Determinants of outcome following PSMA-based radioligand therapy and mechanisms of resistance in patients with metastatic castration-resistant prostate cancer

PSMA放射性配体治疗后转移性去势抵抗性前列腺癌患者的疗效决定因素及耐药机制

阅读:2

Abstract

[(177)Lu]Lu-PSMA has recently been approved for use in the post-taxane, post-novel hormonal-agent setting in patients with metastatic castration-resistant prostate cancer. As a beta-emitting radioligand targeting prostate-specific membrane antigen (PSMA), it delivers radiation to cells expressing PSMA on their surface. In pivotal clinical trials, patients were selected for this treatment based on positron emission tomography (PET)/CT imaging, requiring PSMA-avid disease with no evidence of discordant disease on 2-[(18)F]fluoro-2-deoxy-D-glucose PET/CT or contrast CT scan. Despite exhibiting an optimal imaging phenotype, the response for many patients is not durable, and a minority do not respond to [(177)Lu]Lu-PSMA at all. Disease progression is inevitable even for those who achieve an exceptional initial response. Reasons for both primary and acquired resistance are largely unknown; however, they are likely due to the presence of underlying PSMA-negative disease not identified on imaging, molecular factors conferring radioresistance, and inadequate delivery of lethal radiation, particularly to sites of micrometastatic disease. Biomarkers are urgently needed to optimize patient selection for treatment with [(177)Lu]Lu-PSMA by identifying those who are most and least likely to respond. Retrospective data support using several prognostic and predictive baseline patient- and disease-related parameters; however, robust prospective data is required before these can be translated into widespread use. Further, early on-treatment clinical parameters (in addition to serial prostate-specific antigen [PSA] levels and conventional restaging imaging) may serve as surrogates for predicting treatment response. With little known about the efficacy of treatments given after [(177)Lu]Lu-PSMA, optimal treatment sequencing is paramount, and biomarker-driven patient selection will hopefully improve treatment and survival outcomes.

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。