DNA methylation-based classifier and gene expression signatures detect BRCAness in osteosarcoma

Although osteosarcoma (OS) is a rare cancer, it is the most common primary malignant bone tumor in children and adolescents. BRCAness is a phenotypical trait in tumors with a defect in homologous recombination repair, resembling tumors with inactivation of BRCA1/2, rendering these tumors sensitive t...

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Autores principales:	Maxim Barenboim, Michal Kovac, Baptiste Ameline, David T. W. Jones, Olaf Witt, Stefan Bielack, Stefan Burdach, Daniel Baumhoer, Michaela Nathrath
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/9779539f89114f3db6d4458b6a645f29
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spelling	oai:doaj.org-article:9779539f89114f3db6d4458b6a645f292021-11-18T05:52:51ZDNA methylation-based classifier and gene expression signatures detect BRCAness in osteosarcoma1553-734X1553-7358https://doaj.org/article/9779539f89114f3db6d4458b6a645f292021-11-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8584788/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Although osteosarcoma (OS) is a rare cancer, it is the most common primary malignant bone tumor in children and adolescents. BRCAness is a phenotypical trait in tumors with a defect in homologous recombination repair, resembling tumors with inactivation of BRCA1/2, rendering these tumors sensitive to poly (ADP)-ribose polymerase inhibitors (PARPi). Recently, OS was shown to exhibit molecular features of BRCAness. Our goal was to develop a method complementing existing genomic methods to aid clinical decision making on administering PARPi in OS patients. OS samples with DNA-methylation data were divided to BRCAness-positive and negative groups based on the degree of their genomic instability (n = 41). Methylation probes were ranked according to decreasing variance difference between two groups. The top 2000 probes were selected for training and cross-validation of the random forest algorithm. Two-thirds of available OS RNA-Seq samples (n = 17) from the top and bottom of the sample list ranked according to genome instability score were subjected to differential expression and, subsequently, to gene set enrichment analysis (GSEA). The combined accuracy of trained random forest was 85% and the average area under the ROC curve (AUC) was 0.95. There were 449 upregulated and 1,079 downregulated genes in the BRCAness-positive group (fdr < 0.05). GSEA of upregulated genes detected enrichment of DNA replication and mismatch repair and homologous recombination signatures (FWER < 0.05). Validation of the BRCAness classifier with an independent OS set (n = 20) collected later in the course of study showed AUC of 0.87 with an accuracy of 90%. GSEA signatures computed for this test set were matching the ones observed in the training set enrichment analysis. In conclusion, we developed a new classifier based on DNA-methylation patterns that detects BRCAness in OS samples with high accuracy. GSEA identified genome instability signatures. Machine-learning and gene expression approaches add new epigenomic and transcriptomic aspects to already established genomic methods for evaluation of BRCAness in osteosarcoma and can be extended to cancers characterized by genome instability. Author summary Osteosarcoma (OS) is the most common primary malignant tumor of bone in children and young adults with poor prognosis for patients with refractory or metastatic disease. A common feature, so-called BRCAness, exists in multiple cancers including OS and is characterized by homologous recombination deficiency. Tumors exhibiting BRCAness have been shown to respond to therapy with PARP inhibitors. Currently, BRCAness is mostly assessed by the genomic instability score. This method based on the DNA sequencing requires normal tissue DNA as control and is vulnerable to subjective interpretation of "genomic scarring" events. In this study, we implemented a classifier based on DNA methylation patterns. It is capable of detecting BRCAness in OS samples and does not require control tissue DNA. Therefore, it has the potential to support clinical decision making on administering PARPi in OS patients. We further corroborated the presence of BRCAness in OS by detecting homologous recombination signatures through gene expression analysis.Maxim BarenboimMichal KovacBaptiste AmelineDavid T. W. JonesOlaf WittStefan BielackStefan BurdachDaniel BaumhoerMichaela NathrathPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Maxim Barenboim Michal Kovac Baptiste Ameline David T. W. Jones Olaf Witt Stefan Bielack Stefan Burdach Daniel Baumhoer Michaela Nathrath DNA methylation-based classifier and gene expression signatures detect BRCAness in osteosarcoma
description	Although osteosarcoma (OS) is a rare cancer, it is the most common primary malignant bone tumor in children and adolescents. BRCAness is a phenotypical trait in tumors with a defect in homologous recombination repair, resembling tumors with inactivation of BRCA1/2, rendering these tumors sensitive to poly (ADP)-ribose polymerase inhibitors (PARPi). Recently, OS was shown to exhibit molecular features of BRCAness. Our goal was to develop a method complementing existing genomic methods to aid clinical decision making on administering PARPi in OS patients. OS samples with DNA-methylation data were divided to BRCAness-positive and negative groups based on the degree of their genomic instability (n = 41). Methylation probes were ranked according to decreasing variance difference between two groups. The top 2000 probes were selected for training and cross-validation of the random forest algorithm. Two-thirds of available OS RNA-Seq samples (n = 17) from the top and bottom of the sample list ranked according to genome instability score were subjected to differential expression and, subsequently, to gene set enrichment analysis (GSEA). The combined accuracy of trained random forest was 85% and the average area under the ROC curve (AUC) was 0.95. There were 449 upregulated and 1,079 downregulated genes in the BRCAness-positive group (fdr < 0.05). GSEA of upregulated genes detected enrichment of DNA replication and mismatch repair and homologous recombination signatures (FWER < 0.05). Validation of the BRCAness classifier with an independent OS set (n = 20) collected later in the course of study showed AUC of 0.87 with an accuracy of 90%. GSEA signatures computed for this test set were matching the ones observed in the training set enrichment analysis. In conclusion, we developed a new classifier based on DNA-methylation patterns that detects BRCAness in OS samples with high accuracy. GSEA identified genome instability signatures. Machine-learning and gene expression approaches add new epigenomic and transcriptomic aspects to already established genomic methods for evaluation of BRCAness in osteosarcoma and can be extended to cancers characterized by genome instability. Author summary Osteosarcoma (OS) is the most common primary malignant tumor of bone in children and young adults with poor prognosis for patients with refractory or metastatic disease. A common feature, so-called BRCAness, exists in multiple cancers including OS and is characterized by homologous recombination deficiency. Tumors exhibiting BRCAness have been shown to respond to therapy with PARP inhibitors. Currently, BRCAness is mostly assessed by the genomic instability score. This method based on the DNA sequencing requires normal tissue DNA as control and is vulnerable to subjective interpretation of "genomic scarring" events. In this study, we implemented a classifier based on DNA methylation patterns. It is capable of detecting BRCAness in OS samples and does not require control tissue DNA. Therefore, it has the potential to support clinical decision making on administering PARPi in OS patients. We further corroborated the presence of BRCAness in OS by detecting homologous recombination signatures through gene expression analysis.
format	article
author	Maxim Barenboim Michal Kovac Baptiste Ameline David T. W. Jones Olaf Witt Stefan Bielack Stefan Burdach Daniel Baumhoer Michaela Nathrath
author_facet	Maxim Barenboim Michal Kovac Baptiste Ameline David T. W. Jones Olaf Witt Stefan Bielack Stefan Burdach Daniel Baumhoer Michaela Nathrath
author_sort	Maxim Barenboim
title	DNA methylation-based classifier and gene expression signatures detect BRCAness in osteosarcoma
title_short	DNA methylation-based classifier and gene expression signatures detect BRCAness in osteosarcoma
title_full	DNA methylation-based classifier and gene expression signatures detect BRCAness in osteosarcoma
title_fullStr	DNA methylation-based classifier and gene expression signatures detect BRCAness in osteosarcoma
title_full_unstemmed	DNA methylation-based classifier and gene expression signatures detect BRCAness in osteosarcoma
title_sort	dna methylation-based classifier and gene expression signatures detect brcaness in osteosarcoma
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/9779539f89114f3db6d4458b6a645f29
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DNA methylation-based classifier and gene expression signatures detect BRCAness in osteosarcoma

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