Mutation Analysis (MutSig 2CV v3.1)
Pan-kidney cohort (KICH+KIRC+KIRP) (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1VQ31RG
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Mutation Analysis (MutSig 2CV v3.1). Broad Institute of MIT and Harvard. doi:10.7908/C1VQ31RG
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: KIPAN-TP

  • Number of patients in set: 742

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:KIPAN-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 49

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: 49. 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 TP53 tumor protein p53 1889 17 0 2 29 9 7 2 47 38 40 5.2e-16 0.066 6e-05 1e-16 1.8e-12
2 PBRM1 polybromo 1 5418 1 1 5 29 48 15 58 150 146 139 1e-16 0.49 0.045 3.3e-16 2e-12
3 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 1244 1 0 1 10 7 2 14 33 28 33 3.5e-16 0.016 0.87 3.3e-16 2e-12
4 BAP1 BRCA1 associated protein-1 (ubiquitin carboxy-terminal hydrolase) 2254 12 0 1 21 11 7 14 53 50 48 9.1e-16 0.0035 0.6 4.4e-16 2e-12
5 SETD2 SET domain containing 2 7777 14 3 4 23 22 6 16 67 61 65 1.3e-15 0.04 0.059 8.9e-16 3.2e-12
6 KDM5C lysine (K)-specific demethylase 5C 4879 10 0 2 14 7 1 9 31 31 31 1e-16 0.51 0.63 2.3e-15 6.7e-12
7 NEFH neurofilament, heavy polypeptide 200kDa 3077 36 0 2 10 0 0 10 20 16 11 6.6e-12 1e-05 0.83 2.6e-15 6.7e-12
8 VHL von Hippel-Lindau tumor suppressor 650 0 0 7 110 38 18 74 240 231 139 3.7e-15 0.99 1 1.3e-13 2.9e-10
9 NF2 neurofibromin 2 (merlin) 1894 12 0 1 2 3 6 5 16 16 15 2.3e-12 0.36 0.62 2.8e-11 5.7e-08
10 HNRNPM heterogeneous nuclear ribonucleoprotein M 2255 13 0 0 3 0 9 0 12 12 4 2.8e-06 2e-05 0.037 6.5e-09 0.000012
11 MET met proto-oncogene (hepatocyte growth factor receptor) 4307 109 0 3 19 0 0 1 20 19 17 3e-05 0.00028 0.0058 6.9e-09 0.000012
12 MUC5B mucin 5B, oligomeric mucus/gel-forming 17492 20 0 8 24 0 1 6 31 30 25 0.000064 1e-05 0.5 1.4e-08 0.000022
13 KDM6A lysine (K)-specific demethylase 6A 4318 10 0 1 1 4 1 6 12 11 12 2.3e-09 1 0.74 4.9e-08 0.000064
14 TDG thymine-DNA glycosylase 1269 4 0 2 1 0 4 0 5 5 3 8.4e-07 0.0028 0.23 4.9e-08 0.000064
15 NFE2L2 nuclear factor (erythroid-derived 2)-like 2 1834 81 1 0 9 1 0 0 10 10 8 0.00035 2e-05 0.0085 7.2e-08 0.000088
16 ATM ataxia telangiectasia mutated 9438 5 0 1 12 4 1 3 20 17 19 0.0017 0.0039 0.0019 3.2e-07 0.00037
17 ZNF814 zinc finger protein 814 2576 5 0 2 17 0 0 4 21 13 10 0.0031 1e-05 0.94 5.7e-07 0.00061
18 DNMT3A DNA (cytosine-5-)-methyltransferase 3 alpha 2952 10 0 0 6 1 1 3 11 11 11 1e-06 1 0.28 5.8e-06 0.0059
19 CDK12 cyclin-dependent kinase 12 4525 18 0 0 5 1 2 1 9 9 9 4.6e-07 1 0.7 7.2e-06 0.007
20 CSGALNACT2 chondroitin sulfate N-acetylgalactosaminyltransferase 2 1653 26 0 1 7 1 0 1 9 8 5 0.0055 9e-05 0.98 7.7e-06 0.007
21 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 3287 2 0 0 13 1 0 0 14 14 9 0.1 1e-05 0.68 0.000015 0.013
22 KIAA1751 KIAA1751 2357 4 0 0 5 2 0 1 8 8 6 0.00049 0.0015 0.91 2e-05 0.017
23 MTOR mechanistic target of rapamycin (serine/threonine kinase) 8871 8 0 6 34 0 0 1 35 34 28 0.15 1e-05 0.75 0.000021 0.017
24 RIMBP3 RIMS binding protein 3 4920 41 0 0 0 0 0 5 5 5 4 0.15 0.057 8e-05 0.000022 0.017
25 ZNF598 zinc finger protein 598 2763 0 0 3 10 0 0 1 11 11 2 0.17 1e-05 1 0.000024 0.017
26 GPR50 G protein-coupled receptor 50 1860 3 0 0 2 0 0 3 5 5 4 0.0085 7e-05 0.25 0.000024 0.017
27 CCDC120 coiled-coil domain containing 120 2042 37 0 0 4 0 0 0 4 4 3 0.018 0.00012 0.51 0.000033 0.022
28 SLC6A14 solute carrier family 6 (amino acid transporter), member 14 1981 0 0 0 2 0 0 4 6 5 5 0.01 0.00014 0.83 0.000037 0.024
29 CYP4F11 cytochrome P450, family 4, subfamily F, polypeptide 11 1622 41 0 0 4 0 2 0 6 5 6 0.0034 0.028 0.0092 0.000039 0.024
30 STAG2 stromal antigen 2 3939 8 0 2 5 4 0 5 14 14 14 2.8e-06 1 0.76 0.000039 0.024
31 SKI v-ski sarcoma viral oncogene homolog (avian) 2213 19 0 2 8 0 0 1 9 9 4 0.0038 0.00052 0.99 4e-05 0.024
32 ARID1A AT rich interactive domain 1A (SWI-like) 6934 1 0 2 10 3 0 6 19 19 19 1e-05 1 0.16 0.000043 0.025
33 EGFR epidermal growth factor receptor (erythroblastic leukemia viral (v-erb-b) oncogene homolog, avian) 3999 4 0 0 7 0 0 0 7 7 6 0.061 5e-05 0.67 5e-05 0.028
34 DPCR1 diffuse panbronchiolitis critical region 1 4190 105 0 1 6 0 0 2 8 8 7 0.000028 0.13 0.84 0.000053 0.028
35 GUSB glucuronidase, beta 2000 19 0 1 6 0 0 0 6 4 4 0.44 1e-05 0.85 0.000059 0.031
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)
Copyright © 2015 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle