336:肠道微生物组预测激素受体阳性转移性乳腺癌患者对CDK4/6抑制剂和免疫检查点抑制剂联合治疗的反应

C. Wong, S. Yost, J. S. Lee, S. Highlander, Yuan Yuan
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引用次数: 1

摘要

背景:CDK4/6抑制剂(CDK4/6i)已被证明在临床前环境中调节免疫反应。帕博西尼是激素受体阳性(HR+)转移性乳腺癌(MBC)的一线治疗药物,与免疫检查点抑制剂(ICI)联合使用的研究越来越多。一项正在进行的I期试验旨在测试帕博西尼、派姆单抗和来曲唑在绝经后HR+ HER2- MBC (NCT02778685)妇女中的安全性和有效性。收集并分析粪便微生物组。本研究的目的是通过机器学习模型使用宏基因组测序数据确定肠道微生物群与反应的关联。方法:入选经组织学证实为IV期HR+ HER2- MBC的绝经后妇女。在基线和治疗期间收集粪便样本,使用宏基因组测序进行分析。根据RECIST 1.1将反应分为:反应者(完全反应或部分反应)和无反应者(疾病稳定或进展)。利用宏基因组相对丰度数据,建立了一个梯度增强的树模型,并进行了留一名患者的交叉验证,以预测患者在基线时的反应。Kruskal-Wallis测试用于评估应答者和非应答者之间最重要微生物群相对丰度(由机器学习模型生成)的差异。结果:在基线和治疗期间收集了11例患者的47份粪便样本,并进行了宏基因组测序。在预测建模方面,受试者工作特征曲线下验证面积(AUROC)和精确召回曲线下验证面积(AUPRC)分别为0.71和0.83。在该模型的前5个特征中,相对丰度较高的Gemmiger formicillis患者对治疗的反应概率增加(p引文格式:Chi Wah Wong, Susan E. Yost, Jin Sun Lee, Sarah K. Highlander, Yuan Yuan)。肠道微生物组预测激素受体阳性转移性乳腺癌患者对CDK4/6抑制剂和免疫检查点抑制剂联合治疗的反应[摘要]。见:美国癌症研究协会2021年年会论文集;2021年4月10日至15日和5月17日至21日。费城(PA): AACR;癌症杂志2021;81(13 -增刊):摘要第336期。
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Abstract 336: Gut microbiome predicts response to CDK4/6 inhibitor and immune check point inhibitor combination in patients with hormone receptor positive metastatic breast cancer
Background: CDK4/6 inhibitors (CDK4/6i) have been shown to modulate immune responses in the preclinical setting. Palbociclib is a front-line therapy for hormone receptor positive (HR+) metastatic breast cancer (MBC), and the combination with immune check point inhibitors (ICI) is increasingly being studied. An ongoing phase I trial was designed to test the safety and efficacy of palbociclib, pembrolizumab, and letrozole in postmenopausal women with HR+ HER2- MBC (NCT02778685). Stool microbiome was collected and analyzed. The aim of this study is to determine the association of gut microbiota and response using metagenome sequencing data through a machine learning model. Methods: Postmenopausal women with histologically proven stage IV HR+ HER2- MBC were enrolled. Stool samples were collected at baseline and during treatment for analysis using metagenome sequencing. Response per RECIST 1.1 were grouped into: responders (complete response or partial response) and non-responders (stable disease or progressive disease). Using metagenomic relative abundance data, a gradient-boosted tree model was developed with leave-one-patient-out cross validations to predict patient response at baseline. Kruskal-Wallis tests were used to assess the differences of the most important microbiota relative abundance (generated from the machine learning model) between responders and non-responders. Results: Forty-seven stool samples from 11 patients were collected at baseline and during treatment, and metagenome sequencing was performed. For predictive modeling, the validation Area Under the Receiver Operating Characteristic Curve (AUROC) and Area Under the Precision Recall Curve (AUPRC) were 0.71 and 0.83, respectively. Among the top 5 features from the model, patients with a larger relative abundance of Gemmiger formicillis have increased probability of responding to therapy (p Citation Format: Chi Wah Wong, Susan E. Yost, Jin Sun Lee, Sarah K. Highlander, Yuan Yuan. Gut microbiome predicts response to CDK4/6 inhibitor and immune check point inhibitor combination in patients with hormone receptor positive metastatic breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 336.
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