Mutation Analysis (MutSigCV v0.9)
Rectum Adenocarcinoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1TD9VT5
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/C1TD9VT5
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: 73

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: 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: 3. 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
TP53 68985 20148 0 47 47 30 1 0 4 0.86 5e-15 130 0.042 6e-11
APC 487056 136510 0 94 60 69 0 0 4 0 6.6e-15 260 0.043 6e-11
KRAS 44165 10804 0 39 39 8 0 0 1 0 8.1e-11 76 0.043 5e-07
NRAS 33653 8906 0 6 6 4 0 0 20 0.82 0.00034 17 0.037 1
FBXW7 141766 38909 0 15 11 10 0 1 20 0.97 0.00036 27 0.041 1
PPPDE1 34310 9417 0 3 2 2 0 0 20 0.078 0.00051 8.4 0.028 1
SMAD4 96652 26937 0 9 9 6 0 0 20 0.7 0.00054 23 0.039 1
SIP1 49859 13213 0 3 3 2 0 0 20 0.73 0.0015 15 0.036 1
ZNF706 13870 3358 0 2 2 1 0 0 20 1 0.0018 10 0.032 1
MAPK10 84023 21170 0 5 5 4 0 0 16 1 0.002 19 0.038 1
PFDN1 17593 4307 0 2 2 1 0 0 20 0.62 0.0021 11 0.033 1
ELF3 64897 17812 0 3 3 3 0 0 20 0.59 0.0027 17 0.039 1
TCF7L2 108405 30587 0 7 7 7 2 0 20 1.7 0.003 23 0.04 1
MARCKSL1 33799 10147 0 2 2 1 0 0 20 0.25 0.0034 12 0.035 1
FAM49B 58473 15184 0 3 3 2 0 0 20 1.1 0.004 15 0.038 1
WDR69 73949 19856 0 8 5 6 0 0 20 0.43 0.0043 17 0.038 1
DYNLRB2 17155 4672 0 2 2 1 0 0 20 1.6 0.005 9.8 0.033 1
IQCK 45187 11680 0 3 3 2 0 0 20 0.88 0.005 12 0.036 1
FKBP14 37668 9417 0 3 3 2 0 0 20 0.77 0.0053 12 0.036 1
GLT8D2 62123 16644 0 4 4 2 2 0 20 0.37 0.0053 15 0.039 1
SMAD2 81541 22776 0 6 6 5 0 0 20 0.69 0.0056 17 0.038 1
GLRX 18469 5329 0 2 2 1 0 0 20 1.2 0.0056 10 0.037 1
SYTL3 85483 24455 0 4 4 3 0 0 20 0.39 0.0056 18 0.039 1
FAM9B 33069 7154 0 3 3 2 0 0 6 0.41 0.0058 16 0.04 1
SMAP2 74606 20221 0 3 3 2 0 0 20 0.68 0.0059 15 0.037 1
ELL 79059 23871 0 3 3 2 0 0 20 0.27 0.0063 16 0.038 1
NUCB1 75044 21243 0 3 3 2 0 0 20 0 0.0071 14 0.038 1
BOLA3 21316 4307 0 2 2 1 0 0 20 1.1 0.0077 10 0.032 1
MAPK9 80957 21024 0 4 4 3 0 0 20 0.89 0.008 17 0.038 1
HEBP2 30587 7738 0 2 2 1 0 0 20 0.58 0.0082 10 0.033 1
PECAM1 1606 584 0 1 1 1 0 0 20 0 0.0082 4.4 0.014 1
FAM123B 180748 53582 0 8 6 8 2 0 20 1.3 0.0085 26 0.042 1
AGMAT 47085 15184 0 5 5 3 0 0 20 0.51 0.0087 13 0.037 1
NIT2 48837 13943 0 3 3 1 0 0 14 0.71 0.0087 12 0.036 1
ZIM3 84169 20075 0 8 6 6 0 0 20 1.2 0.0088 16 0.037 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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