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.
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
Exon expression for NCI small cell lung cancer cell line panelNCBI Gene Expression Omnibus, GSE73160.
- Mohit Kumar Jolly
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- Sandeep Krishna, National Centre for Biological Sciences‐Tata Institute of Fundamental Research, India
© 2021, Chauhan et al.
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