Mutation Analysis (MutSig 2CV v3.1)
Kidney Renal Papillary 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/C1TX3D4Q
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: KIRP-TP

  • Number of patients in set: 168

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:KIRP-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 18

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: 18. 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 HNRNPM heterogeneous nuclear ribonucleoprotein M 2255 1 0 0 1 0 10 0 11 11 2 4.4e-08 1e-05 0.033 1.3e-11 2.4e-07
2 NF2 neurofibromin 2 (merlin) 1894 12 0 1 3 2 3 4 12 12 12 9.8e-12 1 0.67 2.6e-10 2.4e-06
3 NEFH neurofilament, heavy polypeptide 200kDa 3077 76 0 1 9 0 0 5 14 10 6 1.8e-06 1e-05 0.96 4.6e-10 2.8e-06
4 TDG thymine-DNA glycosylase 1269 24 0 0 1 0 4 0 5 5 3 4.9e-06 0.0024 0.18 2.3e-07 0.001
5 ZNF598 zinc finger protein 598 2763 5 0 2 14 0 0 0 14 13 5 0.012 1e-05 1 2e-06 0.0075
6 SKI v-ski sarcoma viral oncogene homolog (avian) 2213 36 0 1 6 0 0 0 6 6 1 0.0031 1e-05 0.98 4.5e-06 0.012
7 CSGALNACT2 chondroitin sulfate N-acetylgalactosaminyltransferase 2 1653 9 0 1 7 0 0 0 7 7 3 0.029 1e-05 0.83 4.6e-06 0.012
8 SETD2 SET domain containing 2 7777 71 0 2 7 1 1 7 16 15 16 9.7e-07 1 0.35 6.4e-06 0.015
9 MET met proto-oncogene (hepatocyte growth factor receptor) 4307 2 0 0 15 0 0 1 16 15 14 0.059 0.0033 0.0023 9e-06 0.018
10 MUC5B mucin 5B, oligomeric mucus/gel-forming 17492 26 0 9 18 0 1 1 20 18 16 0.069 1e-05 0.11 1e-05 0.019
11 KDM6A lysine (K)-specific demethylase 6A 4318 20 0 2 4 3 1 4 12 9 11 7.9e-06 0.13 0.62 0.000018 0.03
12 ZNF814 zinc finger protein 814 2576 0 0 4 11 0 0 3 14 8 8 0.26 1e-05 0.87 0.000035 0.054
13 OR2L8 olfactory receptor, family 2, subfamily L, member 8 939 3 0 1 1 0 0 3 4 4 2 0.019 0.00011 0.95 0.000048 0.068
14 MYH6 myosin, heavy chain 6, cardiac muscle, alpha (cardiomyopathy, hypertrophic 1) 5970 31 0 1 5 2 1 0 8 8 7 0.00011 0.034 0.43 0.000052 0.068
15 UNC13A unc-13 homolog A (C. elegans) 5276 54 0 3 8 1 0 0 9 9 7 0.00019 0.021 0.93 0.000058 0.071
16 MED16 mediator complex subunit 16 2694 2 0 0 3 1 0 0 4 4 2 0.053 0.0001 1 0.000076 0.084
17 GLUD2 glutamate dehydrogenase 2 1679 2 0 3 11 0 0 0 11 11 5 0.6 1e-05 1 0.000078 0.084
18 BMS1 BMS1 homolog, ribosome assembly protein (yeast) 3937 10 0 1 14 0 0 0 14 13 5 0.65 1e-05 1 0.000084 0.086
19 FUS fusion (involved in t(12;16) in malignant liposarcoma) 1637 32 0 0 0 0 3 0 3 3 1 0.013 0.001 0.63 0.00016 0.14
20 TP53 tumor protein p53 1889 71 0 0 6 1 0 0 7 7 7 0.000065 1 0.13 0.00016 0.14
21 ACADL acyl-Coenzyme A dehydrogenase, long chain 1333 51 0 0 3 0 1 1 5 4 5 0.0036 0.024 0.14 0.00016 0.14
22 AHNAK2 AHNAK nucleoprotein 2 17412 20 0 6 8 3 0 1 12 10 10 0.085 0.00023 0.33 0.00017 0.14
23 SMARCB1 SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily b, member 1 1190 27 0 1 2 0 0 3 5 5 4 0.00035 0.034 0.63 0.00018 0.14
24 TEKT1 tektin 1 1287 20 0 0 2 0 1 1 4 4 3 0.000091 0.17 0.76 0.00024 0.18
25 MKL1 megakaryoblastic leukemia (translocation) 1 2844 313 0 2 9 0 0 1 10 9 9 0.000098 0.18 0.87 0.00025 0.18
26 MYH7 myosin, heavy chain 7, cardiac muscle, beta 5960 27 0 1 4 0 2 0 6 6 5 0.0026 0.0091 0.91 0.00027 0.19
27 MAP4K3 mitogen-activated protein kinase kinase kinase kinase 3 2819 17 0 0 4 0 1 0 5 5 5 0.0096 0.0052 0.57 0.00029 0.2
28 NEK2 NIMA (never in mitosis gene a)-related kinase 2 1366 29 0 0 3 0 0 0 3 3 1 0.026 0.001 0.093 0.00031 0.2
29 FBF1 Fas (TNFRSF6) binding factor 1 3514 90 0 1 3 3 1 0 7 7 6 0.0017 0.082 0.094 0.00035 0.22
30 ZNF592 zinc finger protein 592 3836 198 0 2 4 0 0 0 4 4 2 0.32 0.00016 0.1 0.00037 0.22
31 PAM peptidylglycine alpha-amidating monooxygenase 3021 7 0 1 4 0 0 2 6 4 6 0.23 0.00017 0.42 0.00041 0.25
32 TMCO3 transmembrane and coiled-coil domains 3 2082 1 0 0 0 0 0 2 2 2 1 0.0039 0.01 0.28 0.00043 0.25
33 BAIAP3 BAI1-associated protein 3 3696 21 0 0 6 1 0 1 8 7 8 0.00041 0.082 0.99 0.00048 0.26
34 FCGR2A Fc fragment of IgG, low affinity IIa, receptor (CD32) 980 13 0 0 4 0 0 1 5 5 4 0.0028 0.037 0.23 0.00052 0.26
35 CUL3 cullin 3 2367 3 0 0 4 2 0 1 7 5 7 0.0012 0.039 1 0.00053 0.26
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)