Mutation Analysis (MutSigCV v0.9)
Esophageal Carcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1736Q8T
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/C1736Q8T
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: ESCA-TP

  • Number of patients in set: 185

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:ESCA-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 16

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: ESCA-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: 16. 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
TP53 174825 51060 38110 177 153 107 1 1 4 1.8 2.6e-15 540 0.17 4.7e-11
CDKN2A 111370 31635 11470 19 19 18 1 3 6 1.4 8.4e-14 82 0.17 7.7e-10
IVL 118030 29970 4440 16 15 16 0 0 20 1.2 2e-09 47 0.22 0.000012
DCDC1 161320 43845 27935 18 16 18 2 4 20 2.2 1e-08 55 0.16 0.000046
TGFBR2 251600 67525 44400 15 15 12 1 1 20 1.2 1.8e-08 56 0.17 0.000064
NOTCH1 738705 213490 70300 22 18 22 2 0 20 0.8 3e-08 75 0.37 0.000092
NFE2L2 260665 69005 14985 18 16 14 0 0 20 1 8.1e-08 51 0.17 0.00021
FBXW7 359270 98605 44215 14 13 12 0 0 20 0.69 1.2e-07 50 0.16 0.00028
SMAD4 244940 68265 41070 14 13 13 1 0 20 1.2 6.4e-07 48 0.17 0.0013
COL6A5 249750 65675 15540 19 18 18 5 0 7 1.3 1.3e-06 53 0.19 0.0024
PIK3CA 480815 123025 73260 21 19 13 0 0 20 0.84 2.3e-06 50 0.2 0.0038
FLG 1699780 504495 8140 36 32 35 5 1 15 0.51 9.6e-06 73 0.16 0.014
ZNF750 303585 97680 8140 10 10 10 0 0 20 1 1e-05 46 0.16 0.014
ARID1A 826395 243460 69560 15 15 15 1 0 2 0.4 0.000072 66 0.23 0.094
PTEN 180190 43290 32190 8 7 8 0 0 20 0.72 0.000083 29 0.15 0.1
CCDC7 210160 50320 55315 12 10 12 0 0 11 1.1 0.000087 34 0.16 0.1
TSHB 61420 16650 7770 2 2 2 0 0 20 0.17 0.000093 14 0.14 0.1
RB1 526880 138935 89910 8 7 8 1 0 20 0.65 0.00016 38 0.19 0.16
SOX11 77515 21090 1295 8 8 7 1 1 20 2.7 0.0002 32 0.15 0.19
GPR52 155400 45140 4440 6 6 6 0 0 20 0.7 0.00037 24 0.18 0.31
OR10R2 141525 44030 4625 6 6 6 0 0 20 0.79 0.00042 23 0.15 0.31
VCP 352240 102860 59385 9 8 8 1 0 20 0.53 0.00042 31 0.17 0.31
CNPY1 42735 9990 11470 3 3 3 1 0 19 1.2 0.00042 17 0.14 0.31
RRBP1 409220 115440 87135 7 7 7 1 0 20 0.45 0.00043 31 0.16 0.31
TTC24 106560 30895 18870 5 5 5 2 0 5 0.68 0.00043 26 0.16 0.31
HIST1H1E 82695 27380 4440 5 4 4 0 0 20 0.58 0.00047 22 0.26 0.33
PRTN3 30525 10360 5180 4 4 4 0 0 20 1.6 0.0006 17 0.18 0.4
ATP8A1 527805 143005 137270 9 9 9 1 0 18 0.41 0.00062 30 0.25 0.4
OR5M3 131165 37185 4625 6 6 6 0 0 20 0.71 0.00078 21 0.16 0.49
ZNF268 155955 26085 8510 7 7 7 1 0 20 0.7 0.0008 22 0.14 0.49
OR11H12 130055 38110 4625 5 5 5 0 0 20 0.57 0.00092 19 0.16 0.54
ZNF880 70855 17575 0 6 6 6 3 2 20 2.7 0.00097 23 0.22 0.54
MRGPRF 42920 12765 5920 5 5 5 1 0 8 0.34 0.00098 16 0.16 0.54
PKD2 344285 92130 51615 7 7 4 1 0 20 0.98 0.0012 32 0.15 0.62
OR5H15 133570 38665 4810 4 4 4 0 0 20 0.66 0.0012 19 0.16 0.62
TP53

Figure S1.  This figure depicts the distribution of mutations and mutation types across the TP53 significant gene.

CDKN2A

Figure S2.  This figure depicts the distribution of mutations and mutation types across the CDKN2A significant gene.

IVL

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

DCDC1

Figure S4.  This figure depicts the distribution of mutations and mutation types across the DCDC1 significant gene.

TGFBR2

Figure S5.  This figure depicts the distribution of mutations and mutation types across the TGFBR2 significant gene.

NOTCH1

Figure S6.  This figure depicts the distribution of mutations and mutation types across the NOTCH1 significant gene.

NFE2L2

Figure S7.  This figure depicts the distribution of mutations and mutation types across the NFE2L2 significant gene.

FBXW7

Figure S8.  This figure depicts the distribution of mutations and mutation types across the FBXW7 significant gene.

SMAD4

Figure S9.  This figure depicts the distribution of mutations and mutation types across the SMAD4 significant gene.

COL6A5

Figure S10.  This figure depicts the distribution of mutations and mutation types across the COL6A5 significant gene.

PIK3CA

Figure S11.  This figure depicts the distribution of mutations and mutation types across the PIK3CA significant gene.

FLG

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

ZNF750

Figure S13.  This figure depicts the distribution of mutations and mutation types across the ZNF750 significant gene.

ARID1A

Figure S14.  This figure depicts the distribution of mutations and mutation types across the ARID1A significant gene.

PTEN

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

CCDC7

Figure S16.  This figure depicts the distribution of mutations and mutation types across the CCDC7 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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