GSS EU Science Funding Gateway

Proposals should address all of the following:

• Obtain a systematic understanding of processes underpinning tumour-host interactions in poorly-understood cancers and their subtypes in childhood, adolescent, adult and elderly cancer patients. Applicants should take into account social, ethnical, cultural and gender aspects, with a focus on the transition from a healthy state to cancer initiation and progression, including in advanced localised or metastatic disease (where relevant), using any relevant in silico, in vitro, in vivo, ex vivo, preclinical, or clinical disease models as well as computational, simulation and visualisation tools and technologies where appropriate.
• Combine knowledge and high-quality data from biomedical and clinical studies, and real-world data, using advanced digital tools and technologies such as computer modelling and artificial intelligence with the objective to understand relevant tumour-host interactions and their impact on treatment and care solutions for cancer patients.
• Demonstrate access to and use of multiple comprehensive databases in and beyond health research or health domains. Proposals should build on longitudinal clinically annotated, stratified patient cohorts, case-control studies, biobanks, registries and many other initiatives, use state-of-the art digital and other tools for data analyses and modelling, wherever possible.
• Based on results obtained, propose socially acceptable, affordable novel treatment or care interventions or health technologies for uptake into health systems in the areas of treatment or care, using approaches that involve the end-user using participative research models.

Challenges include uncovering which patients benefit from interventions or risk potentially debilitating side-effects, as well as ensuring affordability of interventions across Europe, across all age groups. This requires a new dimension and level of investment in innovative research with a view to intercept disease. It also requires investing in high-risk, high-reward research projects to deliver a proof-of-concept of potentially disruptive new approaches. These approaches include monitoring treatment and disease progression and disclosing disease pathways, such as through single-cell -omics technologies, innovative disease models, advanced imaging technologies, or artificial intelligence and machine learning.