Multi-scale computational study of the Warburg effect, reverse Warburg effect and glutamine addiction in solid tumors

Cancer metabolism has received renewed interest as a potential target for cancer therapy. In this study, we use a multi-scale modeling approach to interrogate the implications of three metabolic scenarios of potential clinical relevance: the Warburg effect, the reverse Warburg effect and glutamine addiction. At the intracellular level, we construct a network of central metabolism and perform flux balance analysis (FBA) to estimate metabolic fluxes; at the cellular level, we exploit this metabolic network to calculate parameters for a coarse-grained description of cellular growth kinetics; and at the multicellular level, we incorporate these kinetic schemes into the cellular automata of an agent-based model (ABM), iDynoMiCS. This ABM evaluates the reaction-diffusion of the metabolites, cellular division and motion over a simulation domain. Our multi-scale simulations suggest that the Warburg effect provides a growth advantage to the tumor cells under resource limitation. However, we identify a non-monotonic dependence of growth rate on the strength of glycolytic pathway. On the other hand, the reverse Warburg scenario provides an initial growth advantage in tumors that originate deeper in the tissue. The metabolic profile of stromal cells considered in this scenario allows more oxygen to reach the tumor cells in the deeper tissue and thus promotes tumor growth at earlier stages. Lastly, we suggest that glutamine addiction does not confer a selective advantage to tumor growth with glutamine acting as a carbon source in the tricarboxylic acid (TCA) cycle, any advantage of glutamine uptake must come through other pathways not included in our model (e.g., as a nitrogen donor). Our analysis illustrates the importance of accounting explicitly for spatial and temporal evolution of tumor microenvironment in the interpretation of metabolic scenarios and hence provides a basis for further studies, including evaluation of specific therapeutic strategies that target metabolism.癌症代谢作为癌症治疗的潜在靶点，重新受到学界关注。本研究采用多尺度建模（multi-scale modeling）方法，探究三种具有潜在临床相关性的代谢表型的影响机制：瓦伯格效应（Warburg effect）、反向瓦伯格效应（reverse Warburg effect）以及谷氨酰胺成瘾（glutamine addiction）。在细胞内层面，我们构建中心代谢网络，并通过通量平衡分析（flux balance analysis, FBA）估算代谢通量；在细胞水平，我们利用该代谢网络计算用于粗粒度描述细胞生长动力学的参数；在多细胞水平，我们将这些动力学方案整合至基于智能体的模型（agent-based model, ABM）iDynoMiCS的细胞自动机中。该ABM可在模拟域内评估代谢物的反应-扩散过程、细胞分裂与运动行为。我们的多尺度模拟结果表明，在资源受限条件下，瓦伯格效应可赋予肿瘤细胞生长优势。但研究发现，肿瘤生长速率与糖酵解通路强度之间存在非单调依赖关系。与之相对，反向瓦伯格表型可使起源于组织深部的肿瘤获得初始生长优势。该场景下考虑的基质细胞代谢谱可使更多氧气抵达深部组织中的肿瘤细胞，从而在肿瘤早期阶段促进其生长。最后，本研究表明，当谷氨酰胺作为三羧酸循环（tricarboxylic acid cycle, TCA）的碳源时，谷氨酰胺成瘾并不会为肿瘤生长带来选择优势；谷氨酰胺摄取所带来的任何优势，必然来自本模型未涵盖的其他通路（例如作为氮供体）。本研究的分析结果阐明，在阐释代谢表型时，需明确考虑肿瘤微环境的时空演化过程，这为后续研究（包括评估针对代谢通路的特异性治疗策略）提供了理论基础。