Mutation Analysis (MutSig 2CV v3.1)
Glioma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1VD6XT5
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Mutation Analysis (MutSig 2CV v3.1). Broad Institute of MIT and Harvard. doi:10.7908/C1VD6XT5
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: GBMLGG-TP

  • Number of patients in set: 799

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:GBMLGG-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 128

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: 128. 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 89 0 0 415 0 0 0 415 415 2 1.1e-14 1e-05 1 1e-16 2.3e-13
2 TP53 tumor protein p53 1314 9 0 2 329 23 20 48 420 331 166 8.7e-16 1e-05 1e-05 1e-16 2.3e-13
3 ATRX alpha thalassemia/mental retardation syndrome X-linked (RAD54 homolog, S. cerevisiae) 7615 9 0 9 37 50 20 132 239 214 203 6.9e-16 1e-05 0.24 1e-16 2.3e-13
4 CIC capicua homolog (Drosophila) 4905 6 0 1 66 9 3 49 127 109 90 9.2e-16 1e-05 0.54 1e-16 2.3e-13
5 NF1 neurofibromin 1 (neurofibromatosis, von Recklinghausen disease, Watson disease) 8807 1 0 4 15 23 13 35 86 62 79 1e-16 0.0033 0.97 1e-16 2.3e-13
6 PIK3R1 phosphoinositide-3-kinase, regulatory subunit 1 (alpha) 2361 7 0 2 21 2 3 31 57 54 41 1.3e-15 1e-05 0.22 1e-16 2.3e-13
7 NOTCH1 Notch homolog 1, translocation-associated (Drosophila) 7800 12 0 3 25 2 4 24 55 42 43 1e-16 1e-05 0.18 1e-16 2.3e-13
8 IDH2 isocitrate dehydrogenase 2 (NADP+), mitochondrial 1401 76 0 0 20 0 0 0 20 20 3 1e-16 1e-05 1 1e-16 2.3e-13
9 TCF12 transcription factor 12 (HTF4, helix-loop-helix transcription factors 4) 2278 9 0 0 1 0 2 18 21 19 19 1e-16 0.031 0.13 1.1e-16 2.3e-13
10 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 3287 2 0 0 61 2 5 12 80 72 47 6.5e-13 1e-05 0.018 2.2e-16 4.1e-13
11 RB1 retinoblastoma 1 (including osteosarcoma) 2891 4 0 1 2 9 10 11 32 30 28 6.3e-16 0.094 0.02 3.3e-16 5.5e-13
12 FUBP1 far upstream element (FUSE) binding protein 1 2013 151 0 1 1 11 9 29 50 47 45 4.2e-16 0.016 0.97 6.7e-16 1e-12
13 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 1244 58 0 0 61 18 9 25 113 110 88 3.9e-15 0.008 0.74 2.4e-15 3.4e-12
14 STK19 serine/threonine kinase 19 1186 55 0 0 3 0 0 9 12 11 4 2.6e-09 1e-05 0.6 8.4e-13 1.1e-09
15 ZMIZ1 zinc finger, MIZ-type containing 1 3288 22 0 0 9 0 0 4 13 13 11 3.6e-09 0.0042 0.061 8.5e-11 1e-07
16 ARID1A AT rich interactive domain 1A (SWI-like) 6934 58 0 4 10 7 1 10 28 22 28 1.7e-10 1 0.91 3.9e-09 4.5e-06
17 NIPBL Nipped-B homolog (Drosophila) 8642 6 0 1 10 5 3 8 26 20 25 7.4e-10 0.18 0.75 4.3e-09 4.6e-06
18 GAGE2A G antigen 2A 1486 119 0 0 2 0 0 6 8 8 3 0.000092 1e-05 0.97 2e-08 2e-05
19 NUDT11 nudix (nucleoside diphosphate linked moiety X)-type motif 11 500 0 0 2 1 0 0 10 11 11 2 0.00012 1e-05 1 2.5e-08 0.000024
20 CREBZF CREB/ATF bZIP transcription factor 1065 59 0 0 2 0 0 7 9 9 3 0.00013 1e-05 0.53 2.8e-08 0.000026
21 SLC26A3 solute carrier family 26, member 3 2375 51 0 1 10 1 0 0 11 11 10 4e-08 0.11 0.078 4.1e-08 0.000035
22 PLCG1 phospholipase C, gamma 1 4000 80 0 3 10 0 1 1 12 11 10 0.00027 2e-05 0.093 5.5e-08 0.000046
23 SEMG1 semenogelin I 1399 34 0 0 10 1 0 0 11 11 7 6.1e-07 0.0056 0.31 7e-08 0.000055
24 NUP210L nucleoporin 210kDa-like 5825 21 0 3 9 3 0 0 12 12 8 0.000018 0.00042 0.3 9.6e-08 0.000073
25 PDGFRA platelet-derived growth factor receptor, alpha polypeptide 3358 0 0 6 25 0 0 6 31 21 28 0.00082 0.00092 0.00079 1.6e-07 0.00012
26 STAG2 stromal antigen 2 3939 11 0 1 3 5 7 3 18 16 18 2.5e-08 1 0.61 4.7e-07 0.00033
27 MED9 mediator complex subunit 9 447 4 0 1 0 0 0 3 3 3 1 0.000031 0.001 0.5 5.7e-07 0.00038
28 NKD2 naked cuticle homolog 2 (Drosophila) 1394 10 0 1 1 0 0 3 4 4 3 0.000021 0.05 0.0062 7.1e-07 0.00046
29 IRS4 insulin receptor substrate 4 3778 4 0 1 6 1 0 6 13 11 9 0.0043 6e-05 0.0048 7.7e-07 0.00048
30 REN renin 1259 2 0 0 3 0 0 4 7 7 4 0.000011 0.0024 0.92 8.9e-07 0.00053
31 MUC17 mucin 17, cell surface associated 13532 26 0 19 57 2 0 0 59 50 56 3.2e-07 0.1 0.61 8.9e-07 0.00053
32 HTRA2 HtrA serine peptidase 2 1405 32 0 0 1 0 0 4 5 5 2 0.00012 0.00024 0.74 1.1e-06 0.00065
33 RPL5 ribosomal protein L5 926 298 0 0 5 1 2 2 10 10 10 1.9e-07 1 0.54 2.2e-06 0.0012
34 BRAF v-raf murine sarcoma viral oncogene homolog B1 2371 25 0 1 11 0 0 1 12 11 6 0.018 1e-05 0.0061 3e-06 0.0016
35 KDR kinase insert domain receptor (a type III receptor tyrosine kinase) 4187 15 0 3 11 3 0 0 14 13 14 1.9e-07 1 0.54 3.1e-06 0.0016
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.

NF1

Figure S5.  This figure depicts the distribution of mutations and mutation types across the NF1 significant gene.

PIK3R1

Figure S6.  This figure depicts the distribution of mutations and mutation types across the PIK3R1 significant gene.

NOTCH1

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

IDH2

Figure S8.  This figure depicts the distribution of mutations and mutation types across the IDH2 significant gene.

TCF12

Figure S9.  This figure depicts the distribution of mutations and mutation types across the TCF12 significant gene.

PIK3CA

Figure S10.  This figure depicts the distribution of mutations and mutation types across the PIK3CA significant gene.

RB1

Figure S11.  This figure depicts the distribution of mutations and mutation types across the RB1 significant gene.

FUBP1

Figure S12.  This figure depicts the distribution of mutations and mutation types across the FUBP1 significant gene.

PTEN

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

STK19

Figure S14.  This figure depicts the distribution of mutations and mutation types across the STK19 significant gene.

ZMIZ1

Figure S15.  This figure depicts the distribution of mutations and mutation types across the ZMIZ1 significant gene.

ARID1A

Figure S16.  This figure depicts the distribution of mutations and mutation types across the ARID1A significant gene.

NIPBL

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

GAGE2A

Figure S18.  This figure depicts the distribution of mutations and mutation types across the GAGE2A significant gene.

NUDT11

Figure S19.  This figure depicts the distribution of mutations and mutation types across the NUDT11 significant gene.

CREBZF

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

SLC26A3

Figure S21.  This figure depicts the distribution of mutations and mutation types across the SLC26A3 significant gene.

PLCG1

Figure S22.  This figure depicts the distribution of mutations and mutation types across the PLCG1 significant gene.

SEMG1

Figure S23.  This figure depicts the distribution of mutations and mutation types across the SEMG1 significant gene.

NUP210L

Figure S24.  This figure depicts the distribution of mutations and mutation types across the NUP210L significant gene.

PDGFRA

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

STAG2

Figure S26.  This figure depicts the distribution of mutations and mutation types across the STAG2 significant gene.

MED9

Figure S27.  This figure depicts the distribution of mutations and mutation types across the MED9 significant gene.

NKD2

Figure S28.  This figure depicts the distribution of mutations and mutation types across the NKD2 significant gene.

IRS4

Figure S29.  This figure depicts the distribution of mutations and mutation types across the IRS4 significant gene.

REN

Figure S30.  This figure depicts the distribution of mutations and mutation types across the REN significant gene.

MUC17

Figure S31.  This figure depicts the distribution of mutations and mutation types across the MUC17 significant gene.

HTRA2

Figure S32.  This figure depicts the distribution of mutations and mutation types across the HTRA2 significant gene.

RPL5

Figure S33.  This figure depicts the distribution of mutations and mutation types across the RPL5 significant gene.

BRAF

Figure S34.  This figure depicts the distribution of mutations and mutation types across the BRAF significant gene.

Methods & Data
Methods

MutSig and its evolving algorithms have existed since the youth of clinical sequencing, with early versions used in multiple publications. [1][2][3]

"Three significance metrics [are] calculated for each gene, using the […] methods MutSigCV [4], MutSigCL, and MutSigFN [5]. These measure the significance of mutation burden, clustering, and functional impact, respectively […]. MutSigCV determines the P value for observing the given quantity of non-silent mutations in the gene, given the background model determined by silent (and noncoding) mutations in the same gene and the neighbouring genes of covariate space that form its 'bagel'. […] MutSigCL and MutSigFN measure the significance of the positional clustering of the mutations observed, as well as the significance of the tendency for mutations to occur at positions that are highly evolutionarily conserved (using conservation as a proxy for probably functional impact). MutSigCL and MutSigFN are permutation-based methods and their P values are calculated as follows: The observed nonsilent coding mutations in the gene are permuted T times (to simulate the null hypothesis, T = 108 for the most significant genes), randomly reassigning their positions, but preserving their mutational 'category', as determined by local sequence context. We [use] the following context categories: transitions at CpG dinucleotides, transitions at other C-G base pairs, transversions at C-G base pairs, mutations at A-T base pairs, and indels. Indels are unconstrained in terms of where they can move to in the permutations. For each of the random permutations, two scores are calculated: SCL and SFN, measuring the amount of clustering and function impact (measured by conservation) respectively. SCL is defined to be the fraction of mutations occurring in hotspots. A hotspot is defined as a 3-base-pair region of the gene containing many mutations: at least 2, and at least 2% of the total mutations. SFN is defined to be the mean of the base-pair-level conservation values for the position of each non-silent mutation […]. To determine a PCL, the P value for the observed degree of positional clustering, the observed value of SCL (computed for the mutations actually observed), [is] compared to the distribution of SCL obtained from the random permutations, and the P value [is] defined to be the fraction of random permutations in which SCL [is] at least as large as the observed SCL. The P value for the conservation of the mutated positions, PFN, [is] computed analogously." [6]

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] Getz G, Höfling H, Mesirov JP, Golub TR, Meyerson M, Tibshirani R, Lander ES, Comment on "The Consensus Coding Sequences of Human Breast and Colorectal Cancers", Science 317(5844):1500b (2007)
[3] TCGA, Integrated genomic analyses of ovarian carcinoma, Nature 474(7353):609-615 (2011)
[4] Lawrence MS, et al., Mutational heterogeneity in cancer and the search for new cancer-associated genes, Nature 499(7457):214-218 (2013)
[6] Lawrence MS, et al., Discovery and saturation analysis of cancer genes across 21 tumour types, Nature 505(7484):495-501 (2014)
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