Addressing Modern Diagnostic Pathology for Patient-Derived Soft Tissue Sarcosphere Models in the Era of Functional Precision Oncology.

Responses to therapy often cannot be exclusively predicted by molecular markers, thus evidencing a critical need to develop tools for better patient selection based on relations between tumor phenotype and genotype. Patient-derived cell models could help to better refine patient stratification procedures and lead to improved clinical management. So far, such ex vivo cell models have been used for addressing basic research questions and in preclinical studies. As they now enter the era of functional precision oncology, it is of utmost importance that they meet quality standards to fully represent the molecular and phenotypical architecture of patients' tumors. Well-characterized ex vivo models are imperative for rare cancer types with high patient heterogeneity and unknown driver mutations. Soft tissue sarcomas account for a very rare, heterogeneous group of malignancies that are challenging from a diagnostic standpoint and difficult to treat in a metastatic setting because of chemotherapy resistance and a lack of targeted treatment options. Functional drug screening in patient-derived cancer cell models is a more recent approach for discovering novel therapeutic candidate drugs. However, because of the rarity and heterogeneity of soft tissue sarcomas, the number of well-established and characterized sarcoma cell models is extremely limited. Within our hospital-based platform we establish high-fidelity patient-derived ex vivo cancer models from solid tumors for enabling functional precision oncology and addressing research questions to overcome this problem. We here present 5 novel, well-characterized, complex-karyotype ex vivo soft tissue sarcosphere models, which are effective tools to study molecular pathogenesis and identify the novel drug sensitivities of these genetically complex diseases. We addressed the quality standards that should be generally considered for the characterization of such ex vivo models. More broadly, we suggest a scalable platform to provide high-fidelity ex vivo models to the scientific community and enable functional precision oncology.