A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver Transplantation

Detalhes bibliográficos
Autor(a) principal: Pinto-Marques, Hugo
Data de Publicação: 2022
Outros Autores: Cardoso, Joana, Silva, Sílvia, Neto, João L, Gonçalves-Reis, Maria, Proença, Daniela, Mesquita, Marta, Manso, André, Carapeta, Sara, Sobral, Mafalda, Figueiredo, Antonio, Rodrigues, Clara, Milheiro, Adelaide, Carvalho, Ana, Perdigoto, Rui, Barroso, Eduardo, Pereira-Leal, José B
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/144834
Resumo: Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc.
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spelling A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver TransplantationSDG 3 - Good Health and Well-beingCopyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc.OBJECTIVE: To propose a new decision algorithm combining biomarkers measured in a tumor biopsy with clinical variables, to predict recurrence after liver transplantation (LT). SUMMARY BACKGROUND DATA: Liver cancer is one of the most frequent causes of cancer-related mortality. LT is the best treatment for hepatocellular carcinoma (HCC) patients but the scarcity of organs makes patient selection a critical step. Additionally, clinical criteria widely applied in patient eligibility decisions miss potentially curable patients while selecting patients that relapse after transplantation. METHODS: A literature systematic review singled out candidate biomarkers whose RNA levels were assessed by quantitative PCR in tumor tissue from 138 HCC patients submitted to LT (>5 y follow up, 32% beyond Milan criteria). The resulting four gene signature was combined with clinical variables to develop a decision algorithm using machine learning approaches. The method was named HepatoPredict. RESULTS: HepatoPredict identifies 99% disease-free patients (>5 y) from a retrospective cohort, including many outside clinical criteria (16%-24%), thus reducing the false negative rate. This increased sensitivity is accompanied by an increased positive predictive value (88,5%-94,4%) without any loss of long-term overall survival or recurrence rates for patients deemed eligible by HepatoPredict; those deemed ineligible display marked reduction of survival and increased recurrence in the short and long term. CONCLUSIONS: HepatoPredict outperforms conventional clinical-pathologic selection criteria, (Milan, UCSF) providing superior prognostic information. Accurately identifying which patients most likely benefit from LT enables an objective stratification of waiting lists and information-based allocation of optimal versus suboptimal organs.Centro de Estudos de Doenças Crónicas (CEDOC)NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)RUNPinto-Marques, HugoCardoso, JoanaSilva, SílviaNeto, João LGonçalves-Reis, MariaProença, DanielaMesquita, MartaManso, AndréCarapeta, SaraSobral, MafaldaFigueiredo, AntonioRodrigues, ClaraMilheiro, AdelaideCarvalho, AnaPerdigoto, RuiBarroso, EduardoPereira-Leal, José B2022-10-18T22:12:06Z2022-11-012022-11-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/144834engPURE: 46256659https://doi.org/10.1097/SLA.0000000000005637info: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:RCAAP2024-03-11T05:24:47Zoai:run.unl.pt:10362/144834Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:51:46.478063Repositório 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 A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver Transplantation
title A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver Transplantation
spellingShingle A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver Transplantation
Pinto-Marques, Hugo
SDG 3 - Good Health and Well-being
title_short A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver Transplantation
title_full A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver Transplantation
title_fullStr A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver Transplantation
title_full_unstemmed A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver Transplantation
title_sort A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver Transplantation
author Pinto-Marques, Hugo
author_facet Pinto-Marques, Hugo
Cardoso, Joana
Silva, Sílvia
Neto, João L
Gonçalves-Reis, Maria
Proença, Daniela
Mesquita, Marta
Manso, André
Carapeta, Sara
Sobral, Mafalda
Figueiredo, Antonio
Rodrigues, Clara
Milheiro, Adelaide
Carvalho, Ana
Perdigoto, Rui
Barroso, Eduardo
Pereira-Leal, José B
author_role author
author2 Cardoso, Joana
Silva, Sílvia
Neto, João L
Gonçalves-Reis, Maria
Proença, Daniela
Mesquita, Marta
Manso, André
Carapeta, Sara
Sobral, Mafalda
Figueiredo, Antonio
Rodrigues, Clara
Milheiro, Adelaide
Carvalho, Ana
Perdigoto, Rui
Barroso, Eduardo
Pereira-Leal, José B
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Centro de Estudos de Doenças Crónicas (CEDOC)
NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)
RUN
dc.contributor.author.fl_str_mv Pinto-Marques, Hugo
Cardoso, Joana
Silva, Sílvia
Neto, João L
Gonçalves-Reis, Maria
Proença, Daniela
Mesquita, Marta
Manso, André
Carapeta, Sara
Sobral, Mafalda
Figueiredo, Antonio
Rodrigues, Clara
Milheiro, Adelaide
Carvalho, Ana
Perdigoto, Rui
Barroso, Eduardo
Pereira-Leal, José B
dc.subject.por.fl_str_mv SDG 3 - Good Health and Well-being
topic SDG 3 - Good Health and Well-being
description Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc.
publishDate 2022
dc.date.none.fl_str_mv 2022-10-18T22:12:06Z
2022-11-01
2022-11-01T00:00:00Z
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/10362/144834
url http://hdl.handle.net/10362/144834
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv PURE: 46256659
https://doi.org/10.1097/SLA.0000000000005637
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
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
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection 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
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