ONCO-02 Contributed Talks

Ruby Nixson

Mathematical Institute, University of Oxford

"A structured-PDE approach to targeting a quiescent sub-population under hypoxia and anti-tumour therapies in paediatric glioma."

Paediatric diffuse midline gliomas are highly aggressive, incurable, childhood tumours. Their location in the brainstem limits treatment to radiotherapy, which allows an average survival time of 9-11 months. A sub-population of quiescent tumour cells are thought to be responsible for the poor outcomes of these patient. Quiescence is often viewed as a reversible resting state in which cells temporarily exit the cell cycle, the process controlling DNA replication and cell division. Radiosensitivity varies during the cell cycle, and quiescent cells exhibit a higher relative level of radio-resistance. Hypoxia (physiologically low levels of oxygen) also impacts cell cycle progression and quiescence, as well as response to radiotherapy, contributing to poor patient outcomes. We build on existing mathematical models of cell cycle progression under treatment which account for the radio-resistance of quiescent cells and their ability to re-enter the cell cycle and proliferate. We derive a system of partial differential equations (PDEs), which structures cells by the time spent in each cell cycle phase and allows transitions to and from a quiescent phase. By considering oxygen-dependent cell cycle progression, we use the model to investigate how the proportion of quiescent cells changes when we impose fluctuating oxygen dynamics and treat with radiotherapy. We extend existing studies that optimise treatment schedules using a balance of treatment outcome and toxicity/cost by incorporating a fixed radiotherapy schedule to investigate the impact of a hypothetical drug that alters the transition dynamics to and/or from quiescence. By considering different mechanisms of action for this hypothetical drug, we use our PDE model to identify candidate drugs with the potential to slow tumour progression and improve patient outcomes. This work will inform our clinical collaborators if such an improvement is possible, and what the design of a suitable drug should be.

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Reshmi Patel

The University of Texas at Austin

"MRI-based mathematical modeling to predict the response of cervical cancer patients to chemoradiation"

Concurrent chemoradiation followed by brachytherapy is the standard-of-care treatment for locally advanced cervical cancer (LACC), but 30% of treated patients experience local recurrence [1], indicating a need for patient-specific, optimized therapeutic regimens to improve outcomes. We aim to predict patient-specific response to chemoradiation by applying our established MRI-based mathematical modeling framework [2]. The study cohort consisted of 10 LACC patients who underwent imaging with T2-weighted MRI, dynamic contrast-enhanced MRI, and diffusion-weighted MRI (DWI) before (V1), after two weeks (V2), and after five weeks (V3) of chemoradiation [3]. We registered all patient-specific MRI data within and between visits, and maps of the number of tumor cells (NTC) were calculated from the DWI-derived apparent diffusion coefficients. Our biology-based reaction-diffusion models characterize the spatiotemporal change in NTC as a function of cell diffusion, proliferation, and therapy-induced death. We consider two model options for cell death: (A) distinct chemotherapy (exponential decay) and radiotherapy (instantaneous decrease according to the linear-quadratic model) terms and (B) a single exponential decay term describing chemoradiation. Proliferation and chemoradiation efficacy rates were calibrated to the V1 and V2 NTC maps, and the calibrated model was run forward to predict the NTC at V3. Using Model (A), the concordance correlation coefficient (CCC) between the observed and predicted V1 to V3 change in total tumor cellularity was 0.87, and the CCC between the observed and predicted change in tumor volume was 0.90; using Model (B), the CCC values were 0.97 and 0.90, respectively. These preliminary findings show the promise of our mathematical modeling framework in predicting LACC response to chemoradiation. References: [1]. CCCMAC. Cochrane Database Syst Rev. 2010. [2]. Jarrett et al. Nat Protoc. 2021. [3]. Bowen et al. J Magn Reson Imaging. 2018.

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Fabian Spill

University of Birmingham

"Regulation of Intra- and Intercellular Metabolite Transport in Cancer Metabolism"

Metabolite transport is essential for cellular homeostasis, energy production, and metabolic adaptation. In cancer, dysregulated transport sustains tumor growth and alters redox balance. The mitochondrial solute carrier SLC25A10 facilitates succinate, malate, and phosphate exchange, influencing central carbon metabolism. However, its transport kinetics and physiological directionality remain poorly understood. We present a mathematical model of SLC25A10 based on a ping-pong kinetic mechanism, capturing competitive dynamics between malate and succinate. Our simulations reveal that under normal conditions, malate flux dominates due to its higher binding affinity. However, in succinate dehydrogenase (SDH) dysfunction, excess succinate induces a transient efflux shift and phosphate flux reversal. If experimentally validated, this metabolic shift could serve as a biomarker for tumors with SDH mutations. Integrating our kinetic model with genome-scale metabolic networks, we highlight the role of mitochondrial transport in cancer metabolism. Specifically, in multiple myeloma, metabolic crosstalk between plasma cells and bone marrow stromal cells is key to tumor progression. Our findings demonstrate the power of mathematical modeling in uncovering transport-mediated metabolic vulnerabilities, offering potential therapeutic targets for cancer and metabolic diseases.

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