Cancer cells can autonomously rewire their metabolic pathway activity to meet their increased bioenergetic, biosynthetic, and redox needs (1). These reprogramming activities are observed ubiquitously across many cancer types and are therefore considered a hallmark of cancer (2, 3). Established in the 1920s with Otto Warburg’s pioneering work on aerobic glycolysis (4), the study of cancer metabolism represents one of the oldest areas of research in cancer biology (1).

The concept of exploiting metabolic vulnerabilities as a cancer therapy is longstanding (5), with the first antimetabolite therapy dating back to 1948 (6). At that time, Farber and colleagues demonstrated that aminopterin, a folate analogue blocking de novo nucleotide biosynthesis, could halt tumor progression in children with acute lymphoblastic leukemia (ALL) (6). Since then, several other antimetabolites including methotrexate, 6-mercaptopurine and 5-fluorouracil (5-FU) have found their way into the clinic and are now being extensively used in various cancer treatment regimens (7–9).

Despite these advancements, only a limited number of identified metabolic vulnerabilities have been successfully translated into effective (targeted) therapies to date. This is, at least in part, due to the use of reductionist in vitro models in preclinical studies that fail to recapitulate the complex microenvironment that defines the heterogeneous metabolic landscape of human tumors (10, 11). Furthermore, it has become increasingly appreciated that the artificial environment of cell culture systems dictates the metabolic state of cancer cells and that minor adjustments in cancer modeling (i.e., cell culture architecture and microenvironmental interactions), biochemical (i.e., nutrients, cell culture media) and physicochemical (i.e., oxygen levels, pH) factors could easily alter metabolic pathway activity, thereby influencing metabolic readouts (12, 13).

In this Review, we discuss the impact of current cancer models, biochemical and physiochemical conditions in standard cell culture practices on cancer cell metabolism. Furthermore, recent efforts to improve the modeling capacity of in vitro systems to better recapitulate physiologic conditions are discussed, including their strengths and current limitations.

Conventional cancer cell metabolism studies have predominantly been conducted using two-dimensional (2D) cell cultures. However, cells grown as a monolayer do not accurately replicate the three-dimensional (3D) growth dynamics of a tumor (14). Moreover, gaining comprehensive insights into cancer metabolism requires models that facilitate the study of intra- and intercellular communication and tumor-microenvironment (TME) interactions. 3D cell culture platforms, including tumor spheroids and organoids, replicate many of the pathophysiological features of solid tumors, such as cell-cell contacts as well as pH, oxygen and nutrient gradients (15). Such models are increasingly favored to study tumor biological processes in vitro (16), but also exhibit several drawbacks. Currently used 3D cultures lack many components of the TME that shape the unique metabolic landscape of patient tumors, such as infiltrating stromal and immune cells, vasculature and the tumor interstitial fluid (17). Below, we discuss recent efforts in optimizing cell culture dimensionality (e.g., 3D cultures) and complexity (e.g., co-cultures, tissue explants, microfluidics) that have led to in vitro tumor models that better recapitulate the metabolic landscape of tumors.

3D cell culture models are a promising tool to mitigate the gap between 2D culture systems and cancer tissues to study the metabolic complexity of cancer (18). Various studies tried to assess how the metabolic profile of 3D models compares to that of 2D cultures and are reviewed in Tables 1, 2. Most of these studies report an increase in the glycolytic- (19, 20, 42, 43) and oxidative capacities (20, 21, 42) of 3D cultures. Yet, several studies report the opposite, with diminished glycolysis (21) or oxidative metabolism (19, 43) present in 3D models. Moreover, 3D models showed a higher maintenance of ATP production (20, 42, 44, 45), redox balance (42, 44), and biomass synthesis, including nucleotides (42, 44), amino acids (21, 42, 44), lipids (22, 23, 42–45) and NADPH (42, 44). Nevertheless, reductions in amino acid (43) and especially de novo nucleotide synthesis (24, 43, 45) were also reported for 3D models compared to their 2D counterparts. Because these comparative studies were conducted in models of various cancer types, it is believed that these contradictory results can be partially attributed to the metabolic variation across tumor entities. Although a considerable number of studies have addressed the metabolic differences between 2D and 3D cultures, fewer studies make the comparison between these in vitro systems and the metabolic profiles found in primary tumor tissues. Nevertheless, studies that do investigate this indicate a closer resemblance of 3D models to cancer tissues compared to either the traditional 2D cultures (44) or normal, non-cancerous tissue (45).

Table 1. Cited literature on more common cancer types that review the impact of cell culture conditions on cancer cell metabolism in vitro.

Table 2. Cited literature on more rare cancer types that review the impact of cell culture conditions on cancer cell metabolism in vitro.

Given the metabolic differences identified between 2D and 3D culture conditions, it is not surprising that these models show altered sensitivities to commonly used therapeutic agents. Several findings highlight the importance of considering 3D over or next to 2D models in pre-clinical studies evaluating cancer metabolism and responses to anti-cancer drugs, with numerous studies indicating that 3D models exhibit greater resistance to chemotherapeutics compared to 2D models (24, 25, 57–59), thereby more accurately mimicking drug responses observed in vivo.

A burgeoning number of studies combines 3D organoid models such as patient-derived tumor organoids (PDTO) with metabolomics and stable-isotope tracing approaches to study cancer cell metabolism (60–64). Several reports have demonstrated that this approach could facilitate the assessment of metabolic responses to treatment and aid in the development of novel metabolic treatment strategies. For instance, Neef and colleagues (63) observed dose-dependent alterations in the metabolic profiles of patient-derived colorectal cancer (CRC) organoids subjected to 5-FU treatment. Importantly, the metabolites that exhibited significant changes were primarily associated with purine and pyrimidine metabolism, consistent with the known mechanism of action of 5-FU (63). Furthermore, Ludikhuize et al. (64) investigated the 5-FU response in PDTO models mimicking the different CRC stages. They found that 5-FU induces DNA damage and cell death in p53-deficient CRC organoids due to pyrimidine imbalance, with enhanced toxicity observed in KRASG12D glycolytic CRC organoids when targeting the Warburg effect (64). Together, these studies illustrate the valuable role of 3D organoids in monitoring drug-induced metabolic changes and identifying tumor-specific metabolic sensitivities in vitro.

The advent of co-culture systems has made it possible to incorporate multiple cell types to study metabolic cell-cell communication. These in vitro co-culture techniques have provided fundamental insights into the metabolic crosstalk between tumor cells and stromal cells, encompassing adipocytes (65–68), endothelial cells (69, 70) and fibroblasts (71, 72), as well as immune cells such as macrophages (73–75) and T lymphocytes (76–78). However, such systems still lack the cellular diversity as well as the matrix and vascular compartments found within the TME. To address this issue, several next-generation culture platforms have been developed. For example, several groups have set out to establish patient-derived explants (PDEs) to investigate tumor cell metabolism. PDEs are generated by directly culturing fresh, non-dissociated tumor tissue slices in vitro, thereby preserving native tissue architecture, TME, cell-cell interactions and metabolic crosstalk of the in vivo situation (79). In the past, PDEs have been used for metabolic studies (80, 81), but due to their relatively short-term viability and the lack of consistent PDE culturing methodologies their use remains limited (16).

In addition to PDEs, cancer-on-chip (CoC) platforms are emerging as advanced 3D approaches. A CoC is a micro-fluidic-based device that usually hosts multiple cell types in a more in vivo-like microenvironment where mechanical stimuli, flow, and rate of chemical release can be controlled (82, 83). Sensors can also be integrated to perform real-time monitoring of physicochemical cues, such as pH and O2 levels (84). Recently, Dornhof et al. (85) integrated biosensors to measure oxygen, lactate, and glucose into a microfluidic CoC platform, allowing precise and reproducible on-chip multi-analyte metabolite monitoring in real-time. Even more advanced is the work of Kalfe et al. (86), who embedded a microfluidic tube containing tumor spheroids directly into a miniaturized NMR metabolomics detector, allowing them to monitor 23 metabolites. Moreover, Chen et al. (87) developed a CoC integrated with electrospray ionization mass spectrometry, enabling the simultaneous measurement of drug-induced apoptosis and metabolites with high stability, sensitivity and repeatability. Indeed, microfluidic systems have been demonstrated to be able to mimic the in vivo tumor conditions better than traditional 2D systems (88). Especially with the integration of primary, PDTO cultures, a superior reproduction of the in vivo conditions could be obtained. Still, one important limitation of CoC systems is their simplicity, as these devices currently only incorporate the essential components (89).

Metabolic pathway activity is dynamically regulated in a context-dependent manner to balance the anabolic and catabolic needs within a cell. Cancer cells, which frequently encounter nutrient-poor, acidic microenvironments with restricted oxygen availability, must undergo metabolic reprogramming to adapt to and thrive under these nutritional fluctuations (90, 91). Modeling cancer cells under variable lactate and nutrient concentrations that mimic the cancer microenvironment may therefore enhance our understanding of cancer metabolism in vivo.

Recognition of the impact of cell culture medium composition on the transcriptomic, epigenetic and metabolic profiles of cells has grown considerably over the past years (13, 92, 93). Still, much of our current knowledge on cancer metabolism predominantly stems from studies using cells cultured in standard, nutrient-rich media. Frequently, these standard media include a largely undefined serum component (e.g., fetal bovine serum (FBS)) in conjunction with one of the several defined basal media (e.g., Minimal Essential Medium (MEM), Dulbecco’s Modified Eagle Medium (DMEM), and RPMI 1640). Such standard media were originally designed to promote the proliferation of specific cell types without the need for constant refeeding, rather than to accurately mimic the in vivo metabolic environment (12).

Media formulations mimicking the nutrient concentrations in plasma or the direct microenvironment can improve the biological relevance of in vitro cancer modeling and aid in addressing the metabolic discrepancies between in vivo and in vitro systems. In 2017 and 2019, two physiological media that more accurately represent the metabolic profile of human plasma were formulated, termed human plasma-like medium (HPLM) (26) and Plasmax (27). Multiple studies that used these media formulations (see Tables 1, 2) have indicated a decreased use of glucose and reduced glycolytic activity of cancer cells cultured in physiologic medium, while the use of oxidative metabolism was shown to be increased (26, 28, 29). Profound effects on amino acid, lipid, and nucleotide metabolism have also been reported (26). Furthermore, several studies have demonstrated that culturing of cancer cells in physiological media results in metabolic profiles that more closely resemble the metabolic state of tumors (27, 46), and could lead to a better assessment of the effectiveness of antitumor drugs in vivo (26, 47). For example, Vande Voorde and colleagues (27) compared the metabolic profiles of CAL-120 breast cancer cells cultured in DMEM-F12 and Plasmax, both as 2D monolayers and 3D spheroids, with CAL-120-derived mammary tumors. They found that 3D spheroids cultured in Plasmax had a metabolic profile closest to that of the tumors, suggesting that 3D culture in a physiological medium better approximates the tumor’s metabolic phenotype (27). In addition, Cantor et al. (26) showed that the physiologic uric acid levels present in HPLM directly inhibit uridine monophosphate synthase (UMPS), thereby reducing the sensitivity of cancer cells to antimetabolite 5-fluorouracil.

Tumor cells are not directly exposed to nutrients in circulating plasma, but rather to nutrients present in the extracellular fluid that perfuses the tissue, so-called tumor interstitial fluid (TIF) (94). By extracting both plasma and TIF from murine lung- and pancreatic adenocarcinoma (PDAC) models, Sullivan et al. (95) revealed that the nutrients available in TIF differ from those present in plasma. Building on these findings, TIF medium (TIFM), containing nutrient levels representative of the PDAC microenvironment, was developed by the same group (46). PDAC cells cultured in TIFM more closely resembled the metabolic state of PDAC tumors compared to standard cell culture models. In addition, these TIFM-cultured PDAC models revealed high de novo arginine synthesis activity to be a specific metabolic feature of PDAC tumors (46). Nevertheless, it remains to be determined whether the nutrient concentrations observed in murine TIF are comparable to those in human tumors.

While these advancements in media formulation enhance the metabolic fidelity of cell culture models, physiological media are susceptible to rapid nutrient depletion (96), indicating the necessity of daily media replacement in such cultures. However, the daily renewal of nutrients and growth factors could lead to cyclic metabolic activation of cells and therefore affect experimental readouts. Instead, continuous perfusion of cells with physiologic media using a fluidic system, for example, could at least partially solve this (10).

Both in vitro and in the human body, the physicochemical environment (i.e., temperature, pH, O2 and CO2 levels) is tightly controlled, but not necessarily the same. Under normal physiological conditions, the pH of blood and tissues is tightly regulated to be at pH 7.4 (97). Similarly, a stable pH range of 7.2-7.4 is often maintained in cell culture media by the addition of buffers, such as sodium bicarbonate (NaHCO3) or HEPES (98). In contrast, most conventional cell culture incubators do not regulate O2 levels, resulting in atmospheric O2 concentrations around 18-21% (99), while much lower oxygen levels are found in human tissues, ranging from 3-7.4% (physioxia) (100).

In the microenvironment of human tumors, pH and oxygen levels are subjected to further change. In solid tumors, the microenvironment is frequently acidified due to elevated levels of acidic metabolites, such as lactate, and the secretion of protons (H+) through specific pumps and transporters (101). The latter processes result in a local extracellular pH ranging from 5.5 to 7.0 (102, 103). Extracellular acidosis is therefore considered a hallmark of most human tumors, strongly linked to malignancy, aggressiveness (36) and/or stemness (53) of tumors. Oxygenation in tumors frequently drops to 0.3-4.2% oxygen (hypoxia) (100). Yet, most in vitro cancer studies have been performed under hyperoxic conditions, with 18–21% O2 now frequently referred to as ‘normoxia’ in literature (104). Thus, adjusting O2 levels and pH to more physiologically relevant conditions, as reviewed below (see also Figure 1 and Tables 1, 2), can influence cancer cell metabolism in vitro to provide a more accurate representation of the in vivo tumor milieu.

Figure 1. Revising cancer cell culture conditions: comparing standard practices with physiologically relevant conditions for enhanced cancer metabolism modeling. Standard cell culture practices (left) that could be improved to more accurately reflect human physiology (right) to enhance our understanding of metabolic processes in cancer. Created with BioRender.com.

The supraphysiological O2 levels used in cell culture systems greatly impact cancer cell metabolism, and could confound the metabolic findings between in vitro and in vivo settings (99). These findings could have important implications when studying various cancer drugs that target energy metabolism. Several studies report higher mitochondrial activity at physioxia than at normoxia (29, 30, 48), suggesting that the standard O2 cell culture conditions suppress the actual oxidative capacity of cancer cells in vitro. Hypoxic conditions, on the other hand, have been shown to induce glycolysis (30–34, 49–51) and decrease the overall levels of TCA cycle metabolites and oxidative metabolism as compared to normoxia (33, 49, 50). Although diminished, mitochondrial-dependent metabolism did remain active at hypoxia (31, 33). For instance, Yang and colleagues (33) observed that exposing MDA-MB-231 breast cancer cells to hypoxia decreased the flux of glucose yet increased glutamine flux into the TCA cycle by enhancing glutaminolysis to compensate for the reduced mitochondrial metabolism under hypoxia. In addition to changes in energy metabolism, several studies report increased protein- (31, 33) and lipid catabolism (31, 48) under hypoxic conditions (1% O2). These increased catabolic reactions likely occur to compensate for the impaired mitochondrial activity that could not be corrected by increasing the glycolytic flux (31).

Subjecting cancer cells to physiologically relevant O2 levels might result in metabolic profiles that more accurately reflect tumor metabolism in vivo (48). For example, Blandin and colleagues demonstrated that the hypoxia-induced metabolic switch in cultured pediatric high-grade glioma (pHGG) cells was similar to the metabolic profile of matched relapsed pHGG tumors, indicating that culturing pHGG cells at 1% O2 more closely reflects patient tumor metabolism than cells cultured at normoxia (48). By subjecting identical PDAC surgical samples to 20% or 1% O2, Kumano et al. (52) found that hypoxia generated PDAC organoids with a different morphology, increased EMT-related protein expression and a higher 5-FU resistance. Their results suggest that hypoxia selects for PDAC cells with malignant traits, aiding in the development of effective anticancer treatments.

As with oxygen, culturing cancer cells at a tumor-like acidic pH reprograms their metabolism, revealing vulnerabilities that could improve the prediction of therapeutic effectiveness in vivo. Various studies compared the energy metabolism of cancer cells cultured at physiologic pH 7.4 or acidic pH ranging from 6.0 to 7.0. These revealed that acidosis reduced the general glucose uptake in tumor cells (35, 37, 54), and redirected glucose away from glycolysis and lactate production (35–40, 54) towards oxidative metabolism (36, 39, 53–56). Furthermore, several studies report an increased concomitant use of fatty acid (FA) breakdown and synthesis in cancer cells at acidic pH (37, 41). The latter process endows acid-adapted cancer cells with an increased capacity for utilizing FA for metabolic needs, while limiting glycolysis (41). Moreover, cells experiencing acidosis shift their metabolism towards the pentose phosphate pathway (PPP) (55, 56), important for the production of NADPH. NADPH is crucial for antioxidant defense, and acts in part by recycling glutathione (GSH) to counteract reactive oxygen species (ROS) (38, 105). Several papers report diminished GSH synthesis at low pH (38, 54), thereby increasing the demand for PPP-derived NADPH to recycle existing GSH pools (38). These findings indicating that low environmental pH affects both energy and redox metabolism to maintain homeostasis under acidosis-induced oxidative stress.

Acidosis-induced metabolic rewiring of cancer cells results in novel metabolic vulnerabilities that could potentially be exploited. As shown by Peppicelli et al. (36), treating acid-exposed melanoma cells with the mitochondrial complex I inhibitor Metformin inhibited the acidosis-induced oxidative metabolism and reduced the proliferation and epithelial-to-mesenchymal transition (EMT) of invasive melanoma cells. Chano et al. (55) showed a higher sensitivity of osteosarcoma cells to HDAC inhibitors at pH 6.5, suggesting that acidosis promotes metabolic profiles that contribute to epigenetic maintenance. Lastly, Corbet et al. (39) showed that targeting acid-driven glutamine metabolism in vivo with the glutaminase inhibitor BPTES significantly reduced the growth of tumors comprised of cells pre-adapted to pH 6.5, compared to tumors from cells adapted to neutral pH 7.4.

Most of our current knowledge on cancer cell metabolism stems from reductionist in vitro models grown in a highly artificial environment. In this review, we present how current standard cell culture practices markedly influence cancer cell metabolism and how the use of more physiologically relevant culture conditions could mitigate discrepancies between in vitro and in vivo findings. The main findings of cited studies are compiled in Table 1. Summarized by us in Figure 1 are the current recommendations for standard cell culture practices that could improve the metabolic fidelity of in vitro modeling systems.

However, not all factors described are easily implementable. For example, measuring metabolic activity under low O2 conditions requires the use of expensive hypoxic chambers and incubators that are not routinely used in most laboratories. Furthermore, not all cell culture models can currently be grown or maintained in a 3D setting. Finally, the development of the more complex CoC models requires interdisciplinary knowledge, ranging from biology to microfluidic chip engineering.

Still, tremendous progress has been made in optimizing the culture conditions and models to more accurately study cancer metabolism in vitro. Nevertheless, it is expected that our increasing knowledge on TME physiology as well as continuing technological advances will ultimately result in more representative models to study cancer metabolism. Presumably, these improvements will generate novel and meaningful insights into cancer metabolism that will be more effectively translated into successful anti-cancer therapies.

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