Probability distribution of copy number alterations along the genome: an algorithm to distinguish different tumour profiles

Detalhes bibliográficos
Autor(a) principal: Esteves, Luísa
Data de Publicação: 2020
Outros Autores: Caramelo, Francisco, Ribeiro, Ilda Patrícia, Carreira, Isabel M., Melo, Joana Barbosa de
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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spelling 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
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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
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