In Vitro and In Vivo Drug-Response Profiling Using Patient-Derived High-Grade Glioma

The future of cancer therapy: exploring the potential of patient-derived organoids in drug development

Cancer therapy is on the brink of a significant transformation with the inclusion of patient-derived organoids (PDOs) in drug development. These three-dimensional cell cultures, directly derived from a patient's tumor, accurately replicate the complex structure and genetic makeup of the original cancer. This makes them a promising tool for advancing oncology. In this review, we explore the practical applications of PDOs in clinical drug screening and pharmacognostic assessment, as well as their role in refining therapeutic strategies. We provide insights into the latest advancements in PDO technology and its implications for predicting treatment responses and facilitating novel drug discoveries. Additionally, we address the operational challenges associated with incorporating PDOs into the drug development process, such as scaling up organoid cultures, ensuring consistent results, and addressing the ethical use of patient-derived materials. Aimed at researchers, clinicians, and key stakeholders in oncology, this article aims to succinctly present both the extraordinary potential and the obstacles to integrating PDOs, thereby shedding light on their prospective impact on the future of cancer treatment.

Protocol for high throughput 3D drug screening of patient derived melanoma and renal cell carcinoma

High Throughput Screening (HTS) with 3D cell models is possible thanks to the recent progress and development in 3D cell culture technologies. Results from multiple studies have demonstrated different drug responses between 2D and 3D cell culture. It is now widely accepted that 3D cell models more accurately represent the physiologic conditions of tumors over 2D cell models. However, there is still a need for more accurate tests that are scalable and better imitate the complex conditions in living tissues. Here, we describe ultrahigh throughput 3D methods of drug response profiling in patient derived primary tumors including melanoma as well as renal cell carcinoma that were tested against the NCI oncologic set of FDA approved drugs. We also tested their autologous patient derived cancer associated fibroblasts, varied the in-vitro conditions using matrix vs matrix free methods and completed this in both 3D vs 2D rendered cancer cells. The result indicates a heterologous response to the drugs based on their genetic background, but not on their maintenance condition. Here, we present the methods and supporting results of the HTS efforts using these 3D of organoids derived from patients. This demonstrated the possibility of using patient derived 3D cells for HTS and expands on our screening capabilities for testing other types of cancer using clinically approved anti-cancer agents to find drugs for potential off label use.

Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models

Cancer is a heterogeneous disease in that tumors of the same histology type can respond differently to a treatment. Anti-cancer drug response prediction is of paramount importance for both drug development and patient treatment design. Although various computational methods and data have been used to develop drug response prediction models, it remains a challenging problem due to the complexities of cancer mechanisms and cancer-drug interactions. To better characterize the interaction between cancer and drugs, we investigate the feasibility of integrating computationally derived features of molecular mechanisms of action into prediction models. Specifically, we add docking scores of drug molecules and target proteins in combination with cancer gene expressions and molecular drug descriptors for building response models. The results demonstrate a marginal improvement in drug response prediction performance when adding docking scores as additional features, through tests on large drug screening data. We discuss the limitations of the current approach and provide the research community with a baseline dataset of the large-scale computational docking for anti-cancer drugs.