Biology subjects

Jackson, T. L.

Publications and source records attributed to Jackson, T. L..

10 recordsLinked to original sources

bioRxiv2026

Spatial Agent-Based Modeling and Interpretable Machine Learning Predict Combination Therapy Response in HER2-Heterogeneous Breast Cancer

HER2 heterogeneity and reversible phenotypic plasticity play a central role in breast cancer progression and therapeutic resistance, yet how their interaction shapes treatment response remains poorly understood. HER2-positive and HER2-negative tumor cell states can dynamically interconvert, enabling compensatory population shifts that undermine monotherapies targeting a single phenotype. Because stochastic lineage effects and local cell interactions are averaged out in mean-field population-level ODE models, we develop a spatially resolved agent-based model (ABM) of heterogeneous tumor growth. We consider paclitaxel, which is modeled to preferentially suppress HER2-positive proliferation, and Notch inhibition, which targets HER2-negative populations and alters phenotypic composition. Starting from single-cell lineages, we assess the ABM against theoretical predictions from a population-level switching model and against single-cell-derived experimental measurements, showing consistency with early lineage dynamics and long-term phenotypic equilibria. Simulation results show that monotherapies induce compensatory phenotypic shifts and spatial reorganization that permit tumor persistence. In contrast, combination therapy simultaneously targeting HER2-positive and HER2-negative populations disrupts phenotypic replenishment, fragments spatial structure, and can achieve sustained tumor control across a range of simulated tumor regimes and treatment strengths. Importantly, in matched tumors with identical geometry, total burden, and phenotype counts, peripheral enrichment of HER2-negative cells increased post-treatment escape under paclitaxel, whereas this spatial effect was nearly eliminated by combination therapy. To quantify robustness across heterogeneous tumor parameter regimes, we pair the ABM with an interpretable Random Forest surrogate. Using only pre-treatment and early-trajectory features, the surrogate discriminates sustained control from persistence across held-out simulated parameter regimes and identifies growth-rate asymmetries as dominant drivers of resistance. Together, this integrated mechanistic and data-driven framework clarifies how HER2-mediated plasticity, spatial organization, and competitive growth dynamics shape therapy resistance and provides a scalable approach for analyzing simulated treatment responses across heterogeneous tumor regimes.

cancer biology↗

bioRxiv2026

A Hallmark-Integrated, Agent-Based Framework for Intratumor Heterogeneity in Melanoma Evolution

Advances in multiregion sequencing have revealed extensive intratumor heterogeneity (ITH) - the presence of genetically distinct subclones within a single tumor. ITH profoundly influences tumor behavior, including well-accepted hall-marks of cancer such as sustained proliferation, resistance to apoptosis, and immune evasion, and the emergence of therapeutic resistance. In this work, we introduce a hallmark-integrated branching evolution process agent-based model (BEP-HI) to study the emergence of intratumor heterogeneity (ITH) in melanoma under coupled genetic, immune, and spatial selection pressures. Using this model, we identify three distinct evolutionary ITH modes and demonstrate a mechanistic decoupling between tumor growth kinetics and heterogeneity. We further show that immune recruitment exerts the strongest influence on ITH through nonlinear immune-editing feedback, while motility of melanoma cells shapes complex morphologies in different ITH modes as observed in clinical SSM tumors. This work provides a biologically grounded and adaptable computational framework for exploring how hallmark interactions shape ITH evolution and for generating virtual tumor cohorts that link genetic diversity with tumor behavior, morphology, and treatment resistance.

cancer biology↗

bioRxiv2026

Enhancing Predictive Accuracy in Immunotherapy Models through Data Integration and Parameter Identifiability

Immune checkpoint inhibitors (ICIs), a class of immunotherapy, offer promising benefits but face challenges such as low response rates to be used as a broadly effective treatment for all patients. In this study, we use a set of ordinary different equation (ODE) models and bladder cancer in vivo data as a case study to outline a biologically informed, data-driven framework for formulating, calibrating and validating immunotherapy models, and thus ensuring their predictive reliability. We consider multiple treatment scenarios and distinct immune cell-mediated killing mechanisms for tumor cells of different antigenicity. By integrating sensitivity analysis and identifiability analysis with targeted experimental design, we demonstrate how mathematical models can move beyond qualitative insight to quantitative prediction. We generate virtual cohorts to show that insufficient data integration leads to systematically overestimated therapeutic benefits of ICIs. We also explore dosing schedules that enhance survival or reduce dosage without compromising survival.

cell biology↗

bioRxiv2025

Phenotypic Plasticity and Competition Shape Therapy Sequencing in HER2+/HER2 Breast Cancer: A Mathematical Framework

Tumour heterogeneity and phenotypic plasticity are major drivers of treatment failure in cancer, enabling rapid adaptation under therapeutic pressure. In HER2-positive (HER2+) breast cancer, tumours often contain both HER2+ and HER2-negative (HER2-) cells whose interactions complicate schedule design. We develop a compact ordinary differential equation framework for intratumoral HER2+/- dynamics that integrates phenotypic plasticity with density-dependent growth and inter-phenotype competition. Phenotype-specific therapies are incorporated through simple pharmacodynamic surrogates: Paclitaxel chemotherapy acting primarily on HER2+ cells and Notch-pathway inhibition targeting HER2- cells. We use the model to compare staggered and simultaneous treatment schedules. The results show that treatment order and relative intensity critically shape long-term tumour composition. Targeted-first schedules can exhibit competitive release, whereby subsequent aggressive chemotherapy unintentionally favours HER2- expansion. In contrast, simultaneous initiation suppresses both phenotypes more effectively and avoids strong rebound. These findings highlight the importance of ecological structure in therapy design and support simultaneous combination therapy followed by targeted maintenance.

cancer biology↗

bioRxiv2025

A Mechanistic Model of Oncolytic Virus-CAR T Therapy Identifies Memory-Dependent Control of Solid Tumors

Chimeric antigen receptor (CAR) T-cell therapy can induce durable remissions in hematologic malignancies, yet its efficacy in solid tumors remains limited by antigen heterogeneity, an immunosuppressive tumor microenvironment, and poor T-cell persistence. Oncolytic viruses (OVs) provide a complementary therapeutic strategy by directly lysing tumor cells and reshaping local immune responses. Motivated by emerging evidence that OVs can also induce dual-specific, memory-like CAR T cells, we develop a unified mathematical framework that integrates these interacting modalities. The model distinguishes antigen-positive and antigen-negative tumor populations, tracks key viral and immune compartments, and incorporates OV-mediated microenvironmental activation alongside CAR T-cell stimulation. This baseline formulation provides a platform for assessing whether additional mechanisms--such as OV-induced CAR T-cell memory--are necessary for durable tumor control. Sensitivity analysis of the memory-free system using partial rank correlation coefficients (PRCCs) identifies four dominant drivers of therapeutic outcome: antigen-negative tumor dynamics, viral kinetics, CAR T-cell cytotoxic effectiveness, and PD-1/PD-L1-mediated T-cell exhaustion. While these mechanisms can generate strong early responses, they are insufficient to prevent late relapse driven by antigen-negative tumor regrowth. Introducing a reduced memory variable to represent OV-driven CAR T recall fundamentally alters this behavior: enhanced memory induction and CAR T responsiveness sustain effector T-cell levels and enable long-term control of antigen-negative tumor populations. Together, these results highlight OV-induced CAR T-cell memory as a critical determinant of durable therapeutic efficacy in solid tumors.

cancer biology↗

bioRxiv2024

SMoRe GloS: An efficient and flexible framework for inferring global sensitivity of agent-based model parameters

Agent-based models (ABMs) have become essential tools for simulating complex biological, ecological, and social systems where emergent behaviors arise from the interactions among individual agents. Quantifying uncertainty through global sensitivity analysis is crucial for assessing the robustness and reliability of ABM predictions. However, most global sensitivity methods demand substantial computational resources, making them impractical for highly complex models. Here, we introduce SMoRe GloS (Surrogate Modeling for Recapitulating Global Sensitivity), a novel, computationally efficient method for performing global sensitivity analysis of ABMs. By leveraging explicitly formulated surrogate models, SMoRe GloS allows for comprehensive parameter space exploration and uncertainty quantification without sacrificing accuracy. We demonstrate our methods flexibility by applying it to two biological ABMs: a simple 2D cell proliferation assay and a complex 3D vascular tumor growth model. Our results show that SMoRe GloS is compatible with simpler methods like the Morris one-at-a-time method, and more computationally intensive variance-based methods like eFAST. SMoRe GloS accurately recovered global sensitivity indices in each case while achieving substantial speedups, completing analyses in minutes. In contrast, direct implementation of eFAST amounted to several days of CPU time for the complex ABM. Remarkably, our method also estimates sensitivities for ABM parameters representing processes not explicitly included in the surrogate model, further enhancing its utility. By making global sensitivity analysis feasible for computationally expensive models, SMoRe GloS opens up new opportunities for uncertainty quantification in complex systems, allowing for more in depth exploration of model behavior, thereby increasing confidence in model predictions.

systems biology↗

bioRxiv2024

Agent-Based Modeling of Virtual Tumors Reveals the Critical Influence of Microenvironmental Complexity on Immunotherapy Efficacy

Since the introduction of the first immune checkpoint inhibitor (ICI), immunotherapy has changed the landscape of molecular therapeutics for cancers. However, ICIs do not work equally well on all cancers and for all patients. There has been a growing interest in using mathematical and computational models to optimize clinical responses. Ordinary differential equations (ODEs) have been widely used for mechanistic modeling in immuno-oncology and immunotherapy because they allow rapid simulations of temporal changes in the cellular and molecular populations involved. Nonetheless, ODEs cannot describe the spatial structure in the tumor microenvironment or quantify the influence of spatially-dependent characteristics of tumor-immune dynamics. For these reasons, agent-based models (ABMs) have gained popularity because they can model more detailed phenotypic and spatial heterogeneity that better reflect the complexity seen in vivo. In the context of anti-PD-1 ICIs, we compare treatment outcomes simulated from an ODE model and an ABM to show the importance of including spatial components in computational models of cancer immunotherapy. We consider tumor cells of high and low antigenicity and two distinct cytotoxic T lymphocyte (CTL) killing mechanisms. The preferred mechanism differs based on the antigenicity of tumor cells. Our ABM reveals varied phenotypic shifts within the tumor and spatial organization of tumor and CTLs, despite similarities in key immune parameters, initial conditions of simulation, and early temporal trajectories of the cell populations.

cancer biology↗

bioRxiv2023

Mathematical Model Predicts Tumor Control Patterns Induced by Fast and Slow CTL Killing Mechanisms

Immunotherapy has dramatically transformed the cancer treatment landscape largely due to the efficacy of immune checkpoint inhibitors (ICIs). Although ICIs have shown promising results for many patients, the low response rates in many cancers highlight the ongoing challenges in cancer treatment. Cytotoxic T lymphocytes (CTLs) execute their cell-killing function via two distinct mechanisms: a fast-acting, perforin-mediated process and a slower, Fas ligand (FasL)-driven path-way. Evidence also suggests that the preferred killing mechanism of CTLs depends on the anti-genicity of tumor cells. To determine the critical factors affecting responses to ICIs, we construct an ordinary differential equation model describing in vivo tumor-immune dynamics in the presence of active or blocked PD-1/PD-L1 immune checkpoint. Specifically, we identify important aspects of the tumor-immune landscape that affect tumor size and composition in the short and long term. By generating a virtual cohort with differential tumor and immune attributes, we also simulate the therapeutic outcomes of immune checkpoint blockade in a heterogenous population. In this way, we identify key tumor and immune characteristics that are associated with tumor elimination, dor-mancy, and escape. Our analysis sheds light on which fraction of a population potentially responds well to ICIs and ways to enhance therapeutic outcomes with combination therapy.

cancer biology↗

bioRxiv2022

Phenotype switching in a global method for agent-based models of biological tissue

Agent-based models (ABMs) are an increasingly important tool for understanding the complexities presented by phenotypic and spatial heterogeneity in biological tissue. The resolution a modeler can achieve in these regards is unrivaled by other approaches. However, this comes at a steep computational cost limiting either the scale of such models or the ability to explore, parameterize, analyze, and apply them. When the models involve molecular-level dynamics, especially cell-specific dynamics, the limitations are compounded. We have developed a global method for solving these computationally expensive dynamics significantly decreases the computational time without altering the behavior of the system. Here, we extend this method to the case where cells can switch phenotypes in response to signals in the microenvironment. We find that the global method in this context preserves the temporal population dynamics and the spatial arrangements of the cells while requiring markedly less simulation time. We thus add a tool for efficiently simulating ABMs that captures key facets of the molecular and cellular dynamics in heterogeneous tissue. Author summaryAgent-based models (ABMs) are an important tool for understanding how cells and molecular compounds interact to produce complex, emergent behavior. The principal feature of ABMs that set them apart from other types of models is their ability to capture the diversity of cells in a tissue. However, this feature comes at the cost of long simulation times, reducing the ability to apply the findings of these models to improve our understanding of living organisms. We present here a means of simulating ABMs using a more efficient method, called the global method, when the cells are undergoing discrete, phenotypic changes in response to molecular cues. We demonstrate that the global method preserves the key features of the ABM while performing simulations much faster. This allows for more efficient testing of biological hypotheses in a mathematical framework that captures key facets of the diversity in biological tissue.

systems biology↗

bioRxiv2021

Standing Variations Modeling Captures Inter-Individual Heterogeneity in a Deterministic Model of Prostate Cancer Response to Combination Therapy

Sipuleucel-T (Provenge) is the first live cell vaccine approved for advanced, hormonally refractive prostate cancer. However, survival benefit is modest and the optimal combination or schedule of sipuleucel-T with androgen depletion remains unknown. We employ a nonlinear dynamical systems approach to modeling the response of hormonally refractive prostate cancer to sipuleucel-T. Our mechanistic model incorporates the immune response to the cancer elicited by vaccination, and the effect of androgen depletion therapy. Because only a fraction of patients benefit from sipuleucel-T treatment, inter-individual heterogeneity is clearly crucial. Therefore, we introduce our novel approach, Standing Variations Modeling, which exploits inestimability of model parameters to capture heterogeneity in a deterministic model. We use data from mouse xenograft experiments to infer distributions on parameters critical to tumor growth and to the resultant immune response. Sampling model parameters from these distributions allows us to represent heterogeneity, both at the level of the tumor cells and the individual (mouse) being treated. Our model simulations explain the limited success of sipuleucel-T observed in practice, and predict an optimal combination regime that maximizes predicted efficacy. This approach will generalize to a range of emerging cancer immunotherapies.

systems biology↗