Análisis metabolómico en la caracterización de mecanismos de resistencia a agentes antineoplásicos en el cáncer de ovario y su uso para la identificación de potenciales blancos terapéuticos.
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Date
2023
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Universidad de Concepción
Abstract
La resistencia a la quimioterapia en el cáncer de ovario es un desafío significativo que complica la efectividad de los tratamientos farmacológicos. Esta tesis se centró en la evaluación de dos modelos celulares de cáncer de ovario con distintas respuestas a fármacos antineoplásicos, utilizando una estrategia analítica metabolómica basada cromatografía líquida acoplada a espectrometría de masas de alta resolución. Las células sensibles mostraron un predominio en el metabolismo glucosídico y aminoacídico, mientras que las células resistentes presentaron una sobrerregulación en el metabolismo lipídico y del glutatión. La inhibición farmacológica del glutatión en las células resistentes generó un cambio significativo en la resistencia a los agentes antineoplásicos, aumentó la eficacia farmacológica. Además, mediante análisis lipídico, observamos un aumento en el metabolismo lipídico exclusivamente en las células resistentes, no en las células sensibles, lo que podría contribuir al fenotipo de resistencia a la quimioresistencia. Aunque esta investigación se centró en modelos celulares, las conclusiones obtenidas ofrecen perspectivas prometedoras para la investigación traslacional y futuras aplicaciones clínicas. Los análisis metabolómicos podrían utilizarse para predecir la resistencia a los fármacos en pacientes, permitiendo un enfoque de tratamiento más personalizado. Además, identificar metabolitos como posibles blancos terapéuticos podría ayudar a contrarrestar la quimiorresistencia manipulando las vías metabólicas clave en el cáncer de ovario.
Chemotherapy resistance in ovarian cancer presents a significant challenge that complicates the efficacy of pharmacological treatments. This thesis focused on the evaluation of two cellular models of ovarian cancer with different responses to antineoplastic drugs, using an analytical metabolomic approach based on high-resolution liquid chromatography coupled with mass spectrometry. Drug-sensitive cells exhibited a predominance in glycosidic and amino acid metabolism, while drug-resistant cells showed an overexpression in lipid and glutathione metabolism. The pharmacological depletion of glutathione in resistant cells generated a significant change in resistance to antineoplastic agents, with an increase in pharmacological efficacy. Additionally, through lipidomic analysis, we observed an increase in lipid metabolism exclusively in resistant cells, not in sensitive cells, which could contribute to the chemotherapy-resistant phenotype. Although this research focused on cellular models, the conclusions offer promising perspectives for translational research and future clinical applications. Metabolomic analyses could predict patient drug resistance, allowing for a more personalized treatment approach. Furthermore, identifying metabolites as potential therapeutic targets could help counteract chemotherapy resistance by manipulating critical metabolic pathways in ovarian cancer.
Chemotherapy resistance in ovarian cancer presents a significant challenge that complicates the efficacy of pharmacological treatments. This thesis focused on the evaluation of two cellular models of ovarian cancer with different responses to antineoplastic drugs, using an analytical metabolomic approach based on high-resolution liquid chromatography coupled with mass spectrometry. Drug-sensitive cells exhibited a predominance in glycosidic and amino acid metabolism, while drug-resistant cells showed an overexpression in lipid and glutathione metabolism. The pharmacological depletion of glutathione in resistant cells generated a significant change in resistance to antineoplastic agents, with an increase in pharmacological efficacy. Additionally, through lipidomic analysis, we observed an increase in lipid metabolism exclusively in resistant cells, not in sensitive cells, which could contribute to the chemotherapy-resistant phenotype. Although this research focused on cellular models, the conclusions offer promising perspectives for translational research and future clinical applications. Metabolomic analyses could predict patient drug resistance, allowing for a more personalized treatment approach. Furthermore, identifying metabolites as potential therapeutic targets could help counteract chemotherapy resistance by manipulating critical metabolic pathways in ovarian cancer.
Description
Tesis presentada para optar al grado de Doctor en Ciencias y Tecnología Analítica