Nguyen Toan
It is clear that there is a pressing need to create tools for better patient selection based on relationships between tumour phenotype and genotype because responses to therapy are frequently not entirely predicted by molecular markers. Patient-derived cell models may aid to improve clinical management and patient stratification techniques. These ex vivo cell models have thus far been employed in preclinical research as well as for answering fundamental research problems. It is crucial that they achieve quality requirements as they move towards the era of functional precision oncology in order to accurately depict the molecular and phenotypical architecture of patients' tumours. For uncommon cancer types with considerable patient heterogeneity and unknown driver mutations, wellcharacterized ex vivo models are essential. Because of chemotherapy resistance and a lack of targeted treatment options, soft tissue sarcomas are a relatively uncommon, heterogeneous group of malignancies that are tough to diagnose and challenging to treat in a metastatic context. A relatively recent method for finding innovative therapeutic candidate medications involves functional drug screening in patient-derived cancer cell models. However, the number of well-established and characterised sarcoma cell models is exceedingly small due to the rarity and variety of soft tissue sarcomas. We develop high-fidelity patient-derived ex vivo cancer models from solid tumours within our hospital-based platform to enable functional precision oncology and address research topics to solve this issue. In this article, we provide five brand-new, thoroughly characterised, complex-karyotype ex vivo soft tissue sarcosphere models that can be used to explore the molecular aetiology of these genetically complicated diseases and discover their unique therapeutic sensitivities. We discussed the criteria for quality that should be typically taken into account when characterising such ex vivo models. More generally, we propose a scalable infrastructure to enable functional precision oncology and supply high-fidelity ex vivo models to the scientific community.
