Abstract B019: Mathematical modeling-based optimization of gamma knife surgery after checkpoint blockade in melanoma brain metastases
Authors: Alexander R. J. Silalahi, Caroline Chung, James W. Welsh, Zhihui Wang, Joseph D. Butner
Journal: Clinical Cancer Research (2025)

Abstract Gamma knife radiotherapy (RT) treatment is often used as a salvage therapy after progression of melanoma brain metastasis (MBM) lesions under immune checkpoint inhibitor (ICI) blockade therapy; however, waiting until progression of nonresponsive lesions can result in significant symptomatic progression, and the toxicity of RT is greater for larger lesions as it is directly proportional to lesion volume. Robust and reliable standards for consistently maximizing the benefit of combination ICI + RT therapy remain elusive, likely in part due to the complexity of mechanistic interactions between each therapy. Addressing this clinical need will require optimizing the effects of their reported synergistic interactions such as tumor microenvironment modulation and immune activation while mitigating inhibitory effects such as immunosuppressive radiation effects. The ability to identify lesions unlikely to respond to ICI shortly after start of treatment would offer immediate clinical benefit by enabling earlier RT delivery, reduce the volume-dependent toxicities of RT, and reduce symptoms from lesion progression. To address the unmet clinical need for methods to optimize outcomes of combination ICI and RT therapy, we have developed a mechanistic mathematical model based on the biological and physical underpinnings of these complex therapies, and their interactions, which is able to describe and quantify interactions between ICI and RT and their interactions on the tumor. This builds upon our previous ICI modeling work by expanding the model based on the assumption that therapeutic outcome is not only a result of direct interaction between the treatment and disease, but is also a result of complex interplay between heterogeneous tumor populations that may be sensitive or resistant to treatment and their unique interactions with the immune system that serve to maintain or disrupt the tumor-immune equilibrium. RT further modulates this tumor-immune dynamic both by killing T cells in the tumor interior and by stimulating release of T cell activating antigens which can alter T cell activation exterior to the tumor, thereby affecting T cell recruitment into the tumor. By capturing how the complex time-dependent dynamics of tumor-immune interaction and recruitment influences – and is influenced by – transient shifts in treatment sensitive and resistant populations, our model aims to describe and quantify long-term tumor dynamics after ICI + RT therapy. This platform provides a consistent framework for systematically evaluating various proposed mechanisms underlying treatment interactions and how these may determine treatment failure or success. By applying the model to a retrospective, in-house generated melanoma brain metastasis data set, we are examining and refining our model structure with a goal of capturing a wide range of observed tumor dynamics using only clinically available data. This may lead to robust, quantitative methods to maximize treatment outcomes based on the unique characteristics of individual tumors. Citation Format: Alexander R. J. Silalahi, Caroline Chung, James W. Welsh, Zhihui Wang, Joseph D. Butner. Mathematical modeling-based optimization of gamma knife surgery after checkpoint blockade in melanoma brain metastases. [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Targeted Therapies in Combination with Radiotherapy; 2025 Jan 26-29; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(2_Suppl):Abstract nr B019.