Aim/Introduction: In recent years, cancer immunotherapy has become one of the fastest-growing areas in cancer research. Selecting suitable and cost-effective experimental models for developing and validating immunotherapies is one of the major obstacles researchers face today. To overcome this, patient-derived tumor models are of increasing interest because they can better recapitulate many of the properties and the heterogeneity exhibited by the tumor microenvironment at a relatively low cost. Hereby, we propose a high throughput screening platform for an effective and efficient evaluation of cancer immunotherapies in patient-derived tumor models. Methods: Tumor models were established in vitro from patient-derived tumor biopsies. Established tumoroids were engineered using a luciferase-green fluorescent protein (GFP) lentivirus to generate a reporter pool. Transduced pools were enriched for GFP via flow cytometry and characterized using RNA/scRNA-seq and biomarker-based sequencing. Natural Killer (NK) cells were co-cultured with the enriched pool in various effector-to-target ratios and recorded using a live cell imaging and analysis platform. Cytotoxicity and cell health were measured by GFP intensity, luciferase activity, and caspase-based live staining. Results: A patient-derived tumoroid reporter pool was successfully generated through GFP enrichment using a flow cytometer. The killing efficiency of immune cells with various effector(E) to target(T) ratios has been successfully captured in a ratio-dependent manner via the live cell imaging and analysis platform. NK cell-mediated cytotoxicity was successfully measured through GFP intensity, luciferase, and caspase activity. Conclusions: Traditional cell line generation can be used in patient-derived tumoroid models to generate enriched reporter cell pools without selection pressure. Outside of establishing screening platforms, scientists can use this approach to efficiently engineer patient-derived tumoroid models to meet their specific research goals. Here, we used the reporter pool to develop a multiplex-killing assay to measure cell viability and toxicity. This platform can be used in a variety of immune cell workflows, providing a method that can predict tissue-specific responses, and evaluate solid tumor immunotherapies in high throughput cell-based assays. Citation Format: Andrew Tsao, Xiaoyu Yang, Garrett Wong, Vivek Chandra, Jacob Delgadillo, Lindsay Bailey Steinitz, Brittany Balhouse, Colin Paul, Jakhan Nguyen, Sybelle Djikeng, Shyanne Salen, Jason Sharp, Matt Dallas, David Kuninger. Engineering patient-derived tumors to enable high-throughput screening: Immuno-oncology workflows [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2753.

Cancer drug development is an extremely challenging and resource-consuming process with a dismal success rate of 5%. The high failure rate is partly due to the inadequacy of traditional 2D cell culture model to predict drug efficacy and toxicity. A highly accurate and representative system is therefore urgently needed to improve cancer drug discovery and development process. The emerging 3D cancer models can better recapitulate the in vivo tumor microenvironment and have shown closer gene expression profiles to clinical samples compared to 2D models. Another crucial element of in vitro cancer models is the source of cancer cells: patient-derived models have shown advantages to better capture tumor heterogeneity and clinically relevant genetic alterations of the tissue origin compared to traditional cancer cell lines. Several studies showed that response of patient-derived models to treatment reflects the response of patients from which the organoids are derived, suggesting they are great platform for studies of personalized medicine. Hereby, we performed a proof-of-concept high throughput multiplexed plate-reader based drug screening assay on 3D patient-derived tumoroids. Methods: Patient-derived tumoroids were sequenced for cancer relevant mutations, copy number variants, and altered gene expression using multi-biomarker targeted next generation sequencing (NGS) and RNA-seq. 3D tumoroid lines derived from three different colorectal cancer patient samples, together with a colorectal cancer cell line were treated with chemotherapy, and targeted therapy agents. The same tumoroid and cell lines were cultured and treated in 2D as experimental control. Drug response readout was multiplexed using three different plate reader-based assays, measuring the reducing power, ATP, and the release of lactate dehydrogenase from treated cells. Results: Comparable drug responses were observed from three different viability cytotoxicity assays. Different patient-derived tumoroids demonstrated differential drug sensitivity that correlates with their clinical cancer stages. Both patient-derived and cancer cell lines cultured in 3D models showed markedly increased drug resistance compared 2D adherent culture. Differential response was also observed between cancer cell lines vs patient-derived lines. Conclusion: Taken together, these findings suggest the potential advantages of 3D patient-derived tumoroid models over 2D culture and cancer cell lines for accurate prediction of drug response. Through targeted NGS and RNA-seq, patient-specific drug targets could be identified for personalized drug screening. Selected drug candidates could be further subjected to in vivo drug validation using patient-derived xenograft model generated from the same donor. In combination with other derived models or large donor banks, this workflow provides a platform for large-scale drug discovery workflows and precision medicine. Citation Format: Xiaoyu Yang, Andrew Tsao, Garrett Wong, Chris Yankaskas, Colin Paul, Brittany Balhouse, Amber Bullock, Anthony Chatman, Shyanne Salen, Sybelle Djikeng, Matt Dallas, David Kuninger. Multiplexed plate-reader based drug screening of 3D-tumoroid models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2750.