Designing a 3D tumor model co-culture study in oncology research is a meticulous process that demands precision and expertise. However, when executed correctly, these studies provide invaluable insights into tumor-immune microenvironment interactions, potentially leading to groundbreaking therapeutic advancements. This guide outlines essential steps to design a robust 3D tumor model co-culture study, ensuring you capture high-quality data and actionable insights.
1. Select 3D Tumor Models and Validate Target Expression
- Mechanism of Action: Begin by selecting cancer type, 3D tumor models, and co-culture strategies that are appropriate for the mechanism of action of your therapeutic agent. This involves understanding the biological pathways and processes that your agent targets, ensuring that the 3D tumor models accurately reflect these conditions.
- Target Expression-Driven Model Selection: Select 3D tumor models based on target expression to best set up your ex-vivo co-culture assay. Leveraging a fully characterized bank, such as Champions’ tumor model bank, is essential for successful model selection. Access to transcriptomic data, for example, allows to rank order the 3D tumor models based on different gene expression profiles, making model selection easy and effective. (Champions’ entire database is accessible for model selection in Lumin)
- Validation Techniques: Validate target expression using PCR, western blot, or flow cytometry techniques. PCR can help detect and quantify specific nucleic acid sequences, western blotting can confirm protein expression levels, and flow cytometry can analyze the physical and chemical characteristics of cells, ensuring the presence and activity of your targets. This step is crucial for confirming that your targets are being correctly expressed and are functional within your chosen models.
Read our blog "Using 3D Ex Vivo Tumor Models for Oncology Research: An Expert Guide" to learn more about the advantages of using tumor organoids in cancer research.
2. Select Multiple Immune Cell Donors to Account for Donor-to-Donor Variability
In 3D tumor model co-culture studies, accounting for donor variability is crucial for the robustness of your findings. Selecting immune cell donors from a diverse pool minimizes the risk of skewed results that could arise from individual donor-specific traits. Ensure that immune cells are sourced from multiple donors to account for genetic, epigenetic, and environmental variations. This approach enhances the generalizability and reliability of your study outcomes. Additionally, thorough donor screening and characterization using flow cytometry can further refine your selection process, providing a comprehensive understanding of each donor’s immune profile.
- Autologous Immune Cells: Autologous immune cells, sourced from the same patient as the 3D tumor model, offer several significant benefits. One of the primary advantages is the enhanced compatibility, as these cells are naturally tailored to interact with the patient’s specific tumor microenvironment. This compatibility can lead to more accurate reflections of in vivo interactions, allowing for better predictive modeling of therapeutic responses. Additionally, using autologous cells can help mitigate the risk of immune rejection, further promoting a more sustained interaction in the 3D tumor model co-culture setting. Despite their advantages, autologous immune cells can present challenges, including donor variability and limited availability. The unique immunological characteristics of each patient can create substantial variability in the efficacy and functionality of the immune cells, leading to inconsistencies in reproducibility. Moreover, some patients may not have accessible immune cells, which can hinder the study's progress and breadth.
- Allogeneic Immune Cells: On the other hand, allogeneic immune cells, derived from different donors, offer advantages in terms of standardization and availability. These cells can be sourced from healthy donors or patients with known, consistent immune profiles, allowing for high-throughput studies and reproducible results. This standardization can enhance the robustness of the findings, as variations between immune responses can be systematically controlled and compared. However, allogeneic immune cells come with their own set of challenges. The primary concern is the potential for immune rejection, as these cells may not be recognized as 'self' by the 3D tumor model or surrounding microenvironment. This disconnect can influence the dynamics of tumor-immune interactions and may not accurately represent the patient-specific responses, ultimately resulting in less applicable data for therapeutic strategies. Furthermore, the genetic and environmental differences between donors can lead to variability in immune responses, complicating the interpretation of results.
Champions supports your ex vivo immuno-oncology program offering both autologous and allogeneic immune cells for 3D tumor model co-culture assays. (In our webinar "Autologous Co-culture Models to Humanized Mouse Models: Navigating IO Models" our experts deep dive into the intricate world of immuno-oncology models to help you identify the best model for your study)
3. Assess Tumor-to-Immune Cell Ratio
Achieving the right composition and cell population balance when replicating the tumor microenvironment (TME) ex-vivo is crucial in designing a 3D tumor model co-culture system. One of the strengths of ex-vivo 3D tumor model co-cultures is the ability to customize different parameters to recreate the TME of interest, providing a physiologically relevant platform to predict clinical outcomes.
- Origin of the Cellular Component and Phenotype: It is very important to source each cell population from the right tissue. When introducing a new cell type within the 3D tumor model co-culture system, it is fundamental to make sure each cellular component is functional and expressing the correct differentiation markers. In some cases, it is necessary to differentiate progenitor cells into the desired cell type before adding them to the 3D tumor model co-culture. In other experimental settings, activating the immune cells to induce specific target expression is crucial to resemble diseased TME.
- Autologous vs Allogeneic
- Population Assessment: Verify the necessary stroma and immune cell populations using advanced techniques such as flow cytometry and expression profiling. Flow cytometry allows for the detailed analysis of cell populations, including their size, granularity, and protein expression. Expression profiling further assesses the expression of the correct differentiation markers by stroma cells and the target expression and activation status of the immune cells, ensuring they are primed and ready to attack the tumor cells effectively.
- Cell Ratios: It's essential to determine the optimal ratio of tumor cells to immune cells to ensure the immune cells can effectively target and eliminate the tumor cells. This involves careful calculation and adjustment based on the specific characteristics of the tumor, TME, and immune cells being used.
By carefully balancing cell ratios, type, and status, and thoroughly assessing cell populations, we can control the 3D tumor model co-culture system's ability to evaluate cancer immunotherapies and improve clinical translatability.
4. Identify the Right Assay Controls
Controls are pivotal for obtaining reliable results and ensuring the validity of an assay. There are several types of controls to consider:
- Mechanism-Based Controls: These are crucial for the assay as they should be selected based on the specific mechanism of action of the therapeutic agent being tested. The correct set of controls will allow a better understanding of how the therapeutic agent in preclinical stage of development will behave in the given biological context. In particular, this type of control is essential to interpret mechanistic findings in ex-vivo 3D tumor model co-culture systems.
- Validation: It is essential to implement these controls consistently throughout the entire assay process. This step ensures that the results are accurate and reproducible, minimizing false positive and negative readouts which is fundamental for drawing meaningful conclusions from the data. In addition, using these controls, researchers can ensure the ex-vivo 3D tumor model co-culture system is responding as expected to a given perturbation and it can provide meaningful results in the specific biological context of interest.
By carefully choosing and applying these controls, researchers can significantly improve the reliability and validity of their experimental results.
5. Choose the Best Readout for Tumor Killing
Selecting the right readout method is critical for obtaining meaningful data:
- Imaging: Utilize advanced imaging techniques for visualizing 3D tumor model cell interactions and capturing detailed images that provide insights into cellular behavior and morphology. The high throughput confocal approach provides meaningful images to have a glance into the 3D tumor model co-culture system. Moreover, the image quantification pipeline allows for quantitative data extraction through 3D image reconstruction. Beyond tumor killing, immune cell infiltration into the 3D tumor model is another parameter that can be evaluated through an accurate image analysis.
- Flow Cytometry: Assess different phenotypes within the diverse cell populations composing the co-culture, viability (which provides a direct tumor-killing measure), and specific target expression using flow cytometry. This technique allows for precise quantification and analysis down to the single cell level, dissecting the heterogeneous populations within the 3D tumor model co-culture system.
6. Use Immune Cell Infiltration as an Additional Readout
Immune cell infiltration offers deeper insights into the body's response to cancer:
- Infiltration Analysis: Track immune cell movement into the tumor microenvironment to gauge the effectiveness of your therapeutic agent. This analysis helps in understanding the behavior of immune cells and their interaction with 3D tumor model cells, which is crucial for developing more effective treatments and improving patient outcomes. The 3D organization of the cells in different compartments is important in this kind of analysis to decide which parameter is best to use in evaluating cellular mobility.
7. Assess Immune and Stroma Cell Status Post-Treatment
Post-treatment analysis of the 3D tumor model co-culture system helps interpret therapeutic effects by providing detailed insights into cellular and molecular responses. Here are two key techniques:
- Flow Cytometry: Use this technique to analyze immune and stroma cell phenotypes and statuses. It allows for the identification and quantification of various cell types within a sample, offering a comprehensive overview of changes in the cellular landscape following treatment.
- Cytokine Analysis: Measure cytokine levels to understand the immune response and stroma cell dynamics post-treatment. By quantifying specific cytokines, researchers can infer the activation and regulation of different immune pathways, shedding light on the effectiveness and impact of the therapeutic intervention.
Designing a 3D tumor model co-culture study is a complex yet rewarding endeavor that can yield significant insights into tumor biology and treatment efficacy. By following these steps, you can ensure your study is thorough, accurate, and highly informative.
Ready to take your 3D tumor model co-culture studies to the next level? Partner with Champions Oncology for unrivaled expertise and cutting-edge resources to propel your research forward. We tailor our services to meet your specific research needs. Contact us today to learn more!