Project description
Predicting immunotherapy outcome for lung cancer and melanoma
Immunotherapy represents one of the most promising therapeutic approaches in lung cancer; however, 50 % of lung cancer patients will not respond to it. Extracellular vesicles (EVs) harbour miRNAs that could serve as biomarkers for predicting responses, but standardised detection methods are still needed. Supported by the Marie Skłodowska-Curie Actions programme, the IT-PREDICT project will develop a rapid, non-invasive, and cost-effective test to predict patient responses. It will identify a specific microRNA signature (mi-S) using machine learning on one of the largest patient cohorts. Additionally, the project will develop a novel CRISPR-based assay for cost-effective, scalable detection of the mi-S. This work will establish a foundation for clinical trials and may be extended to other cancers.
Objective
Immunotherapy (IT) has revolutionized the treatment of metastatic lung cancer and melanoma by enhancing the immune system's ability to recognize and attack cancer cells. However, only about 50% of patients respond positively to IT and current methods assessing patient eligibility for IT primarily rely on invasive tumor biopsies and have proven inefficient. With 420,000 new melanoma and lung cancer cases diagnosed annually in the EU, this results in wasted costs, time and resources in healthcare systems. We propose to develop a non-invasive, rapid and cost-effective test to predict patients’ response to IT.
Liquid biopsy offers a promising non-invasive alternative for analyzing blood to detect components secreted by cancer cells. Extracellular vesicles (EVs) have particularly gained attention as they are abundant and their content, including miRNAs, reflect the state of the cells that secrete them. EV miRNAs could therefore be used as biomarkers to predict IT response but we lack a consensus miRNA signature (mi-S) and scalable detection methods suitable for clinical use.
The IT-PREDICT project will 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.
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: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- medical and health sciences clinical medicine oncology skin cancer melanoma
- natural sciences biological sciences cell biology
- medical and health sciences basic medicine immunology immunotherapy
- natural sciences computer and information sciences artificial intelligence machine learning
- natural sciences biological sciences molecular biology
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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(opens in new window) HORIZON-MSCA-2025-PF
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21079 Dijon
France
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