Metabolic reprogramming dynamics in tumor spheroids: Insights from a multicellular, multiscale model

Merging metabolic modeling and imaging for screening therapeutic targets in colorectal cancer

Cancer-associated fibroblasts (CAFs) play a key role in metabolic reprogramming and are well-established contributors to drug resistance in colorectal cancer (CRC). To exploit this metabolic crosstalk, we integrated a systems biology approach that identified key metabolic targets in a data-driven method and validated them experimentally. This process involved a novel machine learning-based method to computationally screen, in a high-throughput manner, the effects of enzyme perturbations predicted by a computational model of CRC metabolism. This approach reveals the network-wide effects of metabolic perturbations. Our results highlighted hexokinase (HK) as the crucial target, which subsequently became our focus for experimental validation using patient-derived tumor organoids (PDTOs). Through metabolic imaging and viability assays, we found that PDTOs cultured in CAF-conditioned media exhibited increased sensitivity to HK inhibition, confirming the model predictions. Our approach emphasizes the critical role of integrating computational and experimental techniques in exploring and exploiting CRC-CAF crosstalk.

Machine learning approach for recognition and morphological analysis of isolated astrocytes in phase contrast microscopy

Astrocytes are glycolytically active cells in the central nervous system playing a crucial role in various brain processes from homeostasis to neurotransmission. Astrocytes possess a complex branched morphology, frequently examined by fluorescent microscopy. However, staining and fixation may impact the properties of astrocytes, thereby affecting the accuracy of the experimental data of astrocytes dynamics and morphology. On the other hand, phase contrast microscopy can be used to study astrocytes morphology without affecting them, but the post-processing of the resulting low-contrast images is challenging. The main result of this work is a novel approach for recognition and morphological analysis of unstained astrocytes based on machine-learning recognition of microscopic images. We conducted a series of experiments involving the cultivation of isolated astrocytes from the rat brain cortex followed by microscopy. Using the proposed approach, we tracked the temporal evolution of the average total length of branches, branching, and area per astrocyte in our experiments. We believe that the proposed approach and the obtained experimental data will be of interest and benefit to the scientific communities in cell biology, biophysics, and machine learning.

3D Models of Sarcomas: The Next-generation Tool for Personalized Medicine

Sarcoma is a complex and heterogeneous cancer that has been difficult to study in vitro. While two-dimensional (2D) cell cultures and mouse models have been the dominant research tools, three-dimensional (3D) culture systems such as organoids have emerged as promising alternatives. In this review, we discuss recent developments in sarcoma organoid culture, with a focus on their potential as tools for drug screening and biobanking. We also highlight the ways in which sarcoma organoids have been used to investigate the mechanisms of gene regulation, drug resistance, metastasis, and immune interactions. Sarcoma organoids have shown to retain characteristics of in vivo biology within an in vitro system, making them a more representative model for sarcoma research. Our review suggests that sarcoma organoids offer a potential path forward for translational research in this field and may provide a platform for developing personalized therapies for sarcoma patients.

Metabolic symbiosis between oxygenated and hypoxic tumour cells: An agent-based modelling study

Deregulated metabolism is one of the hallmarks of cancer. It is well-known that tumour cells tend to metabolize glucose via glycolysis even when oxygen is available and mitochondrial respiration is functional. However, the lower energy efficiency of aerobic glycolysis with respect to mitochondrial respiration makes this behaviour, namely the Warburg effect, counter-intuitive, although it has now been recognized as source of anabolic precursors. On the other hand, there is evidence that oxygenated tumour cells could be fuelled by exogenous lactate produced from glycolysis. We employed a multi-scale approach that integrates multi-agent modelling, diffusion-reaction, stoichiometric equations, and Boolean networks to study metabolic cooperation between hypoxic and oxygenated cells exposed to varying oxygen, nutrient, and inhibitor concentrations. The results show that the cooperation reduces the depletion of environmental glucose, resulting in an overall advantage of using aerobic glycolysis. In addition, the oxygen level was found to be decreased by symbiosis, promoting a further shift towards anaerobic glycolysis. However, the oxygenated and hypoxic populations may gradually reach quasi-equilibrium. A sensitivity analysis using Latin hypercube sampling and partial rank correlation shows that the symbiotic dynamics depends on properties of the specific cell such as the minimum glucose level needed for glycolysis. Our results suggest that strategies that block glucose transporters may be more effective to reduce tumour growth than those blocking lactate intake transporters.

MCT4 blockade increases the efficacy of immune checkpoint blockade

BACKGROUND & AIMS

Intratumoral lactate accumulation and acidosis impair T-cell function and antitumor immunity. Interestingly, expression of the lactate transporter monocarboxylate transporter (MCT) 4, but not MCT1, turned out to be prognostic for the survival of patients with rectal cancer, indicating that single MCT4 blockade might be a promising strategy to overcome glycolysis-related therapy resistance.

METHODS

To determine whether blockade of MCT4 alone is sufficient to improve the efficacy of immune checkpoint blockade (ICB) therapy, we examined the effects of the selective MCT1 inhibitor AZD3965 and a novel MCT4 inhibitor in a colorectal carcinoma (CRC) tumor spheroid model co-cultured with blood leukocytes in vitro and the MC38 murine CRC model in vivo in combination with an antibody against programmed cell death ligand-1(PD-L1).

RESULTS

Inhibition of MCT4 was sufficient to reduce lactate efflux in three-dimensional (3D) CRC spheroids but not in two-dimensional cell-cultures. Co-administration of the MCT4 inhibitor and ICB augmented immune cell infiltration, T-cell function and decreased CRC spheroid viability in a 3D co-culture model of human CRC spheroids with blood leukocytes. Accordingly, combination of MCT4 and ICB increased intratumoral pH, improved leukocyte infiltration and T-cell activation, delayed tumor growth, and prolonged survival in vivo. MCT1 inhibition exerted no further beneficial impact.

CONCLUSIONS

These findings demonstrate that single MCT4 inhibition represents a novel therapeutic approach to reverse lactic-acid driven immunosuppression and might be suitable to improve ICB efficacy.

Computational Modeling to Determine the Effect of Phenotypic Heterogeneity in Tumors on the Collective Tumor-Immune Interactions

Tumor immunotherapy aims to maintain or enhance the killing capability of CD8+ T cells to clear tumor cells. The tumor-immune interactions affect the function of CD8+ T cells. However, the effect of phenotype heterogeneity of a tumor mass on the collective tumor-immune interactions is insufficiently investigated. We developed the cellular-level computational model based on the principle of cellular Potts model to solve the case mentioned above. We considered how asymmetric division and glucose distribution jointly regulated the transient changes in the proportion of proliferating/quiescent tumor cells in a solid tumor mass. The evolution of a tumor mass in contact with T cells was explored and validated by comparing it with previous studies. Our modeling exhibited that proliferating/quiescent tumor cells, exhibiting distinct anti-apoptotic and suppressive behaviors, redistributed within the domain accompanied by the evolution of a tumor mass. Collectively, a tumor mass prone to a quiescent state weakened the collective suppressive functions of a tumor mass on cytotoxic T cells and triggered a decline of apoptosis of tumor cells. Although quiescent tumor cells did not sufficiently do their inhibitory functions, the possibility of long-term survival was improved due to their interior location within a mass. Overall, the proposed model provides a useful framework to investigate collective-targeted strategies for improving the efficiency of immunotherapy.

Tumour growth: An approach to calibrate parameters of a multiphase porous media model based on in vitro observations of Neuroblastoma spheroid growth in a hydrogel microenvironment

To unravel processes that lead to the growth of solid tumours, it is necessary to link knowledge of cancer biology with the physical properties of the tumour and its interaction with the surrounding microenvironment. Our understanding of the underlying mechanisms is however still imprecise. We therefore developed computational physics-based models, which incorporate the interaction of the tumour with its surroundings based on the theory of porous media. However, the experimental validation of such models represents a challenge to its clinical use as a prognostic tool. This study combines a physics-based model with in vitro experiments based on microfluidic devices used to mimic a three-dimensional tumour microenvironment. By conducting a global sensitivity analysis, we identify the most influential input parameters and infer their posterior distribution based on Bayesian calibration. The resulting probability density is in agreement with the scattering of the experimental data and thus validates the proposed workflow. This study demonstrates the huge challenges associated with determining precise parameters with usually only limited data for such complex processes and models, but also demonstrates in general how to indirectly characterise the mechanical properties of neuroblastoma spheroids that cannot feasibly be measured experimentally.

Evaluation of Lung Cancer Patient Response to First-Line Chemotherapy by Integration of Tumor Core Biopsy Metabolomics with Multiscale Modeling

The standard of care for intermediate (Stage II) and advanced (Stages III and IV) non-small cell lung cancer (NSCLC) involves chemotherapy with taxane/platinum derivatives, with or without radiation. Ideally, patients would be screened a priori to allow non-responders to be initially treated with second-line therapies. This evaluation is non-trivial, however, since tumors behave as complex multiscale systems. To address this need, this study employs a multiscale modeling approach to evaluate first-line chemotherapy response of individual patient tumors based on metabolomic analysis of tumor core biopsies obtained during routine clinical evaluation. Model parameters were calculated for a patient cohort as a function of these metabolomic profiles, previously obtained from high-resolution 2DLC-MS/MS analysis. Evaluation metrics were defined to classify patients as Disease-Control (DC) [encompassing complete-response (CR), partial-response (PR), and stable-disease (SD)] and Progressive-Disease (PD) following first-line chemotherapy. Response was simulated for each patient and compared to actual response. The results show that patient classifications were significantly separated from each other, and also when grouped as DC vs. PD and as CR/PR vs. SD/PD, by fraction of initial tumor radius metric at 6 days post simulated bolus drug injection. This study shows that patient first-line chemotherapy response can in principle be evaluated from multiscale modeling integrated with tumor tissue metabolomic data, offering a first step towards individualized lung cancer treatment prognosis.

Hybrid computational models of multicellular tumour growth considering glucose metabolism

Cancer cells metabolize glucose through metabolic pathways that differ from those used by healthy and differentiated cells. In particular, tumours have been shown to consume more glucose than their healthy counterparts and to use anaerobic metabolic pathways, even under aerobic conditions. Nevertheless, scientists have still not been able to explain why cancer cells evolved to present an altered metabolism and what evolutionary advantage this might provide them. Experimental and computational models have been increasingly used in recent years to understand some of these biological questions. Multicellular tumour spheroids are effective experimental models as they replicate the initial stages of avascular solid tumour growth. Furthermore, these experiments generate data which can be used to calibrate and validate computational studies that aim to simulate tumour growth. Hybrid models are of particular relevance in this field of research because they model cells as individual agents while also incorporating continuum representations of the substances present in the surrounding microenvironment that may participate in intracellular metabolic networks as concentration or density distributions. Henceforth, in this review, we explore the potential of computational modelling to reveal the role of metabolic reprogramming in tumour growth.

Glioblastoma immuno-endothelial multicellular microtissue as a 3D in vitro evaluation tool of anti-cancer nano-therapeutics

Despite being the most prevalent and lethal type of adult brain cancer, glioblastoma (GBM) remains intractable. Promising anti-GBM nanoparticle (NP) systems have been developed to improve the anti-cancer performance of difficult-to-deliver therapeutics, with particular emphasis on tumor targeting strategies. However, current disease modeling toolboxes lack close-to-native in vitro models that emulate GBM microenvironment and bioarchitecture, thus partially hindering translation due to poorly predicted clinical responses. Herein, human GBM heterotypic multicellular tumor microtissues (MCTMs) are generated through high-throughput 3D modeling of U-251 MG tumor cells, tissue differentiated macrophages isolated from peripheral monocytes, and brain microvascular primary endothelial cells. GBM MCTMs mimicked tumor spatial organization, extracellular matrix production and necrosis areas. The bioactivity of a model drug, docetaxel (DTX), and of tumor-targeted DTX-loaded polymeric NPs with a surface L-Histidine moiety (H-NPs), were assessed in the MCTMs. MCTMs cell uptake and anti-proliferative effect was 8- and 3-times higher for H-NPs, respectively, compared to the non-targeted NPs and to free DTX. H-NPs provided a decrease of MCTMs anti-inflammatory M2-macrophages, while increasing their pro-inflammatory M1 counterparts. Moreover, H-NPs showed a particular biomolecular signature through reduced secretion of an array of medium cytokines (IFN-γ, IL-1β, IL-1Ra, IL-6, IL-8, TGF-β). Overall, MCTMs provide an in vitro biomimetic model to recapitulate key cellular and structural features of GBM and improve in vivo drug response predictability, fostering future clinical translation of anti-GBM nano-therapeutic strategies.

Organoid Cultures In Silico: Tools or Toys?

The implementation of stem-cell-based organoid culture more than ten years ago started a development that created new avenues for diagnostic analyses and regenerative medicine. In parallel, computational modelling groups realized the potential of this culture system to support their theoretical approaches to study tissues in silico. These groups developed computational organoid models (COMs) that enabled testing consistency between cell biological data and developing theories of tissue self-organization. The models supported a mechanistic understanding of organoid growth and maturation and helped linking cell mechanics and tissue shape in general. What comes next? Can we use COMs as tools to complement the equipment of our biological and medical research? While these models already support experimental design, can they also quantitatively predict tissue behavior? Here, we review the current state of the art of COMs and discuss perspectives for their application.

The modeling study of the effect of morphological behaviors of extracellular matrix fibers on the dynamic interaction between tumor cells and antitumor immune response

Low response rate limits the effective application of immunotherapy, in which the interactions between tumor cells and immune cells play a significant role. The strength of regulation could be mediated by extracellular matrix (ECM) fibers, which is still insufficiently investigated. In the study, the cellular potts model was utilized to explore the role of morphological properties of ECM in tumor-immune interactions. It was observed that high-density random ECM fibers delayed the interaction between tumor cells and T cells. Moreover, the tumor-immune interactions were ECM morphology-specific. Radial ECM fibers exhibited weaker inhibitory role in the process of contact between tumor cells and T cells. This study provided the useful mechanism of tumor-immune interactions from the viewpoint of morphological effect of ECM fibers, facilitating improving the efficiency of immunotherapy.

Modeling of Tumor Growth with Input from Patient-Specific Metabolomic Data

Advances in omic technologies have provided insight into cancer progression and treatment response. However, the nonlinear characteristics of cancer growth present a challenge to bridge from the molecular- to the tissue-scale, as tumor behavior cannot be encapsulated by the sum of the individual molecular details gleaned experimentally. Mathematical modeling and computational simulation have been traditionally employed to facilitate analysis of nonlinear systems. In this study, for the first time tumor metabolomic data are linked via mathematical modeling to the tumor tissue-scale behavior, showing the capability to mechanistically simulate cancer progression personalized to omic information obtainable from patient tumor core biopsy analysis. Generally, a higher degree of metabolic dysregulation has been correlated with more aggressive tumor behavior. Accordingly, key parameters influenced by metabolomic data in this model include tumor proliferation, vascularization, aggressiveness, lactic acid production, monocyte infiltration and macrophage polarization, and drug effect. The model enables evaluating interactions of interest between these parameters which drive tumor growth based on the metabolomic data. The results show that the model can group patients consistently with the clinically observed outcomes of response/non-response to chemotherapy. This modeling approach provides a first step towards evaluation of tumor growth based on tumor-specific metabolomic data.

Mechanistic characterization of endothelial sprouting mediated by pro-angiogenic signaling

Objective

We aim to quantitatively characterize the crosstalk between VEGF‐ and FGF‐mediated angiogenic signaling and endothelial sprouting, to gain mechanistic insights and identify novel therapeutic strategies.

Methods

We constructed an experimentally validated hybrid agent‐based mathematical model that characterizes endothelial sprouting driven by FGF‐ and VEGF‐mediated signaling. We predicted the total sprout length, number of sprouts, and average length by the mono‐ and co‐stimulation of FGF and VEGF.

Results

The experimentally fitted and validated model predicts that FGF induces stronger angiogenic responses in the long‐term compared with VEGF stimulation. Also, FGF plays a dominant role in the combination effects in endothelial sprouting. Moreover, the model suggests that ERK and Akt pathways and cellular responses contribute differently to the sprouting process. Last, the model predicts that the strategies to modulate endothelial sprouting are context‐dependent, and our model can identify potential effective pro‐ and anti‐angiogenic targets under different conditions and study their efficacy.

Conclusions

The model provides detailed mechanistic insight into VEGF and FGF interactions in sprouting angiogenesis. More broadly, this model can be utilized to identify targets that influence angiogenic signaling leading to endothelial sprouting and to study the effects of pro‐ and anti‐angiogenic therapies.

Multiscale modeling in disease

Multiscale computational modeling aims to connect the complex networks of effects at different length and/or time scales. For example, these networks often include intracellular molecular signaling, crosstalk, and other interactions between neighboring cell populations, and higher levels of emergent phenomena across different regions of tissues and among collections of tissues or organs interacting with each other in the whole body. Recent applications of multiscale modeling across intracellular, cellular, and/or tissue levels are highlighted here. These models incorporated the roles of biochemical and biomechanical modulation in processes that are implicated in the mechanisms of several diseases including fibrosis, joint and bone diseases, respiratory infectious diseases, and cancers.

Agent-based modeling reveals benefits of heterogeneous and stochastic cell populations during cGAS-mediated IFNβ production

MOTIVATION

The cGAS pathway is a component of the innate immune system responsible for the detection of pathogenic DNA and upregulation of interferon beta (IFNβ). Experimental evidence shows that IFNβ signaling occurs in highly heterogeneous cells and is stochastic in nature; however, the benefits of these attributes remain unclear. To investigate how stochasticity and heterogeneity affect IFNβ production, an agent-based model is developed to simulate both DNA transfection and viral infection.

RESULTS

We show that heterogeneity can enhance IFNβ responses during infection. Furthermore, by varying the degree of IFNβ stochasticity, we find that only a percentage of cells (20-30%) need to respond during infection. Going beyond this range provides no additional protection against cell death or reduction of viral load. Overall, these simulations suggest that heterogeneity and stochasticity are important for moderating immune potency while minimizing cell death during infection.

AVAILABILITY AND IMPLEMENTATION

Model repository is available at: https://github.com/ImmuSystems-Lab/AgentBasedModel-cGASPathway.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An integrated framework for quantifying immune-tumour interactions in a 3D co-culture model

Investigational in vitro models that reflect the complexity of the interaction between the immune system and tumours are limited and difficult to establish. Herein, we present a platform to study the tumour-immune interaction using a co-culture between cancer spheroids and activated immune cells. An algorithm was developed for analysis of confocal images of the co-culture to evaluate the following quantitatively; immune cell infiltration, spheroid roundness and spheroid growth. As a proof of concept, the effect of the glucocorticoid stress hormone, cortisol was tested on 66CL4 co-culture model. Results were comparable to 66CL4 syngeneic in vivo mouse model undergoing psychological stress. Furthermore, administration of glucocorticoid receptor antagonists demonstrated the use of this model to determine the effect of treatments on the immune-tumour interplay. In conclusion, we provide a method of quantifying the interaction between the immune system and cancer, which can become a screening tool in immunotherapy design.

Recent advances in understanding the role of metabolic heterogeneities in cell migration

Migration is an energy-intensive, multi-step process involving cell adhesion, protrusion, and detachment. Each of these steps require cells to generate and consume energy, regulating their morphological changes and force generation. Given the need for energy to move, cellular metabolism has emerged as a critical regulator of both single cell and collective migration. Recently, metabolic heterogeneity has been highlighted as a potential determinant of collective cell behavior, as individual cells may play distinct roles in collective migration. Several tools and techniques have been developed and adapted to study cellular energetics during migration including live-cell probes to characterize energy utilization and metabolic state and methodologies to sort cells based on their metabolic profile. Here, we review the recent advances in techniques, parsing the metabolic heterogeneities inherent in cell populations and their contributions to cell migration.

SARS-COV-2 infection and lung tumor microenvironment

Coronavirus Disease 2019 (COVID-19) is an acute respiratory syndrome, reported at the end of 2019 in China originally and immediately spread affecting over ten million world population to date. This pandemic is more lethal for the older population and those who previously suffered from other ailments such as cardiovascular diseases, respiratory disorders, and other immune system affecting abnormalities including cancers. Lung cancer is an important comorbidity of COVID-19. In this review, we emphasized the impact of lung tumor microenvironment (TME) on the possibility of enhanced severity of infection caused by the SARS-Co-V2. The compromised lung TME is further susceptible to the attack of viruses. The lung cells are also abundant in the virus entry receptors. Several SARS-Co-V2 proteins can modulate the lung TME by disrupting the fragile immune mechanisms contributing to cytokine storming and cellular metabolic variations. We also discussed the impact of medication used for lung cancer in the scenario of this infection. Since other respiratory infections can be a risk factor for lung cancer, COVID-19 recovered patients should be monitored for tumor development, especially if there is genetic susceptibility or it involves exposure to other risk factors.

Role of Glutathione in Cancer: From Mechanisms to Therapies

Glutathione (GSH) is the most abundant non-protein thiol present at millimolar concentrations in mammalian tissues. As an important intracellular antioxidant, it acts as a regulator of cellular redox state protecting cells from damage caused by lipid peroxides, reactive oxygen and nitrogen species, and xenobiotics. Recent studies have highlighted the importance of GSH in key signal transduction reactions as a controller of cell differentiation, proliferation, apoptosis, ferroptosis and immune function. Molecular changes in the GSH antioxidant system and disturbances in GSH homeostasis have been implicated in tumor initiation, progression, and treatment response. Hence, GSH has both protective and pathogenic roles. Although in healthy cells it is crucial for the removal and detoxification of carcinogens, elevated GSH levels in tumor cells are associated with tumor progression and increased resistance to chemotherapeutic drugs. Recently, several novel therapies have been developed to target the GSH antioxidant system in tumors as a means for increased response and decreased drug resistance. In this comprehensive review we explore mechanisms of GSH functionalities and different therapeutic approaches that either target GSH directly, indirectly or use GSH-based prodrugs. Consideration is also given to the computational methods used to describe GSH related processes for in silico testing of treatment effects.

3D Culture Modelling: An Emerging Approach for Translational Cancer Research in Sarcomas

Sarcomas are tumours of mesenchymal origin, which can arise in bone or soft tissues. They are rare but frequently quite aggressive and with a poor outcome. New approaches are needed to characterise these tumours and their resistance mechanisms to current therapies, responsible for tumour recurrence and treatment failure. This review is focused on the potential of three-dimensional (3D) in vitro models, including multicellular tumour spheroids (MCTS) and organoids, and the latest data about their utility for the study on important properties for tumour development. The use of spheroids as a particularly valuable alternative for compound high throughput screening (HTS) in different areas of cancer biology is also discussed, which enables the identification of new therapeutic opportunities in commonly resistant tumours.

Spatio-temporal aspects of the interplay of cancer and the immune system

The conventional mean-field kinetic models describing the interplay of cancer and the immune system are temporal and predict exponential growth or elimination of the population of tumour cells provided their number is small and their effect on the immune system is negligible. More complex kinetics are associated with non-linear features of the response of the immune system. The generic model presented in this communication takes into account that the rates of the birth and death of tumour cells inside a tumour spheroid can significantly depend on the radial coordinate due to diffusion limitations in the supply of nutrients and/or transport of the species (cells and proteins) belonging to the immune system. In this case, non-trivial kinetic regimes are shown to be possible even without appreciable perturbation of the immune system.