Network topology metrics explaining enrichment of hybrid epithelial/mesenchymal phenotypes in metastasis

PLoS Comput Biol. 2022 Nov 8;18(11):e1010687. doi: 10.1371/journal.pcbi.1010687. eCollection 2022 Nov.

Abstract

Epithelial to Mesenchymal Transition (EMT) and its reverse-Mesenchymal to Epithelial Transition (MET) are hallmarks of metastasis. Cancer cells use this reversible cellular programming to switch among Epithelial (E), Mesenchymal (M), and hybrid Epithelial/Mesenchymal (hybrid E/M) state(s) and seed tumors at distant sites. Hybrid E/M cells are often more aggressive and metastatic than the "pure" E and M cells. Thus, identifying mechanisms to inhibit hybrid E/M cells can be promising in curtailing metastasis. While multiple gene regulatory networks (GRNs) based mathematical models for EMT/MET have been developed recently, identifying topological signatures enriching hybrid E/M phenotypes remains to be done. Here, we investigate the dynamics of 13 different GRNs and report an interesting association between "hybridness" and the number of negative/positive feedback loops across the networks. While networks having more negative feedback loops favor hybrid phenotype(s), networks having more positive feedback loops (PFLs) or many HiLoops-specific combinations of PFLs, support terminal (E and M) phenotypes. We also establish a connection between "hybridness" and network-frustration by showing that hybrid phenotypes likely result from non-reinforcing interactions among network nodes (genes) and therefore tend to be more frustrated (less stable). Our analysis, thus, identifies network topology-based signatures that can give rise to, as well as prevent, the emergence of hybrid E/M phenotype in GRNs underlying EMP. Our results can have implications in terms of targeting specific interactions in GRNs as a potent way to restrict switching to the hybrid E/M phenotype(s) to curtail metastasis.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Benchmarking
  • Epithelial-Mesenchymal Transition*
  • Gene Regulatory Networks
  • Humans
  • Neoplasms* / genetics
  • Phenotype

Grants and funding

This work is supported by the Science and Engineering Research Board through National-Postdoctoral Fellowship (PDF/2020/001235 to MR) and Ramanujan Fellowship (SB/S2/RJN-049/2018 to MKJ), the Department of Science and Technology through INSPIRE Faculty Program (DST/INSPIRE/04/2020/001492 to MR), the InfoSys Foundation Bangalore (to MKJ), and the Prime Ministers Research Fellowship (PMRF to KH). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.