Postgraduate Studies and Research Division, Tecnológico Nacional de México - IT de León, León, Mexico.
Depto. de Física, CUCEI, Universidad de Guadalajara, Guadalajara, Mexico.
BMC Bioinformatics. 2022 May 30;23(1):200. doi: 10.1186/s12859-022-04731-w.
Cancer Immunoediting (CI) describes the cellular-level interaction between tumor cells and the Immune System (IS) that takes place in the Tumor Micro-Environment (TME). CI is a highly dynamic and complex process comprising three distinct phases (Elimination, Equilibrium and Escape) wherein the IS can both protect against cancer development as well as, over time, promote the appearance of tumors with reduced immunogenicity. Herein we present an agent-based model for the simulation of CI in the TME, with the objective of promoting the understanding of this process.
Our model includes agents for tumor cells and for elements of the IS. The actions of these agents are governed by probabilistic rules, and agent recruitment (including cancer growth) is modeled via logistic functions. The system is formalized as an analogue of the Ising model from statistical mechanics to facilitate its analysis. The model was implemented in the Netlogo modeling environment and simulations were performed to verify, illustrate and characterize its operation.
A main result from our simulations is the generation of emergent behavior in silico that is very difficult to observe directly in vivo or even in vitro. Our model is capable of generating the three phases of CI; it requires only a couple of control parameters and is robust to these. We demonstrate how our simulated system can be characterized through the Ising-model energy function, or Hamiltonian, which captures the "energy" involved in the interaction between agents and presents it in clear and distinct patterns for the different phases of CI.
The presented model is very flexible and robust, captures well the behaviors of the target system and can be easily extended to incorporate more variables such as those pertaining to different anti-cancer therapies. System characterization via the Ising-model Hamiltonian is a novel and powerful tool for a better understanding of CI and the development of more effective treatments. Since data of CI at the cellular level is very hard to procure, our hope is that tools such as this may be adopted to shed light on CI and related developing theories.
癌症免疫编辑（CI）描述了肿瘤细胞与免疫系统（IS）在肿瘤微环境（TME）中发生的细胞水平相互作用。CI 是一个高度动态和复杂的过程，包括三个不同的阶段（消除、平衡和逃逸），在此期间，IS 既能防止癌症的发生，又能随着时间的推移促进免疫原性降低的肿瘤的出现。在此，我们提出了一种用于模拟 TME 中 CI 的基于主体的模型，旨在促进对该过程的理解。
我们的模型包括肿瘤细胞和 IS 元素的主体。这些主体的行为由概率规则控制，主体的招募（包括癌症的生长）通过逻辑函数进行建模。该系统被形式化为统计力学中的伊辛模型的模拟，以方便其分析。该模型在 Netlogo 建模环境中实现，并进行了模拟以验证、说明和表征其操作。
我们的模拟产生了一种难以直接在体内甚至在体外观察到的复杂的涌现行为。我们的模型能够产生 CI 的三个阶段；它只需要几个控制参数，并且对这些参数具有鲁棒性。我们展示了如何通过伊辛模型能量函数（或哈密顿量）来描述我们的模拟系统，该函数捕获了主体之间相互作用的“能量”，并以清晰和独特的模式呈现 CI 的不同阶段。
所提出的模型非常灵活和稳健，能够很好地捕捉目标系统的行为，并且可以很容易地扩展到纳入更多的变量，如不同的抗癌疗法相关的变量。通过伊辛模型哈密顿量进行系统特征描述是一种新的、强大的工具，可以更好地理解 CI 和开发更有效的治疗方法。由于在细胞水平上获得 CI 的数据非常困难，我们希望采用这种工具来阐明 CI 及其相关发展理论。
