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Supplementary Material for: Three-Dimensional Primary Cell Culture: A Novel Preclinical Model for Pancreatic Neuroendocrine Tumors

posted on 06.07.2020, 11:49 by April-Monn S.L., Wiedmer T., Skowronska M., Maire R., SchiavoLena M., Trippel M., DiDomenico A., Muffatti F., Andreasi V., Capurso G., Doglioni C., Kim-Fuchs C., Gloor B., Zatelli M.C., Partelli S., Falconi M., Perren A., Marinoni I.
Molecular mechanisms underlying the development and progression of pancreatic neuroendocrine tumors (PanNETs) are still insufficiently understood. Efficacy of currently approved PanNET therapies is limited. While novel treatment options are being developed, patient stratification permitting more personalized treatment selection in PanNET is yet not feasible since no predictive markers are established. The lack of representative in vitro and in vivo models as well as the rarity and heterogeneity of PanNET are prevailing reasons for this. In this study, we describe an in vitro 3-dimensional (3-D) human primary PanNET culture system as a novel preclinical model for more personalized therapy selection. We present a screening platform allowing multicenter sample collection and drug screening in 3-D cultures of human primary PanNET cells. We demonstrate that primary cells isolated from PanNET patients and cultured in vitro form islet-like tumoroids. Islet-like tumoroids retain a neuroendocrine phenotype and are viable for at least 2 weeks in culture with a high success rate (86%). Viability can be monitored continuously allowing for a per-well normalization. In a proof-of-concept study, islet-like tumoroids were screened with three clinically approved therapies for PanNET: sunitinib, everolimus and temozolomide. Islet-like tumoroids display varying in vitro response profiles to distinct therapeutic regimes. Treatment response of islet-like tumoroids differs also between patient samples. We believe that the presented human PanNET screening platform is suitable for personalized drug testing in a larger patient cohort, and a broader application will help in identifying novel markers predicting treatment response and in refining PanNET therapy.