Mutation Analysis (MutSig 2CV v3.1)
Liver Hepatocellular Carcinoma (Primary solid tumor)
17 October 2014  |  analyses__2014_10_17
Maintainer Information
Citation Information
doi:10.7908/C1WM1C8V
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Mutation Analysis (MutSig 2CV v3.1). Broad Institute of MIT and Harvard. doi:10.7908/C1WM1C8V
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): 13

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: 13. 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 1314 25 0 1 41 8 5 9 63 62 50 1e-16 0.0012 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 0.099 1e-16 9.1e-13
3 RB1 retinoblastoma 1 (including osteosarcoma) 2891 80 0 1 3 1 3 10 17 15 17 1.3e-15 1 0.76 4.5e-14 2.8e-10
4 AXIN1 axin 1 2629 160 0 1 1 0 3 6 10 9 10 5.6e-09 1 0.2 4.9e-08 0.00022
5 BAP1 BRCA1 associated protein-1 (ubiquitin carboxy-terminal hydrolase) 2254 5 0 0 3 2 1 4 10 10 10 1.2e-07 0.13 0.15 8.7e-08 0.00032
6 TSC2 tuberous sclerosis 2 5590 16 0 1 2 2 0 5 9 9 9 1.2e-07 1 0.02 1.9e-07 0.00057
7 ARID1A AT rich interactive domain 1A (SWI-like) 6934 0 0 1 5 4 1 7 17 16 16 2.2e-07 0.12 0.64 6.1e-07 0.0016
8 IL6ST interleukin 6 signal transducer (gp130, oncostatin M receptor) 2817 11 0 1 1 1 0 5 7 7 7 6.5e-08 1 0.64 1.1e-06 0.0026
9 ALB albumin 1888 2 0 2 8 0 2 11 21 18 20 3.2e-07 0.3 0.48 1.4e-06 0.0028
10 HNF1A HNF1 homeobox A 1934 58 0 0 8 1 1 2 12 8 12 7.2e-07 1 0.73 0.000011 0.019
11 APOB apolipoprotein B (including Ag(x) antigen) 13804 19 0 3 16 2 0 8 26 24 26 7.6e-06 1 0.046 0.000011 0.019
12 EEF1A1 eukaryotic translation elongation factor 1 alpha 1 1417 111 0 0 6 0 0 0 6 5 3 0.0028 0.0011 0.3 0.000047 0.072
13 KIF19 kinesin family member 19 3073 24 0 0 9 0 0 1 10 10 10 8.5e-06 1 0.47 0.000064 0.089
14 GNAS GNAS complex locus 4050 40 0 0 7 0 0 0 7 7 5 0.0037 0.0012 0.9 0.000087 0.11
15 F5 coagulation factor V (proaccelerin, labile factor) 6771 2 0 0 3 0 1 0 4 4 3 0.1 0.0018 0.0073 0.00013 0.15
16 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 1244 412 0 0 2 1 1 3 7 7 7 0.000012 1 0.78 0.00015 0.17
17 HIST1H1C histone cluster 1, H1c 646 374 0 0 5 0 0 0 5 5 5 0.00035 0.036 0.88 0.00019 0.2
18 DLK2 delta-like 2 homolog (Drosophila) 1172 31 0 0 3 0 0 1 4 4 4 0.000032 1 0.56 0.00036 0.36
19 THADA thyroid adenoma associated 6010 16 0 0 3 2 0 1 6 6 6 0.000054 1 0.65 0.00058 0.56
20 CDKN1A cyclin-dependent kinase inhibitor 1A (p21, Cip1) 503 172 0 0 4 0 0 1 5 4 5 0.00084 1 0.034 0.00066 0.6
21 IDH1 isocitrate dehydrogenase 1 (NADP+), soluble 1277 482 0 0 3 0 0 0 3 3 1 0.074 0.001 0.69 0.00077 0.67
22 PRKDC protein kinase, DNA-activated, catalytic polypeptide 12728 12 0 2 9 1 2 0 12 12 11 0.0022 0.03 0.57 0.00085 0.7
23 GRM8 glutamate receptor, metabotropic 8 2815 7 0 0 5 0 0 0 5 4 4 0.85 6e-05 0.51 0.001 0.83
24 HGS hepatocyte growth factor-regulated tyrosine kinase substrate 2420 2 0 0 2 1 0 1 4 3 4 0.006 0.015 0.66 0.0011 0.87
25 GAS2L1 growth arrest-specific 2 like 1 2069 59 0 0 2 0 1 0 3 2 3 0.038 0.011 0.04 0.0013 0.96
26 MLL2 myeloid/lymphoid or mixed-lineage leukemia 2 16826 9 0 2 6 0 1 4 11 11 11 0.00046 1 0.26 0.0014 0.99
27 KEAP1 kelch-like ECH-associated protein 1 1895 10 0 0 7 0 0 1 8 7 8 0.00063 1 0.18 0.0016 0.99
28 OAS2 2'-5'-oligoadenylate synthetase 2, 69/71kDa 2282 162 0 0 5 0 0 0 5 5 5 0.009 0.015 0.78 0.0016 0.99
29 MFSD3 major facilitator superfamily domain containing 3 1257 157 0 0 1 1 0 0 2 2 2 0.017 1 0.01 0.0016 0.99
30 RXRA retinoid X receptor, alpha 1425 115 0 0 5 0 0 0 5 5 5 0.0017 1 0.068 0.0016 0.99
31 RNF145 ring finger protein 145 2118 102 0 0 2 1 0 0 3 2 3 0.095 0.0019 0.68 0.0017 1
32 ZCCHC2 zinc finger, CCHC domain containing 2 3589 123 0 1 3 1 0 0 4 4 4 0.027 1 0.0008 0.0019 1
33 SPAG6 sperm associated antigen 6 1570 0 0 0 2 0 0 2 4 4 4 0.00024 1 0.98 0.0022 1
34 SENP2 SUMO1/sentrin/SMT3 specific peptidase 2 1834 74 0 0 3 0 0 1 4 4 4 0.012 0.016 0.62 0.0023 1
35 TNFRSF4 tumor necrosis factor receptor superfamily, member 4 860 177 0 0 3 0 0 0 3 3 3 0.013 1 0.0087 0.0029 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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