A Blueprint for Identifying Phenotypes and Drug Targets in Complex Disorders with Empirical Dynamics

The highs and lows of cyclic thrombocytopenia

Cyclic thrombocytopenia (CTP) is characterized by periodic platelet oscillation with substantial amplitude. Most CTP cases have a thrombocytopenic background and are often misdiagnosed as immune thrombocytopenia with erratically effective treatment choices. CTP also occurs during hydroxyurea treatment in patients with myeloproliferative diseases. While the aetiology of CTP remains uncertain, here we evaluate historical, theoretical and clinical findings to provide a framework for understanding CTP pathophysiology. CTP retains the intrinsic oscillatory factors defined by the homeostatic regulation of platelet count, presenting as reciprocal platelet/thrombopoietin oscillations and stable oscillation periodicity. Moreover, CTP patients possess pathogenic factors destabilizing the platelet homeostatic system thereby creating opportunities for external perturbations to initiate and sustain the exaggerated platelet oscillations. Beyond humoral and cell-mediated autoimmunity, we propose recently uncovered germline and somatic genetic variants, such as those of MPL, STAT3 or DNMT3A, as pathogenic factors in thrombocytopenia-related CTP. Likewise, the JAK2 V617F or BCR::ABL1 translocation that drives underlying myeloproliferative diseases may also play a pathogenic role in hydroxyurea-induced CTP, where hydroxyurea treatment can serve as both a trigger and a pathogenic factor of platelet oscillation. Elucidating the pathogenic landscape of CTP provides an opportunity for targeted therapeutic approaches in the future.

Dissecting cell fate dynamics in pediatric glioblastoma through the lens of complex systems and cellular cybernetics

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.

A Review of Mathematical and Computational Methods in Cancer Dynamics

Cancers are complex adaptive diseases regulated by the nonlinear feedback systems between genetic instabilities, environmental signals, cellular protein flows, and gene regulatory networks. Understanding the cybernetics of cancer requires the integration of information dynamics across multidimensional spatiotemporal scales, including genetic, transcriptional, metabolic, proteomic, epigenetic, and multi-cellular networks. However, the time-series analysis of these complex networks remains vastly absent in cancer research. With longitudinal screening and time-series analysis of cellular dynamics, universally observed causal patterns pertaining to dynamical systems, may self-organize in the signaling or gene expression state-space of cancer triggering processes. A class of these patterns, strange attractors, may be mathematical biomarkers of cancer progression. The emergence of intracellular chaos and chaotic cell population dynamics remains a new paradigm in systems medicine. As such, chaotic and complex dynamics are discussed as mathematical hallmarks of cancer cell fate dynamics herein. Given the assumption that time-resolved single-cell datasets are made available, a survey of interdisciplinary tools and algorithms from complexity theory, are hereby reviewed to investigate critical phenomena and chaotic dynamics in cancer ecosystems. To conclude, the perspective cultivates an intuition for computational systems oncology in terms of nonlinear dynamics, information theory, inverse problems, and complexity. We highlight the limitations we see in the area of statistical machine learning but the opportunity at combining it with the symbolic computational power offered by the mathematical tools explored.

Algorithmic reconstruction of glioblastoma network complexity

Glioblastoma is a complex disease that is difficult to treat. Network and data science offer alternative approaches to classical bioinformatics pipelines to study gene expression patterns from single-cell datasets, helping to distinguish genes associated with the control of differentiation and aggression. To identify the key molecular regulators of the networks driving glioblastoma/GSC and predict their cell fate dynamics, we applied a host of data theoretic techniques to gene expression patterns from pediatric and adult glioblastoma, and adult glioma-derived stem cells (GSCs). We identified eight transcription factors (OLIG1/2, TAZ, GATA2, FOXG1, SOX6, SATB2, and YY1) and four signaling genes (ATL3, MTSS1, EMP1, and TPT1) as coordinators of cell state transitions and, thus, clinically targetable putative factors differentiating pediatric and adult glioblastomas from adult GSCs. Our study provides strong evidence of complex systems approaches for inferring complex dynamics from reverse-engineering gene networks, bolstering the search for new clinically relevant targets in glioblastoma.

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Complex cell fate attractors capture glioblastoma differentiation dynamics

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Graph theoretic approaches decode master regulators of GBM glioblastoma cell fate decisions

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Network dynamics of pediatric glioblastoma resemble adult GSCs

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Transcriptional networks may help reprogram glioblastoma behavioral patterns

Longitudinal study of 2 patients with cyclic thrombocytopenia, STAT3 and MPL mutations

Cyclic thrombocytopenia (CTP) is a rare disease of periodic platelet count oscillations. The pathogenesis of CTP remains elusive. To study the underlying pathophysiology and genetic and cellular associations with CTP, we applied systems biology approaches to 2 patients with stable platelet cycling and reciprocal thrombopoietin (TPO) cycling at multiple time points through 2 cycles. Blood transcriptome analysis revealed cycling of platelet-specific genes, which are in parallel with and precede platelet count oscillation, indicating that cyclical platelet production leads platelet count cycling in both patients. Additionally, neutrophil and erythrocyte-specific genes also showed fluctuations correlating with platelet count changes, consistent with TPO effects on hematopoietic progenitors. Moreover, we found novel genetic associations with CTP. One patient had a novel germline heterozygous loss-of-function (LOF) thrombopoietin receptor (MPL) c.1210G>A mutation, and both had pathogenic somatic gain-of-function (GOF) variants in signal transducer and activator of transcription 3 (STAT3). In addition, both patients had clonal T-cell populations that remained stable throughout platelet count cycles. These mutations and clonal T cells may potentially involve in the pathogenic baseline in these patients, rendering exaggerated persistent thrombopoiesis oscillations of their intrinsic rhythm upon homeostatic perturbations. This work provides new insights into the pathophysiology of CTP and possible therapies.

A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks

Cancers are complex dynamical systems. They remain the leading cause of disease-related pediatric mortality in North America. To overcome this burden, we must decipher the state-space attractor dynamics of gene expression patterns and protein oscillations orchestrated by cancer stemness networks. The review provides an overview of dynamical systems theory to steer cancer research in pattern science. While most of our current tools in network medicine rely on statistical correlation methods, causality inference remains primitively developed. As such, a survey of attractor reconstruction methods and machine algorithms for the detection of causal structures applicable in experimentally derived time series cancer datasets is presented. A toolbox of complex systems approaches are discussed for reconstructing the signaling state space of cancer networks, interpreting causal relationships in their time series gene expression patterns, and assisting clinical decision making in computational oncology. As a proof of concept, the applicability of some algorithms are demonstrated on pediatric brain cancer datasets and the requirement of their time series analysis is highlighted.