Mutation Analysis (MutSig 2CV v3.1)
Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (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/C1MW2FVZ
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: 38

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): 1

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: 1. 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 NFE2L2 nuclear factor (erythroid-derived 2)-like 2 1834 32 0 0 5 1 0 1 7 6 6 2.6e-06 0.07 0.056 5.8e-07 0.011
2 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 3287 2 0 0 11 0 0 0 11 9 4 0.39 1e-05 0.022 0.000053 0.49
3 MAPK1 mitogen-activated protein kinase 1 1115 1000 0 0 3 0 0 0 3 3 1 0.014 0.001 0.15 0.00017 1
4 ARID1A AT rich interactive domain 1A (SWI-like) 6934 13 0 0 2 4 0 2 8 5 8 0.000081 1 0.32 0.00038 1
5 SGOL2 shugoshin-like 2 (S. pombe) 3836 49 0 0 2 0 1 0 3 3 3 0.00052 1 0.051 0.0004 1
6 HSD17B4 hydroxysteroid (17-beta) dehydrogenase 4 2303 278 0 1 3 1 0 0 4 4 4 0.00084 1 0.035 0.00045 1
7 NCKAP1 NCK-associated protein 1 3529 113 0 0 2 2 0 0 4 4 4 0.0001 1 0.68 0.0011 1
8 RGAG1 retrotransposon gag domain containing 1 4175 43 0 0 2 0 0 0 2 2 2 0.0095 1 0.01 0.0012 1
9 PTH2 parathyroid hormone 2 311 69 0 0 2 0 0 0 2 2 2 0.0033 1 0.04 0.0017 1
10 DNAJB1 DnaJ (Hsp40) homolog, subfamily B, member 1 1033 191 0 0 4 0 1 0 5 3 5 0.00065 1 0.2 0.002 1
11 TMCC1 transmembrane and coiled-coil domain family 1 1974 71 0 0 1 0 0 2 3 3 3 0.00082 1 0.26 0.0025 1
12 SIGLEC10 sialic acid binding Ig-like lectin 10 2134 103 0 0 3 0 0 0 3 3 3 0.0056 1 0.027 0.0033 1
13 EPPK1 epiplakin 1 7265 13 0 0 0 1 0 0 1 1 1 0.0033 NaN NaN 0.0033 1
14 IQCH IQ motif containing H 3293 27 0 0 2 1 0 0 3 3 3 0.00041 1 0.79 0.0036 1
15 GLDC glycine dehydrogenase (decarboxylating) 3159 11 0 0 0 0 1 1 2 2 2 0.00042 1 0.48 0.0036 1
16 UGT3A2 UDP glycosyltransferase 3 family, polypeptide A2 1596 5 0 0 1 0 0 1 2 2 2 0.013 1 0.022 0.0038 1
17 C15orf56 chromosome 15 open reading frame 56 492 12 0 0 0 0 0 1 1 1 1 0.004 NaN NaN 0.004 1
18 LYZL1 lysozyme-like 1 603 87 0 0 2 1 0 0 3 3 3 0.0005 1 0.52 0.0043 1
19 PPP1R3A protein phosphatase 1, regulatory (inhibitor) subunit 3A 3383 24 0 0 0 1 0 0 1 1 1 0.0044 NaN NaN 0.0044 1
20 CCNF cyclin F 2427 58 0 0 1 0 0 1 2 2 2 0.0084 1 0.049 0.0044 1
21 KIAA0947 KIAA0947 6873 5 0 0 0 0 1 0 1 1 1 0.0045 NaN NaN 0.0045 1
22 MLL2 myeloid/lymphoid or mixed-lineage leukemia 2 16826 3 0 2 2 5 0 0 7 6 7 0.0014 1 0.25 0.0049 1
23 DLG4 discs, large homolog 4 (Drosophila) 3161 39 0 0 0 0 1 0 1 1 1 0.005 NaN NaN 0.005 1
24 HK2 hexokinase 2 2822 11 0 0 0 1 0 0 1 1 1 0.0052 NaN NaN 0.0052 1
25 DNAH2 dynein, axonemal, heavy chain 2 13622 9 0 2 1 2 1 0 4 4 4 0.0074 1 0.071 0.0052 1
26 CTNNBIP1 catenin, beta interacting protein 1 258 34 0 0 0 1 0 0 1 1 1 0.0062 NaN NaN 0.0062 1
27 CEP350 centrosomal protein 350kDa 9502 5 0 0 0 0 1 0 1 1 1 0.0063 NaN NaN 0.0063 1
28 ERF Ets2 repressor factor 1659 40 0 0 0 1 0 0 1 1 1 0.0064 NaN NaN 0.0064 1
29 NF2 neurofibromin 2 (merlin) 1894 0 0 0 1 0 0 1 2 2 2 0.0066 NaN NaN 0.0066 1
30 MYH9 myosin, heavy chain 9, non-muscle 6043 42 0 1 4 1 0 1 6 5 6 0.0028 1 0.21 0.0069 1
31 IQCG IQ motif containing G 1934 205 0 0 2 1 0 0 3 3 3 0.0009 1 0.86 0.0072 1
32 TMEM51 transmembrane protein 51 766 67 0 0 3 0 0 0 3 3 3 0.00093 1 0.86 0.0074 1
33 NTNG2 netrin G2 1617 383 0 0 3 0 0 0 3 3 3 0.0041 1 0.17 0.0077 1
34 VEPH1 ventricular zone expressed PH domain homolog 1 (zebrafish) 3378 15 0 0 1 0 0 1 2 2 2 0.00098 1 0.97 0.0077 1
35 ZNF645 zinc finger protein 645 1280 21 0 0 2 0 0 0 2 2 1 0.1 0.049 0.01 0.0079 1
NFE2L2

Figure S1.  This figure depicts the distribution of mutations and mutation types across the NFE2L2 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)