Capturing the heterogeneity of the pancreatic ductal adenocarcinoma tumor microenvironment: novel triple co-culture spheroids for drug screening and angiogenic evaluation

doi:10.21203/rs.3.rs-3788739/v1

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Capturing the heterogeneity of the pancreatic ductal adenocarcinoma tumor microenvironment: novel triple co-culture spheroids for drug screening and angiogenic evaluation

https://doi.org/10.21203/rs.3.rs-3788739/v1

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Background

Pancreatic ductal adenocarcinoma (PDAC) poses a significant health threat with poor response to current treatment options. The desmoplastic reaction, characteristic of PDAC, hinders therapeutic efficacy and emphasizes the need for novel in vitro models to study the complex tumor microenvironment and increase translatability. Three-dimensional in vitro co-culture models with clinically relevant numbers of cancer-associated fibroblasts and endothelial cells are still lacking and lead to failure of clinical trials and low improvement of patient survival.

Methods

MiaPaCa-2 and BxPC-3 cancer cell lines, RLT-PSC and hPSC21 pancreatic stellate cell lines and the endothelial cell line HMEC-1 were seeded in ultra-low-attachment round-bottomed plates to form triple co-culture spheroids. A growth assay including all cell lines was performed to evaluate if DMEM or MCDB131 is most ideal for spheroid formation and culturing. Multi-color flow cytometry was used to quantify cell populations after three days of spheroid formation to optimize the seeding ratios. Drug response profiles of mono-culture and triple co-culture spheroids were made using a cell viability assay. Finally, a tube formation assay with spheroid-conditioned medium was performed to showcase the potential of our model for angiogenic studies.

Results

We developed a panel of high-throughput triple co-culture spheroid models of pancreatic cancer cells, pancreatic stellate cells and endothelial cells. We were able to capture different facets of PDAC heterogeneity in scope of the tumor microenvironment using two different cancer and stellate cell lines, and one endothelial cell line. Importantly, drug responses varied between mono-culture and triple co-culture spheroids, underlining the impact of the tumor microenvironment, spatial arrangement, and spheroid density on therapeutic outcomes. Gemcitabine and paclitaxel treatments revealed different drug response profiles depending on the combination of BxPC-3 or MiaPaCa-2 with RLT-PSC or hPSC21 in a triple co-culture environment. A tube formation assay showcased the potential of our models to assess angiogenesis, providing a quantitative understanding of a treatment-induced response.

Conclusions

Our study brings sophisticated high-throughput in vitro models that are easy to reproduce and provide valuable insights into PDAC research to improve translatability and preclinical screening efficacy. In addition, our triple co-culture spheroids are cheap and include the heterogeneity of the PDAC tumor microenvironment.

Pancreatic ductal adenocarcinoma

Triple co-culture spheroids

Tumor microenvironment

Drug resistance

Heterogeneity

Angiogenesis

In vitro 3D model

Pancreatic stellate cells

Endothelial cells

Cancer-associated fibroblasts

Pancreatic ductal adenocarcinoma (PDAC) is predicted to become the second leading cause of death by cancer before 2030, clearly emphasizing why improvement in treatment strategy is highly needed (1). PDAC, approximately 85% of all pancreatic cancer cases (2), is characterized by a desmoplastic reaction, which triggers the formation of a dense, fibrous tissue causing high intratumoral pressure. This serves as a physical barrier to therapy and causes vascular compression and hypovascularity, which inhibits drug delivery and induces chemoresistance (3). In addition to their vital role in angiogenesis endothelial cells (ECs) contribute to the complexity of the disease as cancer-associated fibroblasts (CAFs) through an endothelial-to-mesenchymal transition (4, 5). Furthermore, activated pancreatic stellate cells (PSCs), a specific type of fibroblasts, act as the guardians of PDAC and are major contributors to the desmoplasia and the creation of a complex tumor microenvironment (TME). This leads to the acquisition of properties like rapid growth, a highly invasive and metastatic potential, survival in hypoxic and low-nutrient conditions, immunosuppression, among others (6). Single-cell RNA sequencing revealed that roughly 35% of all cells in PDAC tumors are cancerous, 26% are fibroblasts and PSCs, and 12% are ECs (7). However, the extreme cellular heterogeneity between patients (intertumoral) and within the tumor (intratumoral) increases the failure rate of therapies in clinical trials and highlights the importance of utilizing more complex models to capture this heterogeneity during the in vitro stage of research (8). While pharmaceutical research and drug development has been ever-growing, the approval of new drugs is not experiencing the same uptrend (9). This is partially caused by Phase 3 failures originating from inadequate preclinical (high-throughput) screening (10).

To develop novel therapeutic approaches, we need better and more accessible in vitro models that can mimic the TME of PDAC. Simple models such as two-dimensional (2D) cultures have been a useful and economic approach for investigating fundamental questions for decades. However, these models are not sufficient to study cellular responses to innovative treatment strategies that can tackle these complex diseases with high translatability (11). One simple and low-cost alternative is the use of three-dimensional (3D) spheroids, which offer many advantages, including an oxygen gradient, scaffold-free tissue-like cellular organization, cellular crosstalk, high-throughput, and reproducibility (11–13). Mono-culture spheroids of cancer cells lack the stromal component of a tumor-like extracellular matrix (ECM) deposition and a complex TME (12). It has been stated that co-culture spheroids of a 1:1 and 2:1 ratio (cancer:stellate) are not representative for the PDAC stroma content (13–15). In addition, attempts have been made to develop a triple co-culture (TCC) spheroid model for pancreatic cancer with fibroblasts and endothelial cells. However, to our knowledge, they were unsuccessful due to the lack of PSCs, which are very specific to PDAC (16, 17). In these studies, the authors used either lung fibroblasts or extremely low numbers of fibroblasts, which does not truly mimic PDAC tumors, and hence does not provide a full picture of the cell-to-cell interactions governing the PDAC TME. In addition, they did not include the intra- and intertumoral heterogeneity of the PDAC TME.

Patient-derived organoids (PDOs) are gaining more attention due to their clinical relevance and to understand patient-specific drug responses for personalized cancer treatment (18). However, they are reported to have low physical properties and a soft matrix (13, 19, 20). Even though PDOs are highly valuable, in many laboratories there is still a lack of expertise and standardized protocols for processing, culturing and cryopreservation of these tissues with a high success rate (21). In addition, most PDOs do not yet include the complex TME related to endothelial cells and angiogenesis, and they require Matrigel or Cultrex that introduces unknown growth factors (22).

Translatability of research will remain low without a proper 3D model that includes a complex TME and its heterogeneity with clinically relevant numbers of different cell types to be able to study the treatment response correctly. In this study, we present a relevant panel of high-throughput TCC spheroid models of PDAC including both PSCs and ECs. To tackle the heterogeneity of the PDAC TME, we used cell lines with different genetic characteristics for both PDAC cells and PSCs. Altogether, our novel 3D spheroid models can be widely used for drug screening, angiogenic studies, and more, providing an affordable and relevant 3D model to advance the development of suitable therapies for PDAC.

Cell culture

The human PCC lines MiaPaCa-2 (ATCC®) and BxPC-3 (ATCC®) were used in this study. The human immortalized PSC line RLT-PSC was kindly provided by Prof. Ralf Jesenofsky, of the Faculty of Medicine, University of Mannheim (31), while the hPSC21 line was established at Tohoku University, Graduate School of Medicine, kindly provided by Prof. Atsushi Masamune (32). In addition, the immortalized endothelial cell line HMEC-1 (ATCC®) was used. Cancer and stellate cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM, 10938025, Gibco) supplemented with 10% foetal bovine serum (FBS, 10270106, Life Technologies), 100 U/mL penicillin, 100 µg/mL streptomycin (15140122, Life Technologies) and 2 mM L-glutamine (25030024, Life Technologies). Cancer and stellate cells were transduced with NucLight Rapid Red (4741, Essen Biosciences) to express mKate2 and NucLight Green (4475, Essen Biosciences) to express GFP respectively. HMEC-1 cells were cultured in MCDB131 (10372019, ThermoFisher Scientific) supplemented with 10 ng/mL Epidermal Growth Factor (PHG0314, ThermoFisher), 1 µg/mL Hydrocortisone (H0396, Sigma Aldrich), 10 mM L-glutamine (25030024, Life Technologies), 10% foetal bovine serum (10270106, Life Technologies) and 100 U/mL penicillin, 100 µg/mL streptomycin (15140122, Life Technologies). Cells were maintained in a humidified incubator at 37°C and 5% CO₂. HMEC-1 cells for the supplementary proliferation rate were transduced with CMV-GFP.

Proliferation Rate

MiaPaCa-2, BxPC-3, RLT-PSC, hPSC21, and HMEC-1 cells were seeded in a flat 96-well plate (655180, Greiner Bio-One BVBA) in 200 uL (1000, 2000, 1000, 1000, and 1500 cells/well, respectively). Cells were either seeded in MCDB131 or DMEM (with or without supplements) to evaluate proliferation over time. Confluence or green fluorescence of nuclei (HMEC-1 with GFP transduction) was measured every 24 hours using live-cell imaging with the Spark® Cyto (Tecan).

Spheroid formation

3D co-culture spheroids were generated using the previously mentioned cell lines. Spheroids were seeded at 5000 (BxPC-3:RLT-PSC:HMEC-1 in a ratio of 7:2:4), 7000 (BxPC-3:hPSC21:HMEC-1 in a ratio of 6:5:3), 3500 (MiaPaCa-2:RLT-PSC:HMEC-1 in a ratio of 6:3:3) and 4500 (MiaPaCA-2:hPSC21:HMEC-1 in a ratio of 5:6:4) cells per well, including 0.24% methylcellulose in ultra-low adherent (ULA) plates (7007, Corning®). Empty outer wells were filled with 200 µL of water to minimize evaporation and plates were sealed with a breathable membrane (Z380059, Merck). The ULA plates were centrifuged at 453 x g for 10 min. Cells were incubated at 37°C and 5% CO₂ for three days to promote spheroid formation, after which spheroids of 400–500 µm diameter were formed.

Flow Cytometry

The characterization of the different cell types in the TCC spheroids was performed with multicolor flow cytometry. 48 Spheroids per condition were collected after three days of formation and dissociated with 1 mL of TrypLE (12604-021, Life Technologies) for 30 min of shaking, while incubating at 37°C and 5% CO₂. Mechanical stress was applied by pipetting up and down with 0,1% BSA-coated tips every 10 min. 3 mL of medium was added, and cells were pipetted up and down, vortexed, and a single cell suspension was obtained using a 70 µm strainer. All cells were resuspended in 200 µL FACS buffer and seeded in a 96-well round-bottom plate. Subsequently, all cell suspensions were pre-treated with human serum blocking solution (S1-100ML, Merck) for 15 min at room temperature (RT) to avoid non-specific binding and washed twice with FACS buffer. Cells were incubated with PE-Cy7 anti-human CD31 (303118, BioLegend) antibody for 15 min at RT, then washed twice with FACS buffer. Cancer and stellate cells are measured with mKate2 and GFP fluorophores that are expressed by the cell nuclei due to transduction, as explained earlier. Samples were measured on the NovoCyte Quanteon (Agilent Technologies), and analysis of all flow cytometry experiments was performed using the FlowJo v10 software (TreeStar inc.).

Cell Viability Assay

Spheroids were formed over three days, as described earlier. Spheroid medium was refreshed by removing 100 µL of medium and adding 100 µL of fresh MCDB131 medium (50% medium renewal). Gemcitabine (S1714, 10 mM in DMSO, Selleck Chemicals) and Paclitaxel (S1150, 10 mM in DMSO, Selleck Chemicals) were applied as monotherapy or as combinational therapy in a 5:1 ratio using the D300e Digital Dispenser (Tecan). Titration curves were set up by measuring the cell viability with Cell Titer Glo (G7571, Promega) after five days of drug treatment. DMSO normalization was performed based on the highest drug concentration to a maximum of 0.6% DMSO. Staurosporine (5 µM, S1421, SellekChem) was used as a 100% cell death control to normalize data. The AUC value represents the area under the curve and is a measure for drug resistance. The higher the viability, the larger the AUC value will be, therefore, indicating higher drug resistance.

Enzyme-Linked Immunosorbent Assay

Supernatant levels of VEGF in triple co-culture spheroid-conditioned media were quantified using the LEGEND MAX™ Human VEGF ELISA Kit (446507, BioLegend) according to the manufacturer’s instructions. Absorbance was measured at 450nm and 570nm (reference) using the Spark® Cyto (Tecan).

Tube Formation Assay

Assessment of angiogenesis was done with a tube formation assay in a 384-well plate (3764, Corning®). Plates were coated with 8 µL of Cultrex (3533, Bio-Techne), centrifuged at 180 x g for three min and incubated for at least 60 min at 37°C and 5% CO₂. HMEC-1 cells were resuspended at 58000 cells/mL (either in MCDB131 or spheroid-conditioned medium) and 40 µL of cell suspension was seeded on top of the Cultrex coating. 30 µM Suramin (S2671, Merck Life Science) in MCDB131 medium without FBS was used as negative control and was added using the D300e Digital Dispenser (Tecan). Fresh MCDB131 medium was used as a positive control as this highly supports tube formation and loops. After six hours, images of the tubular network were taken using the Spark® Cyto (Tecan) with a 4x objective. Python was used to crop these images to a center-focused circular image to remove the out-of-focus outside regions of the well, caused by the meniscus shape of the cultrex coating. Finally, the IKOSA® tube formation analysis was performed to gain four metrics: number of loops, covered area, total tube length and number of branch points.

Statistical Analysis

All experiments were performed at least three times with three replicates, unless specified otherwise. The significances in the proliferation assay were evaluated using two-way ANOVA with Sidak’s multiple comparison test using Prism v10.1.0 (GraphPad Software, San Diego, CA, USA). Dose-response curves were evaluated for significances using the AUC values (p ≤ 0.05) and a linear mixed model with either the treatment or the spheroid combination as fixed effect using JMP Pro v17.0.0 software. Outlier tests were performed using v10.1.0 (GraphPad Software, San Diego, CA, USA). For multiple comparisons of all experimental groups, Tukey’s honestly significant difference (HSD) was used. Statistical significances between the concentrations of VEGF in spheroid-conditioned media was also evaluated with a linear mixed model using JMP Pro v17.0.0 software and Tukey’s HSD multiple comparisons test.

Spheroid formation is most optimal in MCDB131 medium

To include the heterogeneity of the PDAC TME, we used two pancreatic cancer cell (PCC) lines with different genetic backgrounds and associated characteristics (MiaPaCa-2 with KRAS and TP53 mutations and BxPC-3 with BRAF, SMAD4, and TP53 mutations). Additionally, we used two PSC lines (RLT-PSC originating from chronic pancreatitis and hPSC21 originating from pancreatic cancer) and one endothelial cell line (HMEC-1). As PCCs, PSCs, and ECs were cultured in different media, we evaluated the cell growth of each cell line in both DMEM (PCC and PSC culture medium) and MCDB131 (EC culture medium) to determine the optimal medium for TCC spheroid formation. Clearly, MCDB131 was as good (MiaPaCa-2 and RLT-PSC) or better (BxPC-3, hPSC21 and HMEC-1) than DMEM in sustaining the growth of these cell lines after five days (Fig. 1). DMEM supplemented with VEGF and FGF was not able to provide the required environment for ECs to grow to the same extent as MCDB131 medium (Supplementary Fig. 1). Therefore, MCDB131 medium was chosen for spheroid culture.

Triple co-culture spheroids demonstrate the heterogeneity of the PDAC tumor microenvironment

To determine the optimal seeding densities for each cell type in a TCC environment, we quantified the cell populations after three days of spheroid formation by dissociating the spheroids into single-cell suspensions and subsequently analysing them via flow cytometry (Fig. 2a and Supplementary Fig. 2). Given the solid nature of PDAC tumors, our assessment also encompassed an evaluation of the spheroid diameter, compactness, and overall structure using live imaging (Fig. 2c and Fig. 3).

During our optimization process to mimic PDAC, we amended seeding densities when appropriate. These were made based on the quantitative data from the flow cytometry experiments where, e.g., too few cancer cells and too many endothelial cells were found (BxPC-3:RLT-PSC:HMEC-1 in Supplementary Fig. 3). In others, an extremely low number of endothelial cells was found that did not mimic PDAC accurately (Supplementary Fig. 3). Finally, we selected the seeding ratios of our four distinct combinations of TCC spheroids that better mimicked clinical tumors and the different facets of the heterogeneous spectrum of PDAC (Fig. 2b): BxPC-3:RLT-PSC:HMEC-1 (7:2:4), BxPC-3:RLT-PSC:HMEC-1 (6:5:3), MiaPaCa-2:RLT-PSC:HMEC-1 (6:3:3), and MiaPaCa-2:hPSC21:HMEC-1 (5:6:4). These combinations range from tumors exhibiting high PCC contribution and low EC contribution (MiaPaCa-2:RLT-PSC:HMEC-1) to those characterized by high PSC contribution (BxPC-3:hPSC21:HMEC-1). All four TCC combinations were able to form compact spheroids with a diameter of 300–500 µm (Fig. 2c).

We observed two characteristic aspects about the heterogeneous nature of our 3D spheroids (Fig. 2c): i) the stellate cell line hPSC21 tends to organize itself around the tumor, protecting it from the environment, which is often seen in clinical PDAC tumors and contributes to drug resistance; ii) when a shield is not formed around the tumor (spheroids with RLT-PSC), the invasive nature of MiaPaCa-2 (spreading out of the red cancer cells from the spheroid, Fig. 2c) due to the KRAS mutation becomes evident compared to BxPC-3. Altogether, we successfully obtained four different TCC spheroids that collectively resemble PDAC tumors with significant potential to improve in vitro research.

Contrasting spheroid compactness between BxPC-3 and MiaPaCa-2 spheroids and the influence of the tumor microenvironment

Next, we compared the structure of mono-culture and TCC spheroids. We observed that both mono-culture and TCC spheroids of BxPC-3 cancer cells formed compact spheroids (Fig. 3). In contrast, MiaPaCa-2 mono-culture spheroids exhibited a loose spatial arrangement of roughly 1000 µm in diameter, different from the more solid and tightly packed structure observed in the MiaPaCa-2 TCC spheroids of approximately 400 µm (Fig. 3). This ability of MiaPaCa-2 to form compact spheroids upon co-culturing with PSCs and ECs highlights the influence and importance of the TME on the structure of these spheroids. Furthermore, this notable advantage of our TCC spheroids allows the KRAS-mutated MiaPaCa-2 cell line to be investigated in a clinically relevant model, which is not possible with the overly loose mono-culture aggregates. Indeed, the fragile nature of the mono-culture spheroids makes it difficult to manipulate them without breaking and, importantly, their loose structure does not represent clinical PDAC tumors.

Unravelling drug response profiles: the impact of structural variations and the tumor microenvironment

To determine the effect of these structural differences between the four distinct TCC spheroids in the response to chemotherapeutics currently used for PDAC treatment, we investigated the effect of gemcitabine and paclitaxel as single and combination treatments. Mono-culture and TCC spheroids were treated for 5 days and the area under the cell viability curve (AUC) was used as a metric for drug resistance (Fig. 4a).

Contrasting drug resistance was observed in mono-culture spheroids of BxPC-3 and MiaPaCa-2. BxPC-3 mono-culture spheroids displayed a high drug resistance, while MiaPaCa-2 mono-culture spheroids exhibited lower resistance (Fig. 4b). This can be explained by the structural difference (Fig. 3), intrinsic characteristics and genetic differences of both cell lines. Remarkably, the BxPC-3 mono-culture spheroids exhibited higher resistance compared to BxPC-3 TCC spheroids (Fig. 4c), suggesting that the PSCs and ECs are more sensitive to chemotherapy than BxPC-3, leading to a higher total cell viability of the BxPC-3 mono-culture spheroids.

Next, we investigated the differences between the distinct TCC spheroids in response to chemotherapy, to understand the effects of TME heterogeneity in more detail. BxPC-3 and MiaPaCa-2 mono-culture spheroids responded differently, as explained before (Fig. 4b and 4c), which was also observed in the TCC spheroids with RLT-PSC (Fig. 4d). However, we did not observe differences between BxPC-3 and MiaPaCa-2 in TCC spheroids with hPSC21 (Fig. 4d). Interestingly, BxPC-3:RLT-PSC:HMEC-1 spheroids exhibited higher resistance than any of the other three TCC spheroid models, as seen from the AUC values (Fig. 4d). This suggests a distinct resistance profile in the context of the complex TME represented by this specific TCC spheroid.

Altogether, our results reveal that spheroid compactness and the different TMEs in our four spheroid combinations influence the drug response and why it is crucial to account for TME heterogeneity by incorporating multiple TCC spheroid models.

Triple co-culture spheroids and angiogenesis: exploring treatment response

Lastly, we investigated the value of our TCC spheroid models for angiogenesis studies. After spheroid formation of three days in 200 µl, we either renewed 100 µl of medium, or removed 150 µl of medium and added 50 µl of new medium to a total of 100 µl, which both lead to a 50% medium refresh (Fig. 5a). Next, we used spheroid-conditioned medium in a tube formation assay with endothelial cell monolayers in cultrex-coated well (Fig. 5a). When refreshing the medium, we evaluated whether spheroid-conditioned medium was best collected in the amount of 100 or 200 µl, and after 24 or 72 hours conditioning. We concluded that a medium renewal up to 100 µl and conditioning with TCC spheroids for 72 hours resulted in the highest VEGF concentration and was therefore the chosen condition (Fig. 5b). In addition, we found clear differences between VEGF secretion by BxPC-3 and MiaPaCa-2 TCC spheroids with approximately a two- (BxPC-3:hPSC21:HMEC-1, 214 pg/ml; MiaPaCa-2:hPSC21:HMEC-1; 383 pg/ml p ≤ 0.0001) or three-fold (BxPC-3:RLT-PSC:HMEC-1, 185 pg/ml; MiaPaCa-2:RLT-PSC:HMEC-1; 487 pg/ml p ≤ 0.0001) higher VEGF concentration in MiaPaCa-2 TCC spheroid-conditioned media compared to BxPC-3.

The tube formation of endothelial cell monolayers was evaluated using four key parameters of the IKOSA® tube formation analysis: number of loops, total tube length of the vessel network, total covered area of the vessels, and number branch points (Fig. 5c and 5d). In combination with the number of loops, the total tube length and covered area offered a better understanding of angiogenesis than the number of tubes, as the negative control often gave a high number of tubes due to the presence of single cells and disconnected short tubes. However, the presence of loops (Fig. 5c and Fig. 5d positive control) characterizes a mature network of tubes, which is not present in the negative control or the spheroid-conditioned media, which correlates to the vascular compression and hypovascularity of PDAC.

Compared to our negative and positive control, BxPC-3:RLT-PSC:HMEC-1 and MiaPaCa-2:hPSC21:HMEC-1 spheroid-conditioned media showed a high number of branch points, covered area, and total tube length and are therefore useful to study anti-angiogenic treatment strategies (Fig. 5d). In contrast, BxPC-3:hPSC21:HMEC-1 and MiaPaCa-2:RLT-PSC:HMEC-1 spheroid-conditioned media induced a low number of branch points, total tube length, and covered area (Fig. 5d), and therefore these TCC models would be suitable to study pro-angiogenic treatment strategies. Additionally, the number of loops is an interesting metric to study a pro-angiogenic response, as none of the untreated spheroid-conditioned media resulted in a high number of loops, similar to the negative control (Fig. 5d).

In conclusion, our four TCC spheroids are valuable models to evaluate pro- or anti-angiogenic treatments, as demonstrated by the differential production of VEGF in the spheroid-conditioned medium and angiogenic capacity.

The complex and heterogeneous TME of PDAC, which is characterized by a dense stroma, limits drug delivery and can cause chemoresistance (3). Recognizing the crosstalk between different cell types is essential for tackling PDAC and developing better screening tools for drug discovery (4–6). In this study, we developed an innovative panel of TCC spheroid models that better mimic the complex TME observed in different types of PDAC tumors, with high-throughput screening potential for anti-cancer drugs and angiogenic compounds. Traditional 2D and mono-culture models struggle with translatability towards in vivo and clinical studies, while the adoption of 3D co-culture in vitro models, particularly our clinically relevant and heterogeneous TCC spheroids, signify an important shift in PDAC research (11–13).

To account for the heterogeneity of the TME, a hallmark of PDAC, we used two different PCC and PSC lines, mirroring the inter- and intratumoral complexity and diversity, as observed in patient tumors (13). As mutated KRAS is a key oncogenic driver in PDAC, we chose to include the KRAS mutated MiaPaCa-2 cell line (23). KRAS mutations are known to be correlated to high invasion and metastasis, which is also observed in our model when comparing the BxPC-3:RLT-PSC:HMEC-1 and MiaPaCa-2:RLT-PSC:HMEC-1 spheroids (24). On the other hand, BxPC-3 spheroids mimic the solidity of PDAC and the resulting poor drug penetration and high drug resistance (25).

Based on the seeding ratios and structural considerations, we developed four spheroid combinations representing different facets of the heterogeneity of the PDAC TME. The structural differences observed between mono-culture and TCC spheroids highlight the importance of considering the spatial arrangement and density for mimicking clinical scenarios. The absence of crosstalk between different cell types in mono-culture spheroids impacts various aspects, including responses to immunotherapy, growth rates, survival, invasion, metastasis, angiogenesis, and other critical factors (13, 26). In addition, the structural support provided by PSCs and ECs to the TCC spheroids prevented the formation of loose spheroids with the metastatic MiaPaCa-2 cell line, improving their compactness, which is true to clinical PDAC tumors, and resistance to common laboratory manipulation. We believe our model can be extended to incorporate immune cells to provide an even more comprehensive understanding of the TME, particularly in the context of immunotherapy response. Additionally, our triple co-culture model can easily be extended towards PDOs instead of pancreatic cancer cell lines, which would improve the clinical translatability of our models even further.

To validate the use of our models in drug screening, we assessed the drug response to gemcitabine and paclitaxel in both mono-culture and TCC spheroids. BxPC-3 mono-culture spheroids exhibited higher resistance, highlighting the higher sensitivity of stromal and endothelial cells to drugs in TCC spheroids. Notably, MiaPaCa-2 demonstrates greater sensitivity to chemotherapy in comparison to BxPC-3, highlighting the distinct drug resistance profiles, in agreement with previous studies (27). This signifies the investigation of diverse cell lines and TMEs exhibiting varying sensitivities to chemotherapy and allows for a comprehensive understanding of the dynamics of drug resistance. The differential response observed in TCC spheroids with BxPC-3:RLT-PSC:HMEC-1 suggests a specific interaction between these cell lines of cancer and stromal cells. Indeed, (epi)genetic changes (in different cell lines) contribute to the creation of unique TMEs that cause growth, angiogenesis, drug resistance, among others, where the specific communication between cancer cells and CAFs is a key component (28). The observed difference in drug response in our TCC spheroid models matched the patient-specific drug resistance profiles that were recently shown in PDOs (29). In this regard, we suggest to use at least two spheroid combinations in any study, with BxPC-3:RLT-PSC:HMEC-1 being a suitable model to represent a highly resistant tumor, while, MiaPaCa-2:hPSC21:HMEC-1 spheroids are ideal as more drug-sensitive tumors. Both TCC spheroids consist of different PCC and PSC lines, which ensures heterogeneity. Our drug regimen serves as a proof-of-concept for this TCC spheroid models and can be expanded to a broader spectrum of therapeutic agents for future investigations and high-throughput drug screening.

The variations in VEGF concentrations and angiogenesis found within our four different TCC spheroids highlight the influence of the TME on another hallmark of cancer, such as angiogenesis. As the lack or excess of angiogenesis is related to hypoxia, ineffective drug treatment, invasion, metastasis, and tumour growth, it is important to during in vitro research (30). Using a tube formation assay, our TCC spheroid models proved to be valuable for the quantitative assessment of angiogenesis. With spheroid-conditioned medium, we can study the crosstalk between cancer, stromal, and endothelial cells, specifically for angiogenesis upon a pro- or anti-angiogenic treatment. Combined, our data shows the importance and influence of the TME of PDAC in anti-cancer drug screening and angiogenic studies for which we offer a high-throughput in vitro models.

In comparison to the state-of-the-art of 3D co-culture models (16, 17), we were able to include CAFs, more specifically PSCs and ECs, into our spheroids in a clinically relevant number as determined with flow cytometry, which creates cell-to-cell interactions true to the complex TME of PDAC. Our TCC spheroid models overcome some of the challenges still faced by PDOs, as TCC spheroids incorporate the complex TME of PDAC while remaining a simple, low-cost, and easily accessible model suitable for high-throughput screening. While working with PDOs can still present some challenges due to the lack of standardized protocols and high cost (21), our TCC spheroid model incorporates the complex TME of PDAC and remains a simple, low-cost, and easily accessible model suitable for high-throughput screening. Moreover, our TCC model can be combined and extended with PDOs, following full characterisation and biobanking of the PDOs. Besides our validated applications in high-throughput drug screening and angiogenesis evaluation, our TCC spheroid model can also be expanded to other assays, such as, migration and invasion assays, immunohistochemistry staining, RNA sequencing, immunogenicity assays with natural killer or T cells, among others. To summarize, our TCC spheroid model offers, but is not limited to, high-throughput in vitro 3D drug screening or anti-cancer and angiogenic studies concerning PDAC in the context of a complex TME and its intra- and intertumoral heterogeneity.

In conclusion, we have established a panel of triple co-culture spheroid models that capture the complexity and heterogeneity of the pancreatic ductal adenocarcinoma tumor microenvironment. Unlike other alternatives, we were able to achieve this in a simple, cheap, and easily reproducible method, while ensuring that each cell population is present in a clinically relevant number. We showed the value of our triple co-culture spheroids for high-throughput drug screening and angiogenesis evaluation, however, our model is not limited to these applications. We provide a valuable tool for understanding this devastating disease and exploring new treatment strategies with higher clinical translatability.

PDAC	Pancreatic ductal adenocarcinoma
TME	Tumor microenvironment
PSC	Pancreatic stellate cell
EC	Endothelial cell
TCC	Triple co-culture
CAF	Cancer-associated fibroblast
2D	Two-dimensional
3D	Three-dimensional
ECM	Extracellular matrix
PDO	Patient-derived organoid
AUC	Area under the curve

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

We acknowledge financial support from the Fund for Scientific Research (FWO) Flanders (Grant ID 1SD6522N), the Research Fund of UAntwerp (BOF; FFB210293 and FFB210425) and “Kom op tegen Kanker” (Stand up to Cancer), the Flemish Cancer Society (grant number: 34986). We would also like to thank several patrons, as part of this research was funded by donations from different donors, including Dedert Schilde vzw, Mr. Willy Floren, and the Vereycken family.

Authors' contributions

RV conceived and designed the study, acquired and analysed data, and drafted this article. APM contributed to the concept, design, and coordination of the study, and drafting of this article. JVA and SR contributed to the design and analysis of the flow cytometry experiments and drafting this article. HZ participated in the data analysis and drafting of this article. CD provided resources, contributed to the methodology and drafting of this article. JDW supported the visualization and drafting of this article. APM, ES and AB supervised this study, provided resources, and contributed to the drafting of this article. All authors read and approved the final manuscript.

Acknowledgements

We thank M. Le Compte and E. Cardenas De La Hoz for their help with the data analysis and valuable input. All images were created with BioRender.com.

Rahib L, Smith BD, Aizenberg R, Rosenzweig AB, Fleshman JM, Matrisian LM. Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer Res. 2014;74(11):2913–21.
Hasan S, Jacob R, Manne U, Paluri R. Advances in pancreatic cancer biomarkers. Oncol Rev. 2019;13(1):410.
de Sousa Cavalcante L, Monteiro G. Gemcitabine: metabolism and molecular mechanisms of action, sensitivity and chemoresistance in pancreatic cancer. Eur J Pharmacol. 2014;741:8–16.
Zeisberg EM, Potenta S, Xie L, Zeisberg M, Kalluri R. Discovery of endothelial to mesenchymal transition as a source for carcinoma-associated fibroblasts. Cancer Res. 2007;67(21):10123–8.
Platel V, Faure S, Corre I, Clere N. Endothelial-to-Mesenchymal Transition (EndoMT): Roles in Tumorigenesis, Metastatic Extravasation and Therapy Resistance. J Oncol. 2019;2019:8361945.
Verloy R, Privat-Maldonado A, Smits E, Bogaerts A. Cold Atmospheric Plasma Treatment for Pancreatic Cancer-The Importance of Pancreatic Stellate Cells. Cancers (Basel). 2020;12(10):2782.
Peng J, Sun B-F, Chen C-Y, Zhou J-Y, Chen Y-S, Chen H, et al. Single-cell RNA-seq highlights intra-tumoral heterogeneity and malignant progression in pancreatic ductal adenocarcinoma. Cell Res. 2019;29(9):725–38.
Cros J, Raffenne J, Couvelard A, Pote N. Tumor Heterogeneity in Pancreatic Adenocarcinoma. Pathobiology. 2018;85(1–2):64–71.
Wang Y, Jeon H. 3D cell cultures toward quantitative high-throughput drug screening. Trends Pharmacol Sci. 2022;43(7):569–81.
Parasrampuria DA, Benet LZ, Sharma A. Why Drugs Fail in Late Stages of Development: Case Study Analyses from the Last Decade and Recommendations. AAPS J. 2018;20(3):46.
Brüningk SC, Rivens I, Box C, Oelfke U, ter Haar G. 3D tumour spheroids for the prediction of the effects of radiation and hyperthermia treatments. Sci Rep. 2020;10(1):1653.
Pape J, Emberton M, Cheema U. 3D Cancer Models: The Need for a Complex Stroma, Compartmentalization and Stiffness. Front Bioeng Biotechnol. 2021;9:660502.
Tomas-Bort E, Kieler M, Sharma S, Candido JB, Loessner D. 3D approaches to model the tumor microenvironment of pancreatic cancer. Theranostics. 2020;10(11):5074–89.
Ware MJ, Keshishian V, Law JJ, Ho JC, Favela CA, Rees P, et al. Generation of an in vitro 3D PDAC stroma rich spheroid model. Biomaterials. 2016;108:129–42.
Drifka CR, Loeffler AG, Esquibel CR, Weber SM, Eliceiri KW, Kao WJ. Human pancreatic stellate cells modulate 3D collagen alignment to promote the migration of pancreatic ductal adenocarcinoma cells. Biomed Microdevices. 2016;18(6):105.
Lazzari G, Nicolas V, Matsusaki M, Akashi M, Couvreur P, Mura S. Multicellular spheroid based on a triple co-culture: A novel 3D model to mimic pancreatic tumor complexity. Acta Biomater. 2018;78:296–307.
Steinberg E, Orehov N, Tischenko K, Schwob O, Zamir G, Hubert A et al. Rapid Clearing for High Resolution 3D Imaging of Ex Vivo Pancreatic Cancer Spheroids. Int J Mol Sci. 2020;21(20).
Yang H, Sun L, Liu M, Mao Y. Patient-derived organoids: a promising model for personalized cancer treatment. Gastroenterol Rep (Oxf). 2018;6(4):243–5.
Ohlund D, Handly-Santana A, Biffi G, Elyada E, Almeida AS, Ponz-Sarvise M, et al. Distinct populations of inflammatory fibroblasts and myofibroblasts in pancreatic cancer. J Exp Med. 2017;214(3):579–96.
Tsai S, McOlash L, Palen K, Johnson B, Duris C, Yang Q, et al. Development of primary human pancreatic cancer organoids, matched stromal and immune cells and 3D tumor microenvironment models. BMC Cancer. 2018;18(1):335.
Rae C, Amato F, Braconi C. Patient-Derived Organoids as a Model for Cancer Drug Discovery. Int J Mol Sci. 2021;22(7).
Foo MA, You M, Chan SL, Sethi G, Bonney GK, Yong WP, et al. Clinical translation of patient-derived tumour organoids- bottlenecks and strategies. Biomark Res. 2022;10(1):10.
Luo J. KRAS mutation in pancreatic cancer. Semin Oncol. 2021;48(1):10–8.
Rachagani S, Senapati S, Chakraborty S, Ponnusamy MP, Kumar S, Smith LM, et al. Activated KrasG(1)(2)D is associated with invasion and metastasis of pancreatic cancer cells through inhibition of E-cadherin. Br J Cancer. 2011;104(6):1038–48.
Han SJ, Kwon S, Kim KS. Challenges of applying multicellular tumor spheroids in preclinical phase. Cancer Cell Int. 2021;21(1):152.
Kapalczynska M, Kolenda T, Przybyla W, Zajaczkowska M, Teresiak A, Filas V, et al. 2D and 3D cell cultures - a comparison of different types of cancer cell cultures. Arch Med Sci. 2018;14(4):910–9.
Patki M, Saraswat A, Bhutkar S, Dukhande V, Patel K. In vitro assessment of a synergistic combination of gemcitabine and zebularine in pancreatic cancer cells. Exp Cell Res. 2021;405(2):112660.
Nilendu P, Sarode SC, Jahagirdar D, Tandon I, Patil S, Sarode GS, et al. Mutual concessions and compromises between stromal cells and cancer cells: driving tumor development and drug resistance. Cell Oncol (Dordr). 2018;41(4):353–67.
Le Compte M, De La Hoz EC, Peeters S, Fortes FR, Hermans C, Domen A, et al. Single-organoid analysis reveals clinically relevant treatment-resistant and invasive subclones in pancreatic cancer. NPJ Precis Oncol. 2023;7(1):128.
Nishida N, Yano H, Nishida T, Kamura T, Kojiro M. Angiogenesis in cancer. Vasc Health Risk Manag. 2006;2(3):213–9.
Jesnowski R, Furst D, Ringel J, Chen Y, Schrodel A, Kleeff J, et al. Immortalization of pancreatic stellate cells as an in vitro model of pancreatic fibrosis: deactivation is induced by matrigel and N-acetylcysteine. Lab Invest. 2005;85(10):1276–91.
Hamada S, Masamune A, Takikawa T, Suzuki N, Kikuta K, Hirota M, et al. Pancreatic stellate cells enhance stem cell-like phenotypes in pancreatic cancer cells. Biochem Biophys Res Commun. 2012;421(2):349–54.

RubenVerloyGraphicalAbstract.pdf
RubenVerloySupplementaryFigure1.pdf
Supplementary Figure 1. DMEM with VEGF/FGF supplements is not able to provide sufficient growth factors for HMEC-1 growth. Proliferation rate comparison of HMEC-1 growth in MCDB131 vs DMEM vs DMEM with supplements, based on green fluorescence intensity fold ratio as a measure of confluency normalized to day 1. Data are represented as mean ± SD (n ≥ 9 from three independent experiments).
RubenVerloySupplementaryFigure2.pdf
Supplementary Figure 2. Flow cytometry gating strategy. (a) singlets, (b) cells, (c) double negative population of two markers, (d) positive population of the third marker. (e) FMO control for CD31 on BxPC-3:RLT-PSC:HMEC-1 spheroids. (f) FMO control for CD31 on MiaPaCa-2:hPSC21:HMEC-1.
RubenVerloySupplementaryFigure3.pdf
Supplementary Figure 3.Quantitative flow cytometric data showing the relative contribution of each cell population (each bar represents one flow cytometry measurement of a pool of 48 spheroids). Examples of TCC spheroids that were not selected due to an inaccurate representation of PDAC tumors.

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Capturing the heterogeneity of the pancreatic ductal adenocarcinoma tumor microenvironment: novel triple co-culture spheroids for drug screening and angiogenic evaluation

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Abstract

Figures

Background

Methods

Cell culture

Proliferation Rate

Spheroid formation

Flow Cytometry

Cell Viability Assay

Enzyme-Linked Immunosorbent Assay

Tube Formation Assay

Statistical Analysis

Results

Spheroid formation is most optimal in MCDB131 medium

Triple co-culture spheroids demonstrate the heterogeneity of the PDAC tumor microenvironment

Contrasting spheroid compactness between BxPC-3 and MiaPaCa-2 spheroids and the influence of the tumor microenvironment

Unravelling drug response profiles: the impact of structural variations and the tumor microenvironment

Triple co-culture spheroids and angiogenesis: exploring treatment response

Discussion

Conclusion

List Of Abbreviations

Declarations

References

Supplementary Files

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