Leveraging functional genomics to reveal novel targets for cancer treatment
Ente erogante: Commissione Europea - Horizon Europe
Scadenza
21 settembre 2027
522 giorni rimanenti
Forma
Fondo perduto
Budget totale
Non specificato
Contributo max
Non specificato
Descrizione del bando
Expected Outcome:Proposals under this topic should aim to deliver results that are directed and tailored towards and contribute to all of the following expected outcomes:Researchers and health professionals gain a deeper understanding of the functional effects of tumour heterogeneity during cancer initiation and progression, supporting the identification of more precise and novel therapeutic targets. Researchers, health professionals and innovators benefit from improved knowledge and tools to support the discovery and development of next generation genomically-informed cancer treatments.Researchers, innovators, and professionals across disciplines and sectors contribute to the UNCAN.eu research data platform by ensuring data interoperability, and enabling sustainable access to newly developed digital tools and models for the analysis and management of multimodal clinical data. Scope:The proposed topic will contribute to the EU Cancer Mission objective of improving the understanding of cancer initiation and progression. Of particular interest for this topic are paediatric and adolescent cancers[1] , cancers with limited treatment options, refractory cancers, rare cancers or cancer types with low five-year survival rates. Proposals may include the design of exploratory mechanistic studies using longitudinal patient bio-samples, clinical proof-of-concept studies and/or observational and translational studies involving newly collected or existing data, as appropriate. Applicants are encouraged to leverage technological advances in functional genomics[2] and structural biology including but not limited to rapid gene sequencing, single-cell studies, spatial gene mapping, spatial transcriptomics, epigenetic profiling, liquid biopsies and functional precision oncology pipelines. The use of causal inference, computational modelling and/or artificial intelligence tools is encouraged for the collection, visualisation, analysis and management of large, complex, and heterogeneou
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