Descrizione del progetto
Un nuovo approccio nei confronti della resistenza ai farmaci anticancro
I progressi nella genetica del cancro hanno portato alla scoperta di una serie di terapie anticancro mirate alquanto promettenti. Tuttavia, la medicina oncologica di precisione è spesso ostacolata dall’insorgere della resistenza ai farmaci. Gli scienziati del progetto COMBAT-RES, finanziato dall’UE, intendono affrontare questo problema sviluppando metodi in grado di individuare la resistenza ai farmaci, rilevare i relativi biomarcatori e successivamente predire le combinazioni di farmaci che consentono di superare l’inevitabile resistenza alla monoterapia. Per raggiungere questo obiettivo, gli scienziati svilupperanno metodi computazionali innovativi per lo screening in vitro di farmaci ad alte prestazioni e convalideranno queste previsioni in vivo. L’obiettivo a lungo termine è anticipare l’evoluzione del cancro e prevenire la resistenza alla monoterapia individuando combinazioni intelligenti di farmaci che mostrano un’azione sinergica e migliorano l’indice terapeutico.
Obiettivo
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.
Campo scientifico
- 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
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
Parole chiave
Programma(i)
Argomento(i)
Meccanismo di finanziamento
ERC-STG - Starting GrantIstituzione ospitante
85764 Neuherberg
Germania