Descripción del proyecto
Un método innovador para burlar la resistencia a los medicamentos antineoplásicos
Los avances en la genética del cáncer han permitido descubrir muchas terapias dirigidas contra el cáncer prometedoras. Sin embargo, la medicina antineoplásica de precisión a menudo topa con la aparición de resistencia a los medicamentos. Los científicos del proyecto COMBAT-RES, financiado con fondos europeos, se proponen resolver este problema con el desarrollo de métodos que identifiquen la resistencia a los medicamentos, detecten biomarcadores relevantes y, acto seguido, predigan combinaciones de medicamentos que venzan la de otro modo insuperable resistencia a la monoterapia. Para lograrlo, desarrollarán métodos informáticos innovadores aplicados a ensayos farmacológicos «in vitro» de alto rendimiento y validarán los resultados «in vivo». El objetivo a largo plazo es predecir la evolución del cáncer y la resistencia a la monoterapia mediante la identificación de combinaciones de fármacos inteligentes que generen sinergias y mejoren el índice terapéutico.
Objetivo
Personalising treatments based on tumour genetic profiles enables cancer precision medicine. However, treating cancers using targeted therapies often fails due to the emergence of drug resistance. Here, my goal is to use drug high-throughput screens (HTS) combined with computational methods to identify resistance and its biomarkers, and to overcome it with smart drug combinations to empower cancer precision medicine.
Identifying resistance in HTS is challenging: dissecting meaningful drug responses at high concentrations is impossible due to cytotoxicity, making non-responders and resistant cell lines indistinguishable, thus limiting resistance biomarker discovery to frequently mutated cancer genes. To address this, I will employ three approaches: 1) systematically identify non-responding cell lines carrying low-frequency resistance markers; 2) reveal intrinsic resistance driven by gene expression plasticity by conducting my own RNA sequencing experiments and modelling the maximal effect at high drug concentration; 3) identify drugs which increase cell viability, combined with drugs targeting fast proliferating cells. My paradigm shift, that resistance biomarkers become synergy markers, empowers smart drug combinations.
Additionally, I aim to predict drug synergy based on multi-task deep learning using molecular characterisation, QSAR modelling and monotherapies; and, to boost biomarker discovery by identifying clinically-relevant cancer subtypes based on transfer and reinforcement learning.
COMBAT-RES will benefit from data access to a phase III clinical trial in colorectal cancer (COREAD) and access to the largest human pancreas adenocarcinoma (PAAD) combination HTS (currently unpublished) accelerating the delivery of medicine for COREAD and PAAD patients. COMBAT-RES will interrogate the underpinnings of drug resistance, clinically-relevant subtypes and overcome it with highly synergistic drug combinations, enabling the next generation of precision medicine.
Ámbito científico
- medical and health sciencesclinical medicineoncologyprostate cancer
- natural sciencescomputer and information sciencesartificial intelligencemachine learningreinforcement learning
- medical and health sciencesbasic medicinepharmacology and pharmacydrug resistance
- medical and health sciencesclinical medicineoncologycolorectal cancer
- medical and health scienceshealth sciencespersonalized medicine
Programa(s)
Régimen de financiación
ERC-STG - Starting GrantInstitución de acogida
85764 Neuherberg
Alemania