Abstract 2354: Developing a gene signature from circulating biomarkers to forecast Lu-PSMA response in advanced prostate cancer

Abstract Introduction: 177Lu-PSMA-targeted radioligand therapy (RLT) for metastatic castration-resistant prostate cancer (mCRPC) leverages the high PSMA expression on cancer cells. The radiolabeled molecule binds to PSMA, delivering beta radiation to selectively destroy cancer cells while sparing surrounding healthy tissue. Despite its sophistication, not all mCRPC patients with adequate PSMA expression respond to this therapy. Currently, there is no mean to predict response or optimize patient selection. We performed transcriptomic profiling on patients undergoing Lu-PSMA therapy, aiming to identify a gene signature that can predict treatment response and help select the most suitable patient population. Methods: Whole blood samples were collected from mCRPC patients receiving Lu-PSMA-based RLT and categorized into three groups based on PCWG3 criteria: Responders (R, n=30), Partial Responders (RR, n=10), and Non-Responders (NR, n=26). Total RNA was isolated and subjected to RNA sequencing using the NovaSeq 6000 platform. Differentially expressed genes were identified through data analysis with Partek Flow, and pathway analysis was performed to investigate associated biological processes. Results: RNA sequencing of the three groups (R, NR, and PR) revealed 825 statistically significant genes (FC > 1; P < 0.05) in the R vs. NR comparison, 1, 807 genes in R vs. PR, and 1, 548 genes in PR vs. NR. Furthermore, over 90% of responders exhibited prolonged survival, while more than 95% of non-responders and over 80% of partial responders were recorded as deceased. Conclusion: Our findings suggest that the identified gene signature could serve as a predictive biomarker for clinical response prior to initiating 177Lu-PSMA treatment. Additionally, the pronounced expression of certain genes in non-responders presents an opportunity for the development of novel therapeutic strategies. Citation Format: Srinivas V. Koduru, Mark Kidd, Alin Chirindel, Nicolas Guillaume, Abdel Halim. Developing a gene signature from circulating biomarkers to forecast Lu-PSMA response in advanced prostate cancer [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 2354.