Mutation Analysis (MutSig 2CV v3.1)
Pheochromocytoma and Paraganglioma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C13T9GN0
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/C13T9GN0
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: PCPG-TP

  • Number of patients in set: 179

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:PCPG-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 7

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: 7. 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 HRAS v-Ha-ras Harvey rat sarcoma viral oncogene homolog 659 104 0 0 18 0 0 0 18 18 3 1e-16 1e-05 0.00057 1e-16 1.8e-12
2 NF1 neurofibromin 1 (neurofibromatosis, von Recklinghausen disease, Watson disease) 8807 1 0 0 1 3 2 10 16 15 16 1e-16 1 0.61 3.8e-15 3.4e-11
3 EPAS1 endothelial PAS domain protein 1 2673 195 0 0 8 0 0 0 8 8 4 1.3e-10 1e-05 0.0002 4.6e-14 2.8e-10
4 NUDT11 nudix (nucleoside diphosphate linked moiety X)-type motif 11 500 111 0 0 0 0 0 5 5 5 1 1.7e-08 1e-05 0.99 2.9e-11 1.3e-07
5 RET ret proto-oncogene 3451 95 0 0 7 0 0 0 7 6 4 2.4e-07 0.00048 0.065 5e-10 1.8e-06
6 SHROOM4 shroom family member 4 4516 6 0 0 0 0 0 3 3 3 1 0.00019 0.001 0.72 3.1e-06 0.0093
7 AMMECR1 Alport syndrome, mental retardation, midface hypoplasia and elliptocytosis chromosomal region, gene 1 1024 481 0 0 0 0 0 3 3 3 2 0.000045 0.021 1 0.000014 0.037
8 GPR128 G protein-coupled receptor 128 2454 233 0 0 4 0 0 0 4 4 4 3.3e-06 1 0.96 0.000044 0.1
9 FAM120C family with sequence similarity 120C 3373 84 0 0 0 0 0 2 2 2 1 0.00057 0.01 0.71 0.000074 0.15
10 CD99L2 CD99 molecule-like 2 829 61 0 0 0 0 0 2 2 2 1 0.0011 0.01 0.49 0.00014 0.26
11 CSDE1 cold shock domain containing E1, RNA-binding 2611 47 0 0 0 0 2 2 4 4 4 0.000061 1 0.25 0.00022 0.37
12 FAM83D family with sequence similarity 83, member D 1862 113 0 0 2 0 0 1 3 3 3 0.000032 1 0.68 0.00036 0.51
13 BCAR1 breast cancer anti-estrogen resistance 1 3027 377 0 0 1 1 0 0 2 2 1 0.0034 0.01 0.024 0.00038 0.51
14 CLEC17A C-type lectin domain family 17, member A 916 397 0 0 0 0 0 2 2 2 1 0.0035 0.01 0.68 0.00039 0.51
15 MUC2 mucin 2, oligomeric mucus/gel-forming 8640 0 0 0 0 0 0 2 2 2 2 0.0067 0.01 0.87 0.00071 0.87
16 SUSD4 sushi domain containing 4 1658 417 0 0 2 0 0 1 3 2 3 0.011 0.0089 0.83 0.0011 1
17 MAP3K4 mitogen-activated protein kinase kinase kinase 4 4931 4 0 0 3 0 0 0 3 3 2 0.085 0.0018 0.89 0.0015 1
18 P2RY1 purinergic receptor P2Y, G-protein coupled, 1 1122 113 0 0 1 1 0 0 2 2 2 0.00066 1 0.11 0.0015 1
19 LAMA4 laminin, alpha 4 5788 46 0 1 2 0 0 0 2 2 1 0.017 0.01 0.33 0.0016 1
20 OSBPL6 oxysterol binding protein-like 6 3013 37 0 0 1 0 1 0 2 2 2 0.004 1 0.033 0.0021 1
21 ANKRD27 ankyrin repeat domain 27 (VPS9 domain) 3265 90 0 0 1 0 0 1 2 2 2 0.00087 1 0.25 0.0021 1
22 ATP9B ATPase, class II, type 9B 3562 102 0 0 2 0 0 0 2 2 2 0.00054 1 0.36 0.0021 1
23 SHC1 SHC (Src homology 2 domain containing) transforming protein 1 1799 122 0 0 2 0 0 0 2 2 2 0.0027 1 0.069 0.0022 1
24 NDUFS7 NADH dehydrogenase (ubiquinone) Fe-S protein 7, 20kDa (NADH-coenzyme Q reductase) 674 207 0 0 1 0 0 1 2 2 2 0.00052 1 0.4 0.0022 1
25 JUN jun oncogene 996 21 0 0 0 1 0 0 1 1 1 0.0026 NaN NaN 0.0026 1
26 SLC46A1 solute carrier family 46 (folate transporter), member 1 1399 70 0 0 1 0 0 1 2 2 2 0.0003 1 0.62 0.0027 1
27 EPOR erythropoietin receptor 1555 38 0 0 0 1 0 0 1 1 1 0.0028 NaN NaN 0.0028 1
28 GPR44 G protein-coupled receptor 44 1192 4 0 0 0 0 0 1 1 1 1 0.0028 NaN NaN 0.0028 1
29 USP15 ubiquitin specific peptidase 15 2941 206 0 0 1 1 0 0 2 2 2 0.00031 1 0.65 0.0028 1
30 SRPX sushi-repeat-containing protein, X-linked 1431 1000 0 0 0 0 0 2 2 2 1 0.031 0.01 0.99 0.0028 1
31 FAM47C family with sequence similarity 47, member C 3110 10 0 0 3 0 0 0 3 3 2 0.059 0.0049 0.64 0.0029 1
32 TUBB4 tubulin, beta 4 1347 1000 0 0 2 0 0 0 2 2 1 0.018 0.018 0.79 0.0029 1
33 DVL2 dishevelled, dsh homolog 2 (Drosophila) 2267 39 0 0 2 0 0 0 2 2 2 0.0052 1 0.044 0.0029 1
34 FBF1 Fas (TNFRSF6) binding factor 1 3514 90 0 0 1 0 1 0 2 2 2 0.0016 1 0.17 0.0032 1
35 HNRNPM heterogeneous nuclear ribonucleoprotein M 2255 8 0 0 2 0 1 0 3 3 3 0.0055 1 0.047 0.0033 1
HRAS

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

NF1

Figure S2.  This figure depicts the distribution of mutations and mutation types across the NF1 significant gene.

EPAS1

Figure S3.  This figure depicts the distribution of mutations and mutation types across the EPAS1 significant gene.

RET

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

SHROOM4

Figure S5.  This figure depicts the distribution of mutations and mutation types across the SHROOM4 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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