The Patient-Derived Cancer Organoids: Promises and Challenges as Platforms for Cancer Discovery.

The cancer burden is rapidly increasing in most countries, and thus, new anticancer drugs for effective cancer therapy must be developed. Cancer model systems that recapitulate the biological processes of human cancers are one of the cores of the drug development process. PDCO has emerged as a unique model that preserves the genetic, physiological, and histologic characteristics of original cancer, including inter- and intratumoral heterogeneities. Due to these advantages, the PCDO model is increasingly investigated for anticancer drug screening and efficacy testing, preclinical patient stratification, and precision medicine for selecting the most effective anticancer therapy for patients. Here, we review the prospects and limitations of PDCO compared to the conventional cancer models. With advances in culture success rates, co-culture systems with the tumor microenvironment, organoid-on-a-chip technology, and automation technology, PDCO will become the most promising model to develop anticancer drugs and precision medicine.

3D fluid-dynamic ovarian cancer model resembling systemic drug administration for efficacy assay.

Recently, 3D in vitro cancer models have become important alternatives to animal tests for establishing the efficacy of anticancer treatments. In this work, 3D SKOV-3 cell-laden alginate hydrogels were established as ovarian tumor models and cultured within a fluid-dynamic bioreactor (MIVO®) device able to mimic the capillary flow dynamics feeding the tumor. Cisplatin efficacy tests were performed within the device over time and compared with (i) the in vitro culture under static conditions and (ii) a xenograft mouse model with SKOV-3 cells, by monitoring and measuring cell proliferation or tumor regression, respectively, over time. After one week of treatment with 10 µM cisplatin, viability of cells within the 3D hydrogels cultured under static conditions remained above 80%. In contrast, the viability of cells within the 3D hydrogels cultured within dynamic MIVO® decreased by up to 50%, and very few proliferating Ki67-positive cells were observed through immunostaining. Analysis of drug diffusion, confirmed by computational analysis, explained that these results are due to different cisplatin diffusion mechanisms in the two culture conditions. Interestingly, the outcome of the drug efficacy test in the xenograft model was about 44% of tumor regression after 5 weeks, as predicted in a shorter time in the fluid-dynamic in vitro tests carried out in the MIVO® device. These results indicate that the in vivo-like dynamic environment provided by the MIVO® device allows to better model the 3D tumor environment and predict in vivo drug efficacy than a static in vitro model.

Patient-derived xenografts as compatible models for precision oncology.

Cancer is a very heterogeneous disease, displaying heterogeneity between patients (inter-tumoral heterogeneity) and heterogeneity within a patient (intra-tumoral heterogeneity). Precision oncology is a diagnostic and therapeutic approach for cancers based on the stratification of patients using genomic and molecular profiling of tumors. To develop diagnostic and therapeutic tools for the application of precision oncology, appropriate preclinical mouse models that reflect tumor heterogeneity are required. Patient-derived xenograft (PDX) models are generated by the engraftment of patient tumors into immunodeficient mice that retain several aspects of the patient's tumor characteristics, including inter-tumoral heterogeneity and intra-tumoral heterogeneity. Therefore, PDX models can be applied in various developmental steps of cancer diagnostics and therapeutics, such as biomarker development, companion diagnostics, drug efficacy testing, overcoming drug resistance, and co-clinical trials. This review summarizes the diverse aspects of PDX models, addressing the factors considered for PDX generation, application of PDX models for cancer research, and future directions of PDX models.