3D Modeling: Insights into the Metabolic Reprogramming of Cholangiocarcinoma Cells.

Developing accurate in vitro models that replicate the in vivo tumor environment is essential for advancing cancer research and therapeutic development. Traditional 2D cell cultures often fail to capture the complex structural and functional heterogeneity of tumors, limiting the translational relevance of findings. In contrast, 3D culture systems, such as spheroids, provide a more physiologically relevant context by replicating key aspects of the tumor microenvironment. This study aimed to compare the metabolism of three intrahepatic cholangiocarcinoma cell lines in 2D and 3D cultures to identify metabolic shifts associated with spheroid formation. Cells were cultured in 2D on adhesion plates and in 3D using ultra-low attachment plates. Metabolic exchange rates were measured using NMR, and intracellular metabolites were analyzed using LC-MS. Significant metabolic differences were observed between 2D and 3D cultures, with notable changes in central carbon and glutathione metabolism in 3D spheroids. The results suggest that 3D cultures, which more closely mimic the in vivo environment, may offer a more accurate platform for cancer research and drug testing.

An Efficient Method for Vault Nanoparticle Conjugation with Finely Adjustable Amounts of Antibodies and Small Molecules.

Vaults are eukaryotic ribonucleoproteins consisting of 78 copies of the major vault protein (MVP), which assemble into a nanoparticle with an about 60 nm volume-based size, enclosing other proteins and RNAs. Regardless of their physiological role(s), vaults represent ideal, natural hollow nanoparticles, which are produced by the assembly of the sole MVP. Here, we have expressed in Komagataella phaffi and purified an MVP variant carrying a C-terminal Z peptide (vault-Z), which can tightly bind an antibody's Fc portion, in view of targeted delivery. Via surface plasmon resonance analysis, we could determine a 2.5 nM affinity to the monoclonal antibody Trastuzumab (Tz)/vault-Z 1:1 interaction. Then, we characterized the in-solution interaction via co-incubation, ultracentrifugation, and analysis of the pelleted proteins. This showed virtually irreversible binding up to an at least 10:1 Tz/vault-Z ratio. As a proof of concept, we labeled the Fc portion of Tz with a fluorophore and conjugated it with the nanoparticle, along with either Tz or Cetuximab, another monoclonal antibody. Thus, we could demonstrate antibody-dependent, selective uptake by the SKBR3 and MDA-MB 231 breast cancer cell lines. These investigations provide a novel, flexible technological platform that significantly extends vault-Z's applications, in that it can be stably conjugated with finely adjusted amounts of antibodies as well as of other molecules, such as fluorophores, cell-targeting peptides, or drugs, using the Fc portion as a scaffold.

Tumor heterogeneity: preclinical models, emerging technologies, and future applications.

Heterogeneity describes the differences among cancer cells within and between tumors. It refers to cancer cells describing variations in morphology, transcriptional profiles, metabolism, and metastatic potential. More recently, the field has included the characterization of the tumor immune microenvironment and the depiction of the dynamics underlying the cellular interactions promoting the tumor ecosystem evolution. Heterogeneity has been found in most tumors representing one of the most challenging behaviors in cancer ecosystems. As one of the critical factors impairing the long-term efficacy of solid tumor therapy, heterogeneity leads to tumor resistance, more aggressive metastasizing, and recurrence. We review the role of the main models and the emerging single-cell and spatial genomic technologies in our understanding of tumor heterogeneity, its contribution to lethal cancer outcomes, and the physiological challenges to consider in designing cancer therapies. We highlight how tumor cells dynamically evolve because of the interactions within the tumor immune microenvironment and how to leverage this to unleash immune recognition through immunotherapy. A multidisciplinary approach grounded in novel bioinformatic and computational tools will allow reaching the integrated, multilayered knowledge of tumor heterogeneity required to implement personalized, more efficient therapies urgently required for cancer patients.

Progranulin Oncogenic Network in Solid Tumors.

Progranulin is a pleiotropic growth factor with important physiological roles in embryogenesis and maintenance of adult tissue homeostasis. While-progranulin deficiency is associated with a broad range of pathological conditions affecting the brain, such as frontotemporal dementia and neuronal ceroid lipofuscinosis, progranulin upregulation characterizes many tumors, including brain tumors, multiple myeloma, leiomyosarcoma, mesothelioma and epithelial cancers such as ovarian, liver, breast, bladder, adrenal, prostate and kidney carcinomas. The increase of progranulin levels in tumors might have diagnostic and prognostic significance. In cancer, progranulin has a pro-tumorigenic role by promoting cancer cell proliferation, migration, invasiveness, anchorage-independent growth and resistance to chemotherapy. In addition, progranulin regulates the tumor microenvironment, affects the function of cancer-associated fibroblasts, and modulates tumor immune surveillance. However, the molecular mechanisms of progranulin oncogenic function are not fully elucidated. In bladder cancer, progranulin action relies on the activation of its functional signaling receptor EphA2. Notably, more recent data suggest that progranulin can also modulate a functional crosstalk between multiple receptor-tyrosine kinases, demonstrating a more complex and context-dependent role of progranulin in cancer. Here, we will review what is currently known about the function of progranulin in tumors, with a focus on its molecular mechanisms of action and regulation.

An Optimized Workflow for the Analysis of Metabolic Fluxes in Cancer Spheroids Using Seahorse Technology.

Three-dimensional cancer models, such as spheroids, are increasingly being used to study cancer metabolism because they can better recapitulate the molecular and physiological aspects of the tumor architecture than conventional monolayer cultures. Although Agilent Seahorse XFe96 (Agilent Technologies, Santa Clara, CA, United States) is a valuable technology for studying metabolic alterations occurring in cancer cells, its application to three-dimensional cultures is still poorly optimized. We present a reliable and reproducible workflow for the Seahorse metabolic analysis of three-dimensional cultures. An optimized protocol enables the formation of spheroids highly regular in shape and homogenous in size, reducing variability in metabolic parameters among the experimental replicates, both under basal and drug treatment conditions. High-resolution imaging allows the calculation of the number of viable cells in each spheroid, the normalization of metabolic parameters on a per-cell basis, and grouping of the spheroids as a function of their size. Multivariate statistical tests on metabolic parameters determined by the Mito Stress test on two breast cancer cell lines show that metabolic differences among the studied spheroids are mostly related to the cell line rather than to the size of the spheroid. The optimized workflow allows high-resolution metabolic characterization of three-dimensional cultures, their comparison with monolayer cultures, and may aid in the design and interpretation of (multi)drug protocols.

INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation.

Metabolism is directly and indirectly fine-tuned by a complex web of interacting regulatory mechanisms that fall into two major classes. On the one hand, the expression level of the catalyzing enzyme sets the maximal theoretical flux level (i.e., the net rate of the reaction) for each enzyme-controlled reaction. On the other hand, metabolic regulation controls the metabolic flux through the interactions of metabolites (substrates, cofactors, allosteric modulators) with the responsible enzyme. High-throughput data, such as metabolomics and transcriptomics data, if analyzed separately, do not accurately characterize the hierarchical regulation of metabolism outlined above. They must be integrated to disassemble the interdependence between different regulatory layers controlling metabolism. To this aim, we propose INTEGRATE, a computational pipeline that integrates metabolomics and transcriptomics data, using constraint-based stoichiometric metabolic models as a scaffold. We compute differential reaction expression from transcriptomics data and use constraint-based modeling to predict if the differential expression of metabolic enzymes directly originates differences in metabolic fluxes. In parallel, we use metabolomics to predict how differences in substrate availability translate into differences in metabolic fluxes. We discriminate fluxes regulated at the metabolic and/or gene expression level by intersecting these two output datasets. We demonstrate the pipeline using a set of immortalized normal and cancer breast cell lines. In a clinical setting, knowing the regulatory level at which a given metabolic reaction is controlled will be valuable to inform targeted, truly personalized therapies in cancer patients.

A framework for validating AI in precision medicine: considerations from the European ITFoC consortium.

BACKGROUND: Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks. METHODS: The European "ITFoC (Information Technology for the Future Of Cancer)" consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology. RESULTS: This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the "ITFoC Challenge". This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets. CONCLUSIONS: The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care.

Transcriptomics and Metabolomics Integration Reveals Redox-Dependent Metabolic Rewiring in Breast Cancer Cells.

Rewiring glucose metabolism toward aerobic glycolysis provides cancer cells with a rapid generation of pyruvate, ATP, and NADH, while pyruvate oxidation to lactate guarantees refueling of oxidized NAD+ to sustain glycolysis. CtPB2, an NADH-dependent transcriptional co-regulator, has been proposed to work as an NADH sensor, linking metabolism to epigenetic transcriptional reprogramming. By integrating metabolomics and transcriptomics in a triple-negative human breast cancer cell line, we show that genetic and pharmacological down-regulation of CtBP2 strongly reduces cell proliferation by modulating the redox balance, nucleotide synthesis, ROS generation, and scavenging. Our data highlight the critical role of NADH in controlling the oncogene-dependent crosstalk between metabolism and the epigenetically mediated transcriptional program that sustains energetic and anabolic demands in cancer cells.

The driving role of the Cdk5/Tln1/FAKS732 axis in cancer cell extravasation dissected by human vascularized microfluidic models.

BACKGROUND: Understanding the molecular mechanisms of metastatic dissemination, the leading cause of death in cancer patients, is required to develop novel, effective therapies. Extravasation, an essential rate-limiting process in the metastatic cascade, includes three tightly coordinated steps: cancer cell adhesion to the endothelium, trans-endothelial migration, and early invasion into the secondary site. Focal adhesion proteins, including Tln1 and FAK, regulate the cytoskeleton dynamics: dysregulation of these proteins is often associated with metastatic progression and poor prognosis. METHODS: Here, we studied the previously unexplored role of these targets in each extravasation step using engineered 3D in vitro models, which recapitulate the physiological vascular niche experienced by cancer cells during hematogenous metastasis. RESULTS: Human breast cancer and fibrosarcoma cell lines respond to Cdk5/Tln1/FAK axis perturbation, impairing their metastatic potential. Vascular breaching requires actin polymerization-dependent invadopodia formation. Invadopodia generation requires the structural function of FAK and Tln1 rather than their activation through phosphorylation. Our data support that the inhibition of FAKS732 phosphorylation delocalizes ERK from the nucleus, decreasing ERK phosphorylated form. These findings indicate the critical role of these proteins in driving trans-endothelial migration. In fact, both knock-down experiments and chemical inhibition of FAK dramatically reduces lung colonization in vivo and TEM in microfluidic setting. Altogether, these data indicate that engineered 3D in vitro models coupled to in vivo models, genetic, biochemical, and imaging tools represent a powerful weapon to increase our understanding of metastatic progression. CONCLUSIONS: These findings point to the need for further analyses of previously overlooked phosphorylation sites of FAK, such as the serine 732, and foster the development of new effective antimetastatic treatments targeting late events of the metastatic cascade.

The Multi-Level Mechanism of Action of a Pan-Ras Inhibitor Explains its Antiproliferative Activity on Cetuximab-Resistant Cancer Cells.

Ras oncoproteins play a crucial role in the onset, maintenance, and progression of the most common and deadly human cancers. Despite extensive research efforts, only a few mutant-specific Ras inhibitors have been reported. We show that cmp4-previously identified as a water-soluble Ras inhibitor- targets multiple steps in the activation and downstream signaling of different Ras mutants and isoforms. Binding of this pan-Ras inhibitor to an extended Switch II pocket on HRas and KRas proteins induces a conformational change that down-regulates intrinsic and GEF-mediated nucleotide dissociation and exchange and effector binding. A mathematical model of the Ras activation cycle predicts that the inhibitor severely reduces the proliferation of different Ras-driven cancer cells, effectively cooperating with Cetuximab to reduce proliferation even of Cetuximab-resistant cancer cell lines. Experimental data confirm the model prediction, indicating that the pan-Ras inhibitor is an appropriate candidate for medicinal chemistry efforts tailored at improving its currently unsatisfactory affinity.

Correction: A microphysiological early metastatic niche on a chip reveals how heterotypic cell interactions and inhibition of integrin subunit ß3 impact breast cancer cell extravasation.

Correction for 'A microphysiological early metastatic niche on a chip reveals how heterotypic cell interactions and inhibition of integrin subunit ß3 impact breast cancer cell extravasation' by Martina Crippa et al., Lab Chip, 2021, DOI: .

A microphysiological early metastatic niche on a chip reveals how heterotypic cell interactions and inhibition of integrin subunit ß3 impact breast cancer cell extravasation.

During metastatic progression multiple players establish competitive mechanisms, whereby cancer cells (CCs) are exposed to both pro- and anti-metastatic stimuli. The early metastatic niche (EMN) is a transient microenvironment which forms in the circulation during CC dissemination. EMN is characterized by the crosstalk among CCs, platelets, leukocytes and endothelial cells (ECs), increasing CC ability to extravasate and colonize secondary tissues. To better understand this complex crosstalk, we designed a human "EMN-on-a-chip" which involves the presence of blood cells as compared to standard metastases-on-chip models, hence providing a microenvironment more similar to the in vivo situation. We showed that CC transendothelial migration (TEM) was significantly increased in the presence of neutrophils and platelets in the EMN-on-a-chip compared to CC alone. Moreover, exploiting the EMN-on-chip in combination with multi-culture experiments, we showed that platelets increased the expression of epithelial to mesenchymal transition (EMT) markers in CCs and that the addition of a clinically approved antiplatelet drug (eptifibatide, inhibiting integrin ß3) impaired platelet aggregation and decreased CC expression of EMT markers. Inhibition of integrin ß3 in the co-culture system modulated the activation of the Src-FAK-VE-cadherin signaling axis and partially restored the architecture of inter-endothelial junctions by limiting VE-cadherinY658 phosphorylation and its nuclear localization. These observations correlate with the decreased CC TEM observed in the presence of integrin ß3 inhibitor. Our EMN-on-a-chip can be easily implemented for drug repurposing studies and to investigate new candidate molecules counteracting CC extravasation.

Profiling and Targeting of Energy and Redox Metabolism in Grade 2 Bladder Cancer Cells with Different Invasiveness Properties.

Bladder cancer is one of the most prevalent deadly diseases worldwide. Grade 2 tumors represent a good window of therapeutic intervention, whose optimization requires high resolution biomarker identification. Here we characterize energy metabolism and cellular properties associated with spreading and tumor progression of RT112 and 5637, two Grade 2 cancer cell lines derived from human bladder, representative of luminal-like and basal-like tumors, respectively. The two cell lines have similar proliferation rates, but only 5637 cells show efficient lateral migration. In contrast, RT112 cells are more prone to form spheroids. RT112 cells produce more ATP by glycolysis and OXPHOS, present overall higher metabolic plasticity and are less sensitive than 5637 to nutritional perturbation of cell proliferation and migration induced by treatment with 2-deoxyglucose and metformin. On the contrary, spheroid formation is less sensitive to metabolic perturbations in 5637 than RT112 cells. The ability of metformin to reduce, although with different efficiency, cell proliferation, sphere formation and migration in both cell lines, suggests that OXPHOS targeting could be an effective strategy to reduce the invasiveness of Grade 2 bladder cancer cells.

Natural Products Attenuating Biosynthesis, Processing, and Activity of Ras Oncoproteins: State of the Art and Future Perspectives.

RAS genes encode signaling proteins, which, in mammalian cells, act as molecular switches regulating critical cellular processes as proliferation, growth, differentiation, survival, motility, and metabolism in response to specific stimuli. Deregulation of Ras functions has a high impact on human health: gain-of-function point mutations in RAS genes are found in some developmental disorders and thirty percent of all human cancers, including the deadliest. For this reason, the pathogenic Ras variants represent important clinical targets against which to develop novel, effective, and possibly selective pharmacological inhibitors. Natural products represent a virtually unlimited resource of structurally different compounds from which one could draw on for this purpose, given the improvements in isolation and screening of active molecules from complex sources. After a summary of Ras proteins molecular and regulatory features and Ras-dependent pathways relevant for drug development, we point out the most promising inhibitory approaches, the known druggable sites of wild-type and oncogenic Ras mutants, and describe the known natural compounds capable of attenuating Ras signaling. Finally, we highlight critical issues and perspectives for the future selection of potential Ras inhibitors from natural sources.

Disruption of redox homeostasis for combinatorial drug efficacy in K-Ras tumors as revealed by metabolic connectivity profiling.

BACKGROUND: Rewiring of metabolism induced by oncogenic K-Ras in cancer cells involves both glucose and glutamine utilization sustaining enhanced, unrestricted growth. The development of effective anti-cancer treatments targeting metabolism may be facilitated by the identification and rational combinatorial targeting of metabolic pathways. METHODS: We performed mass spectrometric metabolomics analysis in vitro and in vivo experiments to evaluate the efficacy of drugs and identify metabolic connectivity. RESULTS: We show that K-Ras-mutant lung and colon cancer cells exhibit a distinct metabolic rewiring, the latter being more dependent on respiration. Combined treatment with the glutaminase inhibitor CB-839 and the PI3K/aldolase inhibitor NVP-BKM120 more consistently reduces cell growth of tumor xenografts. Maximal growth inhibition correlates with the disruption of redox homeostasis, involving loss of reduced glutathione regeneration, redox cofactors, and a decreased connectivity among metabolites primarily involved in nucleic acid metabolism. CONCLUSIONS: Our findings open the way to develop metabolic connectivity profiling as a tool for a selective strategy of combined drug repositioning in precision oncology.

Tipifarnib as a Precision Therapy for HRAS-Mutant Head and Neck Squamous Cell Carcinomas.

Tipifarnib is a potent and highly selective inhibitor of farnesyltransferase (FTase). FTase catalyzes the posttranslational attachment of farnesyl groups to signaling proteins that are required for localization to cell membranes. Although all RAS isoforms are FTase substrates, only HRAS is exclusively dependent upon farnesylation, raising the possibility that HRAS-mutant tumors might be susceptible to tipifarnib-mediated inhibition of FTase. Here, we report the characterization of tipifarnib activity in a wide panel of HRAS-mutant and wild-type head and neck squamous cell carcinoma (HNSCC) xenograft models. Tipifarnib treatment displaced both mutant and wild-type HRAS from membranes but only inhibited proliferation, survival, and spheroid formation of HRAS-mutant cells. In vivo, tipifarnib treatment induced tumor stasis or regression in all six HRAS-mutant xenografts tested but displayed no activity in six HRAS wild-type patient-derived xenograft (PDX) models. Mechanistically, drug treatment resulted in the reduction of MAPK pathway signaling, inhibition of proliferation, induction of apoptosis, and robust abrogation of neovascularization, apparently via effects on both tumor cells and endothelial cells. Bioinformatics and quantitative image analysis further revealed that FTase inhibition induces progressive squamous cell differentiation in tipifarnib-treated HNSCC PDXs. These preclinical findings support that HRAS represents a druggable oncogene in HNSCC through FTase inhibition by tipifarnib, thereby identifying a precision therapeutic option for HNSCCs harboring HRAS mutations.

Systems metabolomics: from metabolomic snapshots to design principles.

Metabolomics is a rapidly expanding technology that finds increasing application in a variety of fields, form metabolic disorders to cancer, from nutrition and wellness to design and optimization of cell factories. The integration of metabolic snapshots with metabolic fluxes, physiological readouts, metabolic models, and knowledge-informed Artificial Intelligence tools, is required to obtain a system-level understanding of metabolism. The emerging power of multi-omic approaches and the development of integrated experimental and computational tools, able to dissect metabolic features at cellular and subcellular resolution, provide unprecedented opportunities for understanding design principles of metabolic (dis)regulation and for the development of precision therapies in multifactorial diseases, such as cancer and neurodegenerative diseases.

Single-cell Digital Twins for Cancer Preclinical Investigation.

Laboratory models derived from clinical samples represent a solid platform in preclinical research for drug testing and investigation of disease mechanisms. The integration of these laboratory models with their digital counterparts (i.e., predictive mathematical models) allows to set up digital twins essential to fully exploit their potential to face the enormous molecular complexity of human organisms. In particular, due to the close integration of cell metabolism with all other cellular processes, any perturbation in cellular physiology typically reflect on altered cells metabolic profiling. In this regard, changes in metabolism have been shown, also in our laboratory, to drive a causal role in the emergence of cancer disease. Nevertheless, a unique metabolic program does not describe the altered metabolic profile of all tumour cells due to many causes from genetic variability to intratumour heterogeneous dependency on nutrients consumption and metabolism by multiple co-existing subclones. Currently, fluxomics approaches just match with the necessity of characterizing the overall flux distribution of cells within given samples, by disregarding possible heterogeneous behaviors. For the purpose of stratifying cancer heterogeneous subpopulations, quantification of fluxes at the single-cell level is needed. To this aim, we here present a new computational framework called single-cell Flux Balance Analysis (scFBA) that aims to set up digital metabolic twins in the perspective of being better exploited within a framework that makes also use of laboratory patient cell models. In particular, scFBA aims at integrating single-cell RNA-seq data within computational population models in order to depict a snapshot of the corresponding single-cell metabolic phenotypes at a given moment, together with an unsupervised identification of metabolic subpopulations.

Integration of single-cell RNA-seq data into population models to characterize cancer metabolism.

Metabolic reprogramming is a general feature of cancer cells. Regrettably, the comprehensive quantification of metabolites in biological specimens does not promptly translate into knowledge on the utilization of metabolic pathways. By estimating fluxes across metabolic pathways, computational models hold the promise to bridge this gap between data and biological functionality. These models currently portray the average behavior of cell populations however, masking the inherent heterogeneity that is part and parcel of tumorigenesis as much as drug resistance. To remove this limitation, we propose single-cell Flux Balance Analysis (scFBA) as a computational framework to translate single-cell transcriptomes into single-cell fluxomes. We show that the integration of single-cell RNA-seq profiles of cells derived from lung adenocarcinoma and breast cancer patients into a multi-scale stoichiometric model of a cancer cell population: significantly 1) reduces the space of feasible single-cell fluxomes; 2) allows to identify clusters of cells with different growth rates within the population; 3) points out the possible metabolic interactions among cells via exchange of metabolites. The scFBA suite of MATLAB functions is available at https://github.com/BIMIB-DISCo/scFBA, as well as the case study datasets.

A metabolic core model elucidates how enhanced utilization of glucose and glutamine, with enhanced glutamine-dependent lactate production, promotes cancer cell growth: The WarburQ effect.

Cancer cells share several metabolic traits, including aerobic production of lactate from glucose (Warburg effect), extensive glutamine utilization and impaired mitochondrial electron flow. It is still unclear how these metabolic rearrangements, which may involve different molecular events in different cells, contribute to a selective advantage for cancer cell proliferation. To ascertain which metabolic pathways are used to convert glucose and glutamine to balanced energy and biomass production, we performed systematic constraint-based simulations of a model of human central metabolism. Sampling of the feasible flux space allowed us to obtain a large number of randomly mutated cells simulated at different glutamine and glucose uptake rates. We observed that, in the limited subset of proliferating cells, most displayed fermentation of glucose to lactate in the presence of oxygen. At high utilization rates of glutamine, oxidative utilization of glucose was decreased, while the production of lactate from glutamine was enhanced. This emergent phenotype was observed only when the available carbon exceeded the amount that could be fully oxidized by the available oxygen. Under the latter conditions, standard Flux Balance Analysis indicated that: this metabolic pattern is optimal to maximize biomass and ATP production; it requires the activity of a branched TCA cycle, in which glutamine-dependent reductive carboxylation cooperates to the production of lipids and proteins; it is sustained by a variety of redox-controlled metabolic reactions. In a K-ras transformed cell line we experimentally assessed glutamine-induced metabolic changes. We validated computational results through an extension of Flux Balance Analysis that allows prediction of metabolite variations. Taken together these findings offer new understanding of the logic of the metabolic reprogramming that underlies cancer cell growth.