Mutation Analysis (MutSigCV v0.9)
Pan-kidney cohort (KICH+KIRC+KIRP) (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C18S4P42
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/C18S4P42
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: KIPAN-TP

  • Number of patients in set: 726

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:KIPAN-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 123

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: KIPAN-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: 123. 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
CCDC91 765941 191667 123078 15 15 3 0 0 20 0.66 0 83 0.47 0
TP53 686070 200376 111147 53 41 41 1 0 4 0.44 0 180 1.2 0
VHL 219254 68971 23995 241 233 142 2 0 13 1.2 0 1300 2.2 0
PBRM1 2903309 755047 329026 148 145 137 3 1 20 0.98 2.2e-16 750 0.78 1e-12
SETD2 3581429 959067 200738 68 63 65 3 3 7 1.5 4.4e-16 280 0.7 1.6e-12
BAP1 1145703 344147 160150 29 27 29 1 0 20 2.8 2e-15 140 0.54 6.1e-12
KDM5C 2289642 686742 232478 30 30 30 2 0 20 1.4 4.9e-15 140 0.5 1.3e-11
PTEN 707087 169875 94298 37 31 36 1 0 20 1.1 1.6e-14 160 0.79 3.2e-11
EFNB3 469723 166981 40601 12 12 1 0 0 20 0.44 1.6e-14 75 0.53 3.2e-11
NAPSA 654864 220709 87703 14 13 4 0 0 20 0.62 3.2e-13 75 0.79 5.8e-10
NF2 930013 234499 153026 16 16 15 1 0 20 1.3 1.3e-12 89 0.56 2.2e-09
DNMT1 2643432 719485 391717 24 24 9 0 0 20 0.84 3.5e-12 110 0.95 5.3e-09
PHYH 559748 143022 86723 10 10 2 0 0 20 0.62 5.6e-11 58 0.53 7.9e-08
CIB3 323798 82765 62953 8 8 2 0 0 20 0.22 1.2e-10 50 0.46 1.6e-07
CCDC136 1404082 339768 86359 16 16 3 0 0 20 1.3 4.4e-10 84 0.54 5.4e-07
NEFH 1210980 334689 32535 21 17 11 2 0 20 0.96 5.7e-10 76 0.52 6.5e-07
RRAS2 320899 81314 57005 8 8 1 0 0 20 0.85 1.4e-09 49 1.3 1.5e-06
SDHAF2 333265 87133 50527 9 9 1 0 0 20 1.3 2.8e-09 53 0.73 2.8e-06
TAS2R3 529981 159720 13045 14 13 5 0 0 20 0.3 3.6e-09 46 0.46 3.5e-06
ARPC2 526353 137941 104311 9 9 3 0 0 20 0.76 6.4e-09 52 0.49 5.8e-06
SPRY4 529984 163350 22191 10 10 3 0 0 20 0.8 7.3e-09 53 2.3 6.4e-06
DNAH3 7121336 1926077 659464 33 31 22 2 0 19 0.49 1e-08 120 0.86 8.7e-06
MARK4 928555 278784 161768 9 9 2 0 0 20 0.19 1.5e-08 54 0.52 0.000012
ZMAT2 365904 82038 66373 7 7 1 1 0 20 0.73 2.6e-08 43 0.51 2e-05
HNRNPM 1207343 341222 163163 13 13 5 0 1 20 0.73 3.4e-08 68 1.1 0.000025
DPEP2 721643 221430 103132 9 9 3 0 0 20 0.53 4.2e-08 48 1 3e-05
SORD 461735 138666 69663 10 10 3 0 0 20 1.5 5.1e-08 52 0.76 0.000035
DNMT3A 1504286 417457 235052 16 16 12 0 0 18 0.84 5.8e-08 74 2.1 0.000038
PCGF1 390586 101640 77771 9 8 3 1 0 20 1.3 9.5e-08 47 0.5 6e-05
MRPL10 479886 150282 67328 8 8 3 1 0 20 0.67 1.2e-07 43 0.51 0.000071
LETMD1 623636 183679 99855 10 10 5 1 0 20 0.77 1.6e-07 49 1.4 0.000093
CD4 760838 223604 93432 11 10 4 1 0 20 0.97 1.8e-07 51 1.8 0.0001
DCDC1 633079 172063 82056 9 9 8 1 0 20 1.1 3.2e-07 47 0.45 0.00018
RBM23 732534 222155 130514 12 9 5 0 0 15 0.47 3.3e-07 49 0.75 0.00018
PDE9A 1034549 237402 197851 10 10 3 0 0 20 0.72 4.1e-07 54 0.5 0.00021
CCDC91

Figure S1.  This figure depicts the distribution of mutations and mutation types across the CCDC91 significant gene.

TP53

Figure S2.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

VHL

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

PBRM1

Figure S4.  This figure depicts the distribution of mutations and mutation types across the PBRM1 significant gene.

SETD2

Figure S5.  This figure depicts the distribution of mutations and mutation types across the SETD2 significant gene.

BAP1

Figure S6.  This figure depicts the distribution of mutations and mutation types across the BAP1 significant gene.

KDM5C

Figure S7.  This figure depicts the distribution of mutations and mutation types across the KDM5C significant gene.

PTEN

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

EFNB3

Figure S9.  This figure depicts the distribution of mutations and mutation types across the EFNB3 significant gene.

NAPSA

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

NF2

Figure S11.  This figure depicts the distribution of mutations and mutation types across the NF2 significant gene.

DNMT1

Figure S12.  This figure depicts the distribution of mutations and mutation types across the DNMT1 significant gene.

PHYH

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

CIB3

Figure S14.  This figure depicts the distribution of mutations and mutation types across the CIB3 significant gene.

CCDC136

Figure S15.  This figure depicts the distribution of mutations and mutation types across the CCDC136 significant gene.

NEFH

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

TAS2R3

Figure S17.  This figure depicts the distribution of mutations and mutation types across the TAS2R3 significant gene.

ARPC2

Figure S18.  This figure depicts the distribution of mutations and mutation types across the ARPC2 significant gene.

SPRY4

Figure S19.  This figure depicts the distribution of mutations and mutation types across the SPRY4 significant gene.

DNAH3

Figure S20.  This figure depicts the distribution of mutations and mutation types across the DNAH3 significant gene.

MARK4

Figure S21.  This figure depicts the distribution of mutations and mutation types across the MARK4 significant gene.

ZMAT2

Figure S22.  This figure depicts the distribution of mutations and mutation types across the ZMAT2 significant gene.

HNRNPM

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

DPEP2

Figure S24.  This figure depicts the distribution of mutations and mutation types across the DPEP2 significant gene.

SORD

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

DNMT3A

Figure S26.  This figure depicts the distribution of mutations and mutation types across the DNMT3A significant gene.

PCGF1

Figure S27.  This figure depicts the distribution of mutations and mutation types across the PCGF1 significant gene.

MRPL10

Figure S28.  This figure depicts the distribution of mutations and mutation types across the MRPL10 significant gene.

LETMD1

Figure S29.  This figure depicts the distribution of mutations and mutation types across the LETMD1 significant gene.

CD4

Figure S30.  This figure depicts the distribution of mutations and mutation types across the CD4 significant gene.

DCDC1

Figure S31.  This figure depicts the distribution of mutations and mutation types across the DCDC1 significant gene.

RBM23

Figure S32.  This figure depicts the distribution of mutations and mutation types across the RBM23 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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