Topological signatures in regulatory network enable phenotypic heterogeneity in small cell lung cancer
Abstract
Phenotypic (non-genetic) heterogeneity has significant implications for development and evolution of organs, organisms, and populations. Recent observations in multiple cancers have unravelled the role of phenotypic heterogeneity in driving metastasis and therapy recalcitrance. However, the origins of such phenotypic heterogeneity are poorly understood in most cancers. Here, we investigate a regulatory network underlying phenotypic heterogeneity in small cell lung cancer, a devastating disease with no molecular targeted therapy. Discrete and continuous dynamical simulations of this network reveal its multistable behavior that can explain co-existence of four experimentally observed phenotypes. Analysis of the network topology uncovers that multistability emerges from two teams of players that mutually inhibit each other but members of a team activate one another, forming a 'toggle switch' between the two teams. Deciphering these topological signatures in cancer-related regulatory networks can unravel their 'latent' design principles and offer a rational approach to characterize phenotypic heterogeneity in a tumor.
Data availability
Gene expression profiles of 52 SCLC cell lines were downloaded from Broad Institute's CCLE expression data. Data for GSE73160 was downloaded from NCBI website. All codes used to generate and analyze simulation data, and codes used to analyze gene expression data are available at : https://github.com/uday2607/CSB-SCLC
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Exon expression for NCI small cell lung cancer cell line panelNCBI Gene Expression Omnibus, GSE73160.
Article and author information
Author details
Funding
Science and Engineering Research Board (SB/S2/RJN-049/2018)
- Mohit Kumar Jolly
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2021, Chauhan et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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