Probability distribution of copy number alterations along the genome: an algorithm to distinguish different tumour profiles
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/106713 https://doi.org/10.1038/s41598-020-71859-1 |
Resumo: | Copy number alterations (CNAs) comprise deletions or amplifications of fragments of genomic material that are particularly common in cancer and play a major contribution in its development and progression. High resolution microarray-based genome-wide technologies have been widely used to detect CNAs, generating complex datasets that require further steps to allow for the determination of meaningful results. In this work, we propose a methodology to determine common regions of CNAs from these datasets, that in turn are used to infer the probability distribution of disease profiles in the population. This methodology was validated using simulated data and assessed using real data from Head and Neck Squamous Cell Carcinoma and Lung Adenocarcinoma, from the TCGA platform. Probability distribution profiles were produced allowing for the distinction between different phenotypic groups established within that cohort. This method may be used to distinguish between groups in the diseased population, within well-established degrees of confidence. The application of such methods may be of greater value in the clinical context both as a diagnostic or prognostic tool and, even as a useful way for helping to establish the most adequate treatment and care plans. |
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Probability distribution of copy number alterations along the genome: an algorithm to distinguish different tumour profilesAlgorithmsCohort StudiesComparative Genomic HybridizationDNA Copy Number VariationsGene DosageGene ExpressionGene Expression ProfilingGenomeGenomicsHumansModels, TheoreticalNeoplasmsProbabilityPrognosisSequence Analysis, DNACopy number alterations (CNAs) comprise deletions or amplifications of fragments of genomic material that are particularly common in cancer and play a major contribution in its development and progression. High resolution microarray-based genome-wide technologies have been widely used to detect CNAs, generating complex datasets that require further steps to allow for the determination of meaningful results. In this work, we propose a methodology to determine common regions of CNAs from these datasets, that in turn are used to infer the probability distribution of disease profiles in the population. This methodology was validated using simulated data and assessed using real data from Head and Neck Squamous Cell Carcinoma and Lung Adenocarcinoma, from the TCGA platform. Probability distribution profiles were produced allowing for the distinction between different phenotypic groups established within that cohort. This method may be used to distinguish between groups in the diseased population, within well-established degrees of confidence. The application of such methods may be of greater value in the clinical context both as a diagnostic or prognostic tool and, even as a useful way for helping to establish the most adequate treatment and care plans.Springer Nature2020-09-10info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/106713http://hdl.handle.net/10316/106713https://doi.org/10.1038/s41598-020-71859-1eng2045-2322Esteves, LuísaCaramelo, FranciscoRibeiro, Ilda PatríciaCarreira, Isabel M.Melo, Joana Barbosa deinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-04-18T09:44:11Zoai:estudogeral.uc.pt:10316/106713Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:23:07.721829Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Probability distribution of copy number alterations along the genome: an algorithm to distinguish different tumour profiles |
title |
Probability distribution of copy number alterations along the genome: an algorithm to distinguish different tumour profiles |
spellingShingle |
Probability distribution of copy number alterations along the genome: an algorithm to distinguish different tumour profiles Esteves, Luísa Algorithms Cohort Studies Comparative Genomic Hybridization DNA Copy Number Variations Gene Dosage Gene Expression Gene Expression Profiling Genome Genomics Humans Models, Theoretical Neoplasms Probability Prognosis Sequence Analysis, DNA |
title_short |
Probability distribution of copy number alterations along the genome: an algorithm to distinguish different tumour profiles |
title_full |
Probability distribution of copy number alterations along the genome: an algorithm to distinguish different tumour profiles |
title_fullStr |
Probability distribution of copy number alterations along the genome: an algorithm to distinguish different tumour profiles |
title_full_unstemmed |
Probability distribution of copy number alterations along the genome: an algorithm to distinguish different tumour profiles |
title_sort |
Probability distribution of copy number alterations along the genome: an algorithm to distinguish different tumour profiles |
author |
Esteves, Luísa |
author_facet |
Esteves, Luísa Caramelo, Francisco Ribeiro, Ilda Patrícia Carreira, Isabel M. Melo, Joana Barbosa de |
author_role |
author |
author2 |
Caramelo, Francisco Ribeiro, Ilda Patrícia Carreira, Isabel M. Melo, Joana Barbosa de |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Esteves, Luísa Caramelo, Francisco Ribeiro, Ilda Patrícia Carreira, Isabel M. Melo, Joana Barbosa de |
dc.subject.por.fl_str_mv |
Algorithms Cohort Studies Comparative Genomic Hybridization DNA Copy Number Variations Gene Dosage Gene Expression Gene Expression Profiling Genome Genomics Humans Models, Theoretical Neoplasms Probability Prognosis Sequence Analysis, DNA |
topic |
Algorithms Cohort Studies Comparative Genomic Hybridization DNA Copy Number Variations Gene Dosage Gene Expression Gene Expression Profiling Genome Genomics Humans Models, Theoretical Neoplasms Probability Prognosis Sequence Analysis, DNA |
description |
Copy number alterations (CNAs) comprise deletions or amplifications of fragments of genomic material that are particularly common in cancer and play a major contribution in its development and progression. High resolution microarray-based genome-wide technologies have been widely used to detect CNAs, generating complex datasets that require further steps to allow for the determination of meaningful results. In this work, we propose a methodology to determine common regions of CNAs from these datasets, that in turn are used to infer the probability distribution of disease profiles in the population. This methodology was validated using simulated data and assessed using real data from Head and Neck Squamous Cell Carcinoma and Lung Adenocarcinoma, from the TCGA platform. Probability distribution profiles were produced allowing for the distinction between different phenotypic groups established within that cohort. This method may be used to distinguish between groups in the diseased population, within well-established degrees of confidence. The application of such methods may be of greater value in the clinical context both as a diagnostic or prognostic tool and, even as a useful way for helping to establish the most adequate treatment and care plans. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-09-10 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/106713 http://hdl.handle.net/10316/106713 https://doi.org/10.1038/s41598-020-71859-1 |
url |
http://hdl.handle.net/10316/106713 https://doi.org/10.1038/s41598-020-71859-1 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2045-2322 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Springer Nature |
publisher.none.fl_str_mv |
Springer Nature |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799134118893584384 |