Mutation Analysis (MutSig 2CV v3.1)
Kidney Chromophobe (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C14Q7SRK
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/C14Q7SRK
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: KICH-TP

  • Number of patients in set: 66

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:KICH-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 3

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: 3. 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 1891 52 0 1 17 6 5 2 30 22 28 1e-16 0.65 0.0014 1e-16 1.8e-12
2 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 1244 314 0 0 2 1 0 6 9 6 9 1.8e-08 3e-05 0.69 5.1e-11 4.7e-07
3 PABPC1 poly(A) binding protein, cytoplasmic 1 1966 66 0 0 4 1 0 5 10 7 9 1.4e-07 0.07 0.73 2.3e-07 0.0014
4 URGCP upregulator of cell proliferation 2819 29 0 0 1 0 0 2 3 3 2 0.00039 0.0038 0.38 0.000024 0.11
5 FAM26F family with sequence similarity 26, member F 958 55 0 0 2 0 0 0 2 2 2 0.00041 1 0.01 0.000055 0.2
6 PABPC3 poly(A) binding protein, cytoplasmic 3 1898 75 0 1 9 0 0 0 9 7 9 1e-05 1 1 0.00013 0.38
7 RNF145 ring finger protein 145 2118 72 0 1 3 0 0 0 3 3 2 0.0021 0.0059 0.93 0.00015 0.38
8 GFM1 G elongation factor, mitochondrial 1 2951 44 0 0 1 0 0 2 3 3 2 0.0026 0.0049 0.98 0.00017 0.38
9 RIPPLY2 ripply2 homolog (zebrafish) 399 74 0 0 1 0 0 1 2 2 1 0.0018 0.015 0.12 0.00021 0.43
10 MUC16 mucin 16, cell surface associated 43858 40 0 14 17 0 0 3 20 15 19 0.67 0.0001 0.33 0.00031 0.57
11 UBQLN1 ubiquilin 1 1810 104 0 0 0 0 0 5 5 1 5 0.23 0.00013 0.98 0.00038 0.59
12 NOM1 nucleolar protein with MIF4G domain 1 2625 49 0 1 0 0 0 4 4 1 4 0.35 0.0001 0.49 0.00039 0.59
13 RIMBP3 RIMS binding protein 3 4920 57 0 0 0 0 0 2 2 2 2 0.0037 1 0.01 0.00042 0.59
14 HNF1A HNF1 homeobox A 1934 48 0 0 1 0 0 2 3 3 3 0.000042 1 0.87 0.00047 0.59
15 OR4K17 olfactory receptor, family 4, subfamily K, member 17 1032 207 0 0 0 0 0 4 4 1 4 0.27 0.0001 0.44 0.00048 0.59
16 TAS2R43 taste receptor, type 2, member 43 934 87 0 0 5 0 0 0 5 5 5 0.000053 1 0.47 0.00057 0.63
17 SQRDL sulfide quinone reductase-like (yeast) 1389 322 0 0 1 0 0 2 3 2 3 0.0071 0.0076 0.97 0.00059 0.63
18 ZNF814 zinc finger protein 814 2576 43 0 0 5 0 0 0 5 3 4 0.26 0.00012 0.67 0.00062 0.63
19 CDKN1A cyclin-dependent kinase inhibitor 1A (p21, Cip1) 503 160 0 0 0 1 0 2 3 2 3 0.0025 0.047 0.068 0.00071 0.65
20 UPP1 uridine phosphorylase 1 961 154 0 0 1 0 0 2 3 2 3 0.0061 0.012 0.88 0.00075 0.65
21 RB1 retinoblastoma 1 (including osteosarcoma) 3704 131 0 0 0 0 0 4 4 2 4 0.036 0.01 0.17 0.00082 0.65
22 KDELR3 KDEL (Lys-Asp-Glu-Leu) endoplasmic reticulum protein retention receptor 3 718 395 0 0 1 0 0 2 3 2 3 0.016 0.017 0.18 0.00082 0.65
23 AGRN agrin 6280 168 0 1 1 0 0 3 4 3 3 0.0083 0.0092 0.97 0.00087 0.65
24 NASP nuclear autoantigenic sperm protein (histone-binding) 2502 490 0 0 3 0 0 0 3 2 3 0.015 0.0053 0.34 0.00088 0.65
25 SLK STE20-like kinase (yeast) 3782 13 0 0 1 0 0 2 3 2 3 0.086 0.003 0.042 0.00089 0.65
26 MICALCL MICAL C-terminal like 2120 70 0 0 2 0 0 1 3 3 3 0.0001 1 0.81 0.0011 0.71
27 MUC5B mucin 5B, oligomeric mucus/gel-forming 17492 5 0 3 6 0 0 5 11 11 11 0.00026 1 0.34 0.0011 0.71
28 CBWD6 COBW domain containing 6 1244 31 0 0 0 0 0 2 2 2 1 0.011 0.01 0.12 0.0011 0.71
29 SDHA succinate dehydrogenase complex, subunit A, flavoprotein (Fp) 2116 275 0 0 6 0 0 0 6 5 6 0.00029 1 0.31 0.0012 0.73
30 HLA-A major histocompatibility complex, class I, A 1128 1000 0 0 4 1 0 0 5 5 5 0.00012 1 1 0.0012 0.73
31 STAM signal transducing adaptor molecule (SH3 domain and ITAM motif) 1 1675 172 0 0 0 0 0 2 2 2 1 0.013 0.01 0.96 0.0013 0.73
32 TEX14 testis expressed 14 4875 10 0 0 1 1 0 0 2 2 2 0.0013 NaN NaN 0.0013 0.73
33 ZNF521 zinc finger protein 521 3963 6 0 0 1 0 2 0 3 2 3 0.021 0.0061 0.56 0.0014 0.73
34 ZNF799 zinc finger protein 799 1944 189 0 0 5 0 0 0 5 4 5 0.00053 1 0.16 0.0014 0.73
35 LARP7 La ribonucleoprotein domain family, member 7 1797 17 0 0 1 0 0 2 3 2 3 0.034 0.0077 0.44 0.0014 0.73
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)
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