| Home > Publications database > EpCAM-PSMA: Potential predictors of treatment outcomes for PSMA-targeted alpha therapies in metastatic castration-resistant prostate cancer. |
| Journal Article | DKFZ-2026-00433 |
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2026
Elsevier
[New York]
Abstract: Targeted radionuclide therapy and targeted alpha therapy directed at prostate-specific membrane antigen (PSMA) represent emerging treatment modalities for metastatic castration-resistant prostate cancer (mCRPC). However, therapeutic resistance remains a significant barrier to their clinical success. We discovered that dynamic changes in cell surface levels of epithelial cell adhesion molecule (EpCAM) and PSMA can serve as predictive biomarkers in late-stage mCRPC patients treated with the beta-minus-particle-emitting [177Lu]Lu-PSMA-617, in combination with the alpha-particle-emitting [225Ac]Ac-PSMA-617, and we further explored the underlying molecular mechanisms. Using flow cytometry to profile EpCAM and PSMA on circulating tumor cells (CTCs), we observed that Nonresponders displayed significantly higher EpCAM and lower PSMA levels than Responders, both at baseline and after the first treatment cycle. Over subsequent cycles, both markers declined in Nonresponders, whereas Responder CTCs maintained EpCAM expression but progressively lost PSMA. Transcriptome analysis identified upregulation of hub genes involved in the regulation of key pathways such as enhanced DNA-damage repair, anti-apoptotic activity, increased tumor cell growth, and altered surface marker trafficking and recycling, potentially driving EpCAM-PSMA dynamics and contributing to therapy resistance. Ultimately, integrating surface-marker-driven treatment response predictions with novel treatment strategies may help to overcome treatment resistance in mCRPC.
Keyword(s): CTCs ; EpCAM ; PSMA ; TαT ; circulating tumor cells ; epithelial cell adhesion molecule ; gene expression analysis ; liquid biopsy ; mCRPC ; metastatic castration-resistant prostate cancer ; prostate-specific membrane antigen ; surface marker dynamics ; targeted alpha therapy ; treatment resistance ; treatment response prediction
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