Project description
A novel way to tackle anticancer drug resistance
Advances in cancer genetics have led to the discovery of many promising targeted anticancer therapies. However, cancer precision medicine is often impeded by the emergence of drug resistance. Scientists of the EU-funded COMBAT-RES project aim to address this issue by developing methods that identify drug resistance, detect related biomarkers, and subsequently predict drug combinations to overcome the inevitable monotherapy resistance. To achieve this, they will develop innovative computational methods for in vitro drug high-throughput screenings (HTS), and validate these predictions in in vivo. The long-term aim is to anticipate cancer evolution and prevent monotherapy resistance by identifying smart drug combinations that display synergistic action and improve the therapeutic index.
Objective
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.
Fields of science
Not validated
Not validated
- 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
Keywords
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Funding Scheme
ERC-STG - Starting GrantHost institution
85764 Neuherberg
Germany