Bridging the gap: the role of 3D cell cultures in mimicking tumor microenvironment for enhanced drug testing accuracy

Abstract

Cell culture is a crucial technology in life science research, particularly in cancer studies. The morphology and biological properties of tumor cells, along with the mechanisms of tumor development, are highly dependent on their culture conditions. Antitumor drug sensitivity testing is essential for cancer treatment, helping to identify effective therapies and reduce patient treatment burden. Currently, 2D cell culture remains the primary method for antitumor drug sensitivity testing due to its cost-effectiveness, ease of operation, and high-throughput screening capability. However, it does not accurately replicate the tumor microenvironment. Animal models are important tools for drug development, but they are not suitable for high-throughput screening. Recent advancements in 3D culture technologies have addressed this limitation. These technologies can better mimic the tumor microenvironment and can accurately reflect tumor biological behavior, gene expression, and signaling pathways. This paper summarizes the current in vitro and in vivo culture models, discusses emerging three-dimensional cell culture technologies, and highlights their ability to effectively simulate the tumor microenvironment and their significant potential in drug sensitivity testing.

1 Introduction

Drug sensitivity testing for antitumor drugs is a key method for assessing their efficacy and toxicity on tumor cells. Clinical guidelines for the use of antitumor drugs are generally based on the tumor type and stage of progression, often failing to account for individualized treatment. In addition, antitumor drugs have serious side effects, including bone marrow suppression and damage to the heart, liver, kidneys, and other organs. Accurate drug sensitivity testing can help improve drug efficacy, reduce patient suffering, and alleviate the financial and physical burden on patients. Therefore, a reliable tumor culture model is needed to evaluate the efficacy of candidate therapeutic drugs and develop personalized treatment plans.

Studies have shown that cell surface target expression and response to targeted drugs depend on the culture method (Kaur et al., 2021). Integrative analysis of drug transcriptomics has shown that gene expression profiles capture much of the variation in pharmacological profiles, suggesting the potential to develop predictive biomarkers based on gene expression to guide drug use (Bruun et al., 2020). Therefore, the choice of culture technique is crucial. The 2D culture model is easy to handle, highly standardized and reproducible, with straightforward data interpretation. These advantages make it suitable for high-throughput assays (Karlsson et al., 2012; Subia et al., 2021), which is why it remains the dominant model in antitumor drug research. However, traditional 2D culture lacks a three-dimensional growth environment and physiological conditions. For example, 2D cell culture cannot reproduce cell-cell communication or cell-matrix interactions (Fang and Eglen, 2017). Moreover, more aggressive subclones are selected during cell line establishment, and prolonged passaging leads to the accumulation of mutations (Deer et al., 2010). This means that the drug response of 2D-cultured cancer cells may not accurately reflect the behavior of tumors in vivo. Mouse models play an important role in drug screening and development. However, mouse models are expensive, time-consuming to establish, and not practical for high-throughput screening.

In recent years, advancements in bioengineering and biotechnology have led to the development of novel culture models, providing more options for evaluating the efficacy of antitumor drugs (Liao et al., 2019). Currently, 3D culture technologies include multicellular spheroids, organoids, organ-on-chip, and 3D bioprinting, each with its own advantages. Although these 3D culture techniques differ in their approaches, they can better mimic the morphology, functions, and microenvironment of cells in vivo and are more accurate in studying tumor progression and drug screening, compared with 2D culture (Qu et al., 2021; Ma et al., 2018; Boucherit et al., 2020). In this review, we systematically analyze the merits and limitations of current mainstream in vitro and in vivo culture paradigms (Figure 1). We highlight the application of these culture models in drug sensitivity testing, and incorporate the latest real-time monitoring technologies, such as the Seahorse XF Analyzer and D-OCT. Additionally, we discuss emerging applications of deep learning and artificial intelligence. We aim for this review can serve as a reference for researchers, provide valuable insights, and promote the development of in vivo and in vitro culture models for antitumor drug development and screening.

FIGURE 1

2 In vitro culture model

In vitro tumor culture is an important tool for screening anti-tumor drugs and evaluating treatment efficacy, encompassing monolayer cell culture, three-dimensional cell culture, organoid culture, organ-on-a-chip systems, bioreactor and so on. Cultured objects include primary tumor cells, tumor cell lines, fresh tumor tissue sections, tumor stem cells, etc. Cell culture-based drug sensitivity testing is a promising strategy. Currently available in vitro antitumor drug sensitivity testing includes both two-dimensional culture-based methods (CCK-8 assay, MTS assay, etc.) for assessing cell proliferation and survival, as well as apoptosis and cell cycle assays, along with collagen gel droplet-embedded culture drug sensitivity testing (CD-DST). These in vitro drug sensitivity tests may enable more precise and efficient prediction of tumor cell response to currently available anti-tumor drugs and molecularly targeted therapies under development, thereby informing their future clinical use (Miyazaki et al., 2016).

2.1 2D culture

The two-dimensional cell culture system is a flat-plate-supported monolayer cell culture system (Pampaloni et al., 2007). This system has been widely used since the early 20th century for research (Ferreira et al., 2018), particularly in studying cell heterogeneity through co-culture (Breslin and O’Driscoll, 2013). In 2D cell culture, cells are grown on a flat surface where they maintain direct contact with nutrients and growth factors in the culture medium. 2D cell culture techniques are popular among biologists and clinical researchers due to their simplicity and efficiency. In addition, the transwell culture system, a modified 2D culture system, has been developed as a co-culture system to simulate the in vivo environment (Hira et al., 2020; Noonan et al., 2019). However, these two-dimensional culture methods lack three-dimensional structures necessary for maintaining proper cell polarity and shape, and cannot recreate the complex tumor microenvironment. These limitations lead to altered gene expression and metabolism patterns - critical factors in antitumor drug sensitivity testing (Table 1).

2.2 3D culture

In 1992, Petersen and Bissell used three-dimensional cell culture to simulate breast structures under cancerous and non-cancerous conditions (Petersen et al., 1992). Three-dimensional (3D) tumor culture models are now widely used to study tumorigenesis, etc. The major difference between 3D culture and 2D culture lies in the ability of 3D culture models to mimic the extracellular matrix (ECM) of native tissue. ECM is a dynamic protein network that maintains tissue homeostasis and cellular organization (Redmond et al., 2021). It is a scaffold composed of non-cellular fibronectin, various structural macromolecules and adhesion molecules that provide structural and biochemical support for cells and are involved in proliferation, adhesion, cell communication, and cell death (Henke et al., 2019; Wight et al., 1992). It is essential for many basic processes, such as cell differentiation and tissue repair (Schlie-Wolter et al., 2013). A variety of technologies have been derived from 3D culture in tumor research, including multicellular tumor spheroids, tumor-on-a-chip, and 3D bioprinting technologies. These methods generally take several weeks to establish functional models.

Three-dimensional cell models are established through two primary approaches: scaffold-free and scaffold-based culture methods. The scaffold-free culture approach cultivates cells in suspension, enabling them to self-assemble into the formation of multicellular spheroids (Flörkemeier et al., 2024). In this process, cell aggregation and growth occur solely through intrinsic cellular interactions, independent of external support structures. The scaffold-based culture method provides cells with a biocompatible carrier that is conducive to cell adhesion, proliferation, and migration. These scaffolds comprise either natural materials (e.g., collagen, Matrigel, and chitosan) or synthetic polymers (e.g., polycaprolactone) (Cortella et al., 2025; Risangud et al., 2024). Notably, currently used techniques like organoid culture and 3D bioprinting utilize scaffold-based systems, which constitute the primary focus of this review.

2.2.1 Organoid

Organoids are established using the 3D cell culture system that enables stem cells to proliferate and differentiate into organ-like structures. These structures contain multiple cell types, have a spatial organization similar to their in vivo counterparts, and can recapitulate certain functions of the original organs. The foundation of the organoid culture system lies in the stem cells and the microenvironment. Based on the source of cells, organoids are mainly classified into normal tissue-derived organoids and tumor tissue-derived organoids. Normal tissue-derived organoids, cultured from pluripotent stem cells or adult stem cells, are currently used mainly for research on organ physiology. Tumor-derived organoids, established from tumor stem cells in culture and retain the heterogeneity of the original tumor. Organoids are widely used in cancer research, mainly for solid tumors. For non-solid tumors, such as blood tumors, the application of organoid technology still faces technical challenges (Xu et al., 2018).

Patient-derived tumor organoids (PDTOs) are established by culturing patient cancer cells in a 3D matrix. Extensive characterization demonstrates that PDTO models maintain greater similarity to the original tumor than 2D-cultured cells, while preserving genomic and transcriptomic stability (Beshiri et al., 2018), and bridging the gap between 2D cancer cell lines cultured in vitro and patient-derived tumor xenografts (PDTX) in vivo (Drost and Clevers, 2018; Sachs and Clevers, 2014). More importantly, they can be long-term expanded and cryopreserved, thus enabling the generation of biobanks of tumor organoids (van de Wetering et al., 2015). In cancer research, tumor-derived organoids retain the patient’s genetic alterations (Weeber et al., 2015). The 3D architecture of organoids more accurately recapitulates the histological and phenotypic characteristics of native tumors. Pasch et al. (2019) noted that patient-derived organoids can detect clonal heterogeneity with higher sensitivity than whole-tumor sequencing. For clinicians facing rare tumor cases where standard treatment guidelines are lacking, empirical drug testing often yields uncertain efficacy while potentially increasing treatment toxicity. In this context, patient-derived tumor organoids offer an efficient approach for high-throughput drug screening and personalized treatment optimization (Cao et al., 2022; Meier et al., 2022).

A key limitation in current organoid culture systems is the absence of a functional vascular network, which restricts oxygen and nutrient delivery to the organoid core (Nwokoye and Abilez, 2024). The vascular system plays a vital role in supplying nutrients and oxygen while also facilitating tumor metastasis. To better replicate these functions, researchers have developed vascularized tumor organoid models. These models include strategies such as coating organoids with endothelial cells or mesodermal progenitor cells to enable the spontaneous formation of capillary-like structures (Croft et al., 2019; Humpel, 2015). Another approach involves reprogramming mature endothelial cells into vasculogenic endothelial cells, which then integrate with decellularized tumor organoids to form functional vascular networks (Hutter-Schmid et al., 2015; Mielke et al., 2005). Additionally, vascularized organoids can be created using advanced 3D printing techniques to construct a fully integrated 3D vascular network (Sivakumar et al., 2019; Sönnichsen et al., 2018).

Autologous organoid culture represents an advanced method derived from organoid technology. Conventional organoid culture typically relies on commercial prepared media, mainly fetal bovine serum (FBS). Special growth factors are often added to culture medium to promote organoid formation (Wilson et al., 2014). This approach may ignore individual patient differences and the actual growth conditions of the tumor. Autologous culture uses the patient’s own serum or pleural effusion, ascites, to cultivate the patient’s own tumor cells. As a control, researchers cultured several patient-derived cancer samples under FBS-supplemented conditions and found that these conditions primarily supported the growth of mesenchymal stromal cells rather than epithelial cancer cells (Figure 2) (Tang et al., 2020a). This limitation affects subsequent studies based on such organoids, including drug sensitivity testing of antitumor drugs. These patient-specific fluids naturally contain the complete spectrum of nutrients, hormones, cytokines, and growth factors that sustain tumors in their native microenvironment - a biologically complex ecosystem impossible to replicate commercially. Tang et al. (2020a) applied 3D autologous culture (3D-ACM) to clinical specimens (including malignant effusions and surgical tumor tissue), demonstrating superior preservation of tissue architecture, immune profiles, and cytokine secretion compared to FBS-based cultures. Crucially, 3D-ACM maintained tumor biological properties more faithfully, as evidenced by more reliable chemosensitivity results, suggesting improved predictive accuracy for personalized treatment. Nevertheless, autologous culture presents challenges. First, the body fluids derived from different patients make the culture process difficult to standardize. Second, cancer patients are often frail, and their serum is limited, making it challenging to obtain sufficient quantities for autologous culture in drug sensitivity testing. However, autologous thoracoabdominal fluid is a better source of body fluids and is usually disposed of as medical waste without any physical or economic impact on the patient.

FIGURE 2

2.2.2 Organotypic tissue slice culture

The organotypic tissue slice culture was first used in the 1970s for pharmacological evaluation (Willoughby et al., 1971). Surgically excised tissue is collected and placed in a cold medium, cut into cylinders or rectangles, and sectioned under sterile conditions within 6 h. Well-shaped sections were selected for culture (Cao et al., 2022) (Figure 3). Tumor slice culture (TSC) represents the closest model to the parental tumor because it retain the original tissue structure and cellular heterogeneity. Compared with organoids, the advantage of TSCs lies in the complex spatial organization and anatomical connectivity of intact tissue (Bahr, 1995; Croft et al., 2019). In addition, the tumor tissue culture model maintains vascular cells (Humpel, 2015; Hutter-Schmid et al., 2015). This is an advantage over primary cell line cultures or induced pluripotent stem cells (iPSC) cultures. Likewise, the specific genes and proteins expressed are maintained at levels comparable to those in vivo (Bahr, 1995; Mielke et al., 2005). Sections continue to grow for 10 days with a progressive increase in total viable cells, and key immune cell repertoire and gene expression levels of T and B lymphocytes can be fully preserved for at least 8 days (Cao et al., 2022). Three-dimensional tumor slice culture (3D-TSC) allows rapid and accurate replication of highly complex tumors, meanwhile, it use fluorescent-coupled antibodies and biopsy imaging to easily display multiple cell types (e.g., immune cells, endothelial cells, and cancer cells) and morphological structures (blood vessels and lymphatic vessels) in primary tumor sections, with the advantage of preserving cell repertoire and immune components, identifying tumor invasiveness, determining compound toxicity, rapid assessment of efficacy, and accurately predicting drug response (Sivakumar et al., 2019). More importantly, it can distinguish treatment responders from non-responders, providing a reliable tool for conducting drug sensitivity testing. This method enables the selection of optimal standard treatment plans for individualized therapy (Cao et al., 2022). This approach has been successfully implemented in colorectal cancer (Sönnichsen et al., 2018), breast cancer (Chakrabarty et al., 2022), head and neck squamous cancer (Gerlach et al., 2014), and human glioblastoma (Merz et al., 2013). However, the system is not a reproducible tool, and in addition, it is usually inefficient as it cannot test too many drug responses.

FIGURE 3

2.2.3 3D bioprinting

Bioprinting is an advanced technology that utilizes 3D printing to create biological tissues and organs. Through precise 3D control, bioprinting technology can print cells, biomaterials and biofactors (i.e., bioinks) layer by layer to build tissues that are structurally and functionally similar to those in the body. Bioprinting provides an effective platform to model cancer angiogenesis and enable the construction of perfusable organoid models (Nwokoye and Abilez, 2024). The vascular network is a bridge that facilitates the exchange of gases, nutrients and waste products between the blood and surrounding cells (Monahan et al., 2013; Pittman, 2011), and is involved in the interactions between cells, extracellular matrix, and signaling molecules (Zhao et al., 2021; Naderi-Meshkin et al., 2023), which play an important role in the metastasis of tumors and the immune escape (Liu et al., 2024). At the same time, bioprinting is high-throughput and allows for efficient and standardized cell distribution (Kalla et al., 2024). Tumor heterogeneity is one of the mechanisms leading to drug resistance. The 3D bioprinting method can establish an in vitro model composed of different subtypes of tumor cells and non-tumor cells with controllable tumor microenvironment distribution (Moghimi et al., 2023). This is of great significance for the study of tumor heterogeneity and the exploration of chemotherapy resistance mechanisms.

2.2.4 Bioreactor culture

A bioreactor is an engineered system designed for culturing cells, tissues, or microorganisms. It provides a more controlled environment to facilitate biological reactions and processes. Within bioreactors, drug sensitivity can be assessed more accurately by simulating in vivo conditions, including oxygen concentration, pH, temperature, and nutrient supply. It also enables continuous control and maintenance of culture conditions, improving the reproducibility of experiments. Ackermann et al. developed a xeno-free and chemically defined medium-scale bioreactor platform that enables continuous production of standardized human iPSC-derived hematopoietic-like organoids and macrophages (iPSC-Mac) (Ackermann et al., 2024). Moreover, the bioreactor can efficiently mimic the tumor microenvironment, making the biological behavior of tumor cells more plausible. De Luca et al. used a perfusion bioreactor to prepare scaffold morphologies with different pore sizes to reproduce Saos-2 cell behavior (De Luca et al., 2024). The bioreactor also provided continuous oxygenation and media perfusion to 3D cultured cells, promoting AEC-derived HCC to exhibit a stem cell phenotype (Campinoti et al., 2023). This bioreactor-driven ECM scaffold approach may enhance the functionality of pluripotent stem cells and support the development of more precise 3D cell culture systems (Campinoti et al., 2023).

2.2.5 Organ-on-a-Chip

Micro-physiological systems, combined with tissue engineering, have facilitated the development of more physiologically relevant platforms, one of which is the Organ-on-a-Chip (OoC). The OoC platform is an advanced in vitro miniaturized precision-controlled bionic system designed to mimic the in vivo environment of cells and tissues and circulatory function (Bhatia and Ingber, 2014; Balijepalli and Sivaramakrishan, 2017; Peck et al., 2020). One of the most important advantages of this system is the ability to reproduce the key features of TME in vitro. Cellular interactions in TME often determine drug response and tumor fate. They are a major driver of tumor progression, and are potential therapeutic targets (Hanahan and Weinberg, 2011; Altorki et al., 2019; Grivennikov et al., 2010; Valkenburg et al., 2018). These organ-on-chip systems primarily consist of a cell culture chamber and channels for delivering the culture medium. By modulating the microchannels, they can simulate the structure and physiological state of solid tumor tissue, enabling drug evaluation and screening. The microchannels in the chip are also used to simulate capillaries in vivo, and the perfusion speed of the microchannels on the chip is adjusted to simulate the state of relatively insufficient vascular oxygenation in solid tumor tissues. After perfusion culture, the tumor tissue can be dissociated to analyze the metabolic state at different depths. Microfluidic chip culture can reflect the interaction between cells, cellular microenvironment, concentration gradient formed by various cytokines, etc., and with features such as high controllability, large-scale data generation, and reliable results (Polidoro et al., 2021). A key advantage of microfluidic chips is the ability to model the interplay between tumors, immune cells, and the vascular system, which plays a key role in tumor growth and immune escape (Schaaf et al., 2018). Recent research focus on the improvement of microfluidic devices and the optimization of drug combination screening schemes to meet clinical and industrial needs (Sun et al., 2018; Mulholland et al., 2018; Dorrigiv et al., 2023; Patra et al., 2016; Zhang et al., 2018). Microfluidic devices have achieved precise control of temperature, pH and other conditions to ensure the repeatability of experiments. These systems also integrate multiple functional modules, incorporate highly sensitive detection technologies, and utilize artificial intelligence to reduce operational complexity and improve screening efficiency.

3D cell culture methods have been widely adopted in cell biology, drug screening, and cancer research due to their ability to better mimic the growth environment of cells in vivo. However, reproducibility remains a critical challenge for the widespread application of 3D culture, particularly in preclinical research and drug development, where experimental consistency across studies is essential. The repeatability of 3D culture can be affected by tumor heterogeneity, culture materials, environmental conditions, and experimenters. Standardization of culture technology and the development of automated culture systems are conducive to improving repeatability. The 3D culture system combined with microfluidics technology can be used as a high-throughput screening tool in drug development. Utilizing automated operations, sensitive and rapid detection systems, and advanced data analysis platforms, these systems can test and analyze thousands of reactions simultaneously. This greatly enhances the scale and efficiency of drug screening, significantly reducing the time and costs associated with drug development (Yan et al., 2019).

3 In vivo culture model

New drugs must be tested in at least two animal species before they are allowed to be used in human clinical trials (Prior et al., 2018). Rodent models are widely used for preclinical studies because of their ease of handling, short growth cycles, low maintenance costs, and ease of gene editing (Saikawa et al., 1994). Currently, the major preclinical tumor models in mice include syngeneic mouse tumor models, genetically engineered mouse models (GEMMs), cell line-derived xenograft (CDX), patient-derived xenograft (PDX), and humanized mouse models.

Tumor patient-derived xenograft (PDX) models are established by implanting biopsy specimens, surgically resected tissue, malignant ascites-derived tumor cells, or circulating tumor cells (CTCs) into immunodeficient mice (Siolas and Hannon, 2013; Williams et al., 2015; Li et al., 2017). PDX models overcome many limitations of conventional cell line-derived xenografts (CDX), preserving the genetic and histological features, intratumoral heterogeneity, and tumor microenvironment (TME) of the original patient’s tumor (Hidalgo et al., 2014), traits that can persist even across successive generations in mice (Ding et al., 2010). This makes PDX models a useful tool for mechanistic studies and drug testing of cancer. However, recent studies highlight several limitations of PDX models. First, the engraftment success rate is lower than that of in vitro culture, the establishment time ranges from several weeks to several months, and the cost is high. Second, human tumor stromal cells and extracellular matrix are transplanted into immunodeficient mice, and ECM may gradually be replaced by murine components (Unger et al., 2014), compromising the TME and limiting their utility for cancer immunotherapy research. Finally, PDX relies on immunodeficient hosts, which restricts the evaluation of immunotherapies, and thus limits PDX’s applicability in the study of immunotherapy.

Humanized hematopoietic stem cell (HSC) mouse models are generated by injecting human stem cells derived from umbilical cord blood or fetal tissue into immunodeficient mice with little or no functional immune system. These models reconstitute a functional human immune system, including T cells, B cells, and other immune cell populations, allowing researchers to directly study tumor biology and immune system function (Figure 4). However, humanized mice also have limitations. Establishing these models typically requires 8–12 weeks or longer, and the associated costs are high. In addition, graft-versus-host disease often occurs due to a major histocompatibility complex (MHC) mismatch between mouse hosts and human T lymphocytes (Franklin et al., 2022).

FIGURE 4

In general, animal models offer a stable physiological microenvironment for evaluating drug candidate efficacy in a physiologically relevant system. However, species-specific differences introduce uncontrollable variables, resulting in low success rates and poor reproducibility. In addition, the time and economic costs are high, ethical considerations are complex, and animal models are unsuitable for high-throughput screening, which limits their application in anticancer drug sensitivity testing (Table 2).

4 Comparison of the advantages and disadvantages of different tumor models

4.1 Biological behavior of tumors

The biological behavior of tumor cells, such as proliferation, migration, invasion, and metastasis, is critical for validating drug effectiveness. Torisawa et al. (2005) compared MCF-7 cell proliferation in traditional culture flasks and in silico microarrays. The rate of cell proliferation in 3D culture was significantly lower than that in 2D culture, closely resembling in vivo conditions (Torisawa et al., 2004), though variability exists across techniques and cell types (Barbosa et al., 2021). When long-term drug sensitivity studies are required, especially in brain tissue (Giandomenico et al., 2021) or retinal tissue (Volkner et al., 2021), the 3D spheroid system is preferred due to its functional stability over weeks (Messner et al., 2013). Drug development and screening also face challenges because true efficacy and side effects often emerge only after prolonged observation. Recent advances in organoid technology enable long-term expansion, improving accuracy in assessing drug effectiveness and safety (Sato et al., 2011). Cell migration is another critical metric, especially for immunotherapy (Fitzgerald et al., 2020). Mark et al. demonstrated that while NK cells retain cytotoxicity in 2D, their efficacy drops 5.6-fold in 3D due to impaired migration (Mark et al., 2020). Huang et al. further highlighted how 3D-printed biomimetic microstructures (e.g., channel size, curvature) dictate tumor cell migration patterns, revealing that spatial constraints in 3D environments alter invasion strategies compared to 2D (Huang et al., 2014). Velez et al. expanded on this by showing that 3D collagen architectures induce conserved migratory and transcriptional programs in tumor cells, linked to vasculogenic mimicry—a process where aggressive cancer cells form fluid-conducting networks independent of blood vessels (Velez et al., 2017).

4.2 Cell state and tumor microenvironment

In vivo antitumor responses depend critically on tumor morphology/structure and the cellular components of the tumor microenvironment (TME), including various stromal and immune cells. The hepatocellular carcinoma (HCC) spheroid culture system (Liao et al., 2019) better recapitulates the nutrient/waste exchange gradients found in vivo, unlike the uniform access seen in artificial monolayer cultures (Fontoura et al., 2020). The intricate interactions between cells within the tumor microenvironment (TME) play a pivotal role in driving tumor progression and shaping the response to immunotherapeutic agents. The COC device developed by Chakrabarty et al. is particularly suitable for studying the immune response in tumors, where immune cells can be added in a controlled manner through the inflow of top or bottom channels to build the tumor microenvironment required for tumor cell growth. A key advantage of this system is its ability to quantitatively incorporate human immune components and measure responses in real time, enabling accurate and efficient simulation of the tumor growth environment (Chakrabarty et al., 2022). Autologous culture and air-liquid interface (ALI) culture techniques mix finely cut tumor tissues with stromal matrices, thereby preserving the original microenvironment—including immune and stromal cells—and maintaining the immune characteristics of the tumor (Gu et al., 2024). These culture models show great potential as predictive platforms for precision therapy to evaluate the effectiveness of tumor immunotherapy in cancer patients.

Liao et al. (2019) demonstrated that 3D cell culture significantly influences cell polarity, differentiation, signaling cascades, and gene-expression profiles compared to monolayer culture (Pinto et al., 2017). Torisawa et al. (2005) developed a three-dimensional culture system using an array of cell panels on a silicon chip, a culture technique that preserves the original growth characteristics of tumors while allowing control of cell polarity. These systems also establish oxygen gradients, which profoundly impact tumor biology. Hypoxia within tumors can compromise both conventional therapies and immunotherapy efficacy (Chouaib et al., 2017). More importantly, the oxygen gradient affects the sensitivity of tumor drugs by activating DNA damage repair proteins, altering cellular metabolism, and decreasing proliferation (Riffle and Hegde, 2017; Wang et al., 2018). Additionally, 3D cultures enhance the release of extracellular vesicles (e.g., exosomes) from cancer cells. These vesicles modulate diverse cell types within the TME, promoting tumor progression and influencing both local and systemic immune responses—thereby contributing to immunotherapy resistance (Szajnik et al., 2010; Eguchi et al., 2020; Xie et al., 2019; Hwang et al., 2019; Seo et al., 2018; Xavier et al., 2020; Dai et al., 2020). The gravitational microfluidic platform (GMP) (Wang et al., 2020) and the OoC system (Dsouza et al., 2022) leverage controlled fluid flow to enhance cell functionality, differentiation, and longevity.

4.3 Drug sensitivity of tumor cells

Compared to cancer cells in 2D culture systems, cancer cells in 3D culture systems exhibit altered morphology, structure, and signaling networks, which significantly influence drug responses. For example, 3D cell clusters/spheroids often develop multicellular resistance to antitumor drugs (Desoize and Jardillier, 2000). Liao et al. (2019) demonstrated this using patient-derived HCC cells, confirming greater drug resistance in 3D cultures. Liu et al. (2017) observed IFN resistance in B16 melanoma exclusively in 3D conditions. Muguruma et al. (2020) reported higher IC50 values for cisplatin, paclitaxel, and other drugs in 3D-cultured triple-negative breast cancer versus 2D cultures.

3D-cultured spheroids typically exhibit three distinct zones: a proliferative outer layer, a senescent middle zone, and a necrotic hypoxic core (Edmondson et al., 2014; Yamada and Cukierman, 2007). Tumor stem cells generated under hypoxic conditions overexpress ATP-binding cassette transporters and exhibit drug resistance (Weiswald et al., 2015; Bai et al., 2015). The acidic hypoxic core (mediated by lactate overproduction and carbonic anhydrase IX overexpression) (Amiri et al., 2016; Kazokaitė et al., 2018; Nunes et al., 2019) compromises cellular uptake of weak basic drugs (e.g., doxorubicin, vincristine) by impairing membrane permeability, enhancing chemoresistance (Nunes et al., 2019). More importantly, the oxygen gradient that develops in the tumor microenvironment shapes the tumor phenotype and influences drug sensitivity by activating DNA damage repair proteins, altering cellular metabolism, and reducing proliferation (Riffle and Hegde, 2017; Wang et al., 2018). For example, breast cancer cells cultured in 3D models exhibited greater resistance to doxorubicin and paclitaxel, which correlated with reduced PARP/caspase-3 cleavage and elevated hypoxia levels (Imamura et al., 2015). Baek et al. (2016a), Baek et al. (2016b) demonstrated that osteosarcoma spheroids develop drug-impermeable dense cores where ECM acts as a penetration barrier, elevating IC50 values versus 2D cultures - underscoring 3D models' superiority for drug sensitivity testing.

The behavior exhibited by cells in a 3D environment is closer to the in vivo conditions, allowing drug permeability and distribution to more accurately reflect their in vivo performance (Cardoso et al., 2023). In addition, 3D culture systems can more accurately evaluate the absorption, distribution, metabolism and excretion of drugs (Tchoryk et al., 2019). These parameters are critical for drug development, especially in the early stages to identify potential problems and thus reduce the risk of failure in late development. Particularly valuable for long-term exposure studies, 3D systems can reveal cumulative drug effects undetectable in short-term assays (Kaminska et al., 2021). Compared to in vivo models, using 3D culture models for drug sensitivity testing offers several advantages. They minimize animal use, reducing ethical concerns and costs (Lancaster and Knoblich, 2014), while enabling faster model establishment and high-throughput screening. This approach also effectively reduces the financial and time burden on patients.

5 3D culture in the application of anticancer drug sensitivity testing

It has been demonstrated that 3D-cultured cells exhibit drug responses more closely resembling in vivo behavior compared to monolayer cultures (Hagemann et al., 2017). The efficacy of anticancer drugs varies among individuals (Inoue et al., 2018). To address this, various drug sensitivity testing technologies based on 3D cell culture systems have been developed and applied (Saikawa et al., 1994; Brown and Markman, 1996; Kondo et al., 2000; Kubota et al., 1995; Yamaue et al., 2003; Takamura et al., 2002).

The collagen gel droplet-embedded culture drug sensitivity testing (CD-DST) was once a prominent method (Takamura et al., 2002; Koezuka et al., 1993; Kobayashi et al., 2001). Developed by Kobayashi in 1995 (Sakuma et al., 2020), this assay integrates three-dimensional cell culture, serum-free culture, and image colorimetric analysis technologies. CD-DST addressed numerous challenges associated with conventional drug sensitivity testing (Kobayashi, 2003). It was widely applied to various cancers including colorectal cancer, gastric cancer, lung cancer and breast cancer (Kobayashi et al., 1997). In recent years, with the rise of organoids, patient-derived organoids (PDOs) have been widely utilized for screening potential anticancer drugs due to their ability to maintain the heterogeneity of patients' tumors. Numerous studies have demonstrated that using PDOs to predict patients' drug sensitivity yields reliable results in multiple cancers, including colorectal cancer, gastric cancer, pancreatic cancer, bladder cancer, ovarian cancer (Vlachogiannis et al., 2018; Lee et al., 2018; Tiriac et al., 2018; Ganesh et al., 2019; Nan et al., 2020). Additionally, organoids are also employed to predict the toxic side effects of drugs on non-target tissues. For instance, liver and kidney organoids are used to evaluate the hepatotoxicity and nephrotoxicity of chemotherapy drugs (Andersson, 2017; Takasato et al., 2015).

The tumor slice culture (TSC) provides a unique tool for investigating tumor sensitivity to chemotherapeutic agents (Merz et al., 2013). Breast cancer tissue slices can remain viable for up to 7 days under standard culture conditions, enabling the assessment of tumor resistance or sensitivity to different chemotherapy regimens (Naipal et al., 2016). Chakrabarty et al. (2022) developed a microfluidic platform that evaluates patient treatment responses using tumor tissue slices through precise control of growth conditions. Nguyen et al. (2018) utilized an on-chip reconstituted immunocompetent tumor microenvironment to demonstrate that cancer-associated fibroblasts (CAFs) critically drive drug resistance and modulate immune evasion. The optimized chip-based organotypic culture (COC) platform sustains prolonged proliferative activity in breast and prostate cancer tissues without significant morphological or genetic alterations.

The application of 3D bioprinting technology in drug sensitivity testing offers new possibilities for precision medicine. This 3D bioprinting approach enables the construction of complex multicellular tissue models that can predict treatment response, maintain stem cell characteristics, and assess tumor invasiveness and drug resistance. Pharmaceutical giants such as Roche are utilizing 3D-printed “livers” to evaluate drug toxicity and detect liver injury caused by medications like trovafloxacin (Nguyen et al., 2016). Research has found that 3D-printed biomimetic microenvironments are conducive to the maturation and functional stability of liver cells induced from pluripotent stem cells. The selection of bioinks and the complex kidney structure pose significant challenges for in vitro reconstruction of kidneys and their microenvironments. Using kidney progenitor cells derived from pluripotent stem cells and kidney-derived extracellular matrix, 3D-printed “kidneys” are developed for high-throughput drug-induced nephrotoxicity assays (Lawlor et al., 2021). Tang et al. utilized a 3D bioprinting system to integrate glioma stem cells, astrocytes, neural stem cells, and optionally macrophages to create a dynamic multi-cellular biomimetic glioblastoma model. The findings demonstrate that the 3D bioprinting model more closely recapitulates the transcriptomic profiles of patient-derived glioblastoma tissues and is compatible with CRISPR-Cas9-based large-scale whole-genome screening methods (Tang et al., 2020b).

6 Discussion

Antitumor drug sensitivity testing is essential for screening suitable drugs for precision therapy. Selecting cell culture methods that accurately simulate the in vivo environment is the most critical step in drug sensitivity testing. Therefore, developing reliable tumor culture methods is crucial for anticancer drug development and application. Due to advantages such as simplicity, low cost, and high-throughput screening, 2D culture still remains widely used in antitumor drug development and screening. However, the clinical applicability of 2D primary cultures is constrained by methodological limitations, such as inconsistent drug response prediction and low culture success rates in certain tumor types (Kondo et al., 1966; Salmon et al., 1978). Moreover, monolayer cell culture cannot accurately mimic the in vivo tumor state (Breslin and O'Driscoll, 2013; Yamada and Cukierman, 2007; Weigelt et al., 2014; Lovitt et al., 2014; Rimann and Graf-Hausner, 2012; Hirschhaeuser et al., 2010), making it a suboptimal choice for drug sensitivity testing.

In addition to factors such as nutrient/oxygen gradients and drug diffusion capacity (Boucherit et al., 2020; Weiswald et al., 2015; Langhans, 2018), differences in drug sensitivity are often attributed to variations in microenvironment and gene expression profiles (Bruun et al., 2020; Farhat et al., 2021). Compared to 2D culture, 3D culture systems better preserve original tumor characteristics, simulate the in vivo tumor microenvironment, and provide a superior platform for drug screening. As a highly promising emerging technology, 3D culture techniques have led to the development of various advanced methods, including organoid culture, organ-on-a-chip, and 3D bioprinting. Currently, a variety of 3D culture models have been commercialized. Organoids have attracted the attention of pharmaceutical companies. Many companies are working to develop standardized organoid production processes to reduce costs and improve experimental consistency. Regulatory agencies such as the FDA have gradually recognized organ-on-a-chip technology as a supplementary method for drug development to promote commercialization (Low et al., 2021). Many companies and scientific research institutions are promoting the development of printing materials and equipment to advance the commercialization of 3D bioprinting products.

3D culture systems still face multiple challenges: traditional evaluation methods may not be suitable for 3D models (Bengtsson et al., 2021); reproducibility and standardization of culture protocols are limited; light penetration in 3D structures is poor; and cellular imaging within complex geometric architectures is challenging. Recent studies are actively addressing these limitations. Ooft and colleagues (Ooft et al., 2019) developed a growth rate -based classification tool that calculates drug effects per cell division, thereby eliminating confounding factors from cell proliferation rates. The optimized high-throughput confocal microscopy systems enable automated imaging and quantitative analysis of GFP reporter activity in spheroids (Hiemstra et al., 2019), enhancing image processing fidelity (Yang et al., 2020). In addition, metabolomics analyses can also be performed using gas chromatography-mass spectrometry (GC-MS) or liquid chromatography-mass spectrometry (LC-MS) (Klontzas et al., 2024; Pelosi et al., 2024). As a burgeoning tool in metabolic analysis, the Seahorse XF Analyzer is capable of providing real-time and dynamic monitoring of cellular energy metabolism (Ghiraldelli et al., 2025). For bioreactors, external physical and chemical sensors can be used for real-time monitoring. Han et al. developed a novel microfluidic platform for the flexible construction of 3D co-culture tumor models with spatio-temporal resolution, utilizing digital fabrication techniques such as rapid laser cutting of biocompatible polymethylmethacrylate (PMMA) and digital light processing(DLP)-based 3D bioprinting to enable precise drug sensitivity testing (Han et al., 2024). Electrical impedance tomography (EIT) enables real-time, non-destructive, label-free cell analysis, while label-free dynamic optical coherence tomography (D-OCT) can perform visualization and quantitative assessment (Wu et al., 2018; Abd El-Sadek et al., 2024; Abd El-Sadek et al., 2023). The application of these instruments in microfluidic chips helps to interpret the results of drug sensitivity testing. Chiang et al. introduced a deep learning model based on phase-contrast images, providing a cost-effective solution for continuous detection in microfluidic chips (Chiang et al., 2024). In addition, high-content phenotypic screens with multiple parameters can be used to assess cellular and subcellular responses to classify drugs and optimize 3D screening. This strategy provides integrated insights into drug mechanisms of action and system-level pathway dynamics in response to therapy, as evidenced by automated platforms for patient-derived disease models and real-time targeting of malignant plasticity in cancer (Boussaad et al., 2021; Esquer et al., 2021).

7 Conclusion

This review synthesizes recent advances in tumor cell culture methodologies, encompassing 2D, 3D, and in vivo models. We critically evaluate the strengths and limitations of each approach, with a focus on applications in drug sensitivity testing. Notably, these technologies are not mutually exclusive but can be synergistically integrated. The microfluidic platform, artificial intelligence, and machine learning technologies can be integrated with the 3D culture system to provide novel insights into drug sensitivity testing. Future directions should maximize the potential of existing technologies, refine established systems, and integrating them with cutting-edge approaches, such as multi-omics analyses. This approach will optimize patient-specific drug selection while simultaneously elucidating fundamental drug resistance mechanisms to inform novel clinical strategies.

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