Mutation Analysis (MutSigCV v0.9)
Kidney Renal Clear Cell Carcinoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C1G73CZ2
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/C1G73CZ2
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: 499

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): 149

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: 149. 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
SETD2 2461567 659179 180072 55 50 52 1 3 7 1.9 0 210 0.42 0
VHL 150698 47405 21472 238 230 140 2 0 13 1.9 0 1200 0.46 0
PBRM1 1995501 518960 295728 141 138 130 2 1 20 1.4 1.4e-15 700 0.52 8.8e-12
KDM5C 1573846 472054 210328 27 27 27 1 0 20 1 4.8e-15 130 0.51 2.2e-11
CCDC91 526445 131736 110776 15 15 3 0 0 20 0.28 6.9e-15 87 0.41 2.5e-11
EFNB3 322853 114770 36600 12 12 1 0 0 20 0.54 8.3e-15 74 0.47 2.5e-11
BAP1 787422 236526 143960 24 23 24 1 0 20 3.9 1.1e-14 120 0.41 2.9e-11
PTEN 486026 116766 84912 23 20 22 1 0 20 0.91 1.4e-14 100 0.51 3.1e-11
NAPSA 450098 151696 79056 13 12 3 0 0 20 0.62 1.8e-13 68 0.46 3.7e-10
DNMT1 1816859 494509 352336 21 21 6 0 0 20 0.9 3.4e-13 99 0.42 6.2e-10
CCDC136 965066 233532 77592 16 16 3 0 0 20 1.4 1.3e-12 83 0.41 2.2e-09
RRAS2 220558 55888 51240 8 8 1 0 0 20 0.4 1.7e-12 51 0.47 2.5e-09
PHYH 384729 98303 78080 10 10 2 0 0 20 0.89 1.3e-11 57 0.42 1.7e-08
CIB3 222554 56886 56608 8 8 2 0 0 20 0.19 1.3e-11 49 0.5 1.7e-08
ZMAT2 251496 56387 59536 7 7 1 1 0 20 0.46 2.2e-10 44 0.46 2.6e-07
TAS2R3 364270 109780 11712 14 13 5 0 0 20 0.26 2.4e-10 46 0.55 2.8e-07
SPRY4 364270 112275 20008 10 10 3 0 0 20 0.88 3.4e-10 53 0.46 3.6e-07
MARK4 638221 191616 145424 9 9 2 0 0 20 0.16 9.5e-10 53 0.47 9.7e-07
DPEP2 496006 152195 92720 9 9 3 0 0 20 0.52 1.7e-09 48 0.45 1.6e-06
SDHAF2 229041 59880 45384 9 9 1 0 0 20 2.3 2.2e-09 51 0.41 2e-06
CD4 522952 153692 83936 11 10 4 1 0 20 0.94 3.2e-09 51 0.41 2.8e-06
ARPC2 361775 94810 93696 8 8 2 0 0 20 0.91 3.4e-09 48 0.41 2.8e-06
DNMT3A 1033928 286925 211792 12 12 8 0 0 18 0.18 4e-09 60 0.43 3.2e-06
DCAF11 654189 185628 136640 10 10 6 0 0 20 0.59 4.9e-09 52 0.4 3.7e-06
SMG7 1349795 376246 204960 20 16 4 0 0 20 0.81 6.3e-09 67 0.49 4.6e-06
SPDYE3 301895 80838 41968 12 12 1 0 0 3 0 8.5e-09 72 0.59 6e-06
ARAP3 1677139 531435 284992 16 14 5 0 0 20 0.78 1.1e-08 72 0.59 7.5e-06
DPP3 850795 258482 162016 14 14 2 1 0 14 1.7 1.1e-08 71 0.56 7.5e-06
MRPL10 329839 103293 60512 7 7 2 1 0 20 0.46 1.2e-08 40 0.41 7.8e-06
SORD 317364 95309 61976 10 10 3 0 0 20 2.2 1.6e-08 51 0.41 9.8e-06
PCGF1 268462 69860 70272 7 7 1 1 0 20 1 3.3e-08 41 0.4 0.000019
TP53 471555 137724 100528 18 15 14 1 0 4 0.77 3.6e-08 68 0.54 2e-05
SLC26A4 856284 256985 182024 11 11 3 3 0 20 1.1 5.1e-08 55 0.58 0.000028
CECR1 625247 169161 99064 10 10 4 0 0 20 1 8.4e-08 52 0.47 0.000045
LETMD1 428641 126247 89792 9 9 4 0 0 20 0.9 8.9e-08 45 0.52 0.000047
SETD2

Figure S1.  This figure depicts the distribution of mutations and mutation types across the SETD2 significant gene.

VHL

Figure S2.  This figure depicts the distribution of mutations and mutation types across the VHL significant gene.

PBRM1

Figure S3.  This figure depicts the distribution of mutations and mutation types across the PBRM1 significant gene.

KDM5C

Figure S4.  This figure depicts the distribution of mutations and mutation types across the KDM5C significant gene.

CCDC91

Figure S5.  This figure depicts the distribution of mutations and mutation types across the CCDC91 significant gene.

EFNB3

Figure S6.  This figure depicts the distribution of mutations and mutation types across the EFNB3 significant gene.

BAP1

Figure S7.  This figure depicts the distribution of mutations and mutation types across the BAP1 significant gene.

PTEN

Figure S8.  This figure depicts the distribution of mutations and mutation types across the PTEN significant gene.

NAPSA

Figure S9.  This figure depicts the distribution of mutations and mutation types across the NAPSA significant gene.

DNMT1

Figure S10.  This figure depicts the distribution of mutations and mutation types across the DNMT1 significant gene.

CCDC136

Figure S11.  This figure depicts the distribution of mutations and mutation types across the CCDC136 significant gene.

PHYH

Figure S12.  This figure depicts the distribution of mutations and mutation types across the PHYH significant gene.

CIB3

Figure S13.  This figure depicts the distribution of mutations and mutation types across the CIB3 significant gene.

ZMAT2

Figure S14.  This figure depicts the distribution of mutations and mutation types across the ZMAT2 significant gene.

TAS2R3

Figure S15.  This figure depicts the distribution of mutations and mutation types across the TAS2R3 significant gene.

SPRY4

Figure S16.  This figure depicts the distribution of mutations and mutation types across the SPRY4 significant gene.

MARK4

Figure S17.  This figure depicts the distribution of mutations and mutation types across the MARK4 significant gene.

DPEP2

Figure S18.  This figure depicts the distribution of mutations and mutation types across the DPEP2 significant gene.

CD4

Figure S19.  This figure depicts the distribution of mutations and mutation types across the CD4 significant gene.

ARPC2

Figure S20.  This figure depicts the distribution of mutations and mutation types across the ARPC2 significant gene.

DNMT3A

Figure S21.  This figure depicts the distribution of mutations and mutation types across the DNMT3A significant gene.

DCAF11

Figure S22.  This figure depicts the distribution of mutations and mutation types across the DCAF11 significant gene.

SMG7

Figure S23.  This figure depicts the distribution of mutations and mutation types across the SMG7 significant gene.

SPDYE3

Figure S24.  This figure depicts the distribution of mutations and mutation types across the SPDYE3 significant gene.

ARAP3

Figure S25.  This figure depicts the distribution of mutations and mutation types across the ARAP3 significant gene.

DPP3

Figure S26.  This figure depicts the distribution of mutations and mutation types across the DPP3 significant gene.

MRPL10

Figure S27.  This figure depicts the distribution of mutations and mutation types across the MRPL10 significant gene.

SORD

Figure S28.  This figure depicts the distribution of mutations and mutation types across the SORD significant gene.

PCGF1

Figure S29.  This figure depicts the distribution of mutations and mutation types across the PCGF1 significant gene.

TP53

Figure S30.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

SLC26A4

Figure S31.  This figure depicts the distribution of mutations and mutation types across the SLC26A4 significant gene.

CECR1

Figure S32.  This figure depicts the distribution of mutations and mutation types across the CECR1 significant gene.

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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