Biology subjects

Yoon, N.

Publications and source records attributed to Yoon, N..

7 recordsLinked to original sources

bioRxiv2026

Bifurcation Analysis of Cancer-Immunity Cycle

Despite major advances in cancer immunotherapy, many patients still experience immune escape and disease progression. Understanding the dynamical mechanisms governing the transition between tumor control and immune escape is therefore essential for improving therapeutic strategies. Bifurcation theory and stability analysis provide a mathematical framework for explaining how gradual parameter changes can produce sudden qualitative transitions in biological systems. In the context of tumor-immune interactions, such transitions may correspond to critical thresholds separating immune-limited tumor control from immune-escape behavior. In this study, we investigate a discrete-time model of the cancer-immunity cycle applying the local stability analysis to the system equilibria based on the trace and determinant of the Jacobian matrices. The model incorporates immune suppression through a parameter representing tumor-mediated immune evasion, including mechanisms related to immune checkpoint pathways such as PD-1/PD-L1 signaling. The system exhibits a saddle-node bifurcation associated with the appearance and disappearance of nontrivial equilibria, under weak immune conditions. In addition, under strong immune conditions, the model demonstrates a distinct stability transition in which a stable spiral equilibrium (spiral sink) loses stability and becomes an unstable spiral (spiral source), resulting in the loss of stable tumor-control dynamics before equilibrium disappearance occurs. Additional mathematical analysis and numerical investigations indicate that the system does not generate stable non-equilibrium attractors such as limit cycles over the parameter ranges considered. Consequently, the stable equilibrium remains the only stable attractor in the model, emphasizing the importance of maintaining equilibrium stability for effective immune-mediated tumor suppression. Also, further parameter analyses reveal that both equilibrium existence and stability are highly sensitive near bifurcation boundaries, reflecting the delicate balance between tumor proliferation and immune activation. Overall, this work provides a mathematical framework for distinguishing equilibrium existence from effective tumor control in discrete tumor-immune systems. Beyond the specific model considered, the results highlight the importance of stability analysis in understanding immune escape and may contribute to future approaches in adaptive and personalized immunotherapy modeling.

cancer biology↗

bioRxiv2023

Radio-Immune Response Modelling for Spatially Fractionated Radiotherapy

Radiation-induced cell death is a complex process influenced by physical, chemical and biological phenomena. Strong dose gradient may intensify the complexity and reportedly creates significantly more cell death known as bystander effect. Although consensus on the nature and the mechanism of the bystander effect were not yet made, the immune process presumably plays an important role in many aspects of the radiotherapy including the bystander effect. Immune response of host body and immune suppression of tumor cells are modelled with four compartments in this study; viable tumor cells, T cell lymphocytes, immune triggering cells, and doomed cells. The growth of tumor was analyzed in two distinctive modes of tumor status (immune limited and immune escape) and its bifurcation condition. Tumors in the immune limited mode can grow only up to a finite size, named as terminal tumor volume analytically calculated from the model. The dynamics of the tumor growth in the immune escape mode is much more complex than the tumors in the immune limited mode especially when the status of tumor is close to the bifurcation condition. Radiation can kill tumor cells not only by radiation damage but also by boosting immune reaction. The model demonstrated that the highly heterogeneous dose distribution in spatially fractionated radiotherapy (SFRT) can make a drastic difference in tumor cell killing compared to the homogeneous dose distribution. SFRT can not only enhance but also moderate the cell killing depending on the immune response triggered by many factors such as dose prescription parameters, tumor volume at the time of treatment and tumor characteristics. The model was applied to the lifted data of 67NR tumors on mice and a sarcoma patient treated multiple times over 1200 days for the treatment of tumor recurrence as a demonstration.

cancer biology↗

bioRxiv2020

Modeling of collaterally sensitive drug cycles, and optimization of the drug effect in the spirit of adaptive therapy

In previous work, we focused on the optimal therapeutic strategy with a pair of drugs which are collaterally sensitive to each other, that is, a situation in which evolution of resistance to one drug induces sensitivity to the other, and vice versa. [1] Here, we have extended this exploration to the optimal strategy with a collaterally sensitive drug sequence of an arbitrary length, N([≥] 2). To explore this, we have developed a dynamical model of sequential drug therapies with N drugs. In this model, tumor cells are classified as one of N subpopulations represented as {Ri|i = 1,2,..., N}. Each subpopulation, Ri, is resistant to Drug i and each subpopulation, Ri-1 (or RN, if i = 1), is sensitive to it, so that Ri increases under Drug i as it is resistant to it, and after drug-switching, decreases under Drug i + 1 as it is sensitive to that drug(s). Similar to our previous work examining optimal therapy with two drugs, we found that there is an initial period of time in which the tumor is shaped into a specific makeup of each subpopulation, at which time all the drugs are equally effective [Formula]. After this shaping period, all the drugs are quickly switched with duration relative to their efficacy in order to maintain each subpopulation, consistent with the ideas underlying adaptive therapy. [2, 3] Additionally, we have developed methodologies to administer the optimal regimen under clinical or experimental situations in which no drug parameters and limited information of trackable populations data (all the subpopulations or only total population) are known. The therapy simulation based on these methodologies showed consistency with the theoretical effect of optimal therapy.

cancer biology↗

bioRxiv2018

Resistance to ALK targeting therapies as a gradual Darwinian adaptation to inhibitor specific selective pressures

Despite high initial efficacy, therapies that target oncogenic kinases eventually fail in advanced, metastatic cancers. This failure in initially responsive tumors is the direct result of the evolution of drug resistance under therapy-imposed selective pressures. In contrast to the massive body of experimental research on the molecular mechanisms of resistance, understanding of its evolutionary origins and dynamics remains fragmented. Using a combination of experimental studies and mathematical modeling, we sought to dissect the evolution of resistance to different clinical ALK inhibitors in an experimental model of ALK positive NSCLC. We found that resistance can originate from heterogeneous, weakly resistant, sub-populations with variable sensitivity to different ALK inhibitors. Instead of the commonly assumed stochastic single hit (epi) mutational transition, or drug-induced reprogramming, we found evidence of a hybrid scenario, of gradual, multifactorial development through acquisition of multiple cooperating genetic and epigenetic adaptive changes, amplified by selection. Additionally, we found that intermediate resistance phenotypes might present unique, temporally restricted collateral sensitivities, absent in therapy naive or fully resistant cells, suggesting new opportunities for therapeutic interference.

cancer biology↗

bioRxiv2018

The impact of tumor stromal architecture on therapy response and clinical progression

-- Advances in molecular oncology research culminated in the development of targeted therapies that act on defined molecular targets either on tumor cells directly (such as inhibitors of oncogenic kinases), or indirectly by targeting the tumor microenvironment (such as anti-angiogenesis drugs). These therapies can induce strong clinical responses, when properly matched to patients. Unfortunately, most targeted therapies ultimately fail as tumors evolve resistance. Tumors consist not only of neoplastic cells, but also of stroma, whereby \"stroma\" is the umbrella term for non-tumor cells and extracellular matrix (ECM) within the tumor microenvironment, possibly excluding immune cells1. We know that tumor stroma is an important player in the development of resistance. We also know that stromal architecture is spatially complex, differs from patient to patient and changes with therapy. However, to this date we do not understand the link between spatial and temporal changes in stromal architecture and response of tumors to therapy, in space and time. In this project we sought to address this gap of knowledge using a combination of mathematical and statistical modeling, experimental in vivo studies, and analysis of clinical samples in therapies that target tumor cells directly (in lung and breast cancers) and indirectly (in kidney cancer). This knowledge will inform therapy choices and offer new angles for therapeutic interventions. Our main question is: how does spatial architecture of stroma impact the emergence or evolution of resistance to targeted therapies, and how can we use this knowledge clinically?

cancer biology↗

bioRxiv2017

Dark selection for JAK/STAT-inhibitor resistance in chronic myelomonocytic leukemia

Acquired therapy resistance to cancer treatment is a common and serious clinical problem. The classic U-shape model for the emergence of resistance supposes that: (1) treatment changes the selective pressure on the treatment-naive tumour; (2) this shifting pressure creates a proliferative or survival difference between sensitive cancer cells and either an existing or de novo mutant; (3) the resistant cells then out-compete the sensitive cells and - if further interventions (like drug holidays or new drugs or dosage changes) are not pursued - take over the tumour: returning it to a state dangerous to the patient. The emergence of ruxolitinib resistance in chronic myelomonocytic leukemia (CMML) seems to challenge the classic model: we see the global properties of resistance, but not the drastic change in clonal architecture expected with the selection bottleneck. To study this, we explore three population-level models as alternatives to the classic model of resistance. These three effective models are designed in such a way that they are distinguishable based on limited experimental data on the time-progression of resistance in CMML. We also propose a candidate reductive implementation of the proximal cause of resistance to ground these effective theories. With these reductive implementations in mind, we also explore the impact of oxygen diffusion and spatial structure more generally on the dynamics of CMML in the bone marrow concluding that, even small fluctuations in oxygen availability can seriously impact the efficacy of ruxolitinib. Finally, we look at the ability of spatially distributed cytokine signaling feedback loops to produce a relapse in symptoms similar to what we observe in the clinic.

cancer biology↗

bioRxiv2017

Optimal Therapy Scheduling Based on a Pair of Collaterally Sensitive Drugs

Despite major strides in the treatment of cancer, the development of drug resistance remains a major hurdle. One strategy which has been proposed to address this is the sequential application of drug therapies where resistance to one drug induces sensitivity to another drug, a concept called collateral sensitivity. The optimal timing of drug switching in these situations, however, remains unknown.\n\nTo study this, we developed a dynamical model of sequential therapy on heterogeneous tumors comprised of resistant and sensitive cells. A pair of drugs (DrugA, DrugB) are utilized and are periodically switched during therapy. Assuming resistant cells to one drug are collaterally sensitive to the opposing drug, we classified cancer cells into two groups, AR and BR, each of which is a subpopulation of cells resistant to the indicated drug and concurrently sensitive to the other, and we subsequently explored the resulting population dynamics.\n\nSpecifically, based on a system of ordinary differential equations for AR and BR, we determined that the optimal treatment strategy consists of two stages: an initial stage in which a chosen effective drug is utilized until a specific time point, T, and a second stage in which drugs are switched repeatedly, during which each drug is used for a relative duration (i.e. f{Delta}t-long for DrugA and (1 - f) {Delta}t-long for DrugB with 0 [≤] f [≤] 1 and {Delta}t [≥] 0). We prove that the optimal duration of the initial stage, in which the first drug is administered, T, is shorter than the period in which it remains effective in decreasing the total population, contrary to current clinical intuition.\n\nWe further analyzed the relationship between population makeup, [Formula], and the effect of each drug. We determine a critical ratio, which we term [Formula], at which the two drugs are equally effective. As the first stage of the optimal strategy is applied, [Formula] changes monotonically to [Formula] and then, during the second stage, remains at [Formula] thereafter.\n\nBeyond our analytic results, we explored an individual based stochastic model and presented the distribution of extinction times for the classes of solutions found. Taken together, our results suggest opportunities to improve therapy scheduling in clinical oncology.

cancer biology↗