Adaptive therapy in advanced basal cell carcinoma driven by imaging and predictive mathematical

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• Jill Gallaher (Moffitt Cancer Center)
• Friday 19 September 2025, 10:40-10:45
• Seminar Room 1, Newton Institute.

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OOEW07 - Mathematical Foundations of Oncological Digital Twins

Basal cell carcinoma (BCC) accounts for 80% of skin cancers and in most cases is very treatable. However, roughly 1-10% of BCCs are considered advanced and have infiltrated into surrounding tissue such that it is challenging to clear without causing morbidity. These advanced cancers are treated with a systemic therapy inhibiting the Hedgehog signaling pathway, which is mutated in 85% of BCCs. Whilst Hedgehog inhibitors such as vismodegib have a median progression-free survival (PFS) of approximately 1 year, they come with significant toxicity that often disrupts standard treatment scheduling, and even for those that can tolerate it, there is the possibility of treatment resistance. Here we show interim results from an ongoing clinical trial (NCT05651828) where we guide treatments for BCC patients using image analysis, mathematical models, and an adaptive strategy. Photographic images of skin lesions over time are analyzed to develop a metric for tumor burden over time and guide treatment. This metric uses a random forest algorithm to define the lesion’s current state based the color and texture of a lesion to relate to its aggressiveness. We also define a system of ordinary differential equations comprising sensitive and resistant cells along with compartments to capture the drug’s unique pharmacodynamics to make predictions for future treatments. We fit the model to each patient’s lesion dynamics and predict when to start and stop therapy, iteratively driving treatment decisions to adapt treatment scheduling (either on or off) in our ongoing clinical trial to balance growth control with toxicity and risk of drug resistance. We find considerable heterogeneity amongst patients due to the number, size, and morphology of lesions and amongst lesions within a single patient. Drug response and clearance rate of the drug may differ between patients and lesions, so pharmacokinetics and pharmacodynamics parameters need to be carefully calibrated to make good response predictions. Importantly, we define a new strategy for treatment of multiple lesions that are responding differently to treatment within the same patient. Ultimately, by combining imaging with mathematical modelling, we can predict BCC patient growth and response dynamics to make more informed treatment decisions.

This talk is part of the Isaac Newton Institute Seminar Series series.

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