Mutation Analysis (MutSig 2CV v3.1)
Uveal Melanoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1S1821V
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/C1S1821V
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: UVM-TP

  • Number of patients in set: 80

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:UVM-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 6

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: 6. 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 GNAQ guanine nucleotide binding protein (G protein), q polypeptide 1106 1000 0 0 40 1 0 0 41 40 4 1e-16 1e-05 0.0014 1e-16 6.1e-13
2 GNA11 guanine nucleotide binding protein (G protein), alpha 11 (Gq class) 1104 354 0 0 36 0 0 0 36 36 3 2.6e-16 1e-05 0.0028 1e-16 6.1e-13
3 EIF1AX eukaryotic translation initiation factor 1A, X-linked 459 174 0 0 7 0 2 1 10 10 6 1e-16 1e-05 0.01 1e-16 6.1e-13
4 SF3B1 splicing factor 3b, subunit 1, 155kDa 4035 20 0 0 18 0 0 0 18 18 5 1.1e-11 1e-05 0.92 4e-15 1.8e-11
5 BAP1 BRCA1 associated protein-1 (ubiquitin carboxy-terminal hydrolase) 2254 1 4 0 4 3 0 16 23 17 23 5.5e-10 0.43 0.67 7.1e-09 0.000026
6 CYSLTR2 cysteinyl leukotriene receptor 2 1043 1000 0 0 3 0 0 0 3 3 1 0.00011 0.001 0.0047 1.9e-06 0.0059
7 MAPKAPK5 mitogen-activated protein kinase-activated protein kinase 5 1474 87 0 0 0 0 0 2 2 2 2 3e-05 1 0.037 0.000042 0.11
8 SFRS2 splicing factor, arginine/serine-rich 2 670 533 0 0 0 0 0 3 3 3 3 0.000011 1 0.3 0.000063 0.14
9 SELE selectin E (endothelial adhesion molecule 1) 1885 478 0 0 2 0 0 0 2 2 2 0.00024 1 0.023 0.00014 0.29
10 SPAG5 sperm associated antigen 5 3676 44 0 0 0 1 1 0 2 2 2 0.000021 1 0.4 0.00016 0.3
11 PLCB4 phospholipase C, beta 4 3769 86 0 0 3 0 0 0 3 2 2 0.044 0.001 0.044 0.00048 0.75
12 PLCB2 phospholipase C, beta 2 3682 125 0 0 1 0 1 1 3 3 3 0.0001 1 0.45 0.00049 0.75
13 CSNK1A1L casein kinase 1, alpha 1-like 1014 264 0 0 2 0 0 0 2 2 2 0.000083 1 0.48 0.00087 1
14 NRK Nik related kinase 4861 53 0 0 0 0 0 2 2 2 2 0.00031 1 0.28 0.00095 1
15 FLJ36031 coiled-coil domain containing 71-like 708 0 0 0 0 0 0 1 1 1 1 0.0015 NaN NaN 0.0015 1
16 EIF1B eukaryotic translation initiation factor 1B 354 54 0 0 1 0 0 1 2 2 2 0.00015 1 0.79 0.0015 1
17 ProSAPiP1 2030 259 0 0 1 0 0 1 2 2 2 0.00017 1 0.73 0.0016 1
18 EVPLL envoplakin-like 946 30 0 0 0 0 0 1 1 1 1 0.0016 NaN NaN 0.0016 1
19 WISP1 WNT1 inducible signaling pathway protein 1 1124 48 0 0 2 0 0 0 2 2 2 0.00063 1 0.27 0.0019 1
20 SLC19A1 solute carrier family 19 (folate transporter), member 1 1796 122 0 0 2 0 0 0 2 2 2 0.0022 1 0.04 0.0019 1
21 C13orf36 324 45 0 0 1 0 0 0 1 1 1 0.0019 NaN NaN 0.0019 1
22 GLI2 GLI-Kruppel family member GLI2 4811 32 0 0 0 1 0 0 1 1 1 0.0023 NaN NaN 0.0023 1
23 MGAT4B mannosyl (alpha-1,3-)-glycoprotein beta-1,4-N-acetylglucosaminyltransferase, isozyme B 1849 272 0 0 0 0 1 0 1 1 1 0.0024 NaN NaN 0.0024 1
24 SNRPA1 small nuclear ribonucleoprotein polypeptide A' 802 59 0 0 0 0 0 1 1 1 1 0.003 NaN NaN 0.003 1
25 TPCN1 two pore segment channel 1 2779 48 0 0 1 1 0 0 2 2 2 0.00039 1 0.98 0.0034 1
26 ZMYM3 zinc finger, MYM-type 3 4227 179 0 0 0 0 1 0 1 1 1 0.0035 NaN NaN 0.0035 1
27 MPP1 membrane protein, palmitoylated 1, 55kDa 1459 405 0 0 1 0 0 0 1 1 1 0.0035 NaN NaN 0.0035 1
28 C11orf80 chromosome 11 open reading frame 80 2100 31 0 0 0 0 0 1 1 1 1 0.004 NaN NaN 0.004 1
29 TMEM39A transmembrane protein 39A 1499 283 0 0 2 0 0 0 2 2 2 0.0015 1 0.26 0.0043 1
30 MAOB monoamine oxidase B 1619 114 0 0 1 0 1 0 2 2 2 0.0005 1 0.67 0.0043 1
31 NFE2L2 nuclear factor (erythroid-derived 2)-like 2 1834 170 0 0 0 1 0 0 1 1 1 0.0043 NaN NaN 0.0043 1
32 HSD17B7 hydroxysteroid (17-beta) dehydrogenase 7 1060 439 0 0 2 0 0 0 2 2 2 0.00091 1 0.32 0.0044 1
33 YSK4 yeast Sps1/Ste20-related kinase 4 (S. cerevisiae) 4025 19 0 0 2 0 0 0 2 2 2 0.005 1 0.053 0.0046 1
34 KY kyphoscoliosis peptidase 2028 135 0 0 0 0 0 1 1 1 1 0.0047 NaN NaN 0.0047 1
35 TBX5 T-box 5 1654 108 0 0 0 1 0 0 1 1 1 0.0051 NaN NaN 0.0051 1
GNAQ

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

GNA11

Figure S2.  This figure depicts the distribution of mutations and mutation types across the GNA11 significant gene.

EIF1AX

Figure S3.  This figure depicts the distribution of mutations and mutation types across the EIF1AX significant gene.

SF3B1

Figure S4.  This figure depicts the distribution of mutations and mutation types across the SF3B1 significant gene.

BAP1

Figure S5.  This figure depicts the distribution of mutations and mutation types across the BAP1 significant gene.

CYSLTR2

Figure S6.  This figure depicts the distribution of mutations and mutation types across the CYSLTR2 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)
Copyright © 2016 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle