Abstract 6242: Adaptive therapy in advanced basal cell carcinoma driven by imaging and mathematical modeling

Abstract Basal cell carcinoma (BCC) accounts for 80% of skin cancers and in most cases is very treatable. However, due to either neglect or aggressive growth, roughly 1-10% of BCCs are considered advanced and 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, such as vismodegib, which is mutated in 85% of BCCs. Whilst Hedgehog inhibitors 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 design an adaptive therapy strategy for BCC to increase PFS by controlling growth whilst reducing toxicity and the risk of treatment resistance. We use measurements and photographic images of skin lesions over time integrated with a mathematical model to adapt treatment scheduling (either on or off) in a dynamic patient-specific manner. We develop a burden metric based on the color and texture of a lesion to define its current state. 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 in our ongoing clinical trial. 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. We can predict BCC patient growth and response dynamics by fitting a mathematical model to a novel tumor burden metric based on size, color, and texture. Citation Format: Jill Gallaher, Masud M A, Zeynep Eroglu, Alexander R. A. Anderson. Adaptive therapy in advanced basal cell carcinoma driven by imaging and mathematical modeling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6242.