Development of New Personalised 3D Preclinical Models of Pancreatic Neuroendocrine Tumours

Pancreatic neuroendocrine tumors (PanNETs) are characterized by highly heterogeneous biological behavior. Another distinctive feature of these neoplasms is the presence of a dense vascular network, reminiscent of their physiological counterpart. Indeed, the tumor microenvironment plays a critical role in these neoplasms and influence their therapeutic management. Although they are typically indolent in nature, approximately 40% of PanNET patients are metastatic at diagnosis, requiring non-surgical treatments such as somatostatin therapy, chemotherapy, targeted therapy, and peptide receptor radionuclide therapy (PRRT). Furthermore, 1 out of 5 patients experience disease recurrence after surgical resection, necessitating pharmacological therapies. Currently, no specific recommendations exist on the most effective therapeutic sequence to follow in presence of metastatic disease/disease relapse. One of the main reasons behind this unmet clinical need is the scarcity of available preclinical models of these neoplasms able to accurately reproduce the biology and physiology of the primary tumor and that can be used as valid platforms for drug testing.

The study aims to generate a new 3D in vitro model of PanNET, replicating the complex primary tumor in vitro, including also the tumor microenvironment (e.g., vascular network and stroma, essential elements of these neoplasms), personalised for each patient. Indeed, for the development of the newly proposed models, patients' surgical specimens will be initially evaluated by the Pathological Unit and, in the presence of pathological material in excess not required for the routine diagnostic procedure, it will be employed for the generation of the proposed personalised models. 3D culture techniques (e.g., 3D bioprinting) will be used for the development of the models. Moreover, in order to further mimic in vitro the in vivo environment, the models will be maintained under dynamic conditions.

These newly proposed models will be exploited for studying the mechanisms underlying disease development and progression, as well as for performing drug testing.