Topological signatures in regulatory network enable phenotypic heterogeneity in small cell lung cancer.

Lakshya Chauhan,Uday Ram,Mohit Kumar Jolly,Kishore Hari

doi:10.7554/elife.64522

Abstract

Phenotypic (non-genetic) heterogeneity has significant implications for the development and evolution of organs, organisms, and populations. Recent observations in multiple cancers have unraveled 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.

Highlights

‘Genotype controls phenotype’ has been a prevalent paradigm across multiple biological contexts (Orgogozo et al, 2015)
To obtain the steady-state distributions corresponding to this complex network, we implemented two complementary approaches – one of them is a discrete parameter-independent Boolean modeling approach using Ising model formalism and an asynchronous update mode (FontClos et al, 2018), and the other is RACIPE (Random Circuit Perturbation) (Huang et al, 2017), a parameter-agnostic approach that uses a set of coupled ordinary differential equations (ODEs) with parameters sampled over a wide biologically relevant range
Red line shows the number of states obtained for the small cell lung cancer (SCLC) WT network. (D) (i) Comparison of steady-state frequencies obtained via RACIPE and Boolean

Summary

Introduction

‘Genotype controls phenotype’ has been a prevalent paradigm across multiple biological contexts (Orgogozo et al, 2015). Past few decades have revealed in many biological organisms that a fraction of cells in a genetically identical population can behave differently from others, even under nearly identical environmental conditions This ‘phenotypic heterogeneity’ usually refers to ‘non-genetic’ variations among individual cells in a genetically homogeneous scenario (Grote et al, 2015). In microbial populations, this heterogeneity can manifest as variation in morphologies, growth dynamics, metabolic signatures, and response to antibiotics. This heterogeneity can manifest as variation in morphologies, growth dynamics, metabolic signatures, and response to antibiotics It can enable ‘bet-hedging’, thereby providing cell populations or organisms with higher fitness especially in fluctuating environments (Ackermann, 2015). Decoding mechanistic underpinnings of emergence of phenotypic heterogeneity remains crucial

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Results

Conclusion