3D dynamic cultures of HGSOC organoids to model innovative and standard therapies

Abstract

High-grade serous ovarian cancer (HGSOC) needs new technologies for improving cancer diagnosis and therapy. It is a fatal disease with few options for the patients. In this context, dynamic culture systems coupling with patient-derived cancer 3D microstructures could offer a new opportunity for exploring novel therapeutic approaches. In this study, we optimized a passive microfluidic platform with 3D cancer organoids, which allows a standardized approach among different patients, a minimum requirement of samples, multiple interrogations of biological events, and a rapid response. The passive flow was optimized to improve the growth of cancer organoids, avoiding the disruption of the extracellular matrix (ECM). Under optimized conditions of the OrganoFlow (tilting angle of 15° and an interval of rocking every 8 min), the cancer organoids grow faster than when they are in static conditions and the number of dead cells is reduced over time. To calculate the IC50 values of standard chemotherapeutic drugs (carboplatin, paclitaxel, and doxorubicin) and targeted drugs (ATRA), different approaches were utilized. Resazurin staining, ATP-based assay, and DAPI/PI colocalization assays were compared, and the IC50 values were calculated. The results showed that in the passive flow, the IC50 values are lower than in static conditions. FITC-labeled paclitaxel shows a better penetration of ECM under passive flow than in static conditions, and cancer organoids start to die after 48 h instead of 96 h, respectively. Cancer organoids are the last frontiers for ex vivo testing of drugs that replicate the response of patients in the clinic. For this study, organoids derived from ascites or tissues of patients with Ovarian Cancer have been used. In conclusion, it was possible to develop a protocol for organoid cultures in a passive microfluidic platform with a higher growth rate, faster drug response, and better penetration of drugs into ECM, maintaining the samples’ vitals and collecting the data on the same plate for up to 16 drugs.

Longitudinal Prediction of Drug Response in High-Grade Serous Ovarian Cancer Organoid Cultures Aligning with Clinical Responses

“…These results are not surprising since, as previously reported by Russo Spena et al [35], VS10 is a more potent Pin1 inhibitor compared to ATRA. These findings are in line with the results obtained by Cavarzerani et al who developed a passive microfluidics platform to do drug screening in PDTOs of HGSOC in which 2 out of 5 PDTOs of the study were sensitive to ATRA [36]. Furthermore, in line with findings from Saorin et al…”

“…This highlights a significant gap in current research, emphasizing the need for further exploration into the stability of drug responsiveness in organoid cultures over extended periods. Moreover, we explored the potential to develop novel targeted therapies and testing new inhibitor of Peptidylprolyl Cis/Trans Isomerase, NIMA-Interacting 1 (Pin1), an emerging target of HGSOC as demonstrating by our group [33] [23] [34] [24] [35], [36], [37].…”

Unveiling the promising anticancer activity of palladium(ii)–aryl complexes bearing diphosphine ligands: a structure–activity relationship analysis

“…Therefore, taking advantage of organoid biotechnology, we selected two patient-derived tumoroids (PDTO-1 and PDTO-2), originating from high-grade serous ovarian cancer (HGSOC) and recently isolated and characterized by our research group. 49 Notably, from immunohistochemistry (IHC) analyses, the established organoids captured the histological characteristics of the primary tumors. In particular, hematoxylin as well as PAX8, WT-1, and CA-125 HGOC markers were evaluated by the pathologist.…”

Organ-On-A-Chip (OOC) Image Dataset for Machine Learning and Tissue Model Evaluation

“…Furthermore, dynamic systems often replicate in vivo physiological conditions more closely, which can lead to more physiologically relevant results [4]. For instance, when subjected to dynamic conditions, 3D organoids derived from patients with high-grade serous ovarian cancer exhibited heightened sensitivity to paclitaxel [5]. This is promising technology also in the case of personalized medicine and drug development as it can be used to develop or select therapeutics personalized for individual patients and therefore identify drug candidates with a greater probability of success and thus shorten the clinical trial timeline [6].…”