Abstract C093: <i>In Silico</i> Virtual Microdosimetry Analysis of Spatial Transcriptomics Data Identify Combination Strategies for PSMA Trageted Radionuclide Therapy

Abstract Purpose: Radiopharmaceutical therapy (RPT) targeting the prostate-specific membrane antigen (PSMA) is a transformative treatment for prostate cancer, yet its efficacy is often limited by heterogeneous dose delivery within the tumor microenvironment (TME). At the cellular level, understanding dosimetry and its biological impact is crucial. This study integrates spatial transcriptomics (ST) with modeling to identify clinically available therapeutic targets that can be effectively combined with PSMA to mitigate dose heterogeneity. Methods: High-resolution ST data from 36 prostate cancer tissue samples were used for this study. From these datasets, we extracted spatially-resolved gene expression profiles for PSMA (FOLH1) and a panel of other RPT targets under other investigations (STEAP1, CD46, B7-H3, GRPR, DLL3, and FAP). A Virtual microdosimetry was performed using a physiologically based pharmacokinetic (PBPK) model coupled with a radiation transport kernel to compute spatiotemporal drug distribution and absorbed dose at a microscopic level. We analyzed spot-wise dose correlations, epithelial dose coverage, and the therapeutic benefit of combination strategies. Results: Our in silico virtual microdosimetry analysis demonstrated distinct efficacy patterns across targets. STEAP1 emerged as the most promising candidate to complement PSMA. Cell-level dose analysis revealed a weak correlation between PSMA- and STEAP1-based RPT, indicating that STEAP1 effectively delivers radiation to epithelial regions that receive a low dose from PSMA RPT. Analysis of individual cases highlighted two primary mechanisms for suboptimal PSMA RPT efficacy: 1) significant spatial heterogeneity of FOLH1 expression and 2) a 'binding-site barrier' effect, where high target expression near vasculature limits tissue penetration. Conclusion: The proposed methodology, combining ST with computational modeling, provides a powerful framework for optimizing personalized RPT strategies and next generation RPT programs. Our findings provide a strong rationale for the clinical investigation of a combination PSMA- and STEAP1-targeted RPT to achieve more uniform tumor killing. Citation Format: Hongyoon Choi, Sungwoo Bae, Minki Choi, Doingjoo Lee, Minkyu Kim, Jae Eun Lee, Daeseung Lee. In Silico Virtual Microdosimetry Analysis of Spatial Transcriptomics Data Identify Combination Strategies for PSMA Trageted Radionuclide Therapy [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2025 Oct 22-26; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2025;24(10 Suppl):Abstract nr C093.