PO.EN01.01 · 内分泌肿瘤
基于DNA甲基化的分类器可预测ESR1野生型HR+/HER2-乳腺癌患者的SERD获益
DNA methylation-based classifier predicts SERD benefit in ESR1 wild-type HR+/HER2- breast cancer
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景:激素受体阳性(HR+)转移性乳腺癌(mBC)对芳香化酶抑制剂(AI)的耐药不可避免,且常由ESR1突变驱动。选择性雌激素受体降解剂(SERD)靶向雌激素受体使其降解,并在这一情境下提供临床获益。ESR1突变是已确立的生物标志物,可指导从AI向以SERD为基础的内分泌治疗过渡。然而,许多患者检测不到ESR1突变,且尚无经过验证的生物标志物可用于指导ESR1野生型(ESR1-wt)患者使用SERD。我们研究了一种基于血液、源自DNA甲基化的激素受体(mHR)活性评分能否预测ESR1-wt患者的SERD获益。
方法:mHR活性评分采用逻辑回归模型建立,该模型整合了来自Guardant360 Liquid检测的全基因组甲基化捕获中信息量最高的400余个区域。mHR-高/-低阈值使用超过2,000例克隆性ESR1突变mBC样本进行定义。验证在一个真实世界的ESR1-wt队列(N=765)中进行,样本为来自GuardantINFORM数据库的AI治疗后进展的血浆样本。采用Kaplan-Meier法和多变量Cox模型评估mHR-高/-低组之间以SERD或化疗的治疗中断时间(TTD)定义的临床结局,并对肿瘤分数、年龄和治疗类型进行校正。
结果:在超过12,000例采用Guardant360 Liquid检测的乳腺癌样本中,mHR活性评分与ESR1突变状态呈正相关,并在具有克隆性(相对于亚克隆性)ESR1突变的肿瘤中更高。在ESR1-wt验证队列中,mHR-高患者接受SERD治疗(氟维司群单药或联合)的中位TTD显著长于mHR-低患者(校正风险比:0.44,95%CI:0.21-0.89;p = 0.02;中位TTD为107天对89天)。此外,mHR状态可预测SERD相对于化疗的相对获益:在mHR-高患者中,SERD治疗的TTD显著长于化疗(校正风险比:0.41;95%CI:0.21-0.81;p = 0.01;中位TTD为107天对87天),而mHR-低患者接受SERD相对于化疗则呈现TTD较短的趋势(校正风险比:1.83;95%CI:0.87-3.87;p = 0.11;中位TTD为89天对108天)。
结论:一种基于血液、源自DNA甲基化的mHR评分捕获了激素受体驱动的表观遗传活性,并可预测ESR1-wt HR+ mBC患者从SERD治疗中获益。该分类器可实现对ESR1-wt患者的实时、无创分层,识别出更可能从以SERD为基础的治疗中获益的分子界定亚群。这些发现支持基于甲基化的生物标志物在将精准内分泌治疗扩展至ESR1突变谱分析之外方面的潜在临床应用价值。
查看英文原文 English abstract
Background: Resistance to aromatase inhibitors (AIs) in hormone receptor-positive (HR+) metastatic breast cancer (mBC) is inevitable and frequently driven by ESR1 mutations. Selective estrogen receptor degraders (SERDs) target estrogen receptors for degradation and provide clinical benefit in this setting. ESR1 mutations are established biomarkers guiding transition from AI to SERD-based endocrine therapy. However, many patients lack detectable ESR1 mutations, and no validated biomarker exists to guide SERD use in ESR1 wild-type ( ESR1-wt ) patients. We investigated whether a blood-based, DNA methylation-derived hormone receptor (mHR) activity score could predict SERD benefit in ESR1-wt patients.
Methods: An mHR activity score was developed using a logistic regression model integrating over 400 most informative regions from genome-wide methylation capture of Guardant360 Liquid test. mHR-high/-low thresholds were defined using >2,000 clonal ESR1-mutant mBC samples. Validation was performed in a real-world ESR1-wt cohort (N=765) with post-AI progression plasma samples from the GuardantINFORM database. Clinical outcomes defined by time to treatment discontinuation (TTD) of SERD or chemotherapy were evaluated across mHR-high/-low groups using Kaplan-Meier methods and multivariable Cox models, adjusting for tumor fraction, age, and treatment type.
Results: Across >12,000 breast cancer samples tested with Guardant360 Liquid, the mHR activity score was positively correlated with ESR1 mutation status and higher in tumors with clonal versus subclonal ESR1 mutations. In the ESR1-wt validation cohort, mHR-high patients showed significantly longer median TTD with SERD treatment (Fulvestrant mono or combination) compared to mHR-low patients (adjusted hazard ratio: 0.44, 95%CI: 0.21-0.89; p = 0.02; median TTD 107 vs. 89 days). Moreover, mHR status predicted relative benefit from SERD versus chemotherapy: among mHR-high patients, SERD treatment yielded significantly longer TTD than chemotherapy (adjusted hazard ratio: 0.41; 95%CI: 0.21-0.81; p = 0.01; median TTD 107 vs. 87 days), while mHR-low patients exhibited a trend towards shorter TTD on SERD versus chemotherapy (adjusted hazard ratio: 1.83; 95% CI: 0.87-3.87; p = 0.11; median TTD 89 vs. 108 days).
Conclusions: A blood-based DNA methylation-derived mHR score captures hormone receptor-driven epigenetic activity and predicts benefit from SERD therapy in ESR1 -wt HR+ mBC. This classifier enables real-time, noninvasive stratification of ESR1 -wt patients, identifying a molecularly defined subset more likely to derive benefit from SERD-based therapy. These findings support the potential clinical utility of methylation-based biomarkers to extend precision endocrine therapy beyond ESR1 mutation profiling.
利益披露 Disclosure
S. Zhang,
Guardant Health Employment, Stock.
N. Zhang,
Guardant Health Employment, Stock.
V. Ramani,
Guardant Health Employment, Stock.
T. Jiang,
Guardant Health Employment, Stock.
M. Juntilla,
Guardant Health Employment, Stock.
A. Das,
Guardant Health Employment, Stock.
M. Lefterova,
Guardant Health Employment, Stock.
J. Odegaard,
Guardant Health Employment, Stock.
D. Chudova,
Guardant Health Employment, Stock.
M. Ellis,
Guardant Health Employment, Stock.