Cancer immune control dynamics: a clinical data driven model of systemic immunity in patients with metastatic melanoma

• Main Authors: Becher, L.R.E (Author), Frisch, H.P (Author), Leontovich, A.A (Author), Markovic, S.N (Author), McElroy, V.F (Author), Nevala, W.K (Author), Sprau, A. (Author), Turner, J.D (Author)
• Format: Article
• Language: English
• Published: BioMed Central Ltd 2021
• Subjects: Biological interactions; biological marker; Biomarkers; Blood; Cells; Complex networks; Complex relationships; cytokine; Cytokines; Cytology; Data visualization; Dermatology; Diseases; human; Human immune systems; Humans; Immune system; Immune system behavior; Inverse problems; Math modeling; Mathematical computation; melanoma; Melanoma; Natural killer cells; Oncology; Oscillatory behaviors; Peripheral blood biomarkers; Reverse engineering; Singular value decomposition; Singular value decomposition algorithms; Systemic immunity; Systems analysis; T lymphocyte; T-Lymphocytes
• Online Access: View Fulltext in Publisher

 Background: 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. Results: 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. Conclusions: 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. © 2021, The Author(s).