Deciphering the effects of radiopharmaceutical therapy in the tumor microenvironment of prostate cancer: an in-silico exploration with spatial transcriptomics

Radiopharmaceutical therapy (RPT) is an emerging prostate cancer treatment that delivers radiation to specific molecules within the tumor microenvironment (TME), causing DNA damage and cell death. Given TME heterogeneity, it's crucial to explore RPT dosimetry and biological impacts at the cellular level. We integrated spatial transcriptomics (ST) with computational modeling to investigate the effects of RPT targeting prostate-specific membrane antigen (PSMA), fibroblast activation protein (FAP), and gastrin-releasing peptide receptor (GRPR) each labelled with beta-emitting lutetium-177 (¹⁷⁷Lu) and alpha-emitting actinium-225 (²²⁵Ac). <b>Methods:</b> Three ST datasets from primary tissue samples of two prostate cancer patients were obtained. From these datasets, we extracted gene expressions, including FOLH1, GRPR, FAP, and Harris Hypoxia, and estimated the proportions of different cell types-epithelial, endothelial, and prostate cancer (PC) cells-in the corresponding ST spots. We computed the spatiotemporal distribution of each RPT targeting PSMA, FAP, and GRPR at each ST spot by solving the partial differential equation (PDE) using a convection-reaction-diffusion (CRD) model, assuming similar pharmacokinetic parameters across all ligands. A well-established physiologically based pharmacokinetic (PBPK) model was used to simulate the input function in the prostate, carefully calibrated to deliver 10 Gy to the prostate tumor over 20 days. Dosimetry was estimated using the Medical Internal Radiation Dose (MIRD) formalism, applying the dose point kernels (DVK) method. The survival probability was estimated using the linear quadratic model, applied to both beta-emitting RPT labeled with ¹⁷⁷Lu and ²²⁵Ac. A modified linear quadratic model was used to estimate the bioeffect of the alpha-emitting RPT. <b>Results:</b> The results demonstrate distinct dose-response and efficacy patterns across ST samples, with FAP-targeted RPT exhibiting limited effectiveness in tumor cell-rich areas compared to PSMA- and GRPR-targeted therapies. GRPR-targeted RPT showed higher resistance in hypoxic regions relative to the other therapies. Additionally, ²²⁵Ac-labeled RPT was more effective overall than ¹⁷⁷Lu-labeled RPT, especially in areas with low cancer-cell fraction or high hypoxia. The findings suggest that a combination of ²²⁵Ac-labeled FAP- and PSMA-targeted RPT offers the best therapeutic strategy. <b>Conclusion:</b> The proposed method, which combines ST and computational modeling to determine the dosimetry and cell survival probability of RPT in the TME, holds promise for identifying optimal personalized RPT strategies.