Mutation Analysis (MutSigCV v0.9)
Colon Adenocarcinoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1HQ3XBR
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/C1HQ3XBR
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: COAD-TP

  • Number of patients in set: 154

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: COAD-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: 12. 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
KRAS 93170 22792 106 59 59 9 0 0 1 0 0 130 0.09 0
APC 1027488 287980 291 163 107 108 4 0 4 1 1e-15 490 0.095 9.1e-12
TP53 145530 42504 206 76 74 50 1 0 4 1 1.8e-15 240 0.091 1.1e-11
FBXW7 299068 82082 239 33 29 21 2 0 20 1.5 1.9e-13 86 0.09 8.7e-10
NRAS 70994 18788 82 15 15 7 0 0 20 0.59 5.4e-11 47 0.087 2e-07
FAM123B 381304 113036 98 19 19 17 1 0 20 2 1.1e-08 79 0.089 0.000033
SMAD4 203896 56826 222 22 18 18 0 0 20 1.2 1.5e-08 56 0.087 0.000039
SOX9 145068 42658 52 9 9 9 0 0 20 1.5 5.1e-07 48 0.087 0.0012
PIK3CA 400246 102410 396 33 26 18 1 0 20 1.2 1.2e-06 57 0.091 0.0024
BRAF 265958 75922 339 21 20 3 0 0 19 1.2 0.000013 44 0.087 0.023
ACVR2A 191576 50512 222 10 9 8 1 0 10 1.1 0.000022 38 0.085 0.036
TXNDC3 223454 54362 294 13 10 13 2 0 20 1.1 0.000061 38 0.086 0.093
SMAD2 172018 48048 199 11 10 8 1 0 20 1.4 0.0001 36 0.086 0.14
CASP8 224224 52514 213 11 10 10 0 0 20 0.88 0.00013 34 0.087 0.17
TCF7L2 228690 64526 273 13 11 13 3 0 20 1.4 0.00033 38 0.087 0.4
MGC26647 91476 25256 24 7 7 7 0 0 13 1.4 0.00035 26 0.084 0.4
SAT1 64372 15400 117 4 4 2 0 0 20 0.91 0.00037 21 0.08 0.4
WBSCR17 212674 61292 211 17 17 16 2 0 18 1.5 0.00042 41 0.09 0.4
CCDC160 59906 14476 5 7 5 7 0 0 20 1.2 0.00045 19 0.079 0.4
ESR1 187418 53130 146 12 11 12 0 0 20 0.68 0.00046 30 0.084 0.4
MIER3 205128 52822 236 10 10 10 0 0 20 1.1 0.00046 32 0.087 0.4
ZNF593 25718 7700 43 3 3 3 0 0 20 0.75 0.001 15 0.077 0.85
TBC1D10C 86240 28644 92 4 4 2 0 0 20 1.1 0.0013 23 0.083 1
TMEM105 40964 14476 35 3 3 3 0 0 19 0.4 0.0017 15 0.076 1
CRTC1 199892 63140 274 5 5 3 1 0 20 0.45 0.0017 25 0.083 1
ELF3 136906 37576 158 5 5 5 0 0 20 0.82 0.0019 24 0.082 1
C6orf211 163548 41580 100 5 5 5 0 0 19 0.41 0.002 20 0.081 1
RNF43 256410 81158 175 11 10 10 1 0 8 0.64 0.0021 27 0.085 1
CELA1 83776 24948 138 6 5 6 0 0 20 1.1 0.0021 20 0.08 1
INSL6 76846 21098 40 5 5 5 1 0 20 0.91 0.0022 18 0.08 1
GGA2 216062 62370 320 8 8 8 1 0 20 0.91 0.0026 28 0.097 1
OR2M4 109956 33418 27 8 8 8 0 0 19 1.7 0.0026 24 0.084 1
HSPA1L 245784 74074 35 8 8 8 1 0 20 0.32 0.0039 23 0.096 1
B2M 44198 12320 64 3 3 3 0 0 20 1.1 0.0046 14 0.076 1
SLC16A7 171094 50050 82 10 8 9 2 0 20 1.8 0.0046 28 0.084 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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