Project description
How to identify resistance to antibody-drug conjugates
Antibody-drug conjugates (ADCs) have improved survival rates in solid tumours and blood cancers, with 14 approved and over 300 in development. However, resistance to ADCs remains a critical challenge and current clinical tools and preclinical models fail to effectively identify its underlying causes. The EU-funded OASIS project aims to determine the optimal companion diagnostics for each ADC and to develop an AI-based multimodal score to predict both response and toxicity associated with ADCs. It will use a range of assays, including molecular imaging, circulating tumour cells and machine learning-enhanced digital pathology. It will also create a biobank of patient-derived organoids to reflect ADC-resistance mechanisms. These will work to identify biomarkers of ADC resistance, providing insights to inform future therapeutic strategies.
Objective
Over the past 5 years, antibody drug conjugates (ADCs) have shown impressive improvements in survival outcomes of solid tumors and hematological malignancies. With 14 ADCs already approved across different countries and more than 140 that entered the clinical development, they are intended to replace standard chemotherapies across multiple tumor types over the next decade. Although ADCs show great clinical efficacy, resistance eventually occurs, and it becomes critical to understand resistance mechanisms to guide the choice of the following lines of therapy for patients who progress on a given ADC. Given the complex nature of ADCs, immunocompromised mouse models (nude mice, NOD-SCID or NOG mice) and currently used clinical assays (standard radiology, IHC, WES etc) are not the optimal preclinical and clinical tools, to identify the multiple causes of resistance. The OASIS project aims to generate a biobank of different patient-derived organoids (PDOs), which better recapitulate ADCs resistance and integrate different assays, spanning from whole-body molecular imaging (Ab-radiolabeled PET scan or immunoPET), circulating tumor cells (CTC), plasma proteomics, to multiplex immunofluorescence (MIF) and machine-learning enhanced digital pathology (AI-digital pathology) to capture most of the parallel mechanisms of resistance to ADCs. Such tools will enable to define biomarkers of ADC resistance that can inform further therapeutic decisions.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- natural sciencesbiological sciencesbiochemistrybiomoleculesproteinsproteomics
- medical and health sciencesclinical medicineradiology
- medical and health sciencesbasic medicinepathology
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Programme(s)
Call for proposal
(opens in new window) HORIZON-HLTH-2024-TOOL-05-two-stage
See other projects for this callFunding Scheme
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinator
94805 Villejuif
France