Mutation Analysis (MutSig 2CV v3.1)
Brain Lower Grade Glioma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
Maintainer Information
Citation Information
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/C19P30D8
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: 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): 29

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: 29. 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 IDH1 isocitrate dehydrogenase 1 (NADP+), soluble 1277 448 0 0 220 0 0 0 220 220 2 1e-16 1e-05 1 1e-16 3e-13
2 TP53 tumor protein p53 1314 9 0 2 150 10 9 21 190 146 92 1e-16 1e-05 1e-05 1e-16 3e-13
3 ATRX alpha thalassemia/mental retardation syndrome X-linked (RAD54 homolog, S. cerevisiae) 7615 5 0 2 15 27 12 74 128 118 113 1e-16 0.00057 0.2 1e-16 3e-13
4 CIC capicua homolog (Drosophila) 4905 41 0 1 30 4 1 29 64 54 51 1.7e-16 1e-05 0.87 1e-16 3e-13
5 NOTCH1 Notch homolog 1, translocation-associated (Drosophila) 7800 45 0 1 15 0 4 16 35 29 29 1e-16 1e-05 0.076 1e-16 3e-13
6 IDH2 isocitrate dehydrogenase 2 (NADP+), mitochondrial 1401 202 0 0 12 0 0 0 12 12 3 5.6e-14 1e-05 1 1e-16 3e-13
7 FUBP1 far upstream element (FUSE) binding protein 1 2013 14 0 1 2 6 5 13 26 26 25 1.3e-16 0.64 0.82 4.8e-15 1.2e-11
8 NF1 neurofibromin 1 (neurofibromatosis, von Recklinghausen disease, Watson disease) 8807 1 0 0 4 7 5 13 29 19 26 6.1e-14 0.011 0.61 2.5e-14 5.6e-11
9 PIK3R1 phosphoinositide-3-kinase, regulatory subunit 1 (alpha) 2361 38 0 1 6 0 0 8 14 13 12 4.7e-13 0.008 0.69 2.1e-13 4.3e-10
10 STK19 serine/threonine kinase 19 1186 163 0 0 0 0 0 8 8 8 1 2.4e-09 1e-05 0.92 7.8e-13 1.4e-09
11 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 1244 1000 0 0 10 0 1 2 13 13 13 5.6e-12 1 0.6 1.5e-10 2.5e-07
12 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 3287 2 0 0 20 0 4 4 28 25 16 7.2e-07 1e-05 0.0022 1.9e-10 2.9e-07
13 ARID1A AT rich interactive domain 1A (SWI-like) 6934 3 0 0 1 5 1 6 13 12 13 3.9e-11 1 0.38 6.1e-10 8.6e-07
14 EGFR epidermal growth factor receptor (erythroblastic leukemia viral (v-erb-b) oncogene homolog, avian) 3999 17 0 0 19 1 0 1 21 17 15 0.000014 1e-05 0.0044 3.3e-09 4.3e-06
15 GAGE2B G antigen 2B 368 11 0 0 0 0 0 4 4 4 1 3.6e-09 NaN NaN 3.6e-09 4.4e-06
16 CREBZF CREB/ATF bZIP transcription factor 1065 80 0 0 0 0 0 5 5 5 1 0.000067 1e-05 0.52 1.5e-08 0.000017
17 TCF12 transcription factor 12 (HTF4, helix-loop-helix transcription factors 4) 2278 21 0 0 0 0 1 8 9 8 8 2.9e-08 0.054 0.44 3.1e-08 0.000033
18 SMARCA4 SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 4 5189 6 0 0 9 0 0 4 13 13 11 0.00022 1e-05 0.53 4.5e-08 0.000046
19 VAV3 vav 3 guanine nucleotide exchange factor 2675 54 0 0 0 0 0 8 8 8 1 1.5e-07 NaN NaN 1.5e-07 0.00014
20 TMEM216 transmembrane protein 216 467 73 0 0 0 0 3 0 3 3 1 1e-05 0.0026 1 6.1e-07 0.00055
21 ROBO3 roundabout, axon guidance receptor, homolog 3 (Drosophila) 4269 117 0 0 2 0 0 2 4 4 3 0.00017 0.0097 0.0074 1.3e-06 0.0011
22 IRS4 insulin receptor substrate 4 3778 4 0 1 2 0 0 3 5 5 3 0.012 2e-05 0.024 2e-06 0.0016
23 HTRA2 HtrA serine peptidase 2 1405 98 0 0 0 0 0 4 4 4 1 0.0012 0.0001 0.73 2.1e-06 0.0016
24 EIF1AX eukaryotic translation initiation factor 1A, X-linked 459 174 0 0 4 0 0 0 4 4 3 0.00012 0.043 0.054 7.1e-06 0.0054
25 FAM47C family with sequence similarity 47, member C 3110 31 0 3 7 0 0 0 7 7 2 0.064 1e-05 0.75 9.8e-06 0.0071
26 PLCG1 phospholipase C, gamma 1 4000 45 0 0 3 0 0 1 4 4 3 0.00066 0.0077 0.28 0.000029 0.02
27 DDX5 DEAD (Asp-Glu-Ala-Asp) box polypeptide 5 1893 112 0 0 2 0 0 2 4 4 3 0.0003 0.012 0.23 0.000048 0.033
28 ZNF709 zinc finger protein 709 1938 56 0 0 3 0 0 0 3 3 1 0.0053 0.001 0.97 7e-05 0.046
29 TRERF1 transcriptional regulating factor 1 3655 26 0 0 0 0 0 3 3 3 2 0.0052 0.0021 0.35 0.00015 0.093
30 BCOR BCL6 co-repressor 5324 31 0 3 3 1 0 6 10 9 10 0.000023 1 0.47 0.00019 0.12
31 SRPX sushi-repeat-containing protein, X-linked 1431 783 0 0 0 0 0 3 3 3 1 0.018 0.001 1 0.00021 0.13
32 DOCK5 dedicator of cytokinesis 5 5817 2 0 0 3 0 1 2 6 6 6 0.00098 1 0.01 0.00026 0.15
33 GIGYF2 GRB10 interacting GYF protein 2 4080 76 0 0 3 1 0 0 4 4 4 0.00013 1 0.15 0.00027 0.15
34 CD99L2 CD99 molecule-like 2 829 5 0 1 0 0 0 2 2 2 1 0.0024 0.01 0.49 0.00028 0.15
35 NIPBL Nipped-B homolog (Drosophila) 8642 6 0 0 1 3 0 2 6 6 6 0.000047 1 0.52 0.00028 0.15
IDH1

Figure S1.  This figure depicts the distribution of mutations and mutation types across the IDH1 significant gene.

TP53

Figure S2.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

ATRX

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

CIC

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

NOTCH1

Figure S5.  This figure depicts the distribution of mutations and mutation types across the NOTCH1 significant gene.

IDH2

Figure S6.  This figure depicts the distribution of mutations and mutation types across the IDH2 significant gene.

FUBP1

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

NF1

Figure S8.  This figure depicts the distribution of mutations and mutation types across the NF1 significant gene.

PIK3R1

Figure S9.  This figure depicts the distribution of mutations and mutation types across the PIK3R1 significant gene.

STK19

Figure S10.  This figure depicts the distribution of mutations and mutation types across the STK19 significant gene.

PTEN

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

PIK3CA

Figure S12.  This figure depicts the distribution of mutations and mutation types across the PIK3CA significant gene.

ARID1A

Figure S13.  This figure depicts the distribution of mutations and mutation types across the ARID1A significant gene.

EGFR

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

CREBZF

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

TCF12

Figure S16.  This figure depicts the distribution of mutations and mutation types across the TCF12 significant gene.

SMARCA4

Figure S17.  This figure depicts the distribution of mutations and mutation types across the SMARCA4 significant gene.

TMEM216

Figure S18.  This figure depicts the distribution of mutations and mutation types across the TMEM216 significant gene.

ROBO3

Figure S19.  This figure depicts the distribution of mutations and mutation types across the ROBO3 significant gene.

IRS4

Figure S20.  This figure depicts the distribution of mutations and mutation types across the IRS4 significant gene.

HTRA2

Figure S21.  This figure depicts the distribution of mutations and mutation types across the HTRA2 significant gene.

EIF1AX

Figure S22.  This figure depicts the distribution of mutations and mutation types across the EIF1AX significant gene.

FAM47C

Figure S23.  This figure depicts the distribution of mutations and mutation types across the FAM47C significant gene.

PLCG1

Figure S24.  This figure depicts the distribution of mutations and mutation types across the PLCG1 significant gene.

DDX5

Figure S25.  This figure depicts the distribution of mutations and mutation types across the DDX5 significant gene.

ZNF709

Figure S26.  This figure depicts the distribution of mutations and mutation types across the ZNF709 significant gene.

TRERF1

Figure S27.  This figure depicts the distribution of mutations and mutation types across the TRERF1 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)