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...

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Veröffentlicht in:Cancer research (Chicago, Ill.) Ill.), 2021-07, Vol.81 (13_Supplement), p.336-336
Hauptverfasser: Wong, Chi Wah, Yost, Susan E., Lee, Jin Sun, Highlander, Sarah K., Yuan, Yuan
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Sprache:eng
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Zusammenfassung: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
ISSN:0008-5472
1538-7445
DOI:10.1158/1538-7445.AM2021-336