Impacts of competition and phenotypic plasticity on the viability of adaptive therapy

Cancer is heterogeneous and variability in drug sensitivity is widely documented across cancer types. Adaptive therapy is an emerging modality of cancer treatment that leverages this drug resistance heterogeneity to improve therapeutic outcomes. Current standard treatments typically eliminate a large fraction of drug-sensitive cells, leading to drug-resistant relapse due to competitive release. Adaptive therapy aims to retain some drug-sensitive cells, thereby limiting resistant cell growth by ecological competition. While early clinical trials of such a strategy have shown promise, optimisation of adaptive therapy is a subject of active study. Current methods largely assume cell phenotypes to remain constant, even though cell-state transitions could permit drug-sensitive and -resistant phenotypes to interchange and thus escape therapy. We address this gap using a deterministic model of population growth, in which sensitive and resistant cells grow under competition and undergo cell-state transitions. Based on the model's steady-state behaviour and temporal dynamics, we identify distinct balances of competition and phenotypic transitions that are suitable for effective adaptive versus constant dose therapy. Our data indicate that under adaptive therapy, models with cell-state transitions show a higher frequency of fluctuations than those without, suggesting that the balance between ecological competition and phenotypic transitions could determine population-level dynamical properties. Our analyses also identify key limitations of applying phenomenological models in clinical practice for therapy design and implementation, particularly when cell-state transitions are involved. These findings provide an overall perspective on the relevance of phenotypic plasticity for emerging cancer treatment strategies using population dynamics as an investigation framework.

Dynamical hallmarks of cancer: Phenotypic switching in melanoma and epithelial-mesenchymal plasticity

Phenotypic plasticity was recently incorporated as a hallmark of cancer. This plasticity can manifest along many interconnected axes, such as stemness and differentiation, drug-sensitive and drug-resistant states, and between epithelial and mesenchymal cell-states. Despite growing acceptance for phenotypic plasticity as a hallmark of cancer, the dynamics of this process remains poorly understood. In particular, the knowledge necessary for a predictive understanding of how individual cancer cells and populations of cells dynamically switch their phenotypes in response to the intensity and/or duration of their current and past environmental stimuli remains far from complete. Here, we present recent investigations of phenotypic plasticity from a systems-level perspective using two exemplars: epithelial-mesenchymal plasticity in carcinomas and phenotypic switching in melanoma. We highlight how an integrated computational-experimental approach has helped unravel insights into specific dynamical hallmarks of phenotypic plasticity in different cancers to address the following questions: a) how many distinct cell-states or phenotypes exist?; b) how reversible are transitions among these cell-states, and what factors control the extent of reversibility?; and c) how might cell-cell communication be able to alter rates of cell-state switching and enable diverse patterns of phenotypic heterogeneity? Understanding these dynamic features of phenotypic plasticity may be a key component in shifting the paradigm of cancer treatment from reactionary to a more predictive, proactive approach.

Rethinking dormancy: Antibiotic persisters are metabolically active, non-growing cells

OBJECTIVES

Bacterial persisters are a subpopulation of multidrug-tolerant cells capable of surviving and resuming activity after exposure to bactericidal antibiotic concentrations, contributing to relapsing infections and the development of antibiotic resistance. In this study, we challenge the conventional view that persisters are metabolically dormant by providing compelling evidence that an isogenic population of Escherichia coli remains metabolically active in persistence.

METHODS

Using transcriptomic analysis, we examined E. coli persisters at multiple time points following exposure to bactericidal concentrations of ampicillin (Amp). Some genes were consistently upregulated in Amp treated persisters compared to the untreated controls, a change that can only occur in metabolically active cells capable of increasing RNA levels.

RESULTS

Some of the identified genes have been previously linked to persister cells, while others have not been associated with them before. If persister cells were metabolically dormant, gene expression changes over time would be minimal during Amp treatment. However, network analysis revealed major shifts in gene network activity at various time points of antibiotic exposure.

CONCLUSIONS

These findings reveal that persisters are metabolically active, non-dividing cells, thereby challenging the traditional view that they are dormant.