ONCO Subgroup Contributed Talks

Siti Maghfirotul Ulyah

Khalifa University, Abu Dhabi, United Arab Emirates

"Estimating the Growth Rate of Tumor Cells from Biopsy Samples Using an Extended Mean Field Approximation"

A biopsy is a common procedure used to diagnose diseases like cancer, infections, or inflammatory conditions. In cell population studies, biopsy samples provide valuable data to analyze cellular growth, proliferation rates, and structural abnormalities, which are essential for understanding disease progression. Estimating the growth (proliferation) rate of human cells is a challenging task. To address this, we have developed a method based on the birth-death Markov process to simulate the logistic growth model. We applied an extended Mean Field Approximation (MFA) for birth-death Markov processes, which accounts for fluctuations in the evolution of observables, such as moments. By calculating the theoretical moments from the birth-death process, we solved the inverse problem and estimated the growth rate. Additionally, we performed Markov Chain Monte Carlo (MCMC) simulations for both logistic growth and logistic growth with the Allee effect. The moments of the simulated population were used to predict the growth rate through regression analysis, achieving a high R-squared value. Finally, by applying this approach to biopsy data, one can estimate the proliferation rate of human cells with greater accuracy.

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Hooman Salavati

Ghent University

"Patient-Specific MRI-Integrated Computational Modeling of Tumor Fluid Dynamics and Drug Transport"

Introduction: Mathematical modeling is a key tool for understanding solid tumor biophysics, progression, and treatment resistance. Biophysical changes, such as elevated interstitial fluid pressure (IFP), are identified as major barriers to effective drug delivery. Incorporating patient-specific data into mathematical models offers the potential for personalized prognosis and treatment strategies for cancer patients. In this study, we explored the integration of patient-specific data from dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DW-MRI) into a computational fluid dynamics (CFD) model of solid tumors to estimate the IFP and drug penetration profiles. Methods: As part of a translational study (EC/2019/1330, approved by Ghent University Hospital, Belgium), a patient with peritoneal metastasis underwent multi-sequential MRI, including T1-weighted (T1w) anatomical imaging, DCE-MRI, and DW-MRI. Tumor interstitial fluid pressure (IFP) was directly measured using a pressure transducer-tipped catheter for model validation. The CFD tumor model described interstitial fluid flow using Darcy’s law, the continuity equation, and Starling’s law, while drug penetration was modeled via the convection-diffusion-reaction equation. The 3D tumor geometry was derived from T1w images, vascular permeability from DCE-MRI using the extended-Tofts model, and hydraulic conductivity from DW-MRI. Results: An elevated IFP zone was observed in the central region of the tumor (up to 14 mmHg), while a lower IFP zone appeared at the tumor's edge. The clinically recorded IFP values (12.0 ± 2.5 mmHg) corresponded well with the simulation results. Drug penetration varied across the tumor, with deeper penetration in low-IFP regions. Conclusion: An image-based CFD model captured IFP and drug distribution variability, aligning with clinical data. This approach advances personalized oncology, potentially improving treatment strategies through noninvasive, patient-specific modeling.

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Rachel Sousa

University of California, Irvine

"Identifying Critical Immunological Features of Tumor Control and Escape Using Mathematical Modeling"

The immune system can eradicate cancer, but various immunosuppressive mechanisms active within a tumor curb this beneficial response. Cytotoxic T cells (CD8s), regulatory T cells (Tregs), and antigen-presenting dendritic cells (DCs) play an important role in the immune response; however, it is very cumbersome to unravel the effects of multimodal interactions between tumor and immune cells and their contributions to tumor control using an experimental approach alone. Thus, to better understand the mechanisms that govern the interactions between immune cells and tumor cells and to identify the critical immunological features associated with tumor control and tumor escape, we built a mechanistic mathematical model of CD8s, Tregs, DCs, and tumor cells. The model accounts for tumor immunogenicity, the effects of IL-2 prolonging T cell lifespan, Treg suppression of antitumor immune response through CTLA-4, recruitment of immune cells into the tumor environment, and interferon-gamma upregulation of PD-L1 on DCs and tumor cells to deactivate T cells. We successfully fit the model to experimental data of tumor and immune cell dynamics. Employing Latin Hypercube Sampling, we generated over 1000 parameter sets that capture the sensitivity of αPD-1 immunotherapy. By comparing the parameter sets, we gain an insight into which mechanisms impinge the success of immunotherapy. We are now utilizing the model to explore combination immunotherapies that enhance the immune response in partial- and non-responders of αPD-1 immunotherapy. In particular, we are investigating what combinations of αPD-1, αCTLA-4, αICOSL, and αLAG-3 will stimulate CD8 activation without promoting Treg activation. After identifying the top combination therapies, we will validate our predictions experimentally. This integrated approach of modeling and experimental validation aims to advance our understanding of tumor-immune interactions and guide the development of more effective immunotherapeutic strategies.

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Simon Syga

TUD Dresden University of Technology

"Evolution of phenotypic plasticity leads to tumor heterogeneity with implications for therapy"

Cancer is a significant global health issue, with treatment challenges arising from intratumor heterogeneity. This study examines the complex relationship between somatic evolution and phenotypic plasticity, explicitly focusing on the interplay between cell migration and proliferation [1]. We propose that evolution does not act directly on phenotypic traits, like the proliferation rate, but on the phenotypic plasticity in response to the microenvironment [2]. We study this hypothesis using a novel, spatially explicit model that tracks individual cells' phenotypic and genetic states. We assume cells change between mobile and growing states controlled by inherited and mutation-driven genotypes and the cells' microenvironment. We observe that cells at the tumor edge evolve to favor migration over proliferation and vice versa in the tumor bulk. However, this phenotypic heterogeneity can be realized by distinct regulations of the phenotypic switch, which depend on the apoptosis rate and the cells' ability to sense their environment. Emerging synthetic tumors display varying levels of heterogeneity, which we show are predictors of the cancer's recurrence time after treatment. Interestingly, higher phenotypic heterogeneity predicts poor treatment outcomes, unlike genetic heterogeneity. [1] Hatzikirou, H. et al. (2010). 'Go or Grow': the key to the emergence of invasion in tumour progression? Math. Med. Biol., 29(1), 49-65. [2] Syga S. et al. (2024) Evolution of phenotypic plasticity leads to tumor heterogeneity with implications for therapy. PLOS Comput. Biol. 20(8): e1012003.

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Alexis Farman

UCL (University College London)

"Enhancing immunotherapies: Insights from the mathematical modelling of a microfluidic device"

A pivotal aspect of developing effective immunotherapies for solid tumors is the robust testing of product efficacy inside in vitro platforms.Collaborating with an experimental team that developed a novel microfluidic device at Children’s National Hospital (CNH), we developed a mathematical model to investigate immune cell migration and cytotoxicity within the device. Specifically, we study Chimeric Antigen Receptor (CAR) T-cell migration inside the channels, treating the cell as a moving boundary driven by a chemoattractant concentration gradient. The chemoattractant concentration is governed by two partial differential equations (PDEs) that incorporate key geometric elements of the device. We examine the motion of the cell as a function of its occlusion of the channel and find that certain cell shapes allow for multiple cells to travel inside the channel simultaneously. Additionally, we identify parameter regimes under which cells clog the channel, impairing their movement. All our findings are validated against experimental data provided by CNH. We integrate our model results into a broader model of the device, which also examines the cytotoxicity of CAR T-cells. This provides a tool for distinguishing experimental artifacts from genuine CAR T-cell behavior. This collaboration enabled the team at Children’s National Hospital to refine experimental conditions and uncover mechanisms enhancing CAR T-cell efficacy. [1] D Irimia, G Charras, N Agrawal, T Mitchison, M Toner, Polar stimulation and constrained cell migration in microfluidic channels,, Lab on a Chip 7 (12), 1783-1790

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Magnus Haughey

Barts Cancer Institute

"Extrachromosomal DNA driven oncogene spatial heterogeneity and evolution in glioblastoma"

Extrachromosomal DNA (ecDNA) oncogene amplification is associated with treatment resistance and shorter survival in cancer. Currently, the spatial dynamics of ecDNA, and their evolutionary impact, are poorly understood. Here, we investigate ecDNA spatial-temporal evolution by integrating computational modeling with samples from 94 treatment-naive human IDH-wildtype glioblastoma patients. Random ecDNA segregation combined with ecDNA-conferred fitness advantages induce predictable spatial ecDNA copy-number patterns which depend on ecDNA oncogenic makeup. EGFR-ecDNAs often reach high copy-number, confer strong fitness advantages and do not co-amplify other oncogenes on the same ecDNA. In contrast, PDGFRA-ecDNAs reach lower copy-number, confer weaker fitness advantages and co-amplify other oncogenes. EGFR structural variants occur exclusively on ecDNA, arise from and are intermixed with wild-type EGFR-ecDNAs. Modeling suggests wild-type and variant EGFR-ecDNAs often accumulate before clonal expansion, even in patients co-amplifying multiple ecDNA species. Early emergence of oncogenic ecDNA under strong positive selection is confirmed in vivo and in vitro in mouse neural stem cells. Our results implicate ecDNA as a driver of gliomagenesis, and suggest a potential time window in which early ecDNA detection may facilitate more effective intervention.

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Luke Heirene

University of Oxford

"Data Driven Mathematical Modelling Highlights the Impact of Bivalency on the Optimum Affinity for Monoclonal Antibody Therapies"

Monoclonal antibody (mAb)-based therapeutics are pivotal in treating a wide range of diseases, including cancer. One key mechanism by which these antibodies exert anti-tumour effects is through antibody-dependent cellular cytotoxicity (ADCC). In ADCC, mAbs bind to specific antigens on tumour cells and Fc receptors on immune effector cells. This trimeric complex triggers these effector cells to kill the tumour. ADCC is influenced by multiple factors, notably the properties of the mAb and its interactions with Fc receptors and target antigens. However, the optimum conditions for ADCC remain unclear. In this study, we investigate how variations in target antigen and mAb properties, particularly antibody valency, modulate ADCC response to identify parameters that maximize its potency. We developed an ordinary differential equation (ODE) model to simulate mAb binding within the immune synapse and quantify trimeric complex formation. To link the number of trimeric complexes to ADCC response, we validated the model using Bayesian inference on ADCC assay data. The results suggest that lower-affinity mAbs enhance ADCC by increasing the number of target cell-bound antibodies. Our validated model indicates that a “steric penalty” is necessary for bivalently target-bound versus monovalently target-bound antibodies. Due to constraints from dual antigen binding, these antibodies experience limited mobility, reducing Fc receptor engagement. After model validation, we explored variations in target expression, binding affinity, and antibody valency on ADCC potency, quantified by EC50. Our key finding is that the optimal binding affinity for maximizing ADCC potency depends on antibody valency. Monovalent antibodies are most potent at high affinity, while bivalent antibodies peak at lower affinities. Furthermore, the magnitude of this effect varies with target expression levels.

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