Mutation Analysis (MutSigCV v0.9)
Kidney Renal Clear Cell Carcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1222SSZ
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Mutation Analysis (MutSigCV v0.9). Broad Institute of MIT and Harvard. doi:10.7908/C1222SSZ
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: 548

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): 80

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). If the figure is unpopulated, then full coverage is assumed (e.g. MutSig CV doesn't use WIGs and assumes full coverage).

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: 80. 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
C6orf25 343596 83296 3625 19 19 1 0 0 20 0.77 0 110 0.61 0
PTEN 533752 128232 5046 28 24 23 1 0 20 0.89 3e-15 120 0.61 1.1e-11
VHL 165496 52060 1276 248 239 140 7 0 13 2.7 3e-15 1200 0.79 1.1e-11
GCNT2 1311364 363872 3103 21 21 5 1 0 20 0.77 3.2e-15 110 0.61 1.1e-11
SETD2 2703284 723908 10701 63 56 59 2 3 7 1.7 3.6e-15 220 0.59 1.1e-11
EFNB3 354556 126040 2175 12 12 1 0 0 20 0.74 4.1e-15 72 0.57 1.1e-11
CCDC91 578140 144672 6583 15 15 3 0 0 20 0.55 4.6e-15 82 0.64 1.1e-11
BAP1 864744 259752 8555 48 46 44 1 0 20 0.83 4.7e-15 230 0.6 1.1e-11
KDM5C 1728392 518408 12499 29 29 29 1 0 20 1.2 8e-15 130 0.61 1.6e-11
PBRM1 2191452 569920 17574 155 151 140 4 1 20 1.4 1.7e-14 750 0.63 3e-11
NAPSA 494296 166592 4698 13 12 3 0 0 20 0.55 6.5e-12 67 0.6 1.1e-08
DNMT1 1995268 543068 20938 22 22 7 0 0 20 0.84 4.8e-11 100 0.63 7.3e-08
RRAS2 242216 61376 3045 8 8 1 0 0 20 0.66 3.9e-10 49 0.61 5.5e-07
CIB3 244408 62472 3364 8 8 2 0 0 20 0.19 4.9e-10 49 0.59 6.3e-07
PHYH 422508 107956 4640 10 10 2 0 0 20 0.92 6.1e-10 56 0.61 7.4e-07
CCDC136 1059832 256464 4611 15 15 2 1 0 20 1.2 8.1e-10 78 0.61 9.2e-07
SPRY4 400040 123300 1189 11 11 3 0 0 20 0.85 1.7e-09 55 0.58 1.9e-06
MARK4 700892 210432 8642 9 9 2 0 0 20 0 4.4e-09 55 0.58 4.5e-06
SDHAF2 251532 65760 2697 9 9 1 0 0 20 1.6 6.6e-09 52 0.59 6.4e-06
ZMAT2 276192 61924 3538 7 7 1 1 0 20 0.57 9.6e-09 43 0.58 8.8e-06
SORD 348528 104668 3683 11 11 3 0 0 20 1.9 2.4e-08 54 0.58 0.000021
TAS2R3 400040 120560 696 14 13 5 0 0 20 0.38 4.5e-08 43 0.57 0.000037
TP53 517860 151248 5974 21 18 17 1 0 4 0.7 5.1e-08 79 0.59 0.000041
DCAF11 718428 203856 8120 10 10 6 0 0 20 0.34 7.5e-08 49 0.61 0.000057
ARPC2 397300 104120 5568 8 8 2 0 0 20 0.65 1e-07 45 0.58 0.000074
CD4 574304 168784 4988 11 10 4 1 0 20 0.93 1.1e-07 51 0.61 0.000074
PCGF1 294824 76720 4176 7 7 1 1 0 20 0.81 1.7e-07 42 0.57 0.00011
DNMT3A 1135456 315100 12586 12 12 8 0 0 18 0.17 1.8e-07 59 0.59 0.00012
DPEP2 544712 167140 5510 9 9 3 0 0 20 0.75 2.3e-07 46 0.59 0.00015
MRPL10 362228 113436 3596 7 7 2 1 0 20 0.57 4.7e-07 39 0.67 0.00029
SMG7 1482340 413192 12180 20 16 4 0 0 20 0.77 5.1e-07 66 0.58 0.0003
PRB1 252628 87132 1769 8 8 3 2 0 12 1.6 5.2e-07 41 0.56 0.0003
PXMP2 203308 59184 2378 6 6 1 0 0 20 1.1 9.9e-07 35 0.55 0.00054
DPP3 934340 283864 9628 14 14 2 1 0 14 1.7 1.2e-06 70 0.6 0.00065
PCGF2 280576 77816 4176 7 7 2 1 0 20 0.63 1.3e-06 40 0.57 0.00066
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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