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Predicting immunotherapy response in melanoma and non-small cell lung cancer patients using an extracellular vesicle-derived miRNA signature and a non-invasive CRISPR-based detection assay.

CORDIS · observation · Publication date unknown

identify a mi-S predictive of IT response in lung cancer and melanoma patients. We will leverage one of the largest cohorts of melanoma and lung cancer patients with EV-derived miRNA profiles and use machine learning to define mi-S. We will then further develop a novel CRISPR-based one-pot and multiplex assay to enable cost-effective and scalable detection of the mi-S. This project will be the foundation for prospective clinical trials, with potential applications to other cancers. We also aim to develop the mi-S and its detection method into a commercially available kit. The project's feasibility is supported by my expertise in CRISPR and molecular biology, my supervisor’s specialization in EV research, the host institute's recognition as an anti-cancer center of excellence in IT, and its interdisciplinary collaboration with the Computer Science Laboratory of Burgundy. Immunotherapy, biomarkers, extracellular vesicles, miRNA, CRISPR, machine learning, lung cancer, melanoma, translational medicine, diagnostics, molecular biology, liquid biopsy, nucleic acid detection

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recordType

award

status

SIGNED

region

EU

value

242260.56

unit

EUR