The remarkably high failure rate of clinical trials for oncology drugs is in part due to the failure of in vitro and in vivo models to sufficiently recapitulate the complexity of the human tumor microenvironment (TME) and predicting clinical patient response. Advanced translational human 3D cell culture models, such as patient-derived tumor organoids, still lack the endogenous cells of the TME, such as tumor infiltrating lymphocytes (TILs), fibroblasts, macrophages and other immune cells. These TME components have been shown to express important drug targets themselves and play a critical role in both tumor progression and modulation of the response to drugs. Here we present a novel 3D Ex vivo Patient Tissue platform that combines a short-term 3D ex vivo tumor culture system with high content image (HCI)-based analysis. Patient tumor tissues from surgical resections, pleural fluid, ascites, or biopsy were tested ex vivo with minimal processing leading to preservation of tumor heterogeneity and resident immune cells. Here, we present a quantification of tumor sensitivity to targeted therapies, standard of care, and novel (immune) drugs and drug combinations, tested on patient tissues from different cancer types. Methods Patient tumor tissues were obtained from commercial tissue providers or from ongoing clinical trials in the Netherlands, and processed within 24 hours to preserve the TME. Freshly isolated tumor cells from ovarian, non-small cell lung cancer (NSCLC), cervical and breast cancer patients were embedded in a protein-rich hydrogel and exposed to panels of drug treatments at various doses in a 384-well format for 5-7 days. Phenotypic effects of drugs and combination therapies on physiologically relevant morphological changes, such as tumor cell killing, growth arrest, and immune cell proliferation, were measured using our proprietary automated HCI analysis platform. In addition, IHC and FACS analysis of primary samples as well as cytokine measurements were performed. Results Ex vivo tissue drug sensitivity profiles were generated for each patient sample based on the response to a broad range of drugs including standard of care (e.g., platinum, paclitaxel, gemcitabine), targeted therapies (e.g., PARP and EGFR inhibitors), and activity of immunomodulatory drugs (e.g., ipilimumab, pembrolizumab and STING agonists). Accurate and reproducible response evaluation demonstrates the feasibility of preclinical drug testing on primary material from cancer patients using this platform. Conclusion The 3D ex vivo patient tissue platform successfully combined drug testing protocols using fresh patient tumor tissue with preserved TME components and advanced 3D HCI analysis. Our approach offers a rapid, reliable and patient-relevant approach to test various (clinical) candidate compounds (e.g., antibodies, antibody-drug conjugates and small molecules) for different solid tumour types. It has the potential to significantly improve the preclinical evaluation of drugs and support the decision-making process during progression of drug candidates to the clinic.
