Bayesian analysis of the rate of spontaneous malignant mesothelioma among BAP1 mutant mice in the absence of asbestos exposure

Abstract Cancers of the mesothelium, such as malignant mesothelioma (MM), historically have been attributed solely to exposure to asbestos. Recent large scale genetic and genomic functional studies now show that approximately 20% of all human mesotheliomas are causally linked to highly penetrant inh...

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Main Authors: Dahlia M. Nielsen, Mei Hsu, Michael Zapata, Giovanni Ciavarra, Leonel van Zyl
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Scientific Reports
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Online Access:https://doi.org/10.1038/s41598-024-84069-w
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author Dahlia M. Nielsen
Mei Hsu
Michael Zapata
Giovanni Ciavarra
Leonel van Zyl
author_facet Dahlia M. Nielsen
Mei Hsu
Michael Zapata
Giovanni Ciavarra
Leonel van Zyl
author_sort Dahlia M. Nielsen
collection DOAJ
description Abstract Cancers of the mesothelium, such as malignant mesothelioma (MM), historically have been attributed solely to exposure to asbestos. Recent large scale genetic and genomic functional studies now show that approximately 20% of all human mesotheliomas are causally linked to highly penetrant inherited (germline) pathogenic mutations in numerous cancer related genes. The rarity of these mutations in humans makes it difficult to perform statistically conclusive genetic studies to understand their biological effects. This has created a disconnect between functional and epidemiological studies. However, since the molecular pathogenesis of MM in mice accurately recapitulates that of human disease, this disconnect between functional and epidemiological studies can be overcome by using inbred mouse strains that harbor mutation(s) in genes involved in the disease. Most mouse studies have focused on the effect of asbestos exposure, leaving the effects of genetic mutations in the absence of exposure understudied. Here, using existing peer-reviewed studies, we investigate the rate of spontaneous MM among mice with and without germline genetic mutations, in the absence of asbestos exposure. We leveraged these published data to generate a historical control dataset (HCD) to allow us to improve statistical power and account for genetic heterogeneity between studies. Our Bayesian analyses indicate that the odds of spontaneous MM among germline BAP1 mutant mice is substantially larger than that of wildtype mice. These results support the existing biological study findings that mesotheliomas can arise in the presence of pathogenic germline mutations, independently of asbestos exposure.
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spelling doaj-art-ca446476467e425da7062d9164d95c0f2025-01-05T12:17:28ZengNature PortfolioScientific Reports2045-23222025-01-011511910.1038/s41598-024-84069-wBayesian analysis of the rate of spontaneous malignant mesothelioma among BAP1 mutant mice in the absence of asbestos exposureDahlia M. Nielsen0Mei Hsu1Michael Zapata2Giovanni Ciavarra3Leonel van Zyl4Department of Biological Sciences, North Carolina State UniversityArrayXpress, Inc.ArrayXpress, Inc.Lumanity Clinical and RegulatoryArrayXpress, Inc.Abstract Cancers of the mesothelium, such as malignant mesothelioma (MM), historically have been attributed solely to exposure to asbestos. Recent large scale genetic and genomic functional studies now show that approximately 20% of all human mesotheliomas are causally linked to highly penetrant inherited (germline) pathogenic mutations in numerous cancer related genes. The rarity of these mutations in humans makes it difficult to perform statistically conclusive genetic studies to understand their biological effects. This has created a disconnect between functional and epidemiological studies. However, since the molecular pathogenesis of MM in mice accurately recapitulates that of human disease, this disconnect between functional and epidemiological studies can be overcome by using inbred mouse strains that harbor mutation(s) in genes involved in the disease. Most mouse studies have focused on the effect of asbestos exposure, leaving the effects of genetic mutations in the absence of exposure understudied. Here, using existing peer-reviewed studies, we investigate the rate of spontaneous MM among mice with and without germline genetic mutations, in the absence of asbestos exposure. We leveraged these published data to generate a historical control dataset (HCD) to allow us to improve statistical power and account for genetic heterogeneity between studies. Our Bayesian analyses indicate that the odds of spontaneous MM among germline BAP1 mutant mice is substantially larger than that of wildtype mice. These results support the existing biological study findings that mesotheliomas can arise in the presence of pathogenic germline mutations, independently of asbestos exposure.https://doi.org/10.1038/s41598-024-84069-wMesotheliomaAsbestosBAP1Mouse studiesHistorical control dataBayesian statistics
spellingShingle Dahlia M. Nielsen
Mei Hsu
Michael Zapata
Giovanni Ciavarra
Leonel van Zyl
Bayesian analysis of the rate of spontaneous malignant mesothelioma among BAP1 mutant mice in the absence of asbestos exposure
Scientific Reports
Mesothelioma
Asbestos
BAP1
Mouse studies
Historical control data
Bayesian statistics
title Bayesian analysis of the rate of spontaneous malignant mesothelioma among BAP1 mutant mice in the absence of asbestos exposure
title_full Bayesian analysis of the rate of spontaneous malignant mesothelioma among BAP1 mutant mice in the absence of asbestos exposure
title_fullStr Bayesian analysis of the rate of spontaneous malignant mesothelioma among BAP1 mutant mice in the absence of asbestos exposure
title_full_unstemmed Bayesian analysis of the rate of spontaneous malignant mesothelioma among BAP1 mutant mice in the absence of asbestos exposure
title_short Bayesian analysis of the rate of spontaneous malignant mesothelioma among BAP1 mutant mice in the absence of asbestos exposure
title_sort bayesian analysis of the rate of spontaneous malignant mesothelioma among bap1 mutant mice in the absence of asbestos exposure
topic Mesothelioma
Asbestos
BAP1
Mouse studies
Historical control data
Bayesian statistics
url https://doi.org/10.1038/s41598-024-84069-w
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