

  • Number of patients in set: 126

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:READ-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 45

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: READ-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: 45. 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
APC 840672 235620 0 202 110 138 25 0 4 6.7 0 390 0.095 0
KRTAP4-1 38178 10332 0 12 12 1 0 0 20 0.91 0 73 0.09 0
TP53 119070 34776 0 111 93 71 5 0 4 2.9 0 260 0.094 0
MUC4 573552 185976 0 59 35 28 7 0 2 2.5 1.3e-15 140 0.093 6.1e-12
PTEN 122724 29484 0 49 34 46 22 0 20 3.6 2.4e-15 88 0.09 8.9e-12
SMAD4 166824 46494 0 26 26 22 9 0 20 1.7 6.8e-15 82 0.091 1.3e-11
CRIPAK 123984 41202 0 28 20 14 2 0 20 1.2 7.5e-15 87 0.099 1.3e-11
KRAS 76230 18648 0 64 64 14 4 0 1 6.5 8e-15 130 0.093 1.3e-11
OR6C76 89586 27468 0 13 13 1 0 0 20 1.3 8e-15 75 0.089 1.3e-11
FBXW7 244692 67158 0 41 30 33 12 0 20 2.8 8.1e-15 88 0.091 1.3e-11
NEFH 210168 58086 0 21 21 5 2 0 20 0.97 9e-15 110 0.091 1.3e-11
ORAI1 72954 23058 0 14 14 1 0 0 20 0.84 9e-15 84 0.09 1.3e-11
C17orf97 89082 25200 0 13 13 4 0 0 20 0.87 9.5e-15 66 0.11 1.3e-11
KRTAP10-7 105336 30744 0 18 18 2 0 0 20 0.51 1e-14 100 0.093 1.3e-11
GPRIN2 118566 40320 0 13 11 3 2 0 20 1.1 1.2e-13 63 0.089 1.5e-10
VHL 38052 11970 0 23 18 22 4 0 13 1.6 2e-12 54 0.089 2.3e-09
PIK3CA 327474 83790 0 56 37 47 16 0 20 2.6 9.1e-12 81 0.091 9.7e-09
HLA-DQA1 71064 21294 0 10 7 3 0 0 20 0.13 2.8e-10 43 0.089 2.9e-07
DEFB126 33012 9576 0 7 7 3 0 0 20 0.67 5.9e-10 38 0.087 5.7e-07
ESRRA 132552 44856 0 9 9 3 0 0 20 0.52 1.9e-09 46 0.089 1.8e-06
KRTAP5-2 52164 14994 0 6 6 1 0 0 20 0.92 3.4e-09 37 0.087 2.9e-06
DDHD1 239400 67410 0 14 12 3 0 0 20 1.2 5.1e-09 57 0.089 4.3e-06
VCX2 16128 4536 0 5 5 2 0 0 20 1.2 6.7e-09 30 0.085 5.3e-06
NRAS 58086 15372 0 12 12 6 1 0 20 1 3e-08 36 0.087 0.000023
TNFAIP6 85302 21924 0 9 9 3 0 0 9 0.92 3.2e-08 45 0.088 0.000023
PRKRA 92106 24822 0 8 8 3 0 0 20 0.71 6.4e-08 36 0.087 0.000045
TCF7L2 187110 52794 0 13 13 12 2 0 20 1.4 2.1e-07 48 0.09 0.00014
KRT4 176274 52038 0 8 8 1 4 0 20 0.96 9.1e-07 44 0.089 0.0006
TMEM37 52542 17010 0 5 5 1 1 0 20 1.4 2e-06 30 0.086 0.0013
LOR 15750 5166 0 3 3 1 0 0 20 0.64 2.4e-06 21 0.08 0.0015
MUC2 474516 137466 0 16 13 8 1 0 20 0.82 2.7e-06 53 0.091 0.0016
TFAM 67158 16128 0 5 5 1 0 0 20 1.4 7e-06 29 0.086 0.004
CTNNB1 231714 69300 0 21 19 19 6 0 20 1.7 8e-06 45 0.089 0.0044
LCE4A 29610 8064 0 4 4 3 1 0 20 1.2 8.5e-06 22 0.067 0.0046
KRTAP5-1 79380 24696 0 5 5 2 0 0 20 0.91 0.000013 27 0.085 0.0067
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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