A mathematical model of tumor growth under fluctuating conditions with stochastic phenotypic epithelial-mesenchymal switch as the main mechanism of adaptation is developed, and non-trivial evolutionary outcomes are revealed, depending on the relative time scales of the underlying processes.
Abstract
Tumors evolve under constantly changing microenvironmental conditions, and a key major mechanism by which cancer cells adapt to fluctuating environments is stochastic phenotypic switch via epithelial–mesenchymal plasticity (EMP). Here we demonstrate the existence of EMP in human breast epithelial cell lines and assess the fitness of E and M cells in environments that fluctuate between favorable and harsh conditions. We then develop a theoretical framework for analysis of tumor evolution with E-M stochastic phenotypic switch that integrates the behaviors of E and M cells. We characterize the outcomes of tumor evolution via three characteristic times describing i) tumor growth, ii) phenotypic adaptation and iii) fluctuations of the environment. We identify a tradeoff between tumor growth and survival, with different phenotype switching rates maximizing each of these objectives. An anticancer therapeutic strategy is proposed based on tumor survival analysis, revealing that blocking mesenchymal to epithelial transition, rather than epithelial to mesenchymal transition, is critical under fluctuating conditions. Thus, this study elucidates fundamental evolutionary mechanisms of tumor phenotypic adaptation in fluctuating environments with potentially important clinical implications. SIGNIFICANCE Tumors evolve under constantly changing environmental conditions, to which they adapt primarily via epithelial-mesenchymal plasticity. However, the impact of fluctuating environment on tumor growth and phenotypic composition remains elusive. We developed a mathematical model of tumor growth under fluctuating conditions with stochastic phenotypic epithelial-mesenchymal switch as the main mechanism of adaptation. Motivated by experimentally observed differences in the growth of epithelial and mesenchymal cells under variable conditions, the model reveals non-trivial evolutionary outcomes, depending on the relative time scales of the underlying processes, reminiscent of the Parrondo’s Paradox in game theory. Based on the model analysis, an optimal therapeutic strategy is proposed, with the mesenchymal-epithelial transition identified as the critical target.
Epithelial-to-Mesenchymal transitions in the context of cancer are considered drivers of tumoral plasticity and hallmarks of cancer metastasis, resistance to therapy, and relapse. Although classically seen as processes induced by specific extracellular inputs, recent findings pointed to the question of whether and to what extent transitions could happen spontaneously within the tumor population. Here, we address this issue by comparing EMT dynamics in lung cancer cells under normal growth conditions and in the presence of TGF-β, a known EMT inducer. Combining time-course flow cytometry measurements with stochastic modeling, we found that (i) untreated cells undergo density-independent EMT. In particular, (ii) the long-term populations present a combination of epithelial, hybrid, and mesenchymal states, where (iii) the hybrid state acts as a transient phenotype across all experimental conditions. Instead, the spontaneous dynamics differ from those observed after the withdrawal of the treatment, suggesting (iv) the presence of hysteric effects due to EMT induction. Finally, we discuss how our minimal quantitative framework may provide insights into the structure of the Waddington landscape associated with the epithelial-mesenchymal plasticity.
Domenico Caudo, Federica Mulè, Giovanna Peruzzi et al.· Journal of Physics, Conferen...· 0 citations
Cancer is one of the leading causes of global morbidity and mortality and is characterized by its high heterogeneity, genomic instability and adaptive plasticity. Applying Darwin’s theory of evolution, the concept of tumor evolution has improved our understanding of the biological behavior of advanced cancer, while technological limitations have long left how to dynamically characterize the evolutionary process unsolved. In recent years, the development of high-throughput sequencing, single-cell and spatial omics, lineage tracing, computational modeling, and noninvasive biopsy technology has helped explain tumor heterogeneity and track the evolutionary trajectory of tumors, which has attracted widespread attention in tumor evolution. Here, we discuss the models and drivers of tumor evolution, focusing on how cancer cells adapt and evolve under multidimensional selective pressure, including the intracellular, extracellular, and exogenous levels. We further summarize key signaling pathways and molecular mechanisms involved in genomic instability, epigenetic regulation, metabolic reprogramming, tumor microenvironment remodeling, immune escape and therapy-induced selection. In addition, emerging therapeutic strategies guided by tumor evolution are also discussed, emphasizing that the identification of key genetic and epigenetic targets, together with dynamic monitoring of clonal changes, is crucial for overcoming treatment resistance and improving patient outcomes. By systematically summarizing the signaling pathways and molecular mechanisms underlying tumor evolution, this review aims to provide new targets and conceptual frameworks for promoting precision treatment strategies in oncology in the future.
Kun-Yu Zhang, Xiu-Zhi Zhu, Yuxin Yan et al.· Signal Transduction and Targ...· 1 citation
The immortalized non-tumorigenic breast epithelial cell line (MCF10A) develops neoplastic clones under fluctuating conditions in-vitro that mimic the harsh tumor microenvironment and is identified as a potential therapeutic target against epithelial-mesenchymal plasticity and metastatic spread in breast cancer.
Erez Persi, Rafael R. Canevarolo, P. Sudalagunta et al.· Research Square· 1 citation
Cancer progression is driven by the ability of cells with identical driver mutations to adopt biologically distinct adaptive phenotypes. Yet the population dynamics implied by intratumour phenotypic heterogeneity is poorly understood. Melanoma is an excellent setting to study phenotype switching, in part because phenotypic identity is conferred by melanocyte inducing transcription factor (MITF) activity. Here we develop a multiscale phenotype-structured partial differential equation model for epidermal melanoma cell populations, first considering subcellular MITF and then spatially uniform and spatially heterogeneous populations. The model admits three stable long-term behaviours: slow growth with proliferative cells and non-cycling differentiated cells; faster expansion, with an invasive core; and rapid growth with oscillatory core dynamics. More broadly, the analysis highlights that phenotype reversibility by individual cells does not imply reversibility of phenotype population distributions. Hence, single-cell properties (e.g., reversibility of invasive capacity) must be extrapolated with caution to populations with coupled cell dynamics.
Keith L. Chambers, Richard M. White, Colin R. Goding et al.· 0 citations
Tumor plasticity and microenvironmental heterogeneity are established as an integrated, evolving system that fuels metastasis and limits durable treatment responses.
G. Dagar, M. Dagar, Ashna Gupta et al.· MedComm· 0 citations
Genetic uniqueness of the tumor microenvironment significantly influences cancer growth, survival, and response to therapy, independent of the cancer cell’s intrinsic properties or the adaptive immune system. Using genetically distinct Rag1-/- mouse models, this study shows that different strains exhibit varied tumor growth kinetics and survival outcomes when xenografted with identical leukemic and solid tumor cell lines. This study further highlights the critical role of the myeloid immune compartment and shows that disrupting both lymphoid and myeloid systems alters cancer progression. These results also reveal that the tumor microenvironment can permanently alter cancer cell phenotypes and significantly affect chemotherapy efficacy, as seen with Cisplatin’s varying effects across strains. These findings underscore the importance of considering genetic background in preclinical cancer models, suggesting that reliance upon a single mouse strain may lead to incomplete conclusions about cancer biology and treatment efficacy. SUMMARY STATEMENT Pre-clinical xenograft mammalian models are used to study human diseases. Here we report that the genetic uniqueness of the tumor microenvironment, independent of the immune system, can determine the fate of cancer progression, survival, and therapy response.
Mark A. Warner, Jennifer K. Sargent, Shawnna R. Farley et al.· bioRxiv· 0 citations