Landscape of Epithelial Mesenchymal Plasticity as an emergent property of coordinated teams in regulatory networks

Hari, Kishore; Ullanat, Varun; Balasubramanian, Archana; Gopalan, Aditi; Jolly, Mohit Kumar

doi:10.1101/2021.12.12.472090

Cited by 16 publications

(21 citation statements)

References 67 publications

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“…S2E ), i.e., genes regulating a phenotype are positively correlated with each other and negatively correlated with genes regulating the other phenotype. Such ‘teams’-like interactions have been shown to play a role in regulating phenotype switching and multi-stability in biological systems 41,42 . Transition paths based on the energy landscapes for the two trajectories (de-differentiation and hyperpigmentation) reveal the changes in gene expression as cells transition from one state to another ( Fig.…”

Section: Resultsmentioning

confidence: 99%

Quantitative landscapes reveal trajectories of cell-state transitions associated with drug resistance in melanoma

Pillai

Chen

Jolly

et al. 2022

Preprint

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Drug resistance and tumor relapse in melanoma patients is attributed to a combination of genetic and non-genetic mechanisms. Non-genetic mechanisms of drug resistance commonly involve reversible changes in the cell-state or phenotype, i.e., alterations in molecular profiles that can help cells escape being killed by targeted therapeutics. In melanoma, one of the most common mechanisms of non-genetic resistance is de-differentiation, which is characterized by loss of melanocytic markers. While various molecular attributes of de-differentiation have been identified, the transition dynamics remains poorly understood. Here, we construct cell-state transition landscapes, to quantify the stochastic dynamics driving phenotypic switch in melanoma based on its underlying regulatory network. These landscapes reveal the existence of multiple alternative paths to resistance - de-differentiation and transition to a hyper-pigmented phenotype. Finally, by visualizing the changes in the landscape during in silico molecular perturbations, we identify combinatorial strategies that can lead to the most optimal outcome - a landscape with the minimal occupancy of the two drug-resistant states. Therefore, we present these landscapes as platforms to screen possible therapeutic interventions in terms of their ability to lead to most favourable patient outcomes.

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Section: Resultsmentioning

confidence: 99%

Quantitative landscapes reveal trajectories of cell-state transitions associated with drug resistance in melanoma

Pillai

Chen

Jolly

et al. 2022

Preprint

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“…From both methods, we now have stable steady states as binary strings (0 → Low, 1 → High). We then characterize these steady states into hybrid or “terminal” (referring cumulatively to epithelial and mesenchymal states) based on an EMT score (Hari et al 2021) ( Fig 1C ) and define the ‘hybridness’ of a network as fraction of steady state frequencies that can be classified as hybrid E/M (see details in materials and methods).…”

Section: Resultsmentioning

confidence: 99%

“…Frustrated phenotypes can then be viewed as those resulting from gene expression patterns that may be the outcome of conflicting signals. Recent computational analysis of some EMP networks showed high frustration for hybrid phenotypes compared to the terminal phenotypes (Hari et al 2021). We investigated whether the relationship between frustration and hybridness is maintained across perturbations in these EMP networks.…”

Section: Resultsmentioning

confidence: 99%

“…From both methods, we now have stable steady states as binary strings (0 → Low, 1 → High). We then characterize these steady states into hybrid or "terminal" (referring cumulatively to epithelial and mesenchymal states) based on an EMT score (Hari et al 2021) (Fig 1C) and define the 'hybridness' of a network as fraction of steady state frequencies that can be classified as hybrid E/M (see details in materials and methods). The results obtained from ODE based modeling show that the unperturbed EMP networks (referred to as "wild-type" or WT from hereafter) had low levels of hybridness as compared to most of their perturbed counterparts (Fig 1D).…”

Section: "Hybridness" Of Emp Network Change With Changing Network-top...mentioning

confidence: 99%

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Network Topology Metrics Explaining Enrichment of Hybrid Epithelial Mesenchymal Phenotypes in Metastasis

Rashid

Hari

Thampi

et al. 2022

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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 for hybrid E/M phenotypes remains to be done. Here, we investigate the dynamics of 13 different GRNs and identify which network topologies can enrich for hybrid E/M phenotype(s) across GRNs. We found that increasing negative feedback loops enhances the “hybridness” of these networks, but increasing the number of positive feedback loops or their specific combinations can decrease the frequency of hybrid E/M phenotype. Thus, our analysis 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.

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“…The deeper an attractor (or a Waddington landscape's valley), the more difficult it gets to transition out from that phenotype. Multiple mathematical attempts to construct such landscapes for intracellular transcriptional networks driving E-M plasticity have shown that the "attractors" corresponding to hybrid E/M states are relatively shallow as compared to those corresponding to E and M states (31,32). This salient feature of hybrid E/M cells can help explain their higher plasticity (28)(29)(30).…”

Section: The Composition Of the Population B) Regaining Of Original P...mentioning

confidence: 99%

Stochastic Population Dynamics of Cancer Stemness and Adaptive Response to Therapies

Jain¹,

Duddu²,

Jolly³

2022

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Intratumoral heterogeneity can exist along multiple axes: Cancer Stem Cells (CSCs)/non-CSCs, drug-sensitive/drug-tolerant states and a spectrum of epithelial-hybrid-mesenchymal phenotypes. Further, these diverse cell-states can switch reversibly among one another, thereby posing a major challenge to therapeutic efficacy. Therefore, understanding the origins of phenotypic plasticity and heterogeneity remains an active area of investigation. While genomic components (mutations, chromosomal instability) driving heterogeneity have been well-studied, recent reports highlight the role of non-genetic mechanisms in enabling both phenotypic plasticity and heterogeneity. Here, we discuss various processes underlying phenotypic plasticity such as stochastic gene expression, chromatin reprogramming, asymmetric cell division and the presence of multiple “attractors”. These processes can facilitate a dynamically evolving cell population such that a subpopulation of (drug-tolerant) cells can survive lethal drug exposure and recapitulate population heterogeneity on drug withdrawal, leading to relapse. These drug-tolerant cells can be both pre-existing and also induced by the drug itself through cell-state reprogramming. The dynamics of cell-state transitions both in absence and presence of the drug can be quantified through mathematical models. Such a dynamical systems approach to elucidating patterns of intratumoral heterogeneity by integrating longitudinal experimental data with mathematical models can help design effective combinatorial and/or sequential therapies for better clinical outcomes.

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Landscape of Epithelial Mesenchymal Plasticity as an emergent property of coordinated teams in regulatory networks

Cited by 16 publications

References 67 publications

Quantitative landscapes reveal trajectories of cell-state transitions associated with drug resistance in melanoma

Quantitative landscapes reveal trajectories of cell-state transitions associated with drug resistance in melanoma

Network Topology Metrics Explaining Enrichment of Hybrid Epithelial Mesenchymal Phenotypes in Metastasis

Stochastic Population Dynamics of Cancer Stemness and Adaptive Response to Therapies

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