A predictive endocrine resistance index accurately stratifies luminal breast cancer treatment responders and nonresponders

预测性内分泌抵抗指数能够准确区分管腔型乳腺癌治疗的有效者和无效者。

阅读:2

Abstract

BACKGROUNDEndocrine therapy (ET) with tamoxifen (TAM) or aromatase inhibitors (AI) is highly effective against hormone receptor-positive (HR-positive) early breast cancer (BC), but resistance remains a major challenge. The primary objectives of our study were to understand the underlying mechanisms of primary resistance and to identify potential biomarkers.METHODSWe selected more than 800 patients in 3 subcohorts (Discovery, n = 364, matched pairs; Validation 1, n = 270, Validation 2, n = 176) of the West German Study Group (WSG) ADAPT trial who underwent short-term preoperative TAM or AI treatment. Treatment response was assessed by immunohistochemical labeling of proliferating cells with Ki67 before and after ET. We performed comprehensive molecular profiling, including targeted next-generation sequencing (NGS) and DNA methylation analysis using EPIC arrays, on posttreatment tumor samples.RESULTSTP53 mutations were strongly associated with primary resistance to both TAM and AI. We identified distinct DNA methylation patterns in resistant tumors, suggesting alterations in key signaling pathways and tumor microenvironment composition. Based on these findings and patient age, we developed the Predictive Endocrine ResistanCe Index (PERCI). PERCI accurately stratified responders and nonresponders in both treatment groups in all 3 subcohorts and predicted progression-free survival in an external validation cohort and in the combined subcohorts.CONCLUSIONOur results highlight the potential of PERCI to guide personalized endocrine therapy and improve patient outcomes.TRIAL REGISTRATIONWSG-ADAPT, ClinicalTrials.gov NCT01779206, retrospectively registered 01-25-2013.FUNDINGGerman Cancer Aid (Grant Number 70112954), German Federal Ministry of Education and Research (Grant Number 01ZZ1804C, DIFUTURE).

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。