Mutation Analysis (MutSig 2CV v3.1)
Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1MG7NV6
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/C1MG7NV6
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: CESC-TP

  • Number of patients in set: 194

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:CESC-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 15

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: 15. 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 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 1244 5 0 0 6 6 3 2 17 15 15 5.9e-16 0.35 0.79 9.7e-15 1.8e-10
2 HLA-B major histocompatibility complex, class I, B 1119 47 0 0 3 5 3 1 12 11 9 2.1e-15 0.18 0.34 2.2e-14 2e-10
3 HLA-A major histocompatibility complex, class I, A 1128 60 0 1 4 7 3 3 17 16 14 1.7e-14 0.18 0.66 2.3e-13 1.4e-09
4 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 709 383 0 0 11 0 0 0 11 11 5 6.4e-09 1e-05 0.033 2e-12 9.2e-09
5 MLL2 myeloid/lymphoid or mixed-lineage leukemia 2 16826 3 0 8 16 14 3 2 35 22 35 1.7e-10 1 0.0035 1.4e-10 5.1e-07
6 FBXW7 F-box and WD repeat domain containing 7 2580 4 0 1 15 5 0 0 20 19 14 1.6e-07 9e-05 0.051 3.4e-10 1e-06
7 EP300 E1A binding protein p300 7365 10 0 1 12 11 3 0 26 21 22 4.3e-09 0.039 0.4 5.4e-09 0.000014
8 MAPK1 mitogen-activated protein kinase 1 1115 192 0 0 9 0 0 0 9 9 3 8.3e-06 9e-05 0.0051 7.6e-09 0.000017
9 MLL3 myeloid/lymphoid or mixed-lineage leukemia 3 14968 2 0 1 11 23 1 2 37 29 35 1.1e-09 0.28 0.92 9.8e-09 2e-05
10 ARID1A AT rich interactive domain 1A (SWI-like) 6934 15 0 1 5 9 0 3 17 14 17 4.9e-10 1 0.35 1.1e-08 2e-05
11 NFE2L2 nuclear factor (erythroid-derived 2)-like 2 1834 294 0 1 14 1 0 1 16 12 14 5.2e-06 0.098 0.036 2.2e-06 0.0036
12 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 3287 2 0 0 57 0 1 0 58 53 19 0.051 1e-05 5e-05 8e-06 0.012
13 CASP8 caspase 8, apoptosis-related cysteine peptidase 1749 100 0 1 3 4 2 0 9 9 8 3.5e-06 0.41 0.19 0.000025 0.035
14 FAT2 FAT tumor suppressor homolog 2 (Drosophila) 13140 0 0 2 10 5 0 0 15 11 13 0.00055 0.0031 0.96 4e-05 0.052
15 ZNF750 zinc finger protein 750 2176 15 0 2 4 2 0 4 10 9 10 8.7e-06 1 0.44 0.000061 0.075
16 C12orf43 chromosome 12 open reading frame 43 811 71 0 0 4 0 0 0 4 4 2 0.011 0.00063 0.046 0.00012 0.13
17 C3orf70 chromosome 3 open reading frame 70 757 24 0 1 3 1 0 0 4 4 1 0.022 0.00048 0.98 0.00014 0.15
18 BAP1 BRCA1 associated protein-1 (ubiquitin carboxy-terminal hydrolase) 2254 10 0 0 0 4 0 0 4 4 3 0.000034 0.4 0.99 0.0002 0.19
19 USP28 ubiquitin specific peptidase 28 3332 6 0 0 1 2 1 0 4 4 4 0.000098 1 0.12 0.0002 0.19
20 IFNGR1 interferon gamma receptor 1 1495 179 0 0 2 3 0 1 6 6 5 0.0006 0.093 0.13 0.00023 0.21
21 ERBB3 v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 (avian) 4266 14 0 1 12 0 0 0 12 11 9 0.0046 0.0058 0.42 0.00031 0.27
22 ANKRD36 ankyrin repeat domain 36 6126 120 0 0 7 0 0 0 7 7 5 0.014 0.0018 0.97 0.00035 0.29
23 SPTAN1 spectrin, alpha, non-erythrocytic 1 (alpha-fodrin) 7658 11 0 4 7 3 0 1 11 10 10 0.018 0.054 0.0053 0.00039 0.31
24 RB1 retinoblastoma 1 (including osteosarcoma) 3719 26 0 1 4 4 1 1 10 9 10 0.00017 1 0.18 0.00055 0.42
25 SMAD4 SMAD family member 4 1699 16 0 0 5 2 0 0 7 7 7 0.000059 1 0.39 0.00063 0.45
26 STAM2 signal transducing adaptor molecule (SH3 domain and ITAM motif) 2 1630 10 0 0 0 0 0 2 2 2 1 0.006 0.01 0.055 0.00064 0.45
27 DCHS1 dachsous 1 (Drosophila) 9973 38 0 5 7 3 1 1 12 12 12 0.00027 1 0.14 0.00071 0.48
28 HIST1H4E histone cluster 1, H4e 314 53 0 0 4 0 0 0 4 4 4 0.00023 1 0.27 0.00081 0.52
29 WDR67 WD repeat domain 67 3287 0 0 0 2 0 0 2 4 3 4 0.01 0.0091 0.21 0.00083 0.52
30 FEZF2 FEZ family zinc finger 2 1396 21 0 1 3 0 0 0 3 3 1 0.08 0.0012 0.16 0.00094 0.57
31 SLC25A5 solute carrier family 25 (mitochondrial carrier; adenine nucleotide translocator), member 5 909 75 0 0 4 1 0 0 5 4 4 0.0025 0.038 0.66 0.00096 0.57
32 TRAF3 TNF receptor-associated factor 3 1747 0 0 0 2 0 1 0 3 3 2 0.016 0.019 0.35 0.0011 0.6
33 GRAMD2 GRAM domain containing 2 1363 27 0 0 4 0 1 0 5 5 4 0.002 0.05 0.72 0.0011 0.64
34 SLC19A3 solute carrier family 19, member 3 1511 26 0 1 1 2 0 0 3 3 2 0.0038 0.059 0.23 0.0012 0.67
35 LIN9 lin-9 homolog (C. elegans) 1733 28 0 0 5 2 0 0 7 7 6 0.0013 0.084 0.39 0.0013 0.7
PTEN

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

HLA-B

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

HLA-A

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

KRAS

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

FBXW7

Figure S5.  This figure depicts the distribution of mutations and mutation types across the FBXW7 significant gene.

EP300

Figure S6.  This figure depicts the distribution of mutations and mutation types across the EP300 significant gene.

MAPK1

Figure S7.  This figure depicts the distribution of mutations and mutation types across the MAPK1 significant gene.

ARID1A

Figure S8.  This figure depicts the distribution of mutations and mutation types across the ARID1A significant gene.

NFE2L2

Figure S9.  This figure depicts the distribution of mutations and mutation types across the NFE2L2 significant gene.

PIK3CA

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

FAT2

Figure S11.  This figure depicts the distribution of mutations and mutation types across the FAT2 significant gene.

ZNF750

Figure S12.  This figure depicts the distribution of mutations and mutation types across the ZNF750 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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