Biology subjects

Jolly, M. K.

Publications and source records attributed to Jolly, M. K..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Ecological Dynamics of Pro-tumor and Anti-tumor Teams in the Tumor Microenvironment

Tumor growth occurs within a complex tumor microenvironment (TME) composed of many interacting cell types. The immune cell types in TME tend to organize into two functional communities: a pro-tumor team and an anti-tumor team, each internally cooperative but mutually antagonistic forming a two-team ecosystem. Quantitatively predicting the ecological outcomes of such interactions remains challenging due to cellular diversity and interaction variability, and the exact dynamical regimes accessible to such a two-team ecosystem remain unknown. Here, we model tumor-immune interactions as a structured ecosystem with two competing teams using a generalized Lotka-Volterra framework and analyze it using the cavity method. We derive phase diagrams that delineate when these two communities coexist, when one dominates, and how these outcomes depend on intra-team cooperation, cross-team inhibition, and ecological heterogeneity. Our work provides a foundation for understanding tumor-immune dynamics from a community ecology perspective.

ecology↗

bioRxiv2025

Multi-omics investigation reveals molecular determinants of cancer cell evolution on soft extracellular matrix

Cancer cell adaptation to their physical tumor microenvironment is a key driver of malignancy. Recent experimental evolution experiments show that the soft extracellular matrix (ECM) can impose a selection pressure on genetically variable tumor populations. Over months of sustained culture, the selection pressure leads to enrichment of specific genetic variants with high fitness, but the mechanisms underlying the high fitness of these soft-selected clones are not fully understood. Here, we used a combination of RNA-seq, ATAC-seq, and RRBS-seq to compare soft-selected populations with non-selected ancestral populations cultured on soft ECM. We demonstrate that ancestral populations grown on soft ECM for short durations are characterized by a stressed cell state with low fitness marked by cell cycle arrest and distinct metabolic shifts, whereas sustained culture selects for a robust proliferative phenotype. Mechanistically, selected cells exhibit a silenced ancestral stress response through epigenetic modifications, characterized by reduced chromatin accessibility and de novo DNA methylation, including CDH1 promoter hypermethylation. This repressive landscape supports a high-fitness state defined by elevated MYBL2 and FAK levels. An in-silico mechanism-based model shows that these molecular differences, together with high YAP1 nuclear localization in soft-selected cells, are salient features of genetic clones capable of FAK upregulation. These findings uncover a coordinated genetic and epigenetic mechanism driving cancer cell evolution in mechanically soft microenvironments.

systems biology↗

bioRxiv2025

ONCOchannelome: A computational framework to investigate altered ion channels across tumor types

Conventional approaches for analyzing ion channels in cancer primarily focus on the detection of membrane potential to record the polarization states of the cellular membrane. Although these approaches provide meaningful insights during validation, they face challenges in discovery, especially in identifying the ion channels that govern the membrane potential. Moreover, ion channels are known to exhibit context-dependent activity that often depends on the cancer type and metabolic state of the tumor. Here, we developed a computational framework, ONCOchannelome, that allows the identification of potential ion channels via transcriptomic datasets. We collected publicly available RNA-Seq datasets corresponding to 2421 normal, 5442 primary tumor and 588 metastatic samples of 15 tumor types. Using the data, we designed a computational strategy to merge the datasets, identifying differentially expressed ion channels in the tumor and metastatic states and aiding in the determination of ion channels in different tumor states. We subsequently developed strategies to determine the correlation of altered ion channels with epithelial-to-mesenchymal transition and to identify differentially coexpressed ion channel networks along with their possible transcription factors. The ONCOchannelome allows visualization of altered ion channels in the tumor and metastatic states in addition to revealing changes resulting in changes in biological processes and molecular functions as the tumor progresses. The ONCOchannelome enhances our understanding of the dependence of ion channels in different tumors in addition to revealing their observed alterations in progressing from the primary tumor state to the metastatic state.

bioinformatics↗

bioRxiv2025

Interconnected axes of phenotypic plasticity drive coordinated cellular behaviour and worse clinical outcomes in breast cancer

Phenotypic plasticity plays a key role in cancer progression and metastasis, enabling cancer cells to adapt and evolve, but precisely how distinct axes governing phenotypic plasticity interact to shape tumour progression and patient outcomes remains unclear. We investigated five major interconnected axes of plasticity in ER-positive (ER+) breast cancer: Metabolic Reprogramming, Epithelial-to-Mesenchymal Plasticity (EMP), Luminal-Basal (Lineage) Switching, Stemness, and Drug-resistance using network dynamics simulations, integrative bulk and single-cell transcriptomic analyses and patient survival analyses. We show that these axes are not independent but drive one another, forming two mutually inhibiting teams of nodes enabling specified cellular behaviour. One team (favouring high glycolysis, stem-like, basal-like, mesenchymal/hybrid and tamoxifen-resistant phenotype) was found to be associated with aggressive progression and worse survival. On the other hand, the opposing team (favouring high oxidative phosphorylation, non-stem-like, luminal-like, epithelial and tamoxifen-sensitive phenotype) correlated with better outcomes. Importantly, altering one axis of plasticity often drove coordinated responses along other axes and vice versa. Our findings establish phenotypic plasticity in cancer as a coordinated, multi-axis dynamical process, thus suggesting novel strategies to disrupt systems-level reprogramming enabling metastasis and therapeutic resistance.

systems biology↗

bioRxiv2025

Androgen receptor activation stabilizes a hybrid epithelial/mesenchymal phenotype in presence of Notch-Jagged signaling

Metastasis is a key cause of mortality in cancer. It is driven majorly by clusters of circulating tumor cells which are often comprised of cells in a hybrid epithelial/mesenchymal (E/M) phenotype. Hybrid E/M phenotype has also been shown to be more tumor-initiating and therapy-resistant, but how cells maintain the hybrid E/M phenotype(s) remains an active area of investigation. Here, we develop a mathematical model that couples the intracellular dynamics of key players of Epithelial-Mesenchymal Transition (EMT) with the Androgen Receptor (AR), and cell-cell communication through Notch-Delta-Jagged signaling, in the context of prostate cancer. Our simulations show that AR can stabilize a hybrid E/M phenotype predominantly in the presence of Notch-Jagged, but not Notch-Delta, signaling. Implementing this model on a multi-cellular lattice, we observed that AR can alter the fraction of cells exhibiting a hybrid E/M and a mesenchymal phenotype. Finally, through analysis of transcriptomic and patient survival data, we found that the co-expression of AR and Jagged correlated with worse out-comes. Together, these results highlight the outcome of emergent dynamics between the Notch and AR signaling axis in promoting prostate cancer aggressiveness.

systems biology↗

bioRxiv2025

A phenotype-structured PDE framework for investigating the role of hypoxic memory on tumor invasion under cyclic hypoxia

Tumor growth and angiogenesis drive complex spatiotemporal variation in micro-environmental oxygen levels. Previous experimental studies have observed that cancer cells exposed to chronic hypoxia retained a phenotype characterized by enhanced migration and reduced proliferation, even after being shifted to normoxic conditions, a phenomenon which we refer to as hypoxic memory. However, because dynamic hypoxia and related hypoxic memory effects are challenging to measure experimentally, our understanding of their implications in tumor invasion is quite limited. Here, we propose a novel phenotype-structured partial differential equation modeling framework to elucidate the effects of hypoxic memory on tumor invasion along one spatial dimension in a cyclically varying hypoxic environment. We incorporated hypoxic memory by including time-dependent changes in hypoxic-to-normoxic phenotype transition rate upon continued exposure to hypoxic conditions. Our model simulations demonstrate that hypoxic memory significantly enhances tumor invasion without necessarily reducing tumor volume. This enhanced invasion was sensitive to the induction rate of hypoxic memory, but not the dilution rate. Further, shorter periods of cyclic hypoxia contributed to a more heterogeneous profile of hypoxic memory in the population, with the tumor front dominated by hypoxic cells that exhibited stronger memory. Overall, our model highlighted the complex interplay between hypoxic memory and cyclic hypoxia in shaping heterogeneous tumor invasion patterns.

systems biology↗

bioRxiv2025

Fixed points and multistability in monotone Boolean network models

Gene regulatory networks (GRN) control the expression levels of proteins in cells, and understanding their dynamics is key to potentially controlling disease processes. Steady states of GRNs are interpreted as cellular phenotypes, and the first step in understanding GRN dynamics is describing the collection of steady states the network can support in different conditions. We consider a collection of all monotone Boolean function models compatible with a given GRN, and ask which steady states are supported by most models. We find that for networks with no negative loops, there is an explicit hierarchy in the prevalence of individual steady states, as well as the prevalence of bistability and multistability. The key insight that we use is that monotone Boolean models supporting a given equilibrium are a product of prime ideals and prime filters of the lattices of monotone Boolean functions. To illustrate our result, we show that in the EMT network associated with cancer metastasis, the most common equilibria correspond to epithelial (E) and mesenchymal (M) states, and the bistability between them is the most common bistability among all network-compatible monotone Boolean models. Author summaryCells adjust their behavior in response to external inputs via networks of genes that regulate each others expression, until they arrive at a new steady state. Each interacting network of genes can behave in different ways that depend on internal and external cellular conditions. In this paper we consider, for a given network, an entire collection of particular type of models (monotone Boolean models) that represent all different ways that network can behave. Then, for any given state a network can potentially be in, we describe all monotone Boolean models that have that state as a steady state. We consider those states that are supported by more models to more likely represent the states that the network will be in. We apply our approach to EMT network that is important in cancer metastasis. We show that the most common steady states are those correspond to epithelial and mesenchymal states, and that the bistability between these two states is the most common bistability. This confirms the experimental results that these are the most common states of the EMT network.

systems biology↗

bioRxiv2025

Distinct geometrical landscapes distinguish between modes of tristability in gene regulatory networks

Geometrical models have been recently used to construct landscapes for cell-fate decisions, inferred directly from experimental data. However, such quantitative cell-fate data is available for a few systems only; instead, gene regulatory networks dynamics have been studied for a broader set of biological decision-making scenarios. Thus, connecting the geometry of cell-fate decisions to their underlying regulatory networks remains an open question. Recently, two regulatory networks have been shown to exhibit tristability - a toggle switch with self-activation, and a toggle triad. Here, we show that these two motifs are distinct from a geometrical point of view, and identify two bifurcations in their behaviour: the standard cusp and the elliptic umbillic. We study experimentally accessible signatures of the differences in tristability between the two motifs. We also show how the standard cusp can be used to quantitatively model cell fate transition data for the Epithelial to Mesenchymal transition on TGF-{beta} induction in the context of cancer cells. Our work uncovers geometrical signatures of gene regulatory motifs and demonstrates how different gene regulatory networks can encode tristability in dynamically distinct ways.

systems biology↗

bioRxiv2025

Multi-team conflict resolution is ineffective for stable decision making

Competition between distinct groups is fundamental to complex systems, from political coalitions and economic markets to gene regulatory networks (GRNs) controlling cell fate. Strikingly, cell-fate decision systems predominantly employ binary competition between two mutually inhibitory teams of genes, despite multi-team competition being common in social and ecological contexts. This raises a fundamental question: does binary decision-making offer functional advantages over multi-team systems? We systematically analyzed multi-team competitive networks using Boolean dynamics on signed directed graphs, extending well-characterized two-team GRN architectures to systems with three or more mutually inhibitory teams. Three-team networks produced significantly less stable outcomes than two-team systems, with steady states showing higher structural frustration and increased sensitivity to perturbations. Multi-team systems exhibited unpredictable transitions even under controlled perturbations, especially at lower densities typical of real networks. Networks with four or more teams failed to maintain distinct stable states entirely. Using spectral analysis, we show that team structure can be predicted from network eigenproperties, extending structural balance theory to directed signed networks. Our findings explain why binary decisions dominate biology and provide insights into coalition instability in social systems, market dynamics, and organizational structures. This work establishes fundamental stability principles for competitive networks across disciplines.

systems biology↗

bioRxiv2025

Regulatory network and spatial modeling reveal cooperative mechanisms of resistance and immune escape in ER+ breast cancer

Despite significant progress, the treatment of estrogen receptorpositive (ER+) breast cancer remains clinically challenging due to reversible drug resistance and immune evasion. Drug resistance often arises as cells undergo a dynamic epithelial-to-mesenchymal transition (EMT), while elevated PD-L1 levels contribute to immune escape. While these phenotypic features can variably co-occur, the impact of co-occurrence on the availability of synergistic treatment strategies remains unknown. To investigate their interplay, we constructed an ER-EMT-PD-L1 gene regulatory network and simulated these networks as coupled ordinary differential equations with biologically informed parameters, to generate steady-state expression profiles. Our study revealed that the relevant overarching network generated antagonistic epithelial and mesenchymal modules, capable of producing monostable, bistable, and tristable dynamics. We further examined the link between phenotypes and immune evasion by quantifying average PD-L1 expression, and found that epithelial-sensitive states consistently exhibited low PD-L1. In contrast, hybrid- and mesenchymal-resistant states were associated with high PD-L1, highlighting a strong coupling between EMT, resistance, and immune evasion. Extending on these network-level insights, we further used a spatially explicit agent-based model seeded with GRN-derived phenotypes to probe tumor behavior under therapeutic pressure. Simulations revealed that tumor escape required co-occurrence of therapy resistance, motility, and immune suppression, with plasticity and multistability further promoting adaptive persistence. Lastly, we identified combination therapies predicted to constrain malignant diversification and enhanced immune accessibility. Taken together, our modeling work links regulatory dynamics with tumor-level adaptation and underscores potential strategies to therapeutically reprogram cell states toward sensitivity.

bioengineering↗

bioRxiv2025

Gain of Function p53 mutant R273H confers distinct methylation profiles and consequent partial or full EMT states to colon tumour

p53 is the second most frequently mutated gene in colorectal cancer. While different p53 mutations have been correlated with metastasis, the distinct phenotypes exhibited by site-specific mutations of p53 are not well elucidated. Here, we analyse transcriptomic and methylation data from TCGA-COAD cohort to understand the epigenetic impact of three most prevalent hotspot mutations of p53 (R175H, R273H and R282W). We observed that p53 R273H mutation associates with a partial epithelial-mesenchymal transition (pEMT) state and metastatic progression. In vitro ChIP-seq experiments conducted on p53R27H harbouring HT29 cells revealed an enrichment of mutant p53 R273H at pEMT or mesenchymal gene sets. Further, simulations from a gene regulatory network incorporating the interactions of p53R273H with EMT regulators explain how this mutation shapes the phenotypic landscape accessible to cancer cells. Finally, single-cell transcriptomic analysis of colorectal tumours reveals R273H-linked enrichment of partial and mesenchymal EMT phenotypes across tumour subpopulations in CRC. Overall, we identified distinct epigenetic regulation regulating partial EMT and consequent aggressive behaviour triggered by p53R273H. These findings can help devise effective therapeutic strategies for p53 mutant specific colon tumours. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=177 SRC="FIGDIR/small/662954v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@7d424dorg.highwire.dtl.DTLVardef@18bfcaborg.highwire.dtl.DTLVardef@17950c8org.highwire.dtl.DTLVardef@1a698f9_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical Abstract CaptionC_FLOATNO Mutation in Tp53 at R273H associates with DNA hypermethylation and elevated pEMT and metastatic signatures as compared to R175 and R282W mutations in colon tumours suggesting of devising novel therapeutic intervention strategies based on p53 mutation profiles. C_FIG

bioinformatics↗

bioRxiv2025

Multiscale modeling predicts dependence of mesenchymally transitioned tumor niche fitness on cell-cell and cell-matrix adhesions

Invasion of cancer cells is often characterized by a transition in phenotype of cells or their niches from an epithelial to a mesenchymal state (EMT). Under what conditions do transitioned niches acquire greater fitness than, and outcompete, their parental un-transitioned niches, is not well-understood. Here, we use a Cellular Potts model-based multiscale computational framework to investigate this question. Inducing an EMT in a single cell at the edge of an early-growing tumor surrounded by a fibrillar extracellular matrix (ECM) allows us to temporally trace inter-niche competitions. We observe that the transitioned niche dominates the population it arises from and invades better when surrounded by dense ECM. An increase in cell-ECM adhesion by itself drives domination at 50% probability, such that the transitioned population invades faster and contributes further to collective invasion of the whole tumor. Decrease in inter- and intra-niche cell-cell adhesion by itself is not sufficient to achieve domination. However, added to increased cell-ECM adhesion, loss of intra-niche (but not inter-niche adhesion) restores the probability, but not the extent, with which domination by the transitioned niche is achieved by attenuating its confinement by its parental population. Our simulations reveal the forces regulating such confinement and how cell-cell and cell-ECM adhesion, stochastic invasion dynamics, and ECM density contribute nuancedly to distinct aspects of inter-niche competitions within tumor populations and their fitness.

cancer biology↗

bioRxiv2025

Balance between autophagy and apoptosis determines anoikis resistance: a mathematical model on cell fate

Although several cancer cells are shed into circulation, most die due to stress, including loss of matrix attachment which triggers anoikis. A subset, however, acquires anoikis resistance, and survives to seed metastasis. How matrix-deprived cancer cells balance autophagy and apoptosis to decide cell fate remains unclear. Here, we formulated a deterministic ordinary differential equation (ODE)-based model of Akt-AMPK-ERK signal interaction to study this balance. Simulations revealed that high AMPK activity, with heterogenous ERK and low pAkt levels promote survival, whereas high pAkt shifts towards apoptosis. Molecular perturbation analyses revealed a signaling hierarchy in which Akt dominates over AMPK which further dominates over ERK. This model further predicts that the extent of autophagy determines apoptotic outcomes with high autophagy favoring survival of matrix-detached MDA-MB-231 cells. Together, our study provides a mathematical framework linking signaling dynamics to cell-fate decisions governing anoikis resistance. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=147 HEIGHT=200 SRC="FIGDIR/small/658583v2_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@d40fdorg.highwire.dtl.DTLVardef@25a839org.highwire.dtl.DTLVardef@475401org.highwire.dtl.DTLVardef@dc4e6b_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2025

How memory and adaptation cost shape cell phenotypic dynamics in response to fluctuating environments

Cells invariably encounter unpredictable changes in their microenvironment and adapt by orchestrating substantial alterations in their molecular states, often resulting in appreciable phenotypic changes. The timescale of molecular adaptation depends on how quickly a cell loses its molecular memory of past environmental encounters through the degradation rate of proteins unfavorable to the current environment. Concurrently, de novo synthesis of favorable biomolecules during adaptation imposes an energetic cost that impacts cellular fitness. Here, by developing a phenomenological model of intracellular processing of environmental signals and associated cell-state switching, we study the dynamical implications of cellular memory and adaptation cost on cellular responses to a changing environment. We find that while increasing cellular memory reduces cell fitness in periodic environments, counterintuitively, increasing adaptation cost can improve growth by minimizing mismatches between the environment and the cell state. Similarly, we observed a variable role of memory capacity and adaptation cost for stochastic correlated environments, with increasing memory and cost improving cellular fitness in negatively but not positively correlated environments. Lastly, we show that cellular memory and population heterogeneity in adaptation cost explained reported experimental observations in melanoma: increased population survival during drug treatment when the population was either enriched for rare cells expressing resistance marker genes or primed with low-dose drug treatment before exposure to high-dose treatment. Overall, this work establishes a foundational model for studying how cellular memory dynamics and adaptation cost drive cellular adaptation under different environmental conditions and explain complex cellular behaviors.

systems biology↗

bioRxiv2025

Repurposing statins in combination therapy for effective ablation of metastatic breast cancer

Metastatic breast cancer (mBC) remains an incurable disease with limited treatment options, highlighting the need for novel therapeutic approaches. Combination therapy with chemotherapeutic agents along with targeted therapy are the most common methods of treatment used in the terminal stages of the disease. Eventual development of resistance to these approaches, leading to fatal outcomes suggests the presence or emergence of a heterogenous population of cells intrinsically resistant to commonly used regimens. Previous work identified a heterogeneous population of metastatic tumor cells with distinct molecular characteristics driven by Macc1 (Metastasis Associated in Colon Cancer 1) overexpression, which could be targeted by lovastatin (transcriptional inhibitor of Macc1). Building on this foundation, the efficacy of lovastatin in targeting metastatic cells with high Macc1 expression was evaluated in lung metastasis, and the regulatory pathways governing Macc1 expression and lovastatin treatment in mBC were investigated. The expression of Macc1, in breast cancer biopsies provides insights into the intra and inter tumor heterogeneity of the Macc1 gene and its correlation with disease outcomes. While some studies suggest synergistic effects between statins and chemotherapeutic agents, comprehensive evaluations of various combinations and their therapeutic outcomes are still needed. The therapeutic efficacy of lovastatin combined with chemotherapy to determine the most effective treatment regimen that maximizes tumor cell ablation demonstrated that lovastatin can effectively ablate the chemoresistant tumor cells in mBC. The translational implications of this research will identify patient subgroups that may benefit most from statin-chemotherapy combinations that could support the repurposing of statins as cost-effective adjuvant therapies for mBC.

cancer biology↗

bioRxiv2025

CD4+ T-cells sensitize quasi-mesenchymal breast tumors lacking CD73 to anti-CTLA4 immune checkpoint blockade therapy

Although immune checkpoint blockade therapy has generated dramatic responses in certain cancer types, breast tumors are largely unresponsive. Epithelial-mesenchymal plasticity leads to the assembly of an immunosuppressive tumor microenvironment and drives resistance of breast tumors to immunotherapies. Importantly, targeting CD73 completely sensitizes quasi-mesenchymal breast tumors to anti-CTLA4 immune checkpoint blockade therapy. However, the mechanism(s) of sensitization remained unknown. We demonstrate that targeting CD73 in quasi-mesenchymal breast tumors sensitizes them to anti-CTLA4 immune checkpoint blockade therapy in a CD4+ T-cell dependent manner. Moreover, epithelial-mesenchymal plasticity results in elevated expression of cancer cell-intrinsic CD73 in human triple negative breast cancers. Given the ability of quasi-mesenchymal cancer cells to metastasize and resist multiple therapies, these findings can instruct the formation of novel translational strategies for the treatment of human breast cancers. These findings also bring to the forefront the attractive possibility of utilizing the phenotypic plasticity of cancer cells along with CD73 and CD4+ T-cells as a predictive criterion for immunotherapy responsiveness. TeaserTargeting CD73 sensitizes breast cancer cells with mesenchymal traits to anti-CTLA4 therapy in a CD4+ T-cell dependent manner.

cancer biology↗

bioRxiv2025

Fractal Measures as Predictors of Histopathological Complexity in Breast Carcinoma Mammograms

Breast carcinoma remains the most commonly diagnosed malignancy and a leading cause of cancer-related mortality among women worldwide. While mammography is the gold standard for early detection, challenges such as high breast density often obscure malignancies, reducing diagnostic sensitivity. Conventional parenchymal texture analysis methods have limitations due to struggles with spatial interpretation and noise sensitivity. This study investigates the efficacy of fractal-based global texture features for distinguishing between malignant and normal mammograms and assessing their potential for molecular subtype differentiation. Digital mammograms were analyzed using standardized preprocessing techniques, and fractal measures were computed to capture complexity and connectivity properties within breast tissue structures. Fractal dimension, multifractality strength, and succolarity reservoir were found to effectively characterize specific features of mammographic texture; however, their incorporation into machine learning models yielded moderate discriminatory performance between categories. We introduced succolarity reservoir as a novel parameter accounting for tissues latent connectivity. In addition, while succolarity reservoir exhibits potential for differentiating Luminal B from other molecular subtypes, its overall discriminative power remains limited. This proof-of-concept study underscores the potential of fractal-based texture analysis as a non-invasive biomarker in breast carcinoma diagnosis.

biophysics↗

bioRxiv2025

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 models 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. Significance StatementDrug-sensitive and -resistant cancer cells can compete with each other within the same tumour, and adaptive therapy exploits this competition to control overall tumour growth. The fact that sensitive and resistant cell types can switch phenotypes complicates the implementation of adaptive therapy. Our ODE-based theoretical ecology framework shows that asymmetric competition favouring sensitive cells generally benefits therapy outcomes, while phenotypic plasticity is usually detrimental. Our model also provides cell population-level indicators that can help predict the underlying balance between competition and plasticity. Phenomenological models are limited by whether their parameters can be reliably identified given scarce data, and our study illuminates the scope of such models in understanding cancer population dynamics and the need for mechanistic modelling.

systems biology↗