Mutation Analysis (MutSigCV v0.9)
Liver Hepatocellular Carcinoma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C18G8JG9
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/C18G8JG9
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: LIHC-TP

  • Number of patients in set: 202

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:LIHC-TP.final_analysis_set.maf

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

  • Significantly mutated genes (q ≤ 0.1): 12

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: LIHC-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: 12. 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
CTNNB1 371478 111100 22278 61 57 29 2 0 20 1 4.3e-15 150 0.12 4e-11
TP53 190890 55752 16274 67 65 53 2 0 4 1.7 4.3e-15 210 0.12 4e-11
ALB 300778 77568 22041 27 24 26 4 0 9 1.5 6.9e-12 92 0.12 4.2e-08
RB1 575296 151702 38394 20 18 20 1 0 20 0.74 3.1e-11 84 0.12 1.4e-07
BAGE3 20604 5454 2923 8 8 3 0 0 20 0.93 1.5e-09 34 0.11 5.6e-06
BAP1 318756 95748 23305 12 12 12 1 0 20 0.76 3.1e-07 58 0.12 0.00091
PTEN 196748 47268 13746 12 11 12 1 0 20 0.89 3.5e-07 48 0.26 0.00091
ARID1A 902334 265832 29704 26 25 24 4 0 2 0.67 9.4e-07 97 0.12 0.0021
LCE4A 47470 12928 1896 10 10 3 1 0 20 0.98 1.4e-06 29 0.087 0.0029
DNAH12 218362 56762 15484 23 20 19 5 0 20 1.6 1.7e-06 53 0.11 0.0032
LCE1E 56156 15756 1896 9 7 9 2 0 20 1.4 2.4e-06 31 0.11 0.004
AXIN1 379154 110696 16748 14 12 14 2 0 20 0.88 3.1e-06 54 0.11 0.0047
CHIC1 25856 6464 2607 5 5 5 0 0 20 0.87 0.000074 19 0.1 0.1
PLA2G2F 82416 22018 5609 7 7 7 0 0 20 0.7 0.000097 26 0.11 0.13
FAM104B 59792 13938 4582 5 5 4 1 0 20 1.3 0.00012 25 0.11 0.14
TPRX1 83628 30502 1027 12 9 9 3 0 20 1.2 0.00015 27 0.11 0.17
CDHR4 47672 15958 7979 7 6 7 2 0 20 0.91 0.00029 19 0.19 0.3
ABCA13 2210082 593880 62489 54 37 54 20 0 19 0.76 0.0003 85 0.11 0.3
MAP4K5 252096 67266 20935 11 10 10 1 0 20 0.69 0.00035 33 0.11 0.33
OR2T27 128270 39188 1975 10 10 5 4 0 20 1 0.00036 28 0.11 0.33
VPS37C 94940 29492 5530 7 6 6 0 0 13 0.52 0.00046 21 0.1 0.38
ZNF880 77366 19190 0 12 8 10 3 0 20 1.5 0.00046 25 0.11 0.38
TLX3 61408 18988 1975 6 6 6 0 0 20 1.3 0.00064 23 0.21 0.49
DPCR1 110494 35350 2528 14 14 13 4 0 20 2.4 0.00065 35 0.11 0.49
APOB 2157562 601556 43292 46 39 46 5 0 14 0.88 0.00071 96 0.12 0.52
HNF1A 263004 84840 13904 14 9 14 0 0 20 0.65 0.00088 33 0.11 0.62
ATXN7L1 70700 17978 6241 7 7 7 4 0 20 1.1 0.0011 21 0.1 0.71
URM1 50096 14544 7031 5 5 5 2 0 20 0.76 0.0013 16 0.099 0.81
EIF4E1B 72518 21008 3397 5 5 5 0 0 20 0.55 0.0013 18 0.1 0.81
GPATCH4 229876 59994 15089 11 11 2 1 0 15 0.68 0.0014 29 0.11 0.85
PPIAL4G 78780 21008 1896 9 7 3 0 0 20 0.75 0.0015 19 0.1 0.9
TUBAL3 210484 62216 6557 3 3 3 1 0 20 0.12 0.0019 14 0.095 1
SEC14L4 186244 51510 16669 5 5 5 1 0 20 0.61 0.002 25 0.11 1
CR1 760530 212908 36577 23 19 20 3 0 7 0.79 0.0022 64 0.12 1
CYLC1 263004 64842 6004 12 11 12 3 0 16 1.3 0.0023 34 0.11 1
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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