EpCAM-PSMA: Potential predictors of treatment outcomes for PSMA-targeted alpha therapies in metastatic castration-resistant prostate cancer

EpCAM-PSMA:转移性去势抵抗性前列腺癌中PSMA靶向α疗法治疗结果的潜在预测因子

阅读:5
作者:Gábor Bakos,Ulrike Bauder-Wüst,Jonathan Landry,Mareike Roscher,Beáta Ramasz,Frank Bruchertseifer,Alfred Morgenstern,Clemens Kratochwil,Vladimír Beneš,Martina Benešová-Schäfer

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.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。