Abstract
PURPOSE: Gastric cancer (GC) is the fifth most lethal form of cancer. Because of its late diagnosis, high intratumor heterogeneity, and the presence of a dense stromal compartment, GC is less susceptible to systemic treatments compared to other tumor types. Many systemic therapies, developed to support curative and palliative treatment for GC and other cancers, did not reach the clinic, which might be due to the fact that preclinical data, obtained from simple cell-based models, does not translate to the complex GC tumor structure and the occurrence of drug resistance. Therefore, this study is aimed to establish fibroblast-rich, close-to-patient models for GC and evaluate their robustness by comparison with the primary GC tissue from the respective patients. MATERIAL AND METHODS: Five different GC models were established: I) a subcutaneous and II) orthotopic patient-derived xenograft (PDX) model, III) an in vitro patient-derived organoid (PDO) model, which was IV) subcutaneously engrafted in vivo, and V) co-cultured with GC fibroblasts to form a novel multicellular fibroblast-rich GC model. Histology, immunohistochemistry, mutational status and tumor/stroma composition were compared between the parental tissues and the various models. RESULTS: Both PDX models reflected the histological structure of the parental tissue, consisting of distinct parenchymal and stromal compartments. Primary tumor mutations were maintained in the PDX, as well as markers of clinical interest, like HER2. Orthotopic GC-PDX models showed high growth rates, accompanied by significant stromal accumulation. GC-PDOs showed histological similarities with parental tissues, maintaining the various histological phenotypes. Engrafted subcutaneously in mice, these organoids generated stroma-rich tumors. To mimic these features in vitro, we established a multicellular model composed of GC-associated fibroblasts and GC organoids, aggregating into complex multicellular structures with high similarity to parental GC tumor tissues. CONCLUSION: We generated five close-to-patient GC models, which can potentially facilitate preclinical evaluations of novel therapies.