Mathematical models of cancer metabolism

MACAW: a method for semi-automatic detection of errors in genome-scale metabolic models

Genome-scale metabolic models (GSMMs) are used to predict metabolic fluxes, with applications ranging from identifying novel drug targets to engineering microbial metabolism. Erroneous or missing reactions, scattered throughout densely interconnected networks, are a limiting factor in these applications. We present Metabolic Accuracy Check and Analysis Workflow (MACAW), a suite of algorithms that helps to identify and visualize errors at the level of connected pathways, rather than individual reactions. We show how MACAW highlights inaccuracies of varying severity in manually curated and automatically generated GSMMs for humans, yeast, and bacteria and helps to identify systematic issues to be addressed in future model construction efforts.

Semi-Automatic Detection of Errors in Genome-Scale Metabolic Models

Background

Genome-Scale Metabolic Models (GSMMs) are used for numerous tasks requiring computational estimates of metabolic fluxes, from predicting novel drug targets to engineering microbes to produce valuable compounds. A key limiting step in most applications of GSMMs is ensuring their representation of the target organism's metabolism is complete and accurate. Identifying and visualizing errors in GSMMs is complicated by the fact that they contain thousands of densely interconnected reactions. Furthermore, many errors in GSMMs only become apparent when considering pathways of connected reactions collectively, as opposed to examining reactions individually.

Results

We present Metabolic Accuracy Check and Analysis Workflow (MACAW), a collection of algorithms for detecting errors in GSMMs. The relative frequencies of errors we detect in manually curated GSMMs appear to reflect the different approaches used to curate them. Changing the method used to automatically create a GSMM from a particular organism's genome can have a larger impact on the kinds of errors in the resulting GSMM than using the same method with a different organism's genome. Our algorithms are particularly capable of identifying errors that are only apparent at the pathway level, including loops, and nontrivial cases of dead ends.

Conclusions

MACAW is capable of identifying inaccuracies of varying severity in a wide range of GSMMs. Correcting these errors can measurably improve the predictive capacity of a GSMM. The relative prevalence of each type of error we identify in a large collection of GSMMs could help shape future efforts for further automation of error correction and GSMM creation.

Metabolic control analysis as a strategy to identify therapeutic targets, the case of cancer glycolysis

The use of kinetic modeling and metabolic control analysis (MCA) to identify possible therapeutic targets and to investigate the controlling and regulatory mechanisms in cancer glycolysis is here reviewed. The glycolytic pathway has been considered a target to decrease cancer cell growth; however, its occurrence in normal cells makes it difficult to design therapeutic strategies that target this pathway in pathological cells. Notwithstanding, the over-expression of all enzymes and transporters, as well as the expression of isoenzymes with different kinetic and regulatory properties in cancer cells, suggested a different distribution of the control of glycolytic flux than that observed in normal cells. Kinetic models of glycolysis are constructed with enzyme kinetics experimental data, validated with the steady-state metabolite concentrations and glycolytic fluxes; applying MCA, permitted us to identify the steps with the highest control of glycolysis in cancer cells, but low control in normal cells. The cancer glycolysis main controlling steps under several metabolic conditions were: glucose transport, hexokinase and hexose-6-phosphate isomerase (HPI); whereas in normal cells were: the first two and phosphofructokinase-1. HPI is the best therapeutic target because it exerts high control in cancer glycolytic flux, but not in normal cells. Furthermore, kinetic modeling also contributed to identifying new feed-back and feed-forward regulatory loops in cancer cells glycolysis, and to understanding the mode of metabolic action of glycolytic inhibitors. Thus, MCA and metabolic modeling allowed to propose new strategies for inhibiting glycolysis in cancer cells.

A happy cell stays home: When metabolic stress creates epigenetic advantages in the tumor microenvironment

A paradox of fast-proliferating tumor cells is that they deplete extracellular nutrients that often results in a nutrient poor microenvironment in vivo. Having a better understanding of the adaptation mechanisms cells exhibit in response to metabolic stress will open new therapeutic windows targeting the tumor’s extreme nutrient microenvironment. Glutamine is one of the most depleted amino acids in the tumor core and here, we provide insight into how important glutamine and its downstream by-product, α-ketoglutarate (αKG), are to communicating information about the nutrient environment. This communication is key in the cell’s ability to foster adaptation. We highlight the epigenetic changes brought on when αKG concentrations are altered in cancer and discuss how depriving cells of glutamine may lead to cancer cell de-differentiation and the ability to grow and thrive in foreign environments. When we starve cells, they adapt to survive. Those survival “skills” allow them to go out looking for other places to live and metastasize. We further examine current challenges to modelling the metabolic tumor microenvironment in the laboratory and discuss strategies that consider current findings to target the tumor’s poor nutrient microenvironment.

Metabolic targeting of malignant tumors: a need for systemic approach

PURPOSE

Dysregulated metabolism is now recognized as a fundamental hallmark of carcinogenesis inducing aggressive features and additional hallmarks. In this review, well-established metabolic changes displayed by tumors are highlighted in a comprehensive manner and corresponding therapeutical targets are discussed to set up a framework for integrating basic research findings with clinical translation in oncology setting.

METHODS

Recent manuscripts of high research impact and relevant to the field from PubMed (2000-2021) have been reviewed for this article.

RESULTS

Metabolic pathway disruption during tumor evolution is a dynamic process potentiating cell survival, dormancy, proliferation and invasion even under dismal conditions. Apart from cancer cells, though, tumor microenvironment has an acting role as extracellular metabolites, pH alterations and stromal cells reciprocally interact with malignant cells, ultimately dictating tumor-promoting responses, disabling anti-tumor immunity and promoting resistance to treatments.

CONCLUSION

In the field of cancer metabolism, there are several emerging prognostic and therapeutic targets either in the form of gene expression, enzyme activity or metabolites which could be exploited for clinical purposes; both standard-of-care and novel treatments may be evaluated in the context of metabolism rewiring and indeed, synergistic effects between metabolism-targeting and other therapies would be an attractive perspective for further research.

Integrating a dynamic central metabolism model of cancer cells with a hybrid 3D multiscale model for vascular hepatocellular carcinoma growth

We develop here a novel modelling approach with the aim of closing the conceptual gap between tumour-level metabolic processes and the metabolic processes occurring in individual cancer cells. In particular, the metabolism in hepatocellular carcinoma derived cell lines (HEPG2 cells) has been well characterized but implementations of multiscale models integrating this known metabolism have not been previously reported. We therefore extend a previously published multiscale model of vascular tumour growth, and integrate it with an experimentally verified network of central metabolism in HEPG2 cells. This resultant combined model links spatially heterogeneous vascular tumour growth with known metabolic networks within tumour cells and accounts for blood flow, angiogenesis, vascular remodelling and nutrient/growth factor transport within a growing tumour, as well as the movement of, and interactions between normal and cancer cells. Model simulations report for the first time, predictions of spatially resolved time courses of core metabolites in HEPG2 cells. These simulations can be performed at a sufficient scale to incorporate clinically relevant features of different tumour systems using reasonable computational resources. Our results predict larger than expected temporal and spatial heterogeneity in the intracellular concentrations of glucose, oxygen, lactate pyruvate, f16bp and Acetyl-CoA. The integrated multiscale model developed here provides an ideal quantitative framework in which to study the relationship between dosage, timing, and scheduling of anti-neoplastic agents and the physiological effects of tumour metabolism at the cellular level. Such models, therefore, have the potential to inform treatment decisions when drug response is dependent on the metabolic state of individual cancer cells.

Computational quantification of global effects induced by mutations and drugs in signaling networks of colorectal cancer cells

Colorectal cancer (CRC) is one of the most deadly and commonly diagnosed tumors worldwide. Several genes are involved in its development and progression. The most frequent mutations concern APC, KRAS, SMAD4, and TP53 genes, suggesting that CRC relies on the concomitant alteration of the related pathways. However, with classic molecular approaches, it is not easy to simultaneously analyze the interconnections between these pathways. To overcome this limitation, recently these pathways have been included in a huge chemical reaction network (CRN) describing how information sensed from the environment by growth factors is processed by healthy colorectal cells. Starting from this CRN, we propose a computational model which simulates the effects induced by single or multiple concurrent mutations on the global signaling network. The model has been tested in three scenarios. First, we have quantified the changes induced on the concentration of the proteins of the network by a mutation in APC, KRAS, SMAD4, or TP53. Second, we have computed the changes in the concentration of p53 induced by up to two concurrent mutations affecting proteins upstreams in the network. Third, we have considered a mutated cell affected by a gain of function of KRAS, and we have simulated the action of Dabrafenib, showing that the proposed model can be used to determine the most effective amount of drug to be delivered to the cell. In general, the proposed approach displays several advantages, in that it allows to quantify the alteration in the concentration of the proteins resulting from a single or multiple given mutations. Moreover, simulations of the global signaling network of CRC may be used to identify new therapeutic targets, or to disclose unexpected interactions between the involved pathways.

Potential of Immunotherapies in Treating Hematological Cancer-Infection Comorbidities-A Mathematical Modelling Approach

BACKGROUND

The immune system attacks threats like an emerging cancer or infections like COVID-19 but it also plays a role in dealing with autoimmune disease, e.g., inflammatory bowel diseases, and aging. Malignant cells may tend to be eradicated, to appraoch a dormant state or escape the immune system resulting in uncontrolled growth leading to cancer progression. If the immune system is busy fighting a cancer, a severe infection on top of it may compromise the immunoediting and the comorbidity may be too taxing for the immune system to control.

METHOD

A novel mechanism based computational model coupling a cancer-infection development to the adaptive immune system is presented and analyzed. The model maps the outcome to the underlying physiological mechanisms and agree with numerous evidence based medical observations.

RESULTS AND CONCLUSIONS

Progression of a cancer and the effect of treatments depend on the cancer size, the level of infection, and on the efficiency of the adaptive immune system. The model exhibits bi-stability, i.e., virtual patient trajectories gravitate towards one of two stable steady states: a dormant state or a full-blown cancer-infection disease state. An infectious threshold curve exists and if infection exceed this separatrix for sufficiently long time the cancer escapes. Thus, early treatment is vital for remission and severe infections may instigate cancer progression. CAR T-cell Immunotherapy may sufficiently control cancer progression back into a dormant state but the therapy significantly gains efficiency in combination with antibiotics or immunomodulation.

Phenotypic variation modulates the growth dynamics and response to radiotherapy of solid tumours under normoxia and hypoxia

In cancer, treatment failure and disease recurrence have been associated with small subpopulations of cancer cells with a stem-like phenotype. In this paper, we develop and investigate a phenotype-structured model of solid tumour growth in which cells are structured by a stemness level, which varies continuously between stem-like and terminally differentiated behaviours. Cell evolution is driven by proliferation and death, as well as advection and diffusion with respect to the stemness structure variable. Here, the magnitude and sign of the advective flux are allowed to vary with the oxygen level. We use the model to investigate how the environment, in particular oxygen levels, affects the tumour's population dynamics and composition, and its response to radiotherapy. We use a combination of numerical and analytical techniques to quantify how under physiological oxygen levels the cells evolve to a differentiated phenotype and under low oxygen level (i.e., hypoxia) they de-differentiate. Under normoxia, the proportion of cancer stem cells is typically negligible and the tumour may ultimately become extinct whereas under hypoxia cancer stem cells comprise a dominant proportion of the tumour volume, enhancing radio-resistance and favouring the tumour's long-term survival. We then investigate how such phenotypic heterogeneity impacts the tumour's response to treatment with radiotherapy under normoxia and hypoxia. Of particular interest is establishing how the presence of radio-resistant cancer stem cells can facilitate a tumour's regrowth following radiotherapy. We also use the model to show how radiation-induced changes in tumour oxygen levels can give rise to complex re-growth dynamics. For example, transient periods of hypoxia induced by damage to tumour blood vessels may rescue the cancer cell population from extinction and drive secondary regrowth.

Modeling the Th17 and Tregs Paradigm: Implications for Cancer Immunotherapy

CD4 + T cell differentiation is governed by gene regulatory and metabolic networks, with both networks being highly interconnected and able to adapt to external stimuli. Th17 and Tregs differentiation networks play a critical role in cancer, and their balance is affected by the tumor microenvironment (TME). Factors from the TME mediate recruitment and expansion of Th17 cells, but these cells can act with pro or anti-tumor immunity. Tregs cells are also involved in tumor development and progression by inhibiting antitumor immunity and promoting immunoevasion. Due to the complexity of the underlying molecular pathways, the modeling of biological systems has emerged as a promising solution for better understanding both CD4 + T cell differentiation and cancer cell behavior. In this review, we present a context-dependent vision of CD4 + T cell transcriptomic and metabolic network adaptability. We then discuss CD4 + T cell knowledge-based models to extract the regulatory elements of Th17 and Tregs differentiation in multiple CD4 + T cell levels. We highlight the importance of complementing these models with data from omics technologies such as transcriptomics and metabolomics, in order to better delineate existing Th17 and Tregs bifurcation mechanisms. We were able to recompilate promising regulatory components and mechanisms of Th17 and Tregs differentiation under normal conditions, which we then connected with biological evidence in the context of the TME to better understand CD4 + T cell behavior in cancer. From the integration of mechanistic models with omics data, the transcriptomic and metabolomic reprograming of Th17 and Tregs cells can be predicted in new models with potential clinical applications, with special relevance to cancer immunotherapy.

Gain and loss of function mutations in biological chemical reaction networks: a mathematical model with application to colorectal cancer cells

This paper studies a system of Ordinary Differential Equations modeling a chemical reaction network and derives from it a simulation tool mimicking Loss of Function and Gain of Function mutations found in cancer cells. More specifically, from a theoretical perspective, our approach focuses on the determination of moiety conservation laws for the system and their relation with the corresponding stoichiometric surfaces. Then we show that Loss of Function mutations can be implemented in the model via modification of the initial conditions in the system, while Gain of Function mutations can be implemented by eliminating specific reactions. Finally, the model is utilized to examine in detail the G1-S phase of a colorectal cancer cell.

One-carbon metabolism in cancer cells: a critical review based on a core model of central metabolism

One-carbon metabolism (1C-metabolism), also called folate metabolism because the carbon group is attached to folate-derived tetrahydrofolate, is crucial in metabolism. It is at the heart of several essential syntheses, particularly those of purine and thymidylate. After a short reminder of the organization of 1C-metabolism, I list its salient features as reported in the literature. Then, using flux balance analysis, a core model of central metabolism and the flux constraints for an 'average cancer cell metabolism', I explore the fundamentals underlying 1C-metabolism and its relationships with the rest of metabolism. Some unreported properties of 1C-metabolism emerge, such as its potential roles in mitochondrial NADH exchange with cytosolic NADPH, participation in NADH recycling, and optimization of cell proliferation.

Modeling the bidirectional glutamine/ammonium conversion between cancer cells and cancer-associated fibroblasts

Like in an ecosystem, cancer and other cells residing in the tumor microenvironment engage in various modes of interactions to buffer the negative effects of environmental changes. One such change is the consumption of common nutrients (such as glutamine/Gln) and the consequent accumulation of toxic metabolic byproducts (such as ammonium/NH4). Ammonium is a waste product of cellular metabolism whose accumulation causes cell stress. In tumors, it is known that it can be recycled into nutrients by cancer associated fibroblasts (CAFs). Here we present monoculture and coculture growth of cancer cells and CAFs on different substrates: glutamine and ammonium. We propose a mathematical model to aid our understanding. We find that cancer cells are able to survive on ammonium and recycle it to glutamine for limited periods of time. CAFs are able to even grow on ammonium. In coculture, the presence of CAFs results in an improved survival of cancer cells compared to their monoculture when exposed to ammonium. Interestingly, the ratio between the two cell populations is maintained under various concentrations of NH4, suggesting the ability of the mixed cell system to survive temporary metabolic stress and sustain the size and cell composition as a stable entity.

Mathematical Modeling of p53 Pathways

Cells have evolved balanced systems that ensure an appropriate response to stress. The systems elicit repair responses in temporary or moderate stress but eliminate irreparable cells via apoptosis in detrimental conditions of prolonged or severe stress. The tumor suppressor p53 is a central player in these stress response systems. When activated under DNA damage stress, p53 regulates hundreds of genes that are involved in DNA repair, cell cycle, and apoptosis. Recently, increasing studies have demonstrated additional regulatory roles of p53 in metabolism and mitochondrial physiology. Due to the inherent complexity of feedback loops between p53 and its target genes, the application of mathematical modeling has emerged as a novel approach to better understand the multifaceted functions and dynamics of p53. In this review, we discuss several mathematical modeling approaches in exploring the p53 pathways.

MoVE identifies metabolic valves to switch between phenotypic states

Metabolism is highly regulated, allowing for robust and complex behavior. This behavior can often be achieved by controlling a small number of important metabolic reactions, or metabolic valves. Here, we present a method to identify the location of such valves: the metabolic valve enumerator (MoVE). MoVE uses a metabolic model to identify genetic intervention strategies which decouple two desired phenotypes. We apply this method to identify valves which can decouple growth and production to systematically improve the rate and yield of biochemical production processes. We apply this algorithm to the production of diverse compounds and obtained solutions for over 70% of our targets, identifying a small number of highly represented valves to achieve near maximal growth and production. MoVE offers a systematic approach to identify metabolic valves using metabolic models, providing insight into the architecture of metabolic networks and accelerating the widespread implementation of dynamic flux redirection in diverse systems.

Loss of GINS2 inhibits cell proliferation and tumorigenesis in human gliomas

AIMS

In this study, we examined the expression of GINS2 in glioma and determined its role in glioma development.

METHODS

The protein expression of GINS2 was assessed in 120 human glioma samples via immunohistochemistry. Then, we suppressed the expression of GINS2 in glioma cell strains U87 and U251 using a short hairpin RNA lentiviral vector. In addition, RNA sequencing and bioinformatics analysis were performed on glioma cells before and after GINS2 knockdown. Subsequent co-immunoprecipitation and western blot experiments indicated possible downstream regulatory molecules.

RESULTS

The present results showed that GINS2 can accelerate the growth of glioma cells, whereas the suppression of GINS2 expression decreased the proliferation and tumorigenicity of glioma cells. Mechanism research experiments proved that GINS2 can block the cell cycle by regulating certain downstream molecules, such as MCM2, ATM, and CHEK2.

CONCLUSION

GINS2 is closely related to the occurrence and development of glioma, and is likely to become a prognostic marker for glioma patients, as well as a potential therapeutic target in the treatment of glioma.

Identification of a co-target for enhancing efficacy of sorafenib in HCC through a quantitative modeling approach

Sorafenib (SFB), a multi-kinase inhibitor, is the only approved drug for treating hepatocellular carcinoma (HCC). However, SFB shows low efficacy in many cases. HCC related mortality therefore remains to be high worldwide. SFB, a multi-kinase inhibitor is also known to modulate the redox homeostasis in cancer cells. To understand the effect of SFB on the redox status, a quantitative understanding of the system is necessary. Kinetic modeling of the relevant pathways is a useful approach for obtaining a quantitative understanding of the pathway dynamics and to rank the individual factors based on the extent of influence they wield on the pathway. Here, we report a comprehensive model of the glutathione reaction network (GSH_net ), consisting of four modules and includes SFB-induced redox stress. We compared GSH_net simulations for HCC of six different etiologies with healthy liver, and correctly identified the expected variations in cancer. Next, we studied alterations induced in the system upon SFB treatment and observed differential H₂ O₂ dynamics in all the conditions. Using metabolic control analysis, we identified glutathione S-transferase (GST) as the enzyme with the highest selective control coefficient, making it an attractive co-target for potentiating the action of SFB across all six etiologies. As a proof-of-concept, we selected ethacrynic acid (EA), a known inhibitor of GST, and verified ex vivo that EA synergistically potentiates the cytotoxic effect of SFB. Being an FDA approved drug, EA is a promising candidate for repurposing as a combination therapy with SFB for HCC treatment.

Angiogenesis and evading immune destruction are the main related transcriptomic characteristics to the invasive process of oral tongue cancer

Metastasis of head and neck tumors is responsible for a high mortality rate. Understanding its biochemistry may allow insights into tumorigenesis. To that end we carried out RNA-Seq analyses of 5 SCC9 derived oral cancer cell lines displaying increased invasive potential. Differentially expressed genes (DEGs) were annotated based on p-values and false discovery rate (q-values). All 292 KEGG pathways related to the human genome were compared in order to pinpoint the absolute and relative contributions to the invasive process considering the 8 hallmarks of cancer plus 2 new defined categories, as well as we made with our transcriptomic data. In terms of absolute contribution, the highest correlations were associated to the categories of evading immune destruction and energy metabolism and for relative contributions, angiogenesis and evading immune destruction. DEGs were distributed into each one of all possible modes of regulation, regarding up, down and continuum expression, along the 3 stages of metastatic progression. For p-values twenty-six genes were consistently present along the tumoral progression and 4 for q-values. Among the DEGs, we found 2 novel potentially informative metastatic markers: PIGG and SLC8B1. Furthermore, interactome analysis showed that MYH14, ANGPTL4, PPARD and ENPP1 are amenable to pharmacological interventions.

Epithelial/mesenchymal plasticity: how have quantitative mathematical models helped improve our understanding?

Phenotypic plasticity, the ability of cells to reversibly alter their phenotypes in response to signals, presents a significant clinical challenge to treating solid tumors. Tumor cells utilize phenotypic plasticity to evade therapies, metastasize, and colonize distant organs. As a result, phenotypic plasticity can accelerate tumor progression. A well‐studied example of phenotypic plasticity is the bidirectional conversions among epithelial, mesenchymal, and hybrid epithelial/mesenchymal (E/M) phenotype(s). These conversions can alter a repertoire of cellular traits associated with multiple hallmarks of cancer, such as metabolism, immune evasion, invasion, and metastasis. To tackle the complexity and heterogeneity of these transitions, mathematical models have been developed that seek to capture the experimentally verified molecular mechanisms and act as ‘hypothesis‐generating machines’. Here, we discuss how these quantitative mathematical models have helped us explain existing experimental data, guided further experiments, and provided an improved conceptual framework for understanding how multiple intracellular and extracellular signals can drive E/M plasticity at both the single‐cell and population levels. We also discuss the implications of this plasticity in driving multiple aggressive facets of tumor progression.

Computational Model Predicts the Effects of Targeting Cellular Metabolism in Pancreatic Cancer

Reprogramming of energy metabolism is a hallmark of cancer that enables the cancer cells to meet the increased energetic requirements due to uncontrolled proliferation. One prominent example is pancreatic ductal adenocarcinoma, an aggressive form of cancer with an overall 5-year survival rate of 5%. The reprogramming mechanism in pancreatic cancer involves deregulated uptake of glucose and glutamine and other opportunistic modes of satisfying energetic demands in a hypoxic and nutrient-poor environment. In the current study, we apply systems biology approaches to enable a better understanding of the dynamics of the distinct metabolic alterations in KRAS-mediated pancreatic cancer, with the goal of impeding early cell proliferation by identifying the optimal metabolic enzymes to target. We have constructed a kinetic model of metabolism represented as a set of ordinary differential equations that describe time evolution of the metabolite concentrations in glycolysis, glutaminolysis, tricarboxylic acid cycle and the pentose phosphate pathway. The model is comprised of 46 metabolites and 53 reactions. The mathematical model is fit to published enzyme knockdown experimental data. We then applied the model to perform in silico enzyme modulations and evaluate the effects on cell proliferation. Our work identifies potential combinations of enzyme knockdown, metabolite inhibition, and extracellular conditions that impede cell proliferation. Excitingly, the model predicts novel targets that can be tested experimentally. Therefore, the model is a tool to predict the effects of inhibiting specific metabolic reactions within pancreatic cancer cells, which is difficult to measure experimentally, as well as test further hypotheses toward targeted therapies.

Metabolite and Microbiome Interplay in Cancer Immunotherapy

The role of the host microbiome has come to the forefront as a potential modulator of cancer metabolism and could be a future target for precision medicine. A recent study revealed that in colon cancer, bacteria form polysaccharide matrices called biofilms at a high frequency in the proximal colon. Comprehensive untargeted and stable isotope-assisted metabolomic analysis revealed that the bacteria utilize polyamine metabolites produced from colon adenomas/carcinomas to build these protective biofilms and may contribute to inflammation and proliferation observed in colon cancer. This study highlighted the importance of finding the biological origin of a metabolite and assessing its metabolism and mechanism of action. This led to a better understanding of host and microbial interactions, thereby aiding therapeutic design for cancer. In this review, we will discuss methodologies for identifying the biological origin and roles of metabolites in cancer progression and discuss the interactions of the microbiome and metabolites in immunity and cancer treatment, focusing on the flourishing field of cancer immunotherapy. Cancer Res; 76(21); 6146-52. ©2016 AACR.