Mutation Analysis (MutSigCV v0.9)
Uterine Corpus Endometrioid Carcinoma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C10R9N72
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Mutation Analysis (MutSigCV v0.9). Broad Institute of MIT and Harvard. doi:10.7908/C10R9N72
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: UCEC-TP

  • Number of patients in set: 248

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:UCEC-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 24

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: UCEC-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: 24. 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
PIK3R1 462272 118792 0 118 83 89 2 0 20 1.5 8.9e-16 400 0.14 8.1e-12
ARID1A 1107816 326368 0 107 83 87 5 0 2 0.72 1.1e-15 380 0.15 8.1e-12
TP53 234360 68448 0 74 69 50 2 0 4 0.81 1.3e-15 220 0.16 8.1e-12
PIK3CA 644552 164920 0 172 132 76 3 0 20 1.1 2.3e-15 350 0.14 1.1e-11
CTCF 437472 112096 0 51 44 40 1 0 20 0.54 5.2e-15 190 0.15 1.7e-11
PTEN 241552 58032 0 255 161 149 5 0 20 1.1 5.7e-15 770 0.16 1.7e-11
CTNNB1 456072 136400 0 80 74 25 7 0 20 1.6 9.3e-15 180 0.14 2.4e-11
FBXW7 481616 132184 0 47 39 31 1 0 20 1.4 2.2e-14 110 0.15 5e-11
KRAS 150040 36704 0 53 53 11 2 0 1 1.8 3e-12 110 0.15 6.1e-09
ARID5B 692912 194432 0 35 29 34 8 0 20 1.6 9.4e-10 100 0.14 1.7e-06
SPOP 226920 59520 0 23 21 18 0 0 20 1.2 4.8e-09 65 0.16 7.9e-06
FGFR2 518320 145080 0 35 31 20 3 0 18 1.2 9.1e-09 82 0.15 0.000014
PPP2R1A 328104 102672 0 30 27 18 3 0 17 1.2 8.8e-08 68 0.14 0.00012
MORC4 534936 136896 0 28 20 26 2 0 15 0.9 5.7e-07 68 0.16 0.00075
CHD4 1150472 307024 0 44 35 39 2 0 20 0.57 7.9e-07 78 0.14 0.00096
FAM9A 154008 38192 0 21 14 20 1 0 14 1.3 1.5e-06 48 0.16 0.0018
CCND1 145328 41664 0 14 14 12 1 0 20 1.5 0.000014 45 0.14 0.014
CASP8 361088 84568 0 21 17 19 6 0 20 1.2 0.000014 54 0.14 0.014
FOXA2 199392 55056 0 13 12 13 1 0 20 1.5 0.000015 52 0.14 0.014
SLC48A1 13144 3720 0 5 5 5 0 0 10 0.82 0.000024 20 0.14 0.022
BRS3 229400 69440 0 17 15 17 2 0 20 0.91 0.000051 40 0.14 0.044
RB1 706304 186248 0 29 20 27 4 0 20 0.82 0.000057 58 0.14 0.047
ADSS 259408 71920 0 6 6 6 4 0 20 0.24 0.000091 23 0.13 0.069
NFE2L2 349432 92504 0 15 15 12 0 0 20 0.84 0.000091 44 0.14 0.069
TIAL1 238328 61256 0 15 11 15 2 0 20 0.59 0.00019 37 0.16 0.14
CDHR4 58528 19592 0 8 7 8 4 0 20 0.93 0.00029 22 0.13 0.2
CHIC1 31744 7936 0 4 4 4 0 0 20 0.8 0.00033 18 0.12 0.22
SGK1 364312 97216 0 16 15 14 3 0 20 1.7 0.00037 53 0.16 0.24
RNF43 412920 130696 0 14 12 14 3 0 8 0.67 0.00041 42 0.16 0.26
ETV3 87048 21824 0 11 9 11 0 0 11 0.78 0.00047 28 0.13 0.28
CNPY1 57288 13392 0 8 7 7 0 0 19 1.5 0.00048 23 0.13 0.28
ZNF471 379440 87792 0 21 15 16 1 0 15 1.1 0.00059 42 0.17 0.32
CCDC75 80848 18104 0 5 5 5 0 0 20 0.89 0.0006 23 0.13 0.32
DNER 377952 102920 0 21 18 20 0 0 20 0.85 0.00061 43 0.14 0.32
ZFHX3 2120648 616032 0 79 44 71 13 0 4 1.3 0.00062 120 0.17 0.32
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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