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.

• Luciferase: Employ luciferase-based assays for fast, quantifiable, and direct measurement of cancer cell killing. This endpoint requires the use of luciferase-tagged cells, enabling the measurement of gene expression, cellular activity, and other biological processes through luminescence. (Watch our on-demand webinar "Poster QuickTake: Bioluminescent 3D Tumors with Immune Cell Co-culture for HTS" to learn more about bioluminescence tagged 3D tumor models for co-culture assays) 
  
  

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!

Conducting an Immuno-Oncology in vivo study is a complex process that requires meticulous planning and execution. For oncology researchers and biotech professionals, ensuring the success of these studies is crucial for advancing cancer treatments. Here are the five essential steps to guide you through a successful immuno-oncology in vivo study.

1. Select the Best Tumor Models

One of the first critical steps is choosing the appropriate tumor models. This involves:

• Molecular Data Analysis: Leverage molecular data to select tumor models that express your drug target and most closely represent the target patient population.
• Ex Vivo Screening: Utilize ex vivo screening techniques to evaluate the effectiveness of your drug in various tumor models and select models with the best response profile.

By selecting the most relevant tumor models for your immuno-oncology in vivo study, whether Patient-Derived Xenografts (PDXs) or cell lines, you can ensure that your immuno-oncology in vivo studies will provide more accurate and translatable results. Verified target expression via proteomics and genomics data, and selection of pretreated or naïve models are two extremely important elements of model selection.

2. Choose the Right Mouse Model

The choice of mouse model can significantly impact the outcomes of your immuno-oncology in vivo study. Below we provide some directions on how to navigate the different available mouse model options.

Murine immune system:

• Syngeneic Mouse Models: These are generally used when the candidate immuno-oncology drug is able to interact with the murine version of the human target. These models are generated by inoculating murine cell lines into the mice, providing a controlled environment to study immune response.

• Knock-in Humanized Mouse Models: These models are used when the candidate immuno-oncology drug only interacts with the human target molecule. These models are generated by inserting human genes into the mouse genome, allowing the candidate drug to target the murine immune response.

Humanized immune system:

• Adoptive transfer humanized models: This group of humanized mice allows for the engraftment of mature immune cells such as PBMCs and NK cells. The experimental strategy consists of engrafting human immune cells isolated from peripheral blood in immune-compromised mice to mimic some aspect of the human immune response.

• CD34 HSC humanized model: This approach provides a platform closer to the human immune system by engrafting CD34 hematopoietic stem cells (HSC) from the human cord blood. The presence of the main populations of the human immune system, including both innate and adaptive immune players, ensures an ideal platform to test a variety of immune-targeting drugs.

Selecting the right mouse model for your immuno-oncology in vivo study is crucial for understanding how your treatment will interact with the immune system. (Read our blog "Choosing the RIGHT Model - Syngeneic versus Humanized Mouse Models")

3. Evaluate Drug Toxicity

Before proceeding with efficacy studies, it's essential to evaluate the safety of your drug in the chosen mouse model system:

• Acute Toxicity Testing: Assess the immediate effects of your drug on the mouse models to identify any potential adverse reactions. Usually, a single high dose of the agent is administered to the animals, and toxicity is evaluated within 24 hours from drug administration.

• Chronic Toxicity Testing: Examine the long-term effects to ensure the drug is safe for prolonged use. In this case, animals are exposed to repeated lower doses of the candidate agent to assess toxicity over a longer period of time.

Ensuring the drug's safety through comprehensive toxicity testing is vital for the integrity of your immuno-oncology in vivo study.

4. Evaluate Tumor Killing

Next, conduct tumor-killing studies to evaluate the efficacy of your treatment:

• In Vivo Efficacy Testing: Measure the tumor size reduction and observe the mouse models' overall health.
• Comparison Studies: Compare your drug’s performance with existing treatments to establish its effectiveness and advantage over standard of care therapies.

These studies will provide concrete evidence of your immuno-oncology drug's potential to kill cancer cells, a fundamental step before moving to the clinical research stage.

5. Analyze the Mechanism of Drug Action

Understanding how your drug works at a molecular level is the final step to creating a successful immuno-oncology in vivo study.

• Flow Cytometry: Use flow cytometry to analyze the types and states of cells affected by your treatment. This analysis will reveal the effects of your candidate drug on the number, proliferation, and activation of the different immune cells. (Read our blog "Using Flow Cytometry as an In Vivo Study Endpoint")
• RNA Sequencing (RNA-seq): Perform RNA-seq to examine gene expression changes induced by your drug. Complementary to flow cytometry, RNA-seq, and DRUG-seq can expose the on-target and off-target effects of your candidate drug, possibly revealing new therapeutic and synergistic opportunities. (Read our blog "Exploring DRUG-seq: Revolutionizing RNA-seq in Oncology Research")

These analyses will give you insights into the precise mechanisms through which your immuno-oncology drug operates, paving the way for further development and optimization.

By following these five essential steps, you can enhance the accuracy, efficacy, and safety of your immuno-oncology in vivo study. At Champions Oncology, we are committed to supporting researchers with cutting-edge tools and expertise.

Ready to take your research to the next level? Reach out to our team of experts and see how we can help you achieve groundbreaking results.

Renal cell carcinoma (RCC) is a common cancer of the genitourinary tract that has very poor survival outcomes if metastatic. RCC is now understood to be composed of several different types of cancer with different genetic features and varied clinical responses. Histological diagnosis has been the primary method to diagnose RCC and has been used to define three major RCC subtypes, including the most common subtype, clear cell renal cell carcinoma (ccRCC), papillary renal cell carcinoma (PRCC; further divided into two subtypes), and chromophobe renal cell carcinoma (ChRCC)^[1]. More recent comparative genomic and phenotypic analysis has identified mutations and epigenetic modifications associated with different histological subtypes^[2]. Across all subtypes, increased DNA hypermethylation and gene alterations in CDKN2A were associated with a poor prognosis as was an increased Th2 immune gene signature. For ccRCC, increased levels of mRNA transcripts associated with ribose metabolism and the immune response were associated with poor survival. ccRCC is also defined by the early loss of chromosome 3p, which in turn causes a loss of heterozygosity for the VHL, PBRM1, SETD2, and BAP1 tumor suppressor genes and subsequent mutation of these genes that leads to tumorigenesis^[3]. There is also a subset of ChRCC with a unique metabolic expression pattern that is associated with extremely poor survival^[2]. PRCC can be classified as type 1, for which PBRM1 mutations are linked to poor survival but type 2 PRCC has increased expression of glycolysis and metabolism-related mRNA transcripts^[2].

ccRCC tumors with VHL mutations show overexpression of vascular endothelial growth factor (VEGF) and hypoxia-inducible factors (HIFs) can contribute to angiogenesis and cancer progression. Similarly, some RCCs show hyperactivation of the serine/threonine kinase mammalian target of rapamycin (mTOR), which can lead to the overproduction of VEGF. VEGFR inhibitors and anti-VEGF antibodies have been tested as therapies for RCC, and the anti-VEGF antibody bevacizumab has been approved for use in combination with IFN-α for metastatic RCC^[4]. The mTOR inhibitors everolimus and temsirolimus have also been approved for the treatment of RCC, typically in combination with tyrosine kinase inhibitors (TKIs)^[5]. Combination therapies that target VEGF and mTOR are considered more effective since they work in concert to target tumor growth and vascularization, whereas sequential treatments are typically associated with a greater likelihood of tumors developing resistance^[6]. Unfortunately, these combination therapies are associated with undesirable toxicities and have not been linked with durable responses^[7]. Notably, an HIF-2α inhibitor, belzutifan,  has shown good overall response rate and duration of response and has been approved by the FDA in 2021 for use in adults with VHL-associated RCC and in patients with advanced RCC who have been treated with anti-VEGF therapy and anti-PD-1/PD-L1 therapy^[8]. Combination therapies with belzutifan and other targeted agents or ICB are currently being evaluated in clinical trials^[9].

Advances in immunotherapy have led to transformative treatment options for RCC. Immune checkpoint blockade (ICB) has been one promising treatment strategy, and the FDA approved the use of anti-PD-1/PD-L1 (nivolumab) for the treatment of advanced ccRCC in 2015^[10]. A follow-up study showed that anti-PD-1 was associated with the best clinical benefit in ccRCC carrying loss-of-function mutations in PBRM1, which appears to affect tumor expression profiles in such a way to maintain responsiveness to checkpoint blockade^[11]. Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) is another checkpoint molecule targeted for checkpoint blockade with the monoclonal antibody ipilimumab. Similar to PD-L1 blockade, CTLA-4 blockade was associated with a partial response against metastatic RCC. Combination therapies have shown much more durable responses in patients with advanced RCC, and the FDA approved the use of nivolumab plus ipilimumab for the treatment of intermediate or high-risk metastatic RCC in 2018^[12]. ICB in combination with TKI have also been approved for advanced disease ^{[13,14,15,16]}. Unfortunately, PD-1/PD-L1 blockade has been less successful for PRCC^[17] and ChRCC^[18], and further studies are needed to identify therapeutic targets in these forms of RCC. Clinical studies with other checkpoint blockade targets are currently underway and are likely to provide new treatment options to RCC patients^[19].

The future of RCC therapies relies on the identification of new molecules or pathways in tumor cells that can be targeted therapeutically without causing toxicity or promoting resistance. Advances in single-cell omics are leading the way in terms of target identification and understanding how different forms of RCC progress.

Mouse models have been the workhorses of preclinical immuno-oncology (IO) research, and advances in mouse model development have expanded to applications for nearly all types of solid tumors and hematological malignancies. Preclinical evaluation of experimental immunotherapies has been advanced by syngeneic and humanized mouse models.

Syngeneic mice are one of the most established types of models used in cancer research, whereas humanized mice are a contemporary mouse model that has been critical to the screening of immunotherapeutic agents. Here we highlight features of syngeneic and humanized mouse models and define which models are most relevant to different phases of preclinical IO research.

Syngeneic Tumor Models

Syngeneic tumor models are created by transplantation of tumor cell lines into immunocompetent mice with the same genetic background as the cell line.^[1] Tumors can be transplanted intravenously or subcutaneously into mice and typically grow rapidly over several weeks. Different types of tumor cell lines can be used in this type of model, including spontaneous, transgenic, or carcinogen-induced tumor cell lines. Syngeneic mouse models are best suited for screening novel IO agents or gaining insight into anti-tumor responses in the context of an intact immune system. Given the rapid growth of tumors in syngeneic mice, these models are less well suited to studying early events in tumor growth associated with cancer stem cells or understanding the contributions of heterogeneous tumor microenvironments, and these models typically do not recapitulate the mutational heterogeneity observed in human tumors.^[2]

Humanized Tumor Models

Humanized tumor models are a more recent addition to preclinical IO research that provide valuable insight into how individual tumors from patients (xenografts) respond to experimental therapies. Prior to the development of humanized mouse models, human xenograft models were used for screening cytotoxic or immunotherapeutic agents like chimeric antigen receptor (CAR) T cells^[3], and these models use human tumor cell lines or patient-derived specimens transplanted into immunocompromised host mice. Different immunocompromised models can be used, including athymic mice that lack T cells or severe combined immunodeficiency (SCID) models that lack all adaptive immune responses. Humanized mice have been engineered from immunocompromised mouse strains that include genetic mutations in other adaptive immune functions that allow for the engraftment of human hematopoietic cells. The NOD/SCID IL2rγ chain knockout (NSG) mouse (NOD.Cg-Prkdc^scid Il2rg^tm1Wjl/SzJ) is one of the most used combined immunodeficiency models that can be engrafted with human hematopoietic cells and primary human tumors.^[4] These patient-derived xenograft (PDX) models are useful for evaluating experimental IO therapies in the context of the human immune system and can use human immune cells from the same or different donor as the tumor source. PDX models are suited to evaluating experimental therapies in the context of a genetically heterogeneous tumor and better recapitulate aspects of the tumor microenvironment. Tumors can be grafted either orthotopically or subcutaneously and this also impacts how tumors grow and respond to experimental treatments.^[5] Given the heavily modified nature of the NSG immune system, these models do not always reflect responses observed in humans during clinical trials. Nonetheless, NSG mice and similarly modified humanized mice offer valuable insights into the efficacy of IO candidates.

Mouse models are constantly being refined and improved to better reflect human physiology. Both syngeneic and humanized mouse models serve as valuable tools for preclinical IO research and accelerate the screening and evaluation of novel therapeutics.

Chimeric antigen receptor-mediated T cell (CAR T cell) therapies have revolutionized the treatment of hematologic malignancies and solid tumors. This therapy uses T cells, typically harvested from patients, that are engineered to express chimeric antigen receptors (CAR) specific to tumor cell antigens. CD19-targeting CAR T cell therapy was the first immunotherapy shown to effectively treat acute lymphoblastic leukemia^[1], but a subset of patients relapse due to loss or poor engraftment of CAR T cells. Here we highlight advances in CAR T cell therapy to improve the quality of the immunotherapy product ex vivo for more effective responses in vivo.

Culture Conditions

T cells from peripheral blood mononuclear cells (PBMCs) are the primary cellular product used for CAR T cell therapy, but several steps must be carried out ex vivo to ensure that enough cells are made for therapeutic efficacy. The best treatment outcomes have been linked to high levels of CAR T cell engraftment and persistence upon transfer into a patient^[2]. Ex vivo culture methods have been optimized to expand T cells, and cultures using less differentiated T cells, like stem cell memory T cells or naïve-like T cells, have been linked to better persistence upon transfer^[3]. Recent studies are characterizing phenotypic features of T cells that preserve “stemness” upon ex vivo culture but still allow for expansion and expression of CAR T receptors. Reduced culture time has been one of the most effective methods for improving engraftment and antitumor responses but is limited by the number of cells yielded by this minimal manipulation process^[4].

Biodistribution

The successful engraftment and persistence of CAR T cells depend greatly on where cells migrate upon transfer into patients. Preserving stem cell-like features correlates with better engraftment, especially if donor-derived cells are unavailable and autologous cell sources must be used^[5]. In vivo imaging studies have been very helpful in understanding CAR T cell dynamics and anti-tumor responses. Studies in mouse models have indicated that CAR T cells can get trapped in tissues, including the lungs, which can limit access to tumor targets^[6]. Analysis of CAR T cells with tumor cells also revealed extensive functional heterogeneity, including CAR-T cells that can interact with tumor cells but not exert cytotoxic effector functions. A recent first-in-human study examined the biodistribution of radioisotope-labeled CAR T cells and confirmed that these cells rapidly distribute to tumor tissue but are taken up by the liver and spleen and can persist systemically for up to two weeks^[7]. As new in vivo imaging studies are carried out, these insights will inform how CAR T cell products are made and delivered and are likely to improve treatment outcomes.

Advances in ex vivo methods for CAR T cell preparation are already improving outcomes, and these methods are broadly applicable to donor-derived or autologous T cell products. In vivo imaging methods are also delineating characteristics of CAR T cells that improve tumor targeting or result in misdirected tissue homing. Future in vivo and ex vivo studies are well-poised to further advance CAR T cell applications.

Tumor-infiltrating lymphocyte (TIL)-based immunotherapy is currently at the forefront of cutting-edge immuno-oncology treatments. TILs are a type of adoptive cellular therapy (ACT) using lymphocytes that are found within tumor tissues; most of these lymphocytes are T cells that can specifically target tumor cells. For TIL-based therapies, these T cells are harvested from a tumor biopsy, expanded ex vivo, and infused back into the patient. Advances in TIL-based therapies are driven by preclinical characterization and screening of TILs against a wide array of tumor types.

Consider these factors related to preclinical TIL therapy studies as you develop new research protocols:

1. Wanted Alive, Not Dead: The development of plate-based TIL assays offers users flexibility and scalability, but it is critical to assess the viability of cultured TILs at the outset of any of these assays. Some TIL cultures are prone to high levels of cell death over time, and culture conditions may need to be optimized to include different cytokines and growth factors to promote viability as well as growth and expansion of tumor-specific T cells.
   
   
2.  Cell Selection: Optimal TIL culture conditions should enhance the expansion of tumor-specific T cells but may also involve a step to deplete other cells prior to expansion. This may involve the depletion of adherent cell subsets with immunosuppressive characteristics such as myeloid-derived suppressor cells (MDSCs). Removing these cells from ex vivo culture allows TILs to expand and differentiate into T cells with tumor-specific cytotoxic activity.
   
   
3. TIL Test Drive: Ex vivo expansion of TILs must include functional assays in order to ascertain if these cells have anti-tumor properties. Analysis typically includes flow cytometry-based immunophenotyping and characterization of cytokine production and tumor-specific cytotoxicity. An ideal TIL product will show tumor-specific activity with limited off-target effects, and TILs will retain a phenotype that is predicted to be well-tolerated in a patient upon re-infusion.

Preclinical TIL studies are continuing to expand the usage of TIL-based therapies against a wide range of solid tumors, and this field of study will advance further as ex vivo culture and analysis techniques improve.