Mutation Analysis (MutSigCV v0.9)
Glioblastoma Multiforme (Primary solid tumor)
23 September 2013  |  analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C1959FVK
Maintained by Dan DiCara (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Mutation Analysis (MutSigCV v0.9). Broad Institute of MIT and Harvard. doi:10.7908/C1959FVK
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: GBM-TP

  • Number of patients in set: 283

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).

Figure 1. 

Distribution of Mutation Counts, Coverage, and Mutation Rates Across Samples

Figure 2.  Patients counts and rates file used to generate this plot: GBM-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: 11. 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 267435 78108 0 96 79 59 1 0 4 1 0 240 0.16 0
PTEN 275642 66222 0 90 87 73 0 0 20 0.44 2.2e-15 400 0.16 1.8e-11
EGFR 871640 238286 0 92 74 44 7 0 20 1.6 2.9e-15 190 0.16 1.8e-11
PIK3R1 527512 135557 0 33 32 27 0 0 20 1.1 4.7e-15 140 0.16 2.1e-11
RB1 805984 212533 0 25 24 22 1 0 20 0.8 6.4e-15 140 0.16 2.3e-11
PIK3CA 735517 188195 0 33 30 28 0 0 20 0.82 7.7e-15 100 0.16 2.3e-11
NF1 2663596 746554 0 35 29 34 1 0 0 0.51 4.5e-08 130 0.16 0.00012
STAG2 884375 219608 0 12 12 12 0 0 20 0.71 3.8e-07 61 0.16 0.00086
IDH1 284981 73580 0 14 14 2 0 0 13 0.67 1.1e-06 43 0.16 0.0022
PRB2 249606 90843 0 6 6 2 0 0 12 1.5 2.5e-06 37 0.16 0.0046
GABRA6 306489 86881 0 11 11 10 1 0 20 1 0.000018 34 0.15 0.029
CDKN2C 110653 33677 0 3 3 3 0 0 20 0.95 0.00037 20 0.15 0.56
TPTE2 362523 94239 0 8 8 6 0 0 14 0.63 0.00054 32 0.15 0.75
CDC27 551284 149141 0 7 6 3 0 0 20 0.93 0.00073 32 0.16 0.9
RPL5 206590 52921 0 7 7 7 0 0 2 0.82 0.00074 34 0.15 0.9
LCE4A 66505 18112 0 2 2 1 0 0 20 0.18 0.00085 14 0.11 0.92
OR5AR1 202911 59147 0 7 7 7 0 0 20 1.6 0.00085 22 0.15 0.92
PSPH 166970 48110 0 5 5 3 0 0 19 0.94 0.0012 20 0.14 1
LZTR1 496948 141783 0 10 10 10 0 0 20 0.41 0.0012 31 0.15 1
LRRC55 220457 69052 0 6 6 6 1 0 20 1 0.0015 21 0.17 1
GFRA4 24338 9339 0 2 2 2 1 0 20 1.1 0.0016 11 0.12 1
QKI 251304 72165 0 5 5 5 0 0 12 0.31 0.0016 24 0.15 1
CHD8 1445847 413746 0 10 10 10 0 0 20 1 0.0035 45 0.16 1
SEMA3C 511098 138387 0 11 11 11 1 0 11 1.9 0.0042 32 0.15 1
FOXR2 209703 52921 0 5 5 5 1 0 20 1.3 0.0043 16 0.14 1
KRTAP20-2 42450 12735 0 3 3 3 0 0 20 2.5 0.0048 13 0.13 1
OR5P2 207722 64524 0 4 4 3 0 0 20 1.3 0.0059 16 0.14 1
MTX3 153952 44997 0 3 3 3 0 0 20 0.52 0.0066 11 0.12 1
TXNDC3 410633 99899 0 6 5 6 1 0 20 1.4 0.0072 24 0.15 1
CD3EAP 329412 104993 0 3 3 1 0 0 20 0.19 0.0074 19 0.14 1
BCOR 1008612 301678 0 7 7 7 3 0 20 0.91 0.0076 33 0.15 1
OR8K3 202911 60562 0 7 7 7 1 0 9 2.8 0.0076 24 0.15 1
ZNF697 117445 30281 0 3 3 3 0 0 20 0 0.0077 13 0.13 1
FOXG1 213382 67637 0 4 4 3 1 0 12 1.6 0.0081 22 0.15 1
OR13C5 207439 60845 0 4 4 3 0 0 20 1.9 0.0095 18 0.15 1
TP53

Figure S1.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

PTEN

Figure S2.  This figure depicts the distribution of mutations and mutation types across the PTEN significant gene.

EGFR

Figure S3.  This figure depicts the distribution of mutations and mutation types across the EGFR significant gene.

PIK3R1

Figure S4.  This figure depicts the distribution of mutations and mutation types across the PIK3R1 significant gene.

RB1

Figure S5.  This figure depicts the distribution of mutations and mutation types across the RB1 significant gene.

PIK3CA

Figure S6.  This figure depicts the distribution of mutations and mutation types across the PIK3CA significant gene.

NF1

Figure S7.  This figure depicts the distribution of mutations and mutation types across the NF1 significant gene.

STAG2

Figure S8.  This figure depicts the distribution of mutations and mutation types across the STAG2 significant gene.

IDH1

Figure S9.  This figure depicts the distribution of mutations and mutation types across the IDH1 significant gene.

GABRA6

Figure S10.  This figure depicts the distribution of mutations and mutation types across the GABRA6 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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