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

Cell-Based Glioma Models for Anticancer Drug Screening: From Conventional Adherent Cell Cultures to Tumor-Specific Three-Dimensional Constructs

Gliomas are a group of primary brain tumors characterized by their aggressive nature and resistance to treatment. Infiltration of surrounding normal tissues limits surgical approaches, wide inter- and intratumor heterogeneity hinders the development of universal therapeutics, and the presence of the blood-brain barrier reduces the efficiency of their delivery. As a result, patients diagnosed with gliomas often face a poor prognosis and low survival rates. The spectrum of anti-glioma drugs used in clinical practice is quite narrow. Alkylating agents are often used as first-line therapy, but their effectiveness varies depending on the molecular subtypes of gliomas. This highlights the need for new, more effective therapeutic approaches. Standard drug-screening methods involve the use of two-dimensional cell cultures. However, these models cannot fully replicate the conditions present in real tumors, making it difficult to extrapolate the results to humans. We describe the advantages and disadvantages of existing glioma cell-based models designed to improve the situation and build future prospects to make drug discovery comprehensive and more effective for each patient according to personalized therapy paradigms.

Functional prediction of response to therapy prior to therapeutic intervention is associated with improved survival in patients with high-grade glioma

Patients with high-grade glioma (HGG) have an extremely poor prognosis compounded by a lack of advancement in clinical care over the past few decades. Regardless of classification, most newly diagnosed patients receive the same treatment, radiation and temozolomide (RT/TMZ). We developed a functional precision oncology test that prospectively identifies individual patient's response to this treatment regimen. Tumor tissues isolated from patients with newly diagnosed HGG enrolled in 3D PREDICT REGISTRY were evaluated for response to chemotherapeutic agents using the 3D Predict™ Glioma test. Patients receiving RT/TMZ were followed for 2 years. Clinical outcomes including imaging, assessments, and biomarker measurements were compared to patient matched test-predicted therapy response. Median survival between test-predicted temozolomide responders and test-predicted temozolomide non-responders revealed a statistically significant increase in progression-free survival when using the test to predict response across multiple subgroups including HGG (5.8 months), glioblastoma (4.7 months), and MGMT unmethylated glioblastoma (4.7 months). Overall survival was also positively separated across the subgroups at 7.6, 5.1, and 6.3 months respectively. The strong correlation of 3D Predict Glioma test results with clinical outcomes demonstrates that this functional test is prognostic in patients treated with RT/TMZ and supports aligning clinical treatment to test-predicted response across varying HGG subgroups.

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.