APOLLO11: a bio-data-driven model for clinical and translational research in lung cancer

: Identifying predictive and resistance biomarkers remains one of the most relevant unmet needs in clinical cancer research. Artificial Intelligence (AI) represents a powerful tool to develop predictive algorithms tailored to individual patients. Thanks to its ability to process large quantities of heterogeneous, patient-level information, the AI-based approach is progressively fostering the growth of a data-driven paradigm to complement traditional, hypothesis-driven clinical research. However, the development of reliable AI models requires access to large, high-quality, and continuously updated datasets. Despite this necessity, no infrastructure currently exists to enable federated, multi-omic, standardized, prospective, and large-scale collection and analysis of real-world clinical and biological data in the context of lung cancer. We established the APOLLO11 consortium, a distributed, nationwide, updated Italian lung cancer network designed to build a decentralized, long-term, population-based, real-world data repository and a multilevel biobank, locally stored and centrally annotated. This strategy seeks to lay the foundation for the clinical implementation of data-driven research, ultimately advancing precision oncology.