Projektbeschreibung
Eine neue Möglichkeit, um Resistenz gegen Krebsmedikamente zu bekämpfen
Fortschritte in der Krebsgenetik haben zur Entdeckung vieler vielversprechender und gezielter Krebstherapien geführt. Die Präzisionsmedizin zur Behandlung von Krebs wird jedoch durch die Entstehung von Arzneimittelresistenzen erschwert. Ein am EU-finanzierten Projekt COMBAT-RES beteiligtes Forschungsteam möchte sich dieser Problematik stellen. Dazu wird es Methoden entwickeln, die Arzneimittelresistenzen aufspüren, entsprechende Biomarker erkennen und folglich Kombinationspräparate voraussagen, welche die unvermeidliche Monotherapie-Resistenz beseitigen. Um dieses Ziel zu erreichen, wird das Team innovative Rechenmethoden für das In-vitro-Hochdurchsatz-Screening von Wirkstoffen erarbeiten und diese Vorhersagen in vivo validieren. Langfristiges Ziel ist es, die Krebsentwicklung zu prognostizieren und Monotherapie-Resistenz zu verhindern. Hierzu sollen intelligente Kombinationspräparate gefunden werden, die synergistische Aktivität aufweisen und die therapeutische Breite verbessern.
Ziel
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.
Wissenschaftliches Gebiet
- 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
Programm/Programme
Thema/Themen
Finanzierungsplan
ERC-STG - Starting GrantGastgebende Einrichtung
85764 Neuherberg
Deutschland