177 LuLu-PSMA radioligand therapy targets metastatic castration-resistant prostate cancer by delivering radiation to cells expressing prostate-specific membrane antigen (PSMA). While some patients show remarkable responses, up to 50% show little to no benefit, and disease progression inevitably occurs. Eligibility is mainly based on PSMA avidity by PET imaging, yet responses remain highly variable, highlighting a disconnect between PSMA expression and clinical efficacy. This study used PSMA-positive patient-derived xenografts (PDXs) from the Melbourne Urological Research Alliance (MURAL) to explore mechanisms of response and resistance to 177 LuLu-PSMA treatment. PDXs with variable PSMA expression were treated with 177 LuLu-PSMA-I&T and assessed for radioligand uptake and DNA damage. Histological, genomic and transcriptomic analyses aimed to identify features of radiosensitivity and resistance at a tissue and cellular level. PDXs recapitulate clinical PSMA histology and exhibit variable responses to 177 LuLu-PSMA, mirroring the heterogeneity observed in the clinic. Responses ranged from sustained, transient or delayed tumor reduction to treatment resistant, including a remarkable responder with complete tumor regression for up to 20 weeks post treatment. The magnitude and persistence of DNA damage and tumor cell death varied between PDXs, demonstrating divergent radiosensitivity due to intrinsic tumor characteristics. These characteristics included PSMA receptor density, extent of DNA damage, genomic aberrations and baseline transcriptomic signatures. These findings in PDX tumors emphasize the complexity of predicting 177 LuLu-PSMA response, beyond what cell line models can capture. Comparable radiation doses with variable regression underscores that radiosensitivity is multifactorial across diverse tumors, supporting the need for integrated multimodal approaches for patient stratification. Integrating baseline genomic and transcriptomic profiles may reveal determinants of 177 LuLu-PSMA sensitivity to improve patient selection and identify novel combination therapies.
Everard et al. (2026) studied this question.