Optimizing Cell Culture Models To Mimic the Tumor Microenvironment
Innovative technologies are transforming cancer research by better replicating the tumor microenvironment.
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Cancer remains a serious health threat within society, with 18 million new cancer cases occurring worldwide in 2020. While novel and more effective treatments continue to increase at a rapid rate, testing platforms that accurately mimic the tumor microenvironment (TME) are lacking.
Tumors are complex ecosystems that contain many different cell types exhibiting diverse functional states. The TME is a complex and dynamic environment incorporating not only the cancer cells but also surrounding cancer-associated fibroblasts, immune cells, extracellular matrix components and other signaling molecules (e.g., cytokines and growth factors).
The complexity of the TME in vivo limits the conclusions that can be drawn from in vitro models of cancer progression and therapy response. Optimizing cell culture models to mimic the TME is vital to understanding cancer progression and the development of optimized therapies.
Traditional cell culture models
In vitro models have significantly advanced cancer research and drug discovery by enabling a deeper understanding of human biology. The most common in vitro assays use two-dimensional (2D) cell cultures, where immortalized cells grow in suspension or as monolayers, allowing for easy environmental manipulation. These models are widely used to identify disease-related molecules and assess drug responses.
2D cultures offer advantages such as high-throughput screening, standardization, reproducibility and straightforward result interpretation. However, they fail to replicate the complexity of three-dimensional (3D) tissue architecture and TME. Cells in monolayers experience unnatural stiffness, altering behavior, differentiation, gene expression and drug sensitivity.1 Additionally, primary 2D cultures have limited lifespans, and neither cell lines nor animal studies fully mimic human disease conditions, posing ethical and practical challenges.
“Cell culture models accurately mimicking the tumor microenvironment is important for studying tumor biology, but traditional cell culture models (i.e., 2D models) often fail to represent these complex systems,” Dave Kim, senior scientist at Cancer Research Horizons within the Functional Genomics Center, told Technology Networks.
“Traditional 2D culture models are the most common and simplest models, but they often fail to incorporate features such as cell polarization, extracellular matrix interactions and the spatial organization of different cell types, which are important in modeling the TME,” he continued.
While in vivo animal models remain essential for studying cancer complexity, their limitations, including cost, time and ethical concerns, have driven the development of advanced alternatives. Emerging 3D models, such as spheroids, organoids and microfluidic platforms, better replicate human conditions and reduce reliance on animal studies. These innovations pave the way for more accurate in vitro models that could eventually replace traditional laboratory methods while preserving key biological functions.2
“One of the key challenges in replicating TME in vitro is the availability of reliable clinical samples. However, access to high-quality, consistent and reliable samples is often difficult and there are ethical constraints surrounding access to viable clinical samples,” said Kim.
“Patient-derived models such as patient-derived organoid models have been used more widely in academics and industries offering more complex systems representing tumor heterogeneity and tumor microenvironment. Patient-derived matching tumor and wild-type organoids co-cultured with matching cancer-associated fibroblasts and immune cells suggest a promising representation of TME over traditional cell culture systems,” he added.
The 3D cell culture model revolution
3D cell models have gained attention for their ability to better mimic tumor characteristics, bridging the gap between traditional 2D cultures and in vivo models. Advances in 3D culture systems have improved drug screening accuracy while reducing reliance on animal testing, aligning with the 3Rs (replacement, reduction, refinement) principles and regulatory laws.1
Unlike patient-derived tumor xenografts, 3D models eliminate cross-species incompatibilities and offer greater control over experimental conditions. Compared to 2D cultures, where cells proliferate unnaturally fast, 3D models support more physiologically relevant growth rates, making them ideal for studying long-term drug effects.1
3D cell culture models can be derived from immortalized cell lines, primary or patient-derived cells or stem cells. They can grow, proliferate, differentiate and spatially arrange into different cell types. Further, they are capable of self-renewal in the right culture media and behave, in many ways, like ordinary tissues and organs.1
“Early 3D culture models, such as spheroids, aim to improve 2D model limitations by creating some aspects of cell aggregation and a 3D spatial arrangement but they are still limited to a single cell type, usually from standard cell lines,” Kim said.
“Patient-derived 3D organoid models offer advantages over traditional 2D and 3D spheroids as these self-organizing tissue-like structures resemble the 3D architecture and functionality of the original organ. As these models are derived directly from patient tissues, they maintain the genetic and epigenetic diversity of the original tumors, allowing for a more accurate representation of tumor heterogeneity, genetic alterations and drug resistance,” he explained.
2D cell lines, spheroids, organoids and tumoroids: key differences
- 2D cell lines: used for in vitro assays, where immortalized cells grow in suspension or as monolayers, allowing for easy environmental manipulation. However, they fail to replicate the complexity of 3D tissue architecture and TME.
- Spheroids: 3D cell aggregates derived from immortalized cell lines, capable of proliferation but lacking differentiation or self-organization.
- Organoids: 3D structures from stem cells that self-organize into functional cell types, replicating organ-specific features.
- Tumoroids: Patient-derived cancer cells grown in 3D, best suited for studying complex solid tumors with specialized media.
In a 3D environment, cells experience variable exposure to nutrients and oxygen, simulating TME where some cells face hypoxia or starvation – conditions that influence cancer progression. Drug penetration is also uneven, making 3D systems more predictive of real-world treatment responses. Additionally, 3D cultures offer greater stability and longer lifespans, enabling extended tumor response studies.3
Despite these advantages, 3D models pose challenges. Their thickness makes visualization using microscopes difficult and flow cytometry requires dissociating spheroids, risking data loss.4, 5 High costs, time-intensive protocols, lack of standardized methods and difficulties in replicability further complicate large-scale adoption. Additionally, reliable assays for clinically relevant drug testing remain limited.1
Tumor-on-a-chip devices
To address the limitations of in vitro models, “organ-on-a-chip” technology is revolutionizing cancer research by integrating microfluidics with 3D cell culture to replicate key organ functions. These bionic microengineering devices simulate physiological conditions such as flow pressure, shear stress and drug transport, offering a controlled environment for studying cellular interactions.6
The devices provide high-throughput testing, lower experimental costs, reduce reliance on animal models and enable reproducible experiments. With advancements in this technology, tumor-on-a-chip has emerged as a powerful tool for studying cancer biology and personalized treatment strategies.6
“Advancements in tumor-on-a-chip models allow precise manipulation of aspects of the TME such as fluid flow, which can replicate blood flow, nutrient, oxygen and drug treatment gradients,” Kim said. “These microfluidic chips integrated with organoid models to form complex systems could improve the ability to study tumor progression, drug response and metastasis in a more systematic and controlled environment.”
“Microfluidic systems can improve the ability to conduct high-throughput experiments and further enhance their potential for screening large libraries of compounds and finding novel treatments,” he continued.
Recent advances in tissue engineering and biomanufacturing now allow for the incorporation of TME components into in vitro models. Microfluidic devices further enhance this by recreating dynamic interactions within the TME, bridging the gap between preclinical studies and clinical applications. Tumor-on-a-chip technology offers a transformative approach by accurately replicating the tumor microenvironment and predicting physiological responses to anti-cancer therapies. As a result, it is becoming an essential translational tool for improving cancer research and drug development.
Future directions and considerations
Advancing cancer research requires sophisticated in vitro tumor models to enhance drug discovery and treatment strategies. While 3D cultures and microfluidic tumor-on-a-chip platforms have revolutionized the field, challenges remain.
Future efforts should improve the physiological relevance of 3D models by incorporating immune cells, vascular networks and extracellular matrix elements. Further, standardized protocols are crucial for reproducibility, especially in large-scale drug screening, which needs to be implemented and continually developed.
“With further advancement in organoid models and in vitro tumor models, we would be able to grow and test effective treatment options in organoid/in vitro models in parallel to patient diagnosis and identify the best therapies and predict resistance mechanisms based on the individual enabling a more personalized approach to cancer treatment,” Kim explained.
Additionally, the integration of microfluidics, artificial intelligence (AI) and high-content imaging offers promising refinements through enhanced real-time monitoring and continuous metabolic tracking.
Despite their advantages, in vitro models cannot fully replicate in vivo tumor complexity. Patient-specific organoids and bioprinting advancements could improve predictive accuracy, enabling personalized treatments. However, widespread adoption faces hurdles such as high costs and technical challenges. Streamlining affordability, scalability and automation will be key to integrating these models into routine drug screening and precision medicine.
“Together, with the help of AI, we would be able to build more sophisticated, human-relevant models that can reduce the gap between lab research and clinical application – ultimately making cancer treatments more effective and accessible,” Kim concluded.
