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

• Published in: PLoS computational biology 2022-11, Vol.18 (11), p.e1010687-e1010687
• Main Authors: Rashid, Mubasher, Hari, Kishore, Thampi, John, Santhosh, Nived Krishnan, Jolly, Mohit Kumar
• Format: Article
• Language: English
• Subjects: Biology and Life Sciences; Boolean; Cancer; Colorectal cancer; Computer and Information Sciences; Development and progression; Epithelial cells; Feedback; Feedback loops; Frustration; Genotype & phenotype; Head & neck cancer; Health aspects; Kidney cancer; M cells; Mathematical models; Medicine and Health Sciences; Mesenchyme; Metastases; Metastasis; Negative feedback; Network topologies; Phenotypes; Physical Sciences; Positive feedback; Signatures; Simulation; Stem cells; Topology; Tumors
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1010687

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.