Resistance to Tyrosine Kinase Inhibitors in Chronic Myeloid Leukemia-From Molecular Mechanisms to Clinical Relevance

Detalhes bibliográficos
Autor(a) principal: Alves, Raquel
Data de Publicação: 2021
Outros Autores: Gonçalves, Ana Cristina, Rutella, Sergio, Almeida, António M, De Las Rivas, Javier, Trougakos, Ioannis P, Sarmento-Ribeiro, Ana Bela
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10316/103806
https://doi.org/10.3390/cancers13194820
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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spelling Resistance to Tyrosine Kinase Inhibitors in Chronic Myeloid Leukemia-From Molecular Mechanisms to Clinical RelevanceCMLTKI resistanceepigeneticsimmune systemnew targeted therapiespatient adherencebioinformatics and artificial intelligenceResistance 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.MDPI2021-09-26info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/103806http://hdl.handle.net/10316/103806https://doi.org/10.3390/cancers13194820eng2072-669434638304Alves, RaquelGonçalves, Ana CristinaRutella, SergioAlmeida, António MDe Las Rivas, JavierTrougakos, Ioannis PSarmento-Ribeiro, Ana Belainfo: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:RCAAP2022-11-29T21:39:16Zoai:estudogeral.uc.pt:10316/103806Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:20:34.776343Repositório 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
CML
TKI resistance
epigenetics
immune system
new targeted therapies
patient adherence
bioinformatics and artificial intelligence
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
De Las Rivas, Javier
Trougakos, Ioannis P
Sarmento-Ribeiro, Ana Bela
author_role author
author2 Gonçalves, Ana Cristina
Rutella, Sergio
Almeida, António M
De Las Rivas, Javier
Trougakos, Ioannis P
Sarmento-Ribeiro, Ana Bela
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Alves, Raquel
Gonçalves, Ana Cristina
Rutella, Sergio
Almeida, António M
De Las Rivas, Javier
Trougakos, Ioannis P
Sarmento-Ribeiro, Ana Bela
dc.subject.por.fl_str_mv CML
TKI resistance
epigenetics
immune system
new targeted therapies
patient adherence
bioinformatics and artificial intelligence
topic CML
TKI resistance
epigenetics
immune system
new targeted therapies
patient adherence
bioinformatics and artificial intelligence
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 2021-09-26
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10316/103806
http://hdl.handle.net/10316/103806
https://doi.org/10.3390/cancers13194820
url http://hdl.handle.net/10316/103806
https://doi.org/10.3390/cancers13194820
dc.language.iso.fl_str_mv eng
language eng
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34638304
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