Abstract
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.
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Data Availability and Codes
Raw Datasets Pediatric GBM gene expression count matrix (Neftel et al. 2019): https://singlecell.broadinstitute.org/single_cell/study/SCP393/single-cell-rna-seq-of-adult-and-pediatric-glioblastoma#study-summary The metadata can be used to isolate the n = 8 pediatric GBM patient samples. Pediatric Brain tumors proteome (Petralia et al. 2021): https://cptac-data-portal.georgetown.edu/study-summary/S047 Extract the abundance count matrix from the text file “Gygi_TCMP_HMS_Proteome.tmt11.txt” found in the downloaded folder. All codes and instructions for the PID networks inference and the guided tutorials to obtain the presented results/single-cell analyses are available in the project GitHub link: https://github.com/Abicumaran/Cancer-Cybernetics GitHub https://doi.org/10.5281/zenodo.6432909
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Acknowledgements
Thanks to Dr. Mohan Malleshaiah (IRCM, Montreal) for introducing me to biological cybernetics and single-cell analyses. Figure S2 in the Appendix was reproduced from our results published in Uthamacumaran and Craig (2022) with the permission of both authors and the open-access journal it is published in iScience. Thanks to Dalia Alkayal for illustrating Figure 1 and to Rik Bhattacharja for designing Figure 5. Figure 1 was adapted from an image found in Deshmukh et al. (2021). All other figures were generated by the corresponding author (AU), the codes and datasets for which are provided in the GitHub link below.
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Uthamacumaran, A. Dissecting cell fate dynamics in pediatric glioblastoma through the lens of complex systems and cellular cybernetics. Biol Cybern 116, 407–445 (2022). https://doi.org/10.1007/s00422-022-00935-8
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DOI: https://doi.org/10.1007/s00422-022-00935-8