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A Plausible Accelerating Function of Intermediate States in Cancer Metastasis

Hanah Goetz, Juan R. Melendez-Alvarez, Luonan Chen, Xiao-Jun Tian
doi: https://doi.org/10.1101/828343
Hanah Goetz
1School of Biological and Health Systems Engineering, Arizona State University
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Juan R. Melendez-Alvarez
1School of Biological and Health Systems Engineering, Arizona State University
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Luonan Chen
2Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences
3Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences
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Xiao-Jun Tian
1School of Biological and Health Systems Engineering, Arizona State University
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  • For correspondence: Xiaojun.Tian@asu.edu
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Abstract

Epithelial-to-mesenchymal transition (EMT) is a fundamental cellular process and plays an essential role in development, tissue regeneration, and cancer metastasis. Interestingly, EMT is not a binary process but instead proceeds with multiple partial intermediate states. However, the functions of these intermediate states are not fully understood. Here, we focus on a general question about how the number of partial EMT states affects cell transformation. First, by fitting a hidden Markov model of EMT with experimental data, we propose a statistical mechanism for EMT in which many unobservable microstates may exist within one of the observable macrostates. Furthermore, we find that increasing the number of intermediate states can accelerate the EMT process and that adding parallel paths or transition layers accelerates the process even further. Last, a stabilized intermediate state traps cells in one partial EMT state. This work advances our understanding of the dynamics and functions of EMT plasticity during cancer metastasis.

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Posted November 01, 2019.
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A Plausible Accelerating Function of Intermediate States in Cancer Metastasis
Hanah Goetz, Juan R. Melendez-Alvarez, Luonan Chen, Xiao-Jun Tian
bioRxiv 828343; doi: https://doi.org/10.1101/828343
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A Plausible Accelerating Function of Intermediate States in Cancer Metastasis
Hanah Goetz, Juan R. Melendez-Alvarez, Luonan Chen, Xiao-Jun Tian
bioRxiv 828343; doi: https://doi.org/10.1101/828343

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