Cancer Cell and Tumor Metabolism Profiling and Targeting

2019 John A. Catanzaro

https://www.linkedin.com/pulse/cancer-cell-tumor-metabolism-profiling-targeting-john-catanzaro/

Introduction

Altered metabolism is a hallmark of cancer development. Cancer cells and the tumor microenvironment alike are said to use the metabolism controls of the Warburg effect. There metabolic / cross-talk rewiring that promotes growth, survival, proliferation, resistance and long-term maintenance. The most common postulated feature of this altered metabolism is increased glucose uptake and fermentation of glucose to lactate. This metabolic mechanism of action is observed even in the presence of completely functioning mitochondria. The Warburg Effect has amazing research literature coverage for over 90 years and extensively studied over the past 15–20 years with thousands of papers reporting to have established either its initiators, causes or functions. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4783224/#!po=16.6667 However, with increasing knowledge there are questions that need definitive answers as outlined in the schema below.

Warburg Altercation in Cancer Cell and Tumor Metabolism Adaptation?

“The Warburg effect, high glycolytic metabolism even under normoxic conditions, represents a metabolic strategy that allow cancer cells and the tumor microenvironment to optimally meet energy demands.” Fluctuating tumor environments signal a cross-talk or stochastic between the tumor microenvironment and cancer cells enabling adaptation, survival and resistance. Cancer cells have an increased glucose flux within the aerobic glycolysis schema, demonstrating a significant uptake of glucose, as noticeably observed in FDG-PET imaging, which is a diagnostic method of demonstrating metabolic intensity and proliferation mapping. Over the last two decades, clinical application of FDG-PET has demonstrated that >90% of human cancers exhibit increased glucose uptake indicating aerobic glycolysis is a ubiquitous property of the malignant phenotype. This “energy dysregulation” fits into the cancer hallmark schema. https://doi.org/10.1016/j.cell.2011.02.013 I will speak more on the benefits of diagnostic imaging in profiling and mapping for targeting further in the review.

The Paradox

The conventional model of cancer development utilizing aerobic glycolysis is difficult to reconcile. The conventional carcinogenesis model is considered “somatic evolution.” The impaired production of ATP via anaerobic metabolism of glucose in the presence of oxygen seems inconsistent with maximization of cellular fitness, that should follow from adaptation dynamics. Presumably, a survival model, in a resource-limited environment would strongly favor maximally efficient energy extraction from the limited supply of glucose. Mitochondrial dysfunction initially was postulated to cause this shift, but it is known today that most cancer cells and tumor microenvironments retain functional mitochondrial metabolism and in some cancers even increase. https://www.ncbi.nlm.nih.gov/pubmed/ We now arrive at the crossroad of both primary and secondary metabolic controls in normal and cancer cell metabolism, this being glycolysis and oxidative phosphorylation respectively.

I will attempt to provide some clarity in these complex metabolic controls to demonstrate that understanding the normal metabolic pathway interface, as we know it, currently in contrast to altered metabolism observed in the cancer cell and tumor microenvironment have multifaceted implications.

Glycolytic and Oxidative Phosphorylation Controls In Cancer Metabolic Adaptation

The hallmark of metabolism in cancer is not simplistic by any means. It involves epigenetic shifts in adaptation of key genetic controls. With increased risk of environmental changes and individualized risks associated with health and various exposures including microbes (viruses, bacteria, fungal elements and microbiome influences), as well as, chemical carcinogens the burden and damage on DNA is high. These all have a profound altering and damaging effect upon DNA. Glycolysis and oxidative phosphorylation have strong interplay in other individual metabolic controls, protein, lipid, amino acid, hormone synthesis, nucleic acid metabolism, mitochondrial generation of ATP and DNA methylation pathways, etc The human metabolism map below gives you a sense of integration and feedback mechanisms involved. Every step process in human metabolism is controlled and effected by specific genes and alterations in normal human metabolism effects epigenetic adaptation all these components are encoded by DNA.

Characteristic Changes

• Glucose / Energy flux: Biosensors have improved the evaluation of dynamic flux and metabolite exchange between cells and their environment.
• ATP production and utilization: Both intracellular and extracellular ATP availability is considered in the cancer metabolic matrix in the tumor microenvironment and varies with cancer cell type and distribution http://mcr.aacrjournals.org/content/14/11/1087
• Oxygen / Oxidation: ROS (Reactive Oxygen Species) / RNS (Reactive Nitrogen Species) effects as well as flux in oxygen availability. Cancer mutations can evolve to survive in abundant / and or starvation conditions.
• pH environment: The vigorous production and export of lactate by many tumors is associated with the acidification of the tumor microenvironment, which disrupts the function of tumor associated stromal cells while enhancing the aggressiveness of neoplastic cells.
• DNA methylation disturbance / DNA architectural integrity: Epigenetic mechanisms DNA methylation mechanism. Methylation of cytosine to 5-methylcytosine is catalyzed by DNMTs, through the methyl donor SAM, which is converted to SAH. Hypermethylation of CpG islands of promoter regions leading to transcriptional gene repression. https://www.frontiersin.org/files/Articles/411720/fgene-09-00427-HTML-r1/image_m/fgene-09-00427-g001.jpg

• Signalling transduction and increased progressive metabolic proliferation and distribution

• Inflammatory Patterns: Proliferative metabolic programming and signaling exhibited by effector T cells, inflammatory macrophages and cancer cells versus the catabolic metabolism and signaling in Treg, M2 macrophages, memory T cells and quiescent cancer cells

Good Thoughts on Cancer Metabolism and Targeting

Reflecting on the above normal human metabolic mapping, this video gives a very understandable recap of the differences.

Cancer metabolism and targeted therapies

Warburg Modification of Cancer Cell / Tumor Microenvironment Adaptation

In a very intriguing paper written by Epstein, Gatenby and Brown, they postulate that the Warburg effect in the adaptation of cancer cells demonstrate rapid fluctuations in energy demands. This direct observation by the researchers is stated below and the paper is a must read. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0185085

“The observation of high rates of aerobic glycolysis in cancer and some normal cells is inconsistent with the conventional model of glucose metabolism in which oxidative phosphorylation is viewed as the optimal pathway in all normoxic conditions. We introduced an alternative metabolic model in which glycolysis and oxidative phosphorylation are complementary modes of ATP production that trade-off efficiency and speed for meeting energetic demands. Oxidative phosphorylation is highly efficient in converting glucose to ATP but slow to responds to fluctuations in energy demand. Glycolysis is less efficient than oxidative phosphorylation but its response time is much greater than oxidative phosphorylation and, thus, well-suited to supply fluctuating demand, such as ion transporters on the cell membrane that are necessary for changes in the cell geometry during motion and invasion. In our model, optimal glucose metabolism is determined by both the availability of oxygen and the dynamics of energy demand.”

Two Theoretic Models Warburg Modification

“Here we present two game-theoretic models that investigate optimal ATP-from-glucose production strategies for given energy demand conditions. In the first model we examined optimal ATP production strategies when cells experience periodic short-time increases in demand superimposed on a constant base-line demand. We note that this model is designed to study efficiency of ATP production for supplying short-term energy demand. As such, it does not address the interaction between the two pathways, such as initial ATP production by mitochondrial to support glycolytic metabolism. Our results indicate the existence of two metabolic regimes, based on three critical parameters: the cost of excessive ATP level, the cost of uptaking glucose, and the cost of maintaining glycolytic capacity. We demonstrate that in normal, healthy tissue in which physiological conditions are spatially and temporally homogenous and demand fluctuations are small, it is optimal to produce all ATP by oxidative phosphorylation so that little or no glycolytic capacity is necessary. However, as the amplitude or frequency of demand spikes increases, the optimal metabolic strategy requires metabolic switching in which the constant component of ATP demand is supplied by oxidative phosphorylation and the fluctuating component by glycolysis.”

“The second model we investigate the trade-off between cost and benefit dynamics that governs the cell’s glycolytic capacity in typical cancer environments in which peak demand fluctuates due to alterations in local conditions caused by chaotic blood flow or host response. We studied two types of short-term ATP demands. The first one is generated by an opportunity to increase substrate acquisition in the event, for example, of a sudden increase in blood flow. We assume optimal response must be rapid because the opportunity diminishes with time either due to diffusion into adjacent tissue or consumption by other cells (scramble competition). The second type is a demand that is required to avoid hazard (e.g. the sudden appearance of predator-like host anti-tumor T cells) and therefore must be met immediately to maintain survival. Our modeling results demonstrate that optimization of the glycolytic capacity includes an “acceptance” of the risk that a rare event provided there is long-term benefit to the population gained by reduction in the cost required to maintain sufficient energy capacity to overcome this rare threat. This results is supported by a recent study showing that adaptation to doxorubicin drug by expression of P-glycoprotein (PGP) transporters is followed by increase of glycolytic capacity, where the energy for these transporters is primarily supplied by glycolysis. In contrast, there is little benefit in maintaining excess capacity to exploit uncommon spikes in environmental opportunities that would improve the energy status of the cell. From a broader perspective, our results demonstrate that “fear” is a stronger motivator for glycolytic capacity than “hunger”, which has been also observed in other ecological studies. From a generic view this model address the fundamental economic question of capacity investment under uncertain demand, and therefore can be used in such studies.”

Theoretic Conclusions on Modification

“In summary, our modeling results demonstrate that normal mammalian cells subject to a near constant environment will require limited glycolytic capacity. Normal cells that may be subject to frequent perturbations (on the skin or colon mucosa, for example) will probably have a higher capacity. Tumor cells in a benign, stable lesion (such as a fibroid) will likely have a low glycolytic capacity while cells in an invasive cancer with spatially and temporally heterogeneous blood flow and subject to immune attack, will likely need to maintain a high glycolytic capacity.”

Genetic Controls Cancer Metabolism Adaptation / Pathways

Where does one begin to outline the many genes within specialized areas of human / cancer metabolism. The Cancer Cell Gene Database is a great resource, as it identifies KEGG and REACTOME pathways and the genes within those pathways. In profiling and targeting isolated molecular products from human biopsy (liquid / solid) associated with gene / protein profiling is critical in understanding characteristic changes within the pathways the genes influence and where metabolism itself can loop back and influence the genetic mechanisms within cells and the tumor microenvironment initiating epigenetic adaptation shifts. Patient precision-based treatment innovations require better profiling patient compatibility and real time activity of the disrupted hallmarks we know about in the cancer architecture and metabolism is crucial. Another essential data resource in data mining for profiling specific cancer types and associations is Oncomine, https://www.oncomine.org/resource/login.html

Another very interesting paper that discusses universal patterns of selection in Cancer and Somatic Tissues https://www.cell.com/cell/pdf/S0092-8674(17)31136-4.pdf

Main Search ccmGDB

You can search by KEGG and REACTOME pathways, also by gene ID and chromosome location. Also the feature to search by driver genes.

https://bioinfo.uth.edu/ccmGDB/index.html?csrt=18062152178016536164

KEGG Cell Metabolism Pathways

REACTOME Cell Metabolism Pathways

Gene Profiling and CRISPR

CRISPR (clustered regularly interspaced short palindromic repeats) and CRISPR associated protein 9 (Cas9)-based gene knock out (KO) facilitates genome-wide loss of function screening. It is a viable platform for gene screening, editing and profiling in metabolism and identifying targetable innovations.

Cell Signaling / Metabolism: Bridging a Gap in Cancer Therapies

In this presentation a very detailed review oc cell signaling and cancer metabolism. Cancer cells display an altered metabolism and this has been known for almost a century, but the cell-signaling mechanisms responsible for this phenomenon are just beginning to be understood, with some drugs targeting key regulators of both cell metabolism and tumor progression. The repurposing of old drugs for the activation and/or inhibition of proteins at the center of the cell-signaling and metabolism web has been a popular strategy for addressing these challenges. The Scientist is bringing together a panel of experts to discuss their research into this exciting and recently acknowledged hallmark of cancer. Metabolic symbioses in tumor microenvironments is discussed. Personalized profiling within the Cancer Hallmark Analytics and Immunocentrics schema can integrate well for new patient precision innovations.

Profiling Cancer Metabolism Technological Perspective

The metabolic reprogramming in cancers is complex promoting the production of intermediates for the generation of new biomass, and is driven by mutations or epigenetic adaptations in oncogenes (Ras, NRF2, and PIK3CA, and / or tumor suppressor genes, such as p53, VHL, Rb and others).

Cancer Cell and tumor metabolism is also modified by cancer cell type, mutation grade and expression, migration, inflammation, interaction with other cells in the tumor microenvironment, tumor hypoxia, and nutrient limitations as I discussed above. It is also influenced by tissue-specific signaling. Oncogene expression and cancer cell signal transduction can express differently and vary according to cell type. For clarity, where the same oncogene can alter metabolism in one tissue but not another.

Metabolic reprogramming is a viable avenue for targeted innovation, There are current therapies targeting metabolism are in use in the clinic, and these target multiple processes critical for tumor growth and survival, including nucleotide metabolism, amino acid metabolism, and carbon and fatty acid metabolism. Patient precision-based profiling and analysis can uniquely design a better more personalized compatible treatment design for the patient.

Another review article that can give pointed profile guidance more comprehensively from a technological platform is “Recent advances in cancer metabolism: a technological perspective.” https://www.nature.com/articles/s12276-018-0027-z#Tab2

Technologies for Study of Cancer Metabolism

I will highlight some key features and figures I feel will create some thought stimulation as you move toward a better profiling and innovative treatment design. Details of these technological applications are in the article for reference. I will post the figures and link and you can review for reference. https://www.nature.com/articles/s12276-018-0027-z#Tab2

• Chromatography coupled to mass spectrometry
• Redox couples
• One carbon metabolism
• Absolute quantification
• Metabolic tracing
• Metabolic sensors

Stable Isotope-Labeled Metabolic Tracers

Metabolic Sensors

Genetically Encoded Fluorescent Biosensors

Diagnostic / Therapeutic Imaging in Cancer Metabolism Profiling / Targeting

Clinical evaluation of tumor metabolism by using clinical diagnostic imaging is an established practice, 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is influenced by Warburg metabolism and the propensity for tumors to take up glucose at a higher rate than adjacent normal tissue. Please review the above postulations as discussed earlier in this article.

There are a variety of radiolabeled metabolites that have been used to demonstrate basic metabolic phenotypes of tumors such as amino acid transport or regionalized hypoxia Hyperpolarized 13C-metabolite infusions have been performed for more sophisticated MR-based tumor detection and grading, however the technical advances described here are being translated to patient populations to allow for the capture of metabolic flux with isotope-labeled substrates.

The figures I have included below outline a multi-modality model and noninvasive form of imaging. The data and computational aspects of the data can be integrated in an Cancer Hallmark Analytics / Immunocentrics for patient-precision profiling and therapeutic application. With continual expansion of diagnostic labeled sensors there is opportunity for direct therapeutic implantation of a patient personalized innovation.

Overview of major metabolic pathways that can be detected using different imaging modalities. Glucose uptake can be detected using PET with 18F-FDG; using SIRM or MRS with 13C-Glucose; using 2DG probes for optical imaging. Contribution of pyruvate and lactate to TCA cycle can be measured using SIRM or MRS with 13C-Pyruvate and 13C-Lactate. Glutamine uptake can be detected using PET with 18F-Glutamine or 11C-Glutamine; using SIRM with 13C-Glutamine. Additional probes based on amino acids can be used to detect contribution of amino acids to cellular biomass synthesis. Fatty acid uptake by tumor cells can be evaluated using PET probe 11C-Acetate or using optical imaging with luciferase-tagged free fatty acid. PET probes 18F-FLT, 18F-FAC, 18F-FMAU can be used to determine reliance of tumors on thymidine kinase (TK) and/or deoxycitidine kinase (dCK) and/or cytidine deamidase (CDA). Combining imaging modalities with qIHC allows for quantification of total protein levels as well as phosphorylation. As you can observe there is an expandable platform here in diagnostic imaging as well as therapeutic applications.

Comprehensive profiling of tumor metabolism. Following non-invasive imaging with PET, MRI, MRS, CT patients undergo resection or biopsy of the tumor. Tissue analysis of the resected or biopsied tumor includes metabolomics, qIHC and gene expression analysis. Comprehensive tumor profiling identified preference for glucose in poorly vascularized areas of the tumor, compared to well vascularized tumors that used both glucose and alternative metabolites to fuel their growth.

Overview and Summary of Cancer Cell / Tumor Profiling and Targeting

The Hallmark Cancer Model demonstrates an overview of known components of metabolism effecting each element of the cancer survival cycle. These cancer-specific pathways are identified, many of which are known to contribute to tumorigenesis and neoplastic growth, including folate metabolism, eicosanoid metabolism, and nucleotide metabolism. For example, folate and nucleotide metabolism have previously been chemotherapy targets since they contribute to the increased nucleotide synthesis rate in cancer. Beyond general pathway differences, several reactions were identified as being more frequently associated with cancer models. These included eicosanoid metabolism reactions catalyzed by 5-lipoxygenase, which contributes to angiogenesis and proliferation. It is clearly advantageous to study gene regulatory programs in cancer metabolism in the context of functional metabolic networks as outlined in the figure.

Tumor cells’ metabolism reprogramming. Metabolic reprogramming of tumor cells is characterized by highly glycolytic rates to increase tumor biomass by biosynthetic process. Glycolytic intermediates are used in pentose phosphate pathway (PPP) for anabolic process such as biosynthesis of nucleotides, amino acids and lipids. Glutamine is also an important source for tumor cells through glutaminolysis.

DNA hypermethylation, histone demethylases and histone deacetylases effect on the metabolic enzymes expression involved in glycolysis and glutaminolysis. Epigenetic mechanisms are associated with a metabolic cancer cell reprogramming by transcriptional repression of gluconeogenic enzymes and consequently activation of glycolytic pathway and glutaminolysis, where myc and HIF-1α transcription factors are important modulators. G6P, glucose 6-phoslpatase; GLS, glutaminase; GLUT1, glucose transporter 1; GLUT3, glucose transporter3; FBP1, fructose 1,6-biphosphatase; HK2, hexokinase2; LDHA; lactate dehydrogenase A; MCT4, monocarboxylate transporter 4; PKM2, pyruvate kinase M2; SLC1A5, glutamine transporter ASCT2.

Conclusion

Mapping cancer is not a simple process. I presented some of the elemental considerations from a cancer cell / tumor microenvironment metabolism part of the Cancer Hallmark. A platform of bio-intelligence and therapeutic personalization innovation is crucial in the architecture of patient-precision based medicine. Thus, I speak of Cancer Hallmark Analytics and Immunocentrics as a platform that is expandable to address key components of the Cancer Hallmark schema. I have attempted to outline in an organized fashion the components and complexities of cancer metabolism but also included some very impressive figures, reviews, studies and alternate postulates. I encourage you to add your opinions and thoughts on this process as we continue to advance, expand and develop with an elite patient-centric compatibility mapping. I welcome your comments. I thank all of the researchers, clinicians and scientists that have contributed to this review presentation and I credit them all for their greatest continued labors and achievements in hallmarking this complex multi-dimensional / multi-system / multi-disease called Cancer.