Mutation Analysis (MutSigCV v0.9)
Pan-kidney cohort (KICH+KIRC+KIRP) (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1ST7P7J
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Mutation Analysis (MutSigCV v0.9). Broad Institute of MIT and Harvard. doi:10.7908/C1ST7P7J
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: 847

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): 133

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: 133. 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
EFNB3 548010 194811 45776 12 12 1 0 0 20 0.42 0 75 0.97 0
PTEN 824941 198189 106304 40 33 39 1 0 20 0.85 0 170 0.86 0
SETD2 4178322 1118908 226199 75 68 72 2 3 7 1.4 0 290 0.85 0
VHL 255796 80466 27031 242 234 142 2 0 13 1 1.1e-16 1300 1.1 5.1e-13
BAP1 1336641 401501 180505 36 33 36 2 0 20 2.9 3.2e-15 160 0.88 1.2e-11
PBRM1 3387188 880887 370840 154 150 141 2 1 20 1 9.5e-15 780 1.2 2.9e-11
TP53 800415 233772 125361 56 44 44 1 0 4 0.42 1.3e-14 190 1.1 3.3e-11
KDM5C 2671276 801208 262217 29 29 29 2 0 20 1.2 2.5e-14 140 0.92 5.6e-11
CCDC91 893596 223611 138741 15 15 3 0 0 20 0.79 5.4e-14 82 1.4 1.1e-10
NF2 1085014 273582 172346 16 16 15 0 0 20 1.1 2.8e-13 92 0.84 5e-10
NAPSA 764006 257493 98881 14 13 4 0 0 20 0.59 5.2e-13 75 0.85 8.6e-10
CIB3 377764 96559 70957 8 8 2 0 0 20 0.11 3.3e-11 51 0.87 5e-08
DNMT1 3083993 839396 441535 23 23 8 0 0 20 0.87 7.7e-11 110 0.98 1.1e-07
PHYH 653039 166859 97763 10 10 2 0 0 20 0.6 9.2e-11 59 1.4 1.2e-07
CCDC136 1638096 396396 97330 17 17 4 1 0 20 1.3 4.5e-10 87 1 5.5e-07
CUL3 1549176 397246 182918 18 16 17 1 0 20 0.73 5.6e-10 76 1.2 6.4e-07
RRAS2 374381 94866 64250 8 8 1 0 0 20 0.74 1.2e-09 49 1.1 1.3e-06
NEFH 1412808 390470 36675 14 13 12 1 0 20 0.92 2.9e-09 74 1.5 2.9e-06
SPRY4 618314 190575 25020 10 10 3 0 0 20 0.77 1.1e-08 53 1.4 1e-05
SDHAF2 388804 101653 56944 9 9 1 0 0 20 1.5 1.2e-08 53 1.4 0.000011
TAS2R3 618311 186340 14701 14 13 5 0 0 20 0.36 1.3e-08 45 0.99 0.000011
DNMT3A 1754998 487032 264998 17 17 13 0 0 18 1 2e-08 83 1 0.000016
ARPC2 614078 160931 117559 8 8 2 0 0 20 0.66 2.7e-08 49 0.81 0.000022
DPEP2 841917 258335 116242 9 9 3 0 0 20 0.51 6e-08 49 1.7 0.000044
PCGF1 455684 118580 87707 9 8 3 1 0 20 1 6e-08 48 0.8 0.000044
PCGF2 433654 120270 87742 9 9 4 1 0 20 1 7.2e-08 50 0.78 0.000051
DHX35 1432282 413337 267753 13 13 8 2 0 20 0.72 7.5e-08 64 1.2 0.000051
PARD6B 697082 198198 25543 11 11 10 1 0 20 0.67 8e-08 50 0.93 0.000052
ZMAT2 426888 95711 74791 7 7 1 1 0 20 0.83 8.8e-08 43 0.88 0.000054
SLC26A4 1453461 436207 228489 12 12 4 3 0 20 0.74 8.9e-08 61 1.3 0.000054
CD4 887646 260872 105300 11 10 4 1 0 20 0.82 1.1e-07 52 1.2 0.000064
ARAP3 2846752 902050 356105 17 15 6 1 0 20 0.57 1.1e-07 77 0.88 0.000064
LETMD1 727575 214292 112551 10 10 5 1 0 20 0.74 2.1e-07 49 1.5 0.00011
DNAH3 8308225 2247090 743161 32 30 21 2 0 19 0.53 2.1e-07 110 0.96 0.00011
SORD 538691 161777 78426 10 10 3 0 0 20 1.6 2.2e-07 52 1 0.00011
EFNB3

Figure S1.  This figure depicts the distribution of mutations and mutation types across the EFNB3 significant gene.

PTEN

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

SETD2

Figure S3.  This figure depicts the distribution of mutations and mutation types across the SETD2 significant gene.

VHL

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

BAP1

Figure S5.  This figure depicts the distribution of mutations and mutation types across the BAP1 significant gene.

PBRM1

Figure S6.  This figure depicts the distribution of mutations and mutation types across the PBRM1 significant gene.

TP53

Figure S7.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

KDM5C

Figure S8.  This figure depicts the distribution of mutations and mutation types across the KDM5C significant gene.

CCDC91

Figure S9.  This figure depicts the distribution of mutations and mutation types across the CCDC91 significant gene.

NF2

Figure S10.  This figure depicts the distribution of mutations and mutation types across the NF2 significant gene.

NAPSA

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

CIB3

Figure S12.  This figure depicts the distribution of mutations and mutation types across the CIB3 significant gene.

DNMT1

Figure S13.  This figure depicts the distribution of mutations and mutation types across the DNMT1 significant gene.

PHYH

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

CCDC136

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

CUL3

Figure S16.  This figure depicts the distribution of mutations and mutation types across the CUL3 significant gene.

NEFH

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

SPRY4

Figure S18.  This figure depicts the distribution of mutations and mutation types across the SPRY4 significant gene.

TAS2R3

Figure S19.  This figure depicts the distribution of mutations and mutation types across the TAS2R3 significant gene.

DNMT3A

Figure S20.  This figure depicts the distribution of mutations and mutation types across the DNMT3A significant gene.

ARPC2

Figure S21.  This figure depicts the distribution of mutations and mutation types across the ARPC2 significant gene.

DPEP2

Figure S22.  This figure depicts the distribution of mutations and mutation types across the DPEP2 significant gene.

PCGF1

Figure S23.  This figure depicts the distribution of mutations and mutation types across the PCGF1 significant gene.

PCGF2

Figure S24.  This figure depicts the distribution of mutations and mutation types across the PCGF2 significant gene.

DHX35

Figure S25.  This figure depicts the distribution of mutations and mutation types across the DHX35 significant gene.

PARD6B

Figure S26.  This figure depicts the distribution of mutations and mutation types across the PARD6B significant gene.

ZMAT2

Figure S27.  This figure depicts the distribution of mutations and mutation types across the ZMAT2 significant gene.

SLC26A4

Figure S28.  This figure depicts the distribution of mutations and mutation types across the SLC26A4 significant gene.

CD4

Figure S29.  This figure depicts the distribution of mutations and mutation types across the CD4 significant gene.

ARAP3

Figure S30.  This figure depicts the distribution of mutations and mutation types across the ARAP3 significant gene.

LETMD1

Figure S31.  This figure depicts the distribution of mutations and mutation types across the LETMD1 significant gene.

DNAH3

Figure S32.  This figure depicts the distribution of mutations and mutation types across the DNAH3 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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