Mutation Analysis (MutSig 2CV v3.1)
Liver Hepatocellular Carcinoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Mutation Analysis (MutSig 2CV v3.1). Broad Institute of MIT and Harvard. doi:10.7908/C1NZ86VB
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. MutSig 2CV v3.1 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): 14

Results
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 1.  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 2.  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 3.  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

  • 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: 14. 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).

rank gene longname codelen nnei nncd nsil nmis nstp nspl nind nnon npat nsite pCV pCL pFN p q
1 TP53 tumor protein p53 1890 52 1 0 39 8 7 9 63 62 50 1e-16 0.0018 0.00021 1e-16 9.1e-13
2 CTNNB1 catenin (cadherin-associated protein), beta 1, 88kDa 2406 42 0 2 50 0 0 4 54 51 26 1e-16 1e-05 0.1 1e-16 9.1e-13
3 RB1 retinoblastoma 1 (including osteosarcoma) 3711 1 0 0 3 1 4 10 18 15 18 1e-16 1 0.67 3.8e-15 2.3e-11
4 ARID1A AT rich interactive domain 1A (SWI-like) 6934 0 0 0 5 4 2 7 18 17 17 1.7e-09 0.1 0.54 3.1e-09 0.000014
5 AXIN1 axin 1 2629 160 0 1 1 0 3 6 10 9 10 2.3e-08 1 0.18 1.3e-07 0.00049
6 BAP1 BRCA1 associated protein-1 (ubiquitin carboxy-terminal hydrolase) 2254 51 0 0 3 2 1 5 11 11 11 6.4e-07 0.16 0.06 3.9e-07 0.0012
7 ALB albumin 1888 2 0 2 8 0 2 11 21 18 20 3.4e-07 0.31 0.5 2e-06 0.005
8 TSC2 tuberous sclerosis 2 5590 16 0 1 2 2 0 5 9 9 9 2.1e-06 1 0.015 2.2e-06 0.005
9 IL6ST interleukin 6 signal transducer (gp130, oncostatin M receptor) 2817 22 0 1 1 1 0 5 7 7 7 1.5e-07 1 0.6 2.4e-06 0.005
10 APOB apolipoprotein B (including Ag(x) antigen) 13804 21 0 3 16 2 0 9 27 25 27 1.8e-06 1 0.063 4.9e-06 0.0089
11 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 1244 82 0 0 2 1 1 3 7 7 7 1e-06 1 0.8 0.000015 0.025
12 EEF1A1 eukaryotic translation elongation factor 1 alpha 1 1417 110 0 0 5 0 0 0 5 5 2 0.0038 0.00033 0.036 0.000018 0.028
13 KIF19 kinesin family member 19 3073 24 0 0 9 0 0 1 10 10 10 7.4e-06 1 0.45 0.000054 0.072
14 THADA thyroid adenoma associated 6010 16 0 0 2 2 1 1 6 6 6 4.1e-06 1 0.71 0.000055 0.072
15 PRKDC protein kinase, DNA-activated, catalytic polypeptide 12728 53 0 3 8 1 2 0 11 11 10 0.00061 0.025 0.4 0.00012 0.14
16 HNF1A HNF1 homeobox A 1934 60 0 0 8 1 1 2 12 8 12 1e-05 1 0.75 0.00013 0.14
17 F5 coagulation factor V (proaccelerin, labile factor) 6771 2 0 0 3 0 1 0 4 4 3 0.1 0.0018 0.0068 0.00013 0.14
18 GNAS GNAS complex locus 4050 25 0 0 7 0 0 0 7 7 5 0.0072 0.0018 0.9 0.00016 0.16
19 MLL4 myeloid/lymphoid or mixed-lineage leukemia 2 8293 23 0 2 5 2 3 0 10 10 10 3e-05 1 0.42 0.00019 0.18
20 HIST1H1C histone cluster 1, H1c 646 373 0 0 5 0 0 0 5 5 5 0.00048 0.045 0.89 0.00031 0.28
21 UNC45B unc-45 homolog B (C. elegans) 2872 46 0 0 5 0 1 0 6 6 6 0.0056 0.016 0.25 0.00039 0.34
22 IDH1 isocitrate dehydrogenase 1 (NADP+), soluble 1277 485 0 0 3 0 0 0 3 3 1 0.048 0.001 0.69 0.00053 0.44
23 SEC24B SEC24 related gene family, member B (S. cerevisiae) 3901 12 0 1 1 1 2 0 4 4 4 0.00027 1 0.17 0.00061 0.48
24 C15orf55 chromosome 15 open reading frame 55 3479 3 0 0 2 0 2 0 4 4 4 0.001 1 0.17 0.0019 1
25 SPAG6 sperm associated antigen 6 1570 0 0 0 2 0 0 2 4 4 4 0.0002 1 0.96 0.0019 1
26 AKAP13 A kinase (PRKA) anchor protein 13 8656 35 0 2 5 1 2 0 8 8 8 0.0056 0.032 0.87 0.0021 1
27 STXBP3 syntaxin binding protein 3 1853 30 0 0 3 0 1 0 4 4 4 0.007 1 0.024 0.0022 1
28 RBL2 retinoblastoma-like 2 (p130) 3504 2 0 1 2 1 1 1 5 5 5 0.00082 1 0.23 0.0024 1
29 PTGR1 prostaglandin reductase 1 1080 24 0 1 1 0 0 2 3 3 3 0.00031 1 0.68 0.0028 1
30 GPR132 G protein-coupled receptor 132 1147 112 0 0 2 0 0 1 3 2 3 0.12 0.014 0.11 0.0029 1
31 KEAP1 kelch-like ECH-associated protein 1 1895 10 0 0 7 0 0 1 8 7 8 0.0012 1 0.18 0.003 1
32 ZCCHC2 zinc finger, CCHC domain containing 2 3589 52 0 1 3 1 0 0 4 4 4 0.032 1 0.0007 0.0032 1
33 OAS2 2'-5'-oligoadenylate synthetase 2, 69/71kDa 2282 95 0 0 5 0 0 0 5 5 5 0.015 0.018 0.79 0.0034 1
34 MLL2 myeloid/lymphoid or mixed-lineage leukemia 2 16826 8 0 2 6 0 1 4 11 11 11 0.0011 1 0.27 0.0034 1
35 MFSD3 major facilitator superfamily domain containing 3 1274 204 0 0 1 1 0 0 2 2 2 0.041 1 0.01 0.0036 1
TP53

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

CTNNB1

Figure S2.  This figure depicts the distribution of mutations and mutation types across the CTNNB1 significant gene.

RB1

Figure S3.  This figure depicts the distribution of mutations and mutation types across the RB1 significant gene.

ARID1A

Figure S4.  This figure depicts the distribution of mutations and mutation types across the ARID1A significant gene.

BAP1

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

ALB

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

TSC2

Figure S7.  This figure depicts the distribution of mutations and mutation types across the TSC2 significant gene.

IL6ST

Figure S8.  This figure depicts the distribution of mutations and mutation types across the IL6ST significant gene.

APOB

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

PTEN

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

EEF1A1

Figure S11.  This figure depicts the distribution of mutations and mutation types across the EEF1A1 significant gene.

KIF19

Figure S12.  This figure depicts the distribution of mutations and mutation types across the KIF19 significant gene.

THADA

Figure S13.  This figure depicts the distribution of mutations and mutation types across the THADA 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)