Mutation Analysis (MutSigCV v0.9)
Liver Hepatocellular Carcinoma (Primary solid tumor)
14 July 2016  |  awg_lihc__2016_07_14
Maintainer Information
Citation Information
doi:10.7908/C1TH8M79
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/C1TH8M79
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: 373

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): 22

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: 22. 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 352485 102948 4944 117 114 85 1 1 4 2.3 2.3e-15 430 0.4 1.7e-11
ALB 555397 143232 6696 52 43 44 3 0 9 2.6 2.9e-15 200 0.43 1.7e-11
CTNNB1 685947 205150 6768 103 97 27 3 0 20 1.6 3.8e-15 260 0.42 1.7e-11
RB1 1062304 280123 11664 25 21 24 0 0 20 0.77 3.8e-15 110 0.39 1.7e-11
AXIN1 700121 204404 5088 25 24 24 1 0 20 0.93 1.1e-14 130 0.41 3.8e-11
BAP1 588594 176802 7080 20 19 20 1 0 20 1.3 2.6e-12 94 0.41 8e-09
KEAP1 484900 141740 2088 19 17 19 0 0 20 0.41 5.4e-10 63 0.4 1.4e-06
ARID1A 1666191 490868 9024 34 32 32 2 0 2 1.2 4.1e-09 140 0.41 8.8e-06
PTEN 363302 87282 4176 11 11 11 1 0 20 0.78 4.3e-09 57 0.39 8.8e-06
ARID2 1586369 475575 9768 25 22 25 2 0 5 0.75 1.5e-06 94 0.41 0.0027
CDC27 726604 196571 8424 15 15 8 1 0 20 1.6 1.7e-06 67 0.4 0.0027
KRT2 531152 162255 4104 12 12 11 2 0 20 1.8 3e-06 62 0.39 0.0045
ACVR2A 464012 122344 5328 14 11 14 1 0 10 1.3 3.2e-06 52 0.41 0.0045
APOB 3984013 1110794 13152 44 39 42 4 0 14 1.2 5.8e-06 130 0.41 0.0075
FILIP1 1067526 294670 2424 13 13 12 2 0 20 0.4 6.2e-06 58 0.4 0.0075
KCNN3 581507 173072 4344 12 12 9 0 0 20 0.36 7.7e-06 45 0.39 0.0088
CELA1 202912 60426 3312 6 6 3 0 0 20 0.81 0.000032 31 0.39 0.034
NFE2L2 525557 139129 1944 15 13 12 0 0 20 0.91 0.000038 43 0.39 0.039
CDKN1A 135399 45506 984 6 6 6 1 0 20 0.92 0.000056 27 0.39 0.051
DNAH12 403213 104813 4704 10 10 10 4 0 20 1.3 0.000056 42 0.4 0.051
LCE1F 102202 29467 648 5 5 5 0 0 20 1.2 0.000093 24 0.38 0.081
CDKN2A 224546 63783 1488 11 11 11 0 1 6 1 0.00011 46 0.4 0.095
NEFH 622164 171953 1440 11 10 10 6 0 20 1.3 0.00025 47 0.4 0.2
IL6ST 822838 219324 7800 12 12 12 1 0 7 1.2 0.00027 53 0.4 0.2
UBE2D3 178294 47371 4512 4 4 4 0 0 20 0.69 0.00035 23 0.37 0.26
DEFB113 59680 14920 744 4 4 4 0 0 20 1.4 0.00038 19 0.37 0.27
COL5A1 1359212 438275 34896 14 14 14 1 0 10 0.29 0.00048 51 0.4 0.32
KRTAP5-7 145097 40284 576 4 4 3 1 0 20 1 0.0005 23 0.36 0.32
CDHR4 88028 29467 2424 6 4 6 1 0 20 1.1 0.00067 18 0.37 0.42
TAF1B 530033 129431 6504 6 6 3 0 0 20 0.62 0.00085 32 0.38 0.51
RCCD1 172326 53339 2496 5 5 4 0 0 20 1.1 0.00086 24 0.37 0.51
EVC2 1055217 296535 9408 15 15 15 2 0 20 0.71 0.00093 44 0.4 0.52
HNRNPL 412165 113019 5304 8 8 7 0 0 20 0.71 0.00097 32 0.38 0.52
CHIC1 47744 11936 792 3 3 3 0 0 20 1.3 0.00097 15 0.36 0.52
C6orf62 209626 52966 2472 4 4 4 1 0 20 0.54 0.0011 20 0.37 0.55
TP53

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

ALB

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

CTNNB1

Figure S3.  This figure depicts the distribution of mutations and mutation types across the CTNNB1 significant gene.

RB1

Figure S4.  This figure depicts the distribution of mutations and mutation types across the RB1 significant gene.

BAP1

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

KEAP1

Figure S6.  This figure depicts the distribution of mutations and mutation types across the KEAP1 significant gene.

ARID1A

Figure S7.  This figure depicts the distribution of mutations and mutation types across the ARID1A significant gene.

PTEN

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

ARID2

Figure S9.  This figure depicts the distribution of mutations and mutation types across the ARID2 significant gene.

CDC27

Figure S10.  This figure depicts the distribution of mutations and mutation types across the CDC27 significant gene.

KRT2

Figure S11.  This figure depicts the distribution of mutations and mutation types across the KRT2 significant gene.

ACVR2A

Figure S12.  This figure depicts the distribution of mutations and mutation types across the ACVR2A significant gene.

APOB

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

FILIP1

Figure S14.  This figure depicts the distribution of mutations and mutation types across the FILIP1 significant gene.

KCNN3

Figure S15.  This figure depicts the distribution of mutations and mutation types across the KCNN3 significant gene.

CELA1

Figure S16.  This figure depicts the distribution of mutations and mutation types across the CELA1 significant gene.

NFE2L2

Figure S17.  This figure depicts the distribution of mutations and mutation types across the NFE2L2 significant gene.

CDKN1A

Figure S18.  This figure depicts the distribution of mutations and mutation types across the CDKN1A significant gene.

DNAH12

Figure S19.  This figure depicts the distribution of mutations and mutation types across the DNAH12 significant gene.

LCE1F

Figure S20.  This figure depicts the distribution of mutations and mutation types across the LCE1F significant gene.

CDKN2A

Figure S21.  This figure depicts the distribution of mutations and mutation types across the CDKN2A 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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