Mutation Analysis (MutSig 2CV v3.1)
Kidney Renal Clear Cell Carcinoma (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/C1RB73BQ
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: KIRC-TP

  • Number of patients in set: 491

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:KIRC-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 21

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: 21. 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 SETD2 SET domain containing 2 7777 25 3 1 20 18 5 9 52 47 50 2.6e-16 0.0046 0.027 1e-16 1.8e-12
2 BAP1 BRCA1 associated protein-1 (ubiquitin carboxy-terminal hydrolase) 2254 12 0 1 17 11 7 9 44 42 40 8.5e-16 0.002 0.21 2.2e-16 2e-12
3 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 1244 33 0 0 5 5 3 8 21 18 21 1e-16 0.19 0.57 7.8e-16 4.7e-12
4 PBRM1 polybromo 1 5418 1 1 4 24 48 13 54 139 136 129 1.7e-16 0.42 0.078 1.7e-15 7.3e-12
5 KDM5C lysine (K)-specific demethylase 5C 4879 10 0 1 10 7 1 9 27 27 27 1e-16 0.45 0.52 2e-15 7.3e-12
6 VHL von Hippel-Lindau tumor suppressor 650 0 0 7 105 38 17 72 232 223 134 3.7e-15 0.99 1 1.3e-13 3.9e-10
7 FAM200A family with sequence similarity 200, member A 1726 9 0 0 6 0 0 0 6 5 6 8.6e-08 0.32 0.014 9e-08 0.00023
8 TP53 tumor protein p53 1889 25 0 1 8 1 1 0 10 9 10 7e-07 1 0.033 1.1e-06 0.0024
9 NEFH neurofilament, heavy polypeptide 200kDa 3077 3 0 0 1 0 0 5 6 6 5 3.8e-06 0.044 0.14 1.3e-06 0.0025
10 CCDC120 coiled-coil domain containing 120 2042 37 0 0 4 0 0 0 4 4 3 0.0066 0.0001 0.51 1e-05 0.018
11 MTOR mechanistic target of rapamycin (serine/threonine kinase) 8871 12 0 3 26 0 0 0 26 25 22 0.084 1e-05 0.85 0.000013 0.021
12 GUSB glucuronidase, beta 2000 19 0 1 6 0 0 0 6 4 4 0.13 1e-05 0.85 0.000019 0.029
13 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 3287 2 0 0 11 1 0 0 12 12 9 0.16 1e-05 0.73 0.000023 0.032
14 PCK1 phosphoenolpyruvate carboxykinase 1 (soluble) 1905 55 0 1 1 0 0 3 4 4 4 0.00052 0.011 0.26 0.000036 0.046
15 ARID1A AT rich interactive domain 1A (SWI-like) 6934 1 0 1 5 3 0 4 12 12 12 0.000014 1 0.14 0.000042 0.052
16 ATM ataxia telangiectasia mutated 9438 5 0 1 9 4 0 2 15 12 15 0.0024 0.039 0.012 0.000051 0.059
17 DPCR1 diffuse panbronchiolitis critical region 1 4190 18 0 1 4 0 0 2 6 6 5 0.000053 0.076 0.63 0.000056 0.06
18 FGFR3 fibroblast growth factor receptor 3 (achondroplasia, thanatophoric dwarfism) 2642 69 0 1 3 1 0 0 4 4 3 0.003 0.019 0.02 6e-05 0.06
19 PTCH1 patched homolog 1 (Drosophila) 4640 51 0 3 7 0 0 0 7 7 6 0.052 0.0001 0.26 0.00011 0.096
20 RBMX RNA binding motif protein, X-linked 1265 0 1 0 1 0 0 3 4 4 3 0.00037 0.019 0.95 0.00011 0.096
21 ARAP3 ArfGAP with RhoGAP domain, ankyrin repeat and PH domain 3 4763 2 0 0 1 2 0 0 3 3 2 0.0065 0.0098 0.051 0.00011 0.096
22 GPR172B G protein-coupled receptor 172B 1359 32 0 0 2 0 0 2 4 4 3 0.00039 0.016 1 0.00013 0.11
23 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 0.00014 0.67 0.00014 0.11
24 IL1RAP interleukin 1 receptor accessory protein 2491 34 0 0 4 0 0 0 4 4 3 0.0024 0.0085 0.28 0.00014 0.11
25 BCL6 B-cell CLL/lymphoma 6 (zinc finger protein 51) 2153 38 0 0 4 1 0 0 5 5 5 0.000015 1 0.93 0.00019 0.13
26 NFAT5 nuclear factor of activated T-cells 5, tonicity-responsive 4706 2 0 0 6 1 0 0 7 6 6 0.17 4e-05 0.23 0.00019 0.13
27 LARP1 La ribonucleoprotein domain family, member 1 3134 8 0 0 2 1 0 2 5 5 5 0.00019 1 0.07 0.00019 0.13
28 KIAA1751 KIAA1751 2357 4 0 0 4 2 0 0 6 6 4 0.041 0.0003 0.98 0.00024 0.16
29 INSRR insulin receptor-related receptor 4008 14 0 0 3 0 1 0 4 4 4 0.0012 1 0.016 0.00035 0.22
30 TDRD10 tudor domain containing 10 1471 47 0 0 3 0 0 0 3 3 2 0.015 0.013 0.013 0.0004 0.24
31 TFDP2 transcription factor Dp-2 (E2F dimerization partner 2) 1498 97 0 0 4 0 0 1 5 5 5 0.000038 1 0.84 0.00042 0.24
32 RANBP3 RAN binding protein 3 1768 35 0 0 3 0 0 0 3 3 2 0.0029 0.014 0.42 0.00045 0.24
33 TNRC18 trinucleotide repeat containing 18 9019 9 0 3 5 0 0 4 9 8 9 0.00056 0.056 0.62 0.00045 0.24
34 GPR172A G protein-coupled receptor 172A 2641 12 0 0 1 0 0 2 3 3 2 0.0059 0.0067 1 0.00045 0.24
35 ZFYVE26 zinc finger, FYVE domain containing 26 7784 8 0 2 9 0 0 0 9 8 8 0.22 0.0001 0.94 0.00047 0.25
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)