Mutation Analysis (MutSigCV v0.9)
Kidney Renal Clear Cell Carcinoma (Primary solid tumor)
23 September 2013  |  analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C1416VC6
Maintained by Dan DiCara (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Mutation Analysis (MutSigCV v0.9). Broad Institute of MIT and Harvard. doi:10.7908/C1416VC6
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. MutSigCV v0.9 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
Results
Target Coverage for Each Individual

The x axis represents the samples. The y axis represents the exons, one row per exon, and they are sorted by average coverage across samples. For exons with exactly the same average coverage, they are sorted next by the %GC of the exon. (The secondary sort is especially useful for the zero-coverage exons at the bottom).

Figure 1. 

Distribution of Mutation Counts, Coverage, and Mutation Rates Across Samples

Figure 2.  Patients counts and rates file used to generate this plot: KIRC-TP.patients.counts_and_rates.txt

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 3.  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 4.  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 5.  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

  • nflank = number of noncoding mutations from this gene's flanking region, across the individual set

  • 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: 7. 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).

gene Nnon Nsil Nflank nnon npat nsite nsil nflank nnei fMLE p score time q
KDM5C 1548614 464486 3017 27 27 27 1 0 20 1.2 1.6e-15 120 0.34 2.8e-11
SETD2 2422103 648611 2583 53 48 51 1 3 7 1.7 5.1e-15 200 0.38 2.8e-11
PBRM1 1963509 510640 4242 140 137 130 4 1 20 1.7 6.2e-15 680 0.39 2.8e-11
VHL 148282 46645 308 234 223 135 7 0 13 3 6.2e-15 1100 0.46 2.8e-11
BAP1 774798 232734 2065 44 42 40 1 0 20 1 7.9e-15 200 0.36 2.9e-11
PTEN 478234 114894 1218 21 18 21 0 0 20 0.55 1.2e-14 100 0.35 3.7e-11
NEFH 818988 226351 420 12 12 11 0 0 20 0.72 2.9e-10 69 0.57 7.6e-07
TSPAN19 163012 39771 392 5 5 5 0 0 13 0.63 0.000092 24 0.51 0.21
FAM200A 74141 19640 84 6 5 6 0 0 20 1.6 0.00013 19 0.3 0.27
DPCR1 268577 85925 224 8 8 6 1 0 20 1.8 0.00022 31 0.59 0.39
TMEM37 204747 66285 154 4 4 3 0 0 20 0.67 0.00059 21 0.56 0.97
TBC1D3F 137480 35843 420 3 3 2 0 0 19 0.65 0.00076 18 0.3 1
LMOD3 591655 150737 259 4 4 3 0 0 20 0.53 0.0014 25 0.32 1
CD7 168904 55483 287 4 3 3 0 0 19 1 0.002 20 0.33 1
ZDHHC1 330934 100655 875 4 4 3 0 0 20 0.58 0.0025 20 0.3 1
C1orf159 68740 22586 2240 2 2 2 0 0 20 0.26 0.0026 12 0.26 1
C5orf13 83470 22095 448 2 2 2 0 0 20 1.1 0.0028 14 0.28 1
C1orf52 192963 51064 343 4 4 4 0 0 20 0.94 0.0032 17 0.3 1
GATA2 429625 136007 539 5 5 5 0 0 19 0.66 0.0032 23 0.31 1
C15orf40 299510 71195 686 3 3 2 0 0 20 0.29 0.0038 17 0.3 1
TFDP2 467923 126678 1561 5 5 5 0 0 20 0.71 0.0048 20 0.57 1
FAM70B 312767 105074 945 4 4 4 0 0 20 0.89 0.0049 20 0.45 1
KRTAP19-6 66285 20131 168 2 2 1 0 0 20 2.3 0.0051 14 0.28 1
KLRC2 267104 69231 812 4 4 3 0 0 20 1.4 0.0052 19 0.52 1
NDUFC1 65303 18167 287 2 2 2 0 0 20 1.6 0.0053 11 0.26 1
RPS21 86907 22586 581 2 2 2 0 0 16 0.93 0.0054 14 0.28 1
COL5A3 1737158 594601 8148 11 10 11 0 0 20 0.64 0.0057 40 0.33 1
CD300E 233716 73650 574 5 5 5 0 0 16 0.58 0.0064 17 0.52 1
PGPEP1L 189526 60393 266 4 4 3 1 0 10 1.2 0.0072 20 0.3 1
PLA2G4E 740428 212603 1400 6 6 4 0 0 20 0.95 0.0072 27 0.44 1
SAMD5 97218 27987 224 2 2 2 0 0 20 1.5 0.0076 14 0.28 1
KRTAP9-2 203274 53519 168 3 3 2 0 0 20 1.3 0.0082 17 0.35 1
NDUFA4L2 171850 46154 826 2 2 1 0 0 20 0.94 0.0083 14 0.44 1
OR2T35 115385 36825 7 2 2 1 2 0 20 1.8 0.0085 13 0.51 1
FGFR3 608840 190017 2198 6 6 4 1 0 18 1.2 0.0091 27 0.48 1
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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