Mutation Analysis (MutSigCV v0.9)
Brain Lower Grade Glioma (Primary solid tumor)
17 October 2014  |  analyses__2014_10_17
Maintainer Information
Citation Information
doi:10.7908/C12F7MBR
Maintained by David Heiman (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/C12F7MBR
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: 289

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:LGG-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 20

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: 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: 20. 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 1744115 430321 201213 124 114 111 2 0 1 2.8 1e-15 530 0.17 1.8e-11
PIK3CA 751111 192185 112068 28 25 16 0 0 20 1.1 4.2e-15 93 0.16 1.9e-11
CIC 901102 326570 92824 64 54 51 1 0 8 1.9 4.3e-15 220 0.17 1.9e-11
TP53 273105 79764 58298 190 146 92 2 0 4 5.7 5.4e-15 450 0.17 1.9e-11
IDH1 291023 75140 45846 220 220 2 0 0 13 0.61 5.8e-15 730 0.18 1.9e-11
NOTCH1 1153977 333506 107540 35 29 29 1 0 20 1.7 6.3e-15 120 0.17 1.9e-11
FUBP1 444771 131206 112068 26 26 25 1 0 5 1.9 7.2e-15 140 0.16 1.9e-11
PTEN 281486 67626 49242 13 13 13 0 0 20 0.75 8.7e-12 53 0.16 2e-08
PCDHAC2 623951 198543 17546 21 17 21 9 0 20 1 1.4e-11 55 0.16 2.9e-08
PIK3R1 538696 138431 97635 14 13 12 1 0 20 1.1 9.6e-11 61 0.16 1.7e-07
IDH2 265302 69938 50657 12 12 3 0 0 20 0 4.5e-10 43 0.16 7.4e-07
EGFR 890120 243338 166404 21 17 15 0 0 20 0 5.5e-10 56 0.16 8.3e-07
CREBZF 217039 73695 4245 5 5 1 0 0 20 1.2 2.8e-06 32 0.15 0.0039
VAV3 598808 152014 159329 8 8 1 0 0 6 0.79 8.4e-06 46 0.16 0.011
TCF12 506617 145078 113483 9 8 8 0 0 7 1.6 9.6e-06 46 0.16 0.012
TMEM216 61846 18785 11037 3 3 1 0 0 20 0.85 0.000014 21 0.3 0.016
SMARCA4 936071 264724 155933 13 13 11 0 0 20 1.4 0.000033 47 0.16 0.036
BCOR 1029996 308074 58015 10 9 10 3 0 20 1.9 0.000035 45 0.16 0.036
NF1 2720068 762382 335072 29 19 26 0 0 0 0 0.000062 85 0.16 0.06
EIF1AX 102306 23987 33960 4 4 3 0 0 20 0 0.0001 16 0.14 0.092
ARID1A 1290963 380324 106408 13 12 13 0 0 2 0 0.00013 61 0.16 0.11
SPANXE 67626 17629 12169 4 4 4 0 0 20 1.5 0.00016 16 0.14 0.14
ING3 299982 78319 74712 4 4 1 0 0 18 0.98 0.00041 25 0.15 0.33
SLC6A3 376567 114733 67071 8 7 8 0 0 15 0.56 0.00082 27 0.15 0.62
PAGE1 86700 23409 23206 2 2 2 0 0 20 0.34 0.00087 14 0.13 0.64
DBNDD2 77452 22253 11886 2 2 2 0 0 20 0 0.0014 14 0.13 1
ATG5 196231 47107 39620 3 3 3 0 0 20 1.7 0.0015 17 0.14 1
MED9 87856 23698 11886 2 2 1 0 0 20 0.41 0.0016 14 0.13 1
FAM48B1 436968 132940 6226 4 4 4 0 0 20 0 0.0021 17 0.14 1
C8orf47 238714 70516 11886 3 3 3 0 0 20 0.31 0.0025 14 0.13 1
ZNF41 527425 127449 16414 4 4 4 0 0 20 0.67 0.0029 19 0.15 1
HTRA2 484942 154904 91409 4 4 1 0 0 14 0.52 0.0031 24 0.15 1
ACPP 298826 81209 62543 3 3 3 0 0 20 0.25 0.0037 14 0.13 1
BHLHE22 74851 25432 2830 2 2 2 0 0 20 1.9 0.0039 14 0.13 1
PCDHGC5 618460 204323 20659 20 20 20 9 0 5 6.7 0.0044 45 0.16 1
ATRX

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

PIK3CA

Figure S2.  This figure depicts the distribution of mutations and mutation types across the PIK3CA significant gene.

CIC

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

TP53

Figure S4.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

IDH1

Figure S5.  This figure depicts the distribution of mutations and mutation types across the IDH1 significant gene.

NOTCH1

Figure S6.  This figure depicts the distribution of mutations and mutation types across the NOTCH1 significant gene.

FUBP1

Figure S7.  This figure depicts the distribution of mutations and mutation types across the FUBP1 significant gene.

PTEN

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

PCDHAC2

Figure S9.  This figure depicts the distribution of mutations and mutation types across the PCDHAC2 significant gene.

PIK3R1

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

IDH2

Figure S11.  This figure depicts the distribution of mutations and mutation types across the IDH2 significant gene.

EGFR

Figure S12.  This figure depicts the distribution of mutations and mutation types across the EGFR significant gene.

CREBZF

Figure S13.  This figure depicts the distribution of mutations and mutation types across the CREBZF significant gene.

TCF12

Figure S14.  This figure depicts the distribution of mutations and mutation types across the TCF12 significant gene.

TMEM216

Figure S15.  This figure depicts the distribution of mutations and mutation types across the TMEM216 significant gene.

SMARCA4

Figure S16.  This figure depicts the distribution of mutations and mutation types across the SMARCA4 significant gene.

BCOR

Figure S17.  This figure depicts the distribution of mutations and mutation types across the BCOR significant gene.

NF1

Figure S18.  This figure depicts the distribution of mutations and mutation types across the NF1 significant gene.

EIF1AX

Figure S19.  This figure depicts the distribution of mutations and mutation types across the EIF1AX 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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