Abstract 6223: PD3D®models: New age in cancer research and clinical diagnostics

Abstract

Abstract Patient-derived 3D cell culture models (PD3D®) developed as a powerful tool for disease modelling, biomarker and drug discovery. Currently, they are gaining increasing significance in the field of personalized oncology, as they recapitulate the histopathology of the original tumor tissue and preserve its genetic markup. PD3D® can be used to model intratumoral heterogeneity and for medium and high throughput drug screens. Using a reverse clinical engineering approach, PD3D® models allow identification of chemoresistance/sensitivity signatures (i.e., biomarkers) and can be applied in personalized oncology to identify treatment for an individual patient. We successfully established PD3D® models from more than 300 tumor tissue samples, ranging from more prevalent cancers like colorectal, breast and pancreas carcinoma, to rare tumor entities including various sarcoma types and thymoma. PD3D® models from different tumor entities differ in morphology and culture media requirements. When treating PD3D® from the same tumor entity with standard of care drugs, we found that their response differed, as does clinical response of patients. Furthermore, we successfully used PD3D® models to identify a biomarker for predicting chemosensitivity towards a targeted drug. For application of PD3D® in truly personalized oncology, we developed a protocol that allows us to generate a PD3D® culture and perform a drug sensitivity assay for an individual patient within a therapy-relevant timeframe. Using this protocol, we identified a combination therapy for a pretreated, metastasized appendix carcinoma within 29 days, that resulted in stable disease of the patient. In conclusion, PD3D® models can be derived from various cancer entities and used to analyze drug response in cohorts of models for drug development or identification of signatures related to drug resistance or sensitivity. Furthermore, PD3D® models can be used to predict a patient tumor’s drug response in a personalized manner, supporting the oncologist to identify the best treatment option for the patient. Citation Format: Alina Pflaume, Samantha Exner, Katja Herrera-Glomm, Jürgen Loskutov, Ulrike Pfohl, Manuela Regenbrecht, Sushmitha Sankarasubramanian, Lena Wedeken, Sabine Finkler, Larissa Ruhe, Quirin Graf Adelmann, Christoph Reinhard, Philipp Stroebel, Christian R. Regenbrecht. PD3D®models: New age in cancer research and clinical diagnostics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6223.