Payload Systems Engineering Branch, Emeritus, NASA, Annapolis, MD, USA.
Math for Medicine, Inc., Rochester, MN, USA.
BMC Bioinformatics. 2021 Apr 16;22(1):197. doi: 10.1186/s12859-021-04025-7.
Recent clinical advances in cancer immuno-therapeutics underscore the need for improved understanding of the complex relationship between cancer and the multiple, multi-functional, inter-dependent, cellular and humoral mediators/regulators of the human immune system. This interdisciplinary effort exploits engineering analysis methods utilized to investigate anomalous physical system behaviors to explore immune system behaviors. Cancer Immune Control Dynamics (CICD), a systems analysis approach, attempts to identify differences between systemic immune homeostasis of 27 healthy volunteers versus 14 patients with metastatic malignant melanoma based on daily serial measurements of conventional peripheral blood biomarkers (15 cell subsets, 35 cytokines). The modeling strategy applies engineering control theory to analyze an individual's immune system based on the biomarkers' dynamic non-linear oscillatory behaviors. The reverse engineering analysis uses a Singular Value Decomposition (SVD) algorithm to solve the inverse problem and identify a solution profile of the active biomarker relationships. Herein, 28,605 biologically possible biomarker interactions are modeled by a set of matrix equations creating a system interaction model. CICD quantifies the model with a participant's biomarker data then computationally solves it to measure each relationship's activity allowing a visualization of the individual's current state of immunity.
CICD results provide initial evidence that this model-based analysis is consistent with identified roles of biomarkers in systemic immunity of cancer patients versus that of healthy volunteers. The mathematical computations alone identified a plausible network of immune cells, including T cells, natural killer (NK) cells, monocytes, and dendritic cells (DC) with cytokines MCP-1 [CXCL2], IP-10 [CXCL10], and IL-8 that play a role in sustaining the state of immunity in advanced cancer.
With CICD modeling capabilities, the complexity of the immune system is mathematically quantified through thousands of possible interactions between multiple biomarkers. Therefore, the overall state of an individual's immune system regardless of clinical status, is modeled as reflected in their blood samples. It is anticipated that CICD-based capabilities will provide tools to specifically address cancer and treatment modulated (immune checkpoint inhibitors) parameters of human immunity, revealing clinically relevant biological interactions.
癌症免疫治疗的最近临床进展强调需要更好地理解癌症与人类免疫系统的多种多功能、相互依存的细胞和体液介质/调节剂之间的复杂关系。这项跨学科工作利用工程分析方法来研究异常物理系统的行为，以探索免疫系统的行为。癌症免疫控制动力学（CICD）是一种系统分析方法，试图根据 27 名健康志愿者和 14 名转移性恶性黑色素瘤患者的常规外周血生物标志物（15 个细胞亚群，35 种细胞因子）的每日连续测量结果，识别两者之间的系统性免疫稳态差异。该建模策略应用工程控制理论根据生物标志物的动态非线性振荡行为来分析个体的免疫系统。反向工程分析使用奇异值分解（SVD）算法来解决逆问题并识别活性生物标志物关系的解决方案分布。在此，通过一组矩阵方程对 28605 种可能的生物标志物相互作用进行建模，创建了一个系统相互作用模型。CICD 使用参与者的生物标志物数据对模型进行量化，然后通过计算求解来测量每个关系的活动，从而可以直观地了解个体当前的免疫状态。
CICD 结果初步表明，这种基于模型的分析与在癌症患者与健康志愿者的系统性免疫中鉴定出的生物标志物的作用一致。仅数学计算就确定了一个可行的免疫细胞网络，包括 T 细胞、自然杀伤（NK）细胞、单核细胞和树突状细胞（DC），以及细胞因子 MCP-1 [CXCL2]、IP-10 [CXCL10]和 IL-8，它们在维持晚期癌症患者的免疫状态方面发挥作用。
通过 CICD 建模功能，可以通过多个生物标志物之间的数千种可能相互作用来对免疫系统的复杂性进行数学量化。因此，无论临床状况如何，个体的免疫系统的整体状态都可以通过其血液样本进行建模。预计 CICD 为基础的功能将提供工具来专门解决癌症和治疗调节（免疫检查点抑制剂）的人类免疫参数，揭示临床相关的生物学相互作用。
