Mutation Analysis (MutSig 2CV v3.1)
Glioblastoma Multiforme (Primary solid tumor)
17 October 2014  |  analyses__2014_10_17
Maintainer Information
Citation Information
doi:10.7908/C1X065XW
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Mutation Analysis (MutSig 2CV v3.1). Broad Institute of MIT and Harvard. doi:10.7908/C1X065XW
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. MutSig 2CV v3.1 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

  • MAF used for this analysis:GBM-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 27

Results
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 1.  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 2.  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 3.  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

  • 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: 27. 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).

rank gene longname codelen nnei nncd nsil nmis nstp nspl nind nnon npat nsite pCV pCL pFN p q
1 TP53 tumor protein p53 1314 49 0 1 78 4 6 8 96 79 59 1e-16 1e-05 1e-05 1e-16 6.8e-13
2 PIK3R1 phosphoinositide-3-kinase, regulatory subunit 1 (alpha) 2361 3 0 0 14 1 2 16 33 32 27 1e-16 1e-05 0.044 1e-16 6.8e-13
3 RB1 retinoblastoma 1 (including osteosarcoma) 2891 63 0 1 1 12 5 7 25 24 22 1e-16 0.17 0.021 1.1e-16 6.8e-13
4 NF1 neurofibromin 1 (neurofibromatosis, von Recklinghausen disease, Watson disease) 8807 1 0 1 4 12 5 14 35 29 34 1e-16 0.21 0.87 1.2e-15 5.6e-12
5 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 1244 58 0 0 48 15 5 22 90 87 73 3.9e-15 0.048 0.47 1.1e-14 4.2e-11
6 IDH1 isocitrate dehydrogenase 1 (NADP+), soluble 1277 583 0 0 14 0 0 0 14 14 2 1.9e-13 0.0086 0.93 7.7e-14 2.3e-10
7 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 3287 2 0 0 26 2 0 5 33 30 28 9.5e-08 1e-05 0.19 2.7e-11 7.1e-08
8 STAG2 stromal antigen 2 3939 13 0 0 2 4 3 3 12 12 12 8e-11 1 0.66 1.9e-09 4.4e-06
9 SEMG1 semenogelin I 1399 142 0 0 7 1 0 0 8 8 7 6.1e-10 0.73 0.65 1.3e-08 0.000027
10 RPL5 ribosomal protein L5 926 298 0 0 2 1 2 2 7 7 7 9.2e-08 1 0.11 4.5e-07 0.00081
11 SLC26A3 solute carrier family 26, member 3 2375 51 0 0 6 1 0 0 7 7 6 7.5e-07 0.22 0.044 5.6e-07 0.00094
12 MAP3K1 mitogen-activated protein kinase kinase kinase 1 4615 55 0 2 6 0 0 0 6 6 4 0.0008 6e-05 0.097 8.6e-07 0.0013
13 ATRX alpha thalassemia/mental retardation syndrome X-linked (RAD54 homolog, S. cerevisiae) 7615 3 0 2 5 3 0 8 16 16 16 4.9e-07 1 0.14 2.1e-06 0.0029
14 BRAF v-raf murine sarcoma viral oncogene homolog B1 2371 49 0 1 6 0 0 0 6 6 2 0.022 1e-05 0.083 3.5e-06 0.0046
15 EGFR epidermal growth factor receptor (erythroblastic leukemia viral (v-erb-b) oncogene homolog, avian) 3999 1 0 7 88 0 0 4 92 74 44 0.081 1e-05 1e-05 0.000012 0.014
16 PDGFRA platelet-derived growth factor receptor, alpha polypeptide 3358 31 0 1 11 0 0 2 13 11 12 0.000029 0.079 0.28 0.000013 0.014
17 KDR kinase insert domain receptor (a type III receptor tyrosine kinase) 4187 23 0 0 6 2 0 0 8 8 8 5.5e-06 1 0.23 2e-05 0.022
18 TMPRSS6 transmembrane protease, serine 6 2504 21 0 0 5 0 0 1 6 6 5 0.000053 0.13 0.16 0.000026 0.027
19 CHD8 chromodomain helicase DNA binding protein 8 7904 2 0 0 3 2 2 2 9 9 9 4.2e-06 1 0.41 0.000031 0.03
20 SEMA3C sema domain, immunoglobulin domain (Ig), short basic domain, secreted, (semaphorin) 3C 2324 46 0 1 10 0 1 0 11 11 11 3.8e-06 1 0.56 0.000051 0.047
21 ZDHHC4 zinc finger, DHHC-type containing 4 1061 51 0 0 3 0 0 0 3 3 1 0.00031 0.009 0.96 0.000056 0.048
22 MMP13 matrix metallopeptidase 13 (collagenase 3) 1454 19 0 0 6 0 0 0 6 5 6 0.000044 1 0.041 0.000057 0.048
23 PRKCD protein kinase C, delta 2099 100 0 1 1 0 0 2 3 3 2 0.0046 0.0059 0.0045 0.000062 0.049
24 REN renin 1259 222 0 0 3 0 0 2 5 5 4 0.00017 0.018 0.89 0.000065 0.05
25 LRRC55 leucine rich repeat containing 55 1030 30 0 1 5 0 1 0 6 6 6 5.5e-06 1 0.84 0.000072 0.052
26 MUC17 mucin 17, cell surface associated 13532 26 0 7 22 0 0 0 22 22 21 0.000033 0.27 0.44 0.000088 0.062
27 CD209 CD209 molecule 1241 39 0 1 5 0 0 0 5 5 4 0.000047 0.41 0.19 0.00011 0.077
28 DDX5 DEAD (Asp-Glu-Ala-Asp) box polypeptide 5 1893 86 0 0 1 0 0 3 4 4 3 0.0016 0.011 0.67 0.00021 0.13
29 TP63 tumor protein p63 2258 105 0 1 5 1 0 0 6 6 6 0.000018 1 0.54 0.00021 0.13
30 CD1D CD1d molecule 1028 820 0 0 1 0 1 2 4 4 3 0.12 0.00015 0.96 0.00027 0.17
31 LZTR1 leucine-zipper-like transcription regulator 1 2604 24 0 0 9 0 0 1 10 10 10 6e-05 1 0.32 0.00031 0.18
32 TEX15 testis expressed 15 8382 80 0 3 6 2 0 0 8 8 7 0.00037 0.29 0.11 0.00034 0.19
33 FBN3 fibrillin 3 8680 54 0 1 12 0 0 0 12 11 11 0.0003 0.1 0.58 0.00038 0.21
34 ODF4 outer dense fiber of sperm tails 4 782 140 0 0 3 0 0 0 3 3 2 0.0038 0.072 0.022 0.00047 0.25
35 PTX4 pentraxin 4, long 1429 39 0 0 4 0 0 0 4 4 4 0.00015 1 0.28 0.00049 0.26
TP53

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

PIK3R1

Figure S2.  This figure depicts the distribution of mutations and mutation types across the PIK3R1 significant gene.

RB1

Figure S3.  This figure depicts the distribution of mutations and mutation types across the RB1 significant gene.

NF1

Figure S4.  This figure depicts the distribution of mutations and mutation types across the NF1 significant gene.

PTEN

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

IDH1

Figure S6.  This figure depicts the distribution of mutations and mutation types across the IDH1 significant gene.

PIK3CA

Figure S7.  This figure depicts the distribution of mutations and mutation types across the PIK3CA significant gene.

STAG2

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

SEMG1

Figure S9.  This figure depicts the distribution of mutations and mutation types across the SEMG1 significant gene.

RPL5

Figure S10.  This figure depicts the distribution of mutations and mutation types across the RPL5 significant gene.

SLC26A3

Figure S11.  This figure depicts the distribution of mutations and mutation types across the SLC26A3 significant gene.

MAP3K1

Figure S12.  This figure depicts the distribution of mutations and mutation types across the MAP3K1 significant gene.

ATRX

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

BRAF

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

EGFR

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

PDGFRA

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

KDR

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

TMPRSS6

Figure S18.  This figure depicts the distribution of mutations and mutation types across the TMPRSS6 significant gene.

CHD8

Figure S19.  This figure depicts the distribution of mutations and mutation types across the CHD8 significant gene.

SEMA3C

Figure S20.  This figure depicts the distribution of mutations and mutation types across the SEMA3C significant gene.

ZDHHC4

Figure S21.  This figure depicts the distribution of mutations and mutation types across the ZDHHC4 significant gene.

MMP13

Figure S22.  This figure depicts the distribution of mutations and mutation types across the MMP13 significant gene.

PRKCD

Figure S23.  This figure depicts the distribution of mutations and mutation types across the PRKCD significant gene.

REN

Figure S24.  This figure depicts the distribution of mutations and mutation types across the REN significant gene.

LRRC55

Figure S25.  This figure depicts the distribution of mutations and mutation types across the LRRC55 significant gene.

MUC17

Figure S26.  This figure depicts the distribution of mutations and mutation types across the MUC17 significant gene.

CD209

Figure S27.  This figure depicts the distribution of mutations and mutation types across the CD209 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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