Mutation Analysis (MutSigCV v0.9)
Brain Lower Grade Glioma (Primary solid tumor)
Due to issues with GAF 3.0, we strongly advise caution in the use of these results.
23 September 2013  |  analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C1JQ0Z9W
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/C1JQ0Z9W
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: LGG-TP

  • Number of patients in set: 220

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: LGG-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
ATRX 1327700 327580 711 108 97 95 2 0 1 1.2 0 430 0.17 0
IDH1 221540 57200 162 167 167 2 0 0 13 0.56 0 520 0.16 0
CIC 685960 248600 328 44 39 37 0 0 8 0.27 1.8e-15 150 0.16 1.1e-11
FUBP1 338580 99880 396 22 21 21 1 0 5 1.3 3.3e-15 110 0.16 1.5e-11
TP53 207900 60720 206 158 116 82 2 0 4 3.5 4.9e-15 330 0.17 1.8e-11
IL32 89760 23540 109 9 9 1 0 0 20 1 1.4e-14 58 0.15 4.1e-11
NOTCH1 878460 253880 380 28 18 25 3 0 20 1.1 9.7e-10 76 0.16 2.5e-06
PIK3CA 571780 146300 396 21 19 13 0 0 20 1.5 2.3e-09 64 0.15 4.6e-06
PTEN 214280 51480 174 13 13 13 0 0 20 1.1 2.3e-09 49 0.23 4.6e-06
PIK3R1 410080 105380 345 14 13 13 1 0 20 1.6 1.7e-08 57 0.15 0.000032
PCDHAC2 474980 151140 62 32 14 32 21 0 20 1 8.2e-08 41 0.15 0.00014
CRIPAK 216480 71940 23 5 5 4 2 0 20 1.1 0.00021 25 0.16 0.31
IDH2 201960 53240 179 8 8 2 0 0 20 0.93 0.00028 26 0.15 0.39
NOX4 308440 82940 351 6 5 3 0 0 13 0.83 0.00043 25 0.15 0.56
CREBZF 165220 56100 15 4 4 1 0 0 20 1.3 0.00053 25 0.15 0.65
TIMD4 195580 60060 180 5 5 3 1 0 20 1.1 0.00068 24 0.15 0.76
ARID1A 982740 289520 376 17 14 17 0 0 2 0.46 0.00071 67 0.16 0.76
SMARCA4 712580 201520 551 13 13 11 4 0 20 1.2 0.0013 42 0.16 1
ZNRF2 49060 12320 181 3 2 3 0 0 20 0.69 0.0016 12 0.14 1
MUC7 182160 67760 44 9 6 7 0 0 20 1.1 0.0017 18 0.14 1
EGFR 677600 185240 588 13 11 10 0 0 20 0.15 0.0032 29 0.15 1
PAGE1 66000 17820 82 2 2 2 0 0 20 0.98 0.0041 13 0.12 1
SPANXE 51480 13420 43 3 3 3 0 0 20 1 0.0044 11 0.12 1
ATG5 149380 35860 140 3 3 3 0 0 20 1.6 0.006 16 0.13 1
EIF1AX 77880 18260 120 3 3 3 1 0 20 0.57 0.0067 11 0.22 1
TCF12 385660 110440 401 7 7 6 0 0 7 3.6 0.0076 38 0.15 1
SERPINB7 201960 53460 143 5 5 5 0 0 8 0.58 0.011 19 0.14 1
EMG1 149600 45540 123 2 2 2 0 0 20 0.61 0.011 13 0.13 1
PRB2 194040 70620 53 4 4 3 1 0 12 1.6 0.015 16 0.14 1
BTBD1 205700 53680 141 3 3 3 0 0 20 0.97 0.015 14 0.13 1
YIPF1 162140 46640 161 3 3 3 0 0 20 0.97 0.016 14 0.19 1
TFAM 117260 28160 121 2 2 1 0 0 20 1.1 0.017 13 0.13 1
GNG12 38720 10340 44 1 1 1 0 0 20 0.98 0.018 7 0.1 1
SUMF2 207020 55880 192 3 2 3 1 0 17 0.24 0.018 11 0.12 1
BCOR 784080 234520 205 8 7 8 4 0 20 1.3 0.018 28 0.15 1
ATRX

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

IDH1

Figure S2.  This figure depicts the distribution of mutations and mutation types across the IDH1 significant gene.

CIC

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

FUBP1

Figure S4.  This figure depicts the distribution of mutations and mutation types across the FUBP1 significant gene.

TP53

Figure S5.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

IL32

Figure S6.  This figure depicts the distribution of mutations and mutation types across the IL32 significant gene.

NOTCH1

Figure S7.  This figure depicts the distribution of mutations and mutation types across the NOTCH1 significant gene.

PIK3CA

Figure S8.  This figure depicts the distribution of mutations and mutation types across the PIK3CA significant gene.

PTEN

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

PIK3R1

Figure S10.  This figure depicts the distribution of mutations and mutation types across the PIK3R1 significant gene.

PCDHAC2

Figure S11.  This figure depicts the distribution of mutations and mutation types across the PCDHAC2 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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