Department of Physics (Alumni), Concordia University, Montreal, QC, Canada.
Biol Cybern. 2022 Aug;116(4):407-445. doi: 10.1007/s00422-022-00935-8. Epub 2022 Jun 9.
Cancers are complex dynamic ecosystems. Reductionist approaches to science are inadequate in characterizing their self-organized patterns and collective emergent behaviors. Since current approaches to single-cell analysis in cancer systems rely primarily on single time-point multiomics, many of the temporal features and causal adaptive behaviors in cancer dynamics are vastly ignored. As such, tools and concepts from the interdisciplinary paradigm of complex systems theory are introduced herein to decode the cellular cybernetics of cancer differentiation dynamics and behavioral patterns. An intuition for the attractors and complex networks underlying cancer processes such as cell fate decision-making, multiscale pattern formation systems, and epigenetic state-transitions is developed. The applications of complex systems physics in paving targeted therapies and causal pattern discovery in precision oncology are discussed. Pediatric high-grade gliomas are discussed as a model-system to demonstrate that cancers are complex adaptive systems, in which the emergence and selection of heterogeneous cellular states and phenotypic plasticity are driven by complex multiscale network dynamics. In specific, pediatric glioblastoma (GBM) is used as a proof-of-concept model to illustrate the applications of the complex systems framework in understanding GBM cell fate decisions and decoding their adaptive cellular dynamics. The scope of these tools in forecasting cancer cell fate dynamics in the emerging field of computational oncology and patient-centered systems medicine is highlighted.
癌症是复杂的动态生态系统。在描述其自我组织模式和集体涌现行为时，简化论方法是不够的。由于当前癌症系统中单细胞分析的方法主要依赖于单点多组学，癌症动力学中的许多时间特征和因果适应性行为都被严重忽略了。因此，本文引入了来自复杂系统理论跨学科范例的工具和概念，以解码癌症分化动力学和行为模式的细胞控制论。本文发展了对癌症过程（如细胞命运决策、多尺度模式形成系统和表观遗传状态转变）的吸引子和复杂网络的直觉。讨论了复杂系统物理学在靶向治疗和精准肿瘤学中因果模式发现方面的应用。儿科高级别神经胶质瘤被用作模型系统，以证明癌症是复杂的自适应系统，其中异质细胞状态和表型可塑性的出现和选择是由复杂的多尺度网络动力学驱动的。具体来说，小儿成胶质细胞瘤（GBM）被用作概念验证模型，说明了复杂系统框架在理解 GBM 细胞命运决定和解码其适应性细胞动力学方面的应用。强调了这些工具在计算肿瘤学和以患者为中心的系统医学这一新兴领域中预测癌症细胞命运动力学的范围。
