Resistance to tyrosine kinase inhibitors in chronic myeloid leukemia — from molecular mechanisms to clinical relevance
Autor(a) principal: | |
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Data de Publicação: | 2021 |
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/10400.14/35386 |
Resumo: | Resistance to targeted therapies is a complex and multifactorial process that culminates in the selection of a cancer clone with the ability to evade treatment. Chronic myeloid leukemia (CML) was the first malignancy recognized to be associated with a genetic alteration, the t(9;22)(q34;q11). This translocation originates the BCR-ABL1 fusion gene, encoding the cytoplasmic chimeric BCR-ABL1 protein that displays an abnormally high tyrosine kinase activity. Although the vast majority of patients with CML respond to Imatinib, a tyrosine kinase inhibitor (TKI), resistance might occur either de novo or during treatment. In CML, the TKI resistance mechanisms are usually subdivided into BCR-ABL1-dependent and independent mechanisms. Furthermore, patients’ compliance/adherence to therapy is critical to CML management. Techniques with enhanced sensitivity like NGS and dPCR, the use of artificial intelligence (AI) techniques, and the development of mathematical modeling and computational prediction methods could reveal the underlying mechanisms of drug resistance and facilitate the design of more effective treatment strategies for improving drug efficacy in CML patients. Here we review the molecular mechanisms and other factors involved in resistance to TKIs in CML and the new methodologies to access these mechanisms, and the therapeutic approaches to circumvent TKI resistance. |
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Resistance to tyrosine kinase inhibitors in chronic myeloid leukemia — from molecular mechanisms to clinical relevanceBioinformatics and artificial intelligenceCMLEpigeneticsImmune systemNew targeted therapiesPatient adherenceTKI resistanceResistance to targeted therapies is a complex and multifactorial process that culminates in the selection of a cancer clone with the ability to evade treatment. Chronic myeloid leukemia (CML) was the first malignancy recognized to be associated with a genetic alteration, the t(9;22)(q34;q11). This translocation originates the BCR-ABL1 fusion gene, encoding the cytoplasmic chimeric BCR-ABL1 protein that displays an abnormally high tyrosine kinase activity. Although the vast majority of patients with CML respond to Imatinib, a tyrosine kinase inhibitor (TKI), resistance might occur either de novo or during treatment. In CML, the TKI resistance mechanisms are usually subdivided into BCR-ABL1-dependent and independent mechanisms. Furthermore, patients’ compliance/adherence to therapy is critical to CML management. Techniques with enhanced sensitivity like NGS and dPCR, the use of artificial intelligence (AI) techniques, and the development of mathematical modeling and computational prediction methods could reveal the underlying mechanisms of drug resistance and facilitate the design of more effective treatment strategies for improving drug efficacy in CML patients. Here we review the molecular mechanisms and other factors involved in resistance to TKIs in CML and the new methodologies to access these mechanisms, and the therapeutic approaches to circumvent TKI resistance.Veritati - Repositório Institucional da Universidade Católica PortuguesaAlves, RaquelGonçalves, Ana CristinaRutella, SergioAlmeida, António M.Rivas, Javier De LasTrougakos, Ioannis P.Ribeiro, Ana Bela Sarmento2021-10-04T15:20:46Z2021-10-012021-10-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.14/35386eng2072-669410.3390/cancers1319482085115641235PMC850837834638304000707334800001info: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:RCAAP2023-12-12T01:34:37Zoai:repositorio.ucp.pt:10400.14/35386Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:28:44.048681Repositório 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 |
Resistance to tyrosine kinase inhibitors in chronic myeloid leukemia — from molecular mechanisms to clinical relevance |
title |
Resistance to tyrosine kinase inhibitors in chronic myeloid leukemia — from molecular mechanisms to clinical relevance |
spellingShingle |
Resistance to tyrosine kinase inhibitors in chronic myeloid leukemia — from molecular mechanisms to clinical relevance Alves, Raquel Bioinformatics and artificial intelligence CML Epigenetics Immune system New targeted therapies Patient adherence TKI resistance |
title_short |
Resistance to tyrosine kinase inhibitors in chronic myeloid leukemia — from molecular mechanisms to clinical relevance |
title_full |
Resistance to tyrosine kinase inhibitors in chronic myeloid leukemia — from molecular mechanisms to clinical relevance |
title_fullStr |
Resistance to tyrosine kinase inhibitors in chronic myeloid leukemia — from molecular mechanisms to clinical relevance |
title_full_unstemmed |
Resistance to tyrosine kinase inhibitors in chronic myeloid leukemia — from molecular mechanisms to clinical relevance |
title_sort |
Resistance to tyrosine kinase inhibitors in chronic myeloid leukemia — from molecular mechanisms to clinical relevance |
author |
Alves, Raquel |
author_facet |
Alves, Raquel Gonçalves, Ana Cristina Rutella, Sergio Almeida, António M. Rivas, Javier De Las Trougakos, Ioannis P. Ribeiro, Ana Bela Sarmento |
author_role |
author |
author2 |
Gonçalves, Ana Cristina Rutella, Sergio Almeida, António M. Rivas, Javier De Las Trougakos, Ioannis P. Ribeiro, Ana Bela Sarmento |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Veritati - Repositório Institucional da Universidade Católica Portuguesa |
dc.contributor.author.fl_str_mv |
Alves, Raquel Gonçalves, Ana Cristina Rutella, Sergio Almeida, António M. Rivas, Javier De Las Trougakos, Ioannis P. Ribeiro, Ana Bela Sarmento |
dc.subject.por.fl_str_mv |
Bioinformatics and artificial intelligence CML Epigenetics Immune system New targeted therapies Patient adherence TKI resistance |
topic |
Bioinformatics and artificial intelligence CML Epigenetics Immune system New targeted therapies Patient adherence TKI resistance |
description |
Resistance to targeted therapies is a complex and multifactorial process that culminates in the selection of a cancer clone with the ability to evade treatment. Chronic myeloid leukemia (CML) was the first malignancy recognized to be associated with a genetic alteration, the t(9;22)(q34;q11). This translocation originates the BCR-ABL1 fusion gene, encoding the cytoplasmic chimeric BCR-ABL1 protein that displays an abnormally high tyrosine kinase activity. Although the vast majority of patients with CML respond to Imatinib, a tyrosine kinase inhibitor (TKI), resistance might occur either de novo or during treatment. In CML, the TKI resistance mechanisms are usually subdivided into BCR-ABL1-dependent and independent mechanisms. Furthermore, patients’ compliance/adherence to therapy is critical to CML management. Techniques with enhanced sensitivity like NGS and dPCR, the use of artificial intelligence (AI) techniques, and the development of mathematical modeling and computational prediction methods could reveal the underlying mechanisms of drug resistance and facilitate the design of more effective treatment strategies for improving drug efficacy in CML patients. Here we review the molecular mechanisms and other factors involved in resistance to TKIs in CML and the new methodologies to access these mechanisms, and the therapeutic approaches to circumvent TKI resistance. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-10-04T15:20:46Z 2021-10-01 2021-10-01T00:00:00Z |
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/10400.14/35386 |
url |
http://hdl.handle.net/10400.14/35386 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2072-6694 10.3390/cancers13194820 85115641235 PMC8508378 34638304 000707334800001 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
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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) |
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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1799132006578126848 |