Mutation Analysis (MutSigCV v0.9)
Liver Hepatocellular Carcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1M32TT4
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/C1M32TT4
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: 198

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): 9

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: 9. 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 364122 108900 0 54 51 26 2 0 20 1.7 2.2e-15 130 0.15 4.1e-11
TP53 187110 54648 0 63 62 50 1 0 4 2.5 5.7e-15 200 0.15 5.2e-11
RB1 563904 148698 0 17 15 17 1 0 20 1.3 4.8e-11 76 0.14 2.9e-07
ALB 294822 76032 0 21 18 20 2 0 9 2.2 1.5e-09 72 0.15 6.8e-06
AXIN1 371646 108504 0 10 9 10 1 0 20 0.99 7.4e-08 52 0.2 0.00027
BAP1 312444 93852 0 11 11 11 0 0 20 0.97 1.4e-07 53 0.19 0.00043
PTEN 192852 46332 0 7 7 7 0 0 20 0.7 2.6e-06 36 0.15 0.0069
LCE1E 55044 15444 0 4 4 4 0 0 20 0.81 0.000033 20 0.13 0.074
HNF1A 257796 83160 0 12 8 12 0 0 20 0.66 0.000036 35 0.22 0.074
ABCA13 2166318 582120 0 24 21 24 5 0 19 0.76 0.00019 63 0.17 0.34
BAGE3 20196 5346 0 3 3 3 0 0 20 1.3 0.00063 12 0.13 1
CDKN1A 71874 24156 0 5 4 5 0 0 20 1 0.0017 16 0.15 1
DEFB113 31680 7920 0 3 3 3 0 0 20 1.1 0.0023 12 0.24 1
SPRR4 37818 9702 0 3 3 3 0 0 20 1.2 0.0024 12 0.12 1
SAA1 52668 14256 0 3 3 3 0 0 11 0.31 0.0028 16 0.12 1
ATXN7L1 69300 17622 0 4 4 4 0 0 20 0.64 0.003 13 0.13 1
CCDC53 61182 17622 0 3 3 3 0 0 20 0.75 0.0033 13 0.12 1
ARID1A 884466 260568 0 17 16 16 1 0 2 0.88 0.0034 64 0.15 1
URM1 49104 14256 0 3 3 3 2 0 20 0.52 0.0041 11 0.12 1
IL6ST 436788 116424 0 7 7 7 1 0 7 1.5 0.0041 35 0.15 1
SPINK6 39798 10494 0 3 3 3 0 0 20 0.71 0.0049 10 0.11 1
EIF4E1B 71082 20592 0 4 4 4 0 0 20 0.41 0.005 13 0.13 1
PDX1 48906 14058 0 4 4 4 0 0 20 0.86 0.005 13 0.13 1
LDOC1L 102960 36630 0 5 5 5 0 0 20 1.3 0.0051 18 0.14 1
APOB 2114838 589644 0 27 25 27 3 0 14 1.4 0.0056 74 0.15 1
KIF19 329076 101376 0 10 10 10 0 0 17 0.95 0.0061 28 0.14 1
SUGT1 161370 39600 0 3 3 3 0 0 20 0.95 0.0067 17 0.16 1
CYLC1 257796 63558 0 6 6 6 2 0 16 1.7 0.0078 24 0.14 1
ADSS 207108 57420 0 2 2 2 0 0 20 0.083 0.0083 7.4 0.11 1
TLX3 60192 18612 0 3 3 3 0 0 20 1.5 0.0085 13 0.12 1
TMEM151B 32076 9900 0 3 3 3 0 0 20 1.3 0.0092 10 0.11 1
COMMD3 73458 18810 0 3 3 3 0 0 20 0.8 0.0093 13 0.26 1
KRTAP5-7 77022 21384 0 2 2 2 0 0 20 0.9 0.0097 13 0.12 1
RCN2 133650 29106 0 3 3 3 0 0 20 0.18 0.012 10 0.11 1
GNG12 34848 9306 0 1 1 1 0 0 20 0.21 0.012 7 0.1 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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