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Circulating Tumor Cells Prediction in Hormone Receptor Positive HER2-Negative Advanced Breast Cancer: A Retrospective Analysis of the MONARCH 2 Trial.
- Source :
- Oncologist; Feb2024, Vol. 29 Issue 2, p123-131, 9p
- Publication Year :
- 2024
-
Abstract
- Background The MONARCH 2 trial (NCT02107703) showed the efficacy of abemaciclib, a cyclin-dependent kinase 4 & 6 inhibitor (CDK4/6i), in combination with fulvestrant for hormone receptor-positive, HER2-negative metastatic breast cancer (MBC). The aim of this analysis was to explore the prediction of circulating tumor cells (CTCs) stratification using machine learning for hypothesis generation of biomarker-driven clinical trials. Patients and Methods Predicted CTCs were computed in the MONARCH 2 trial through a K nearest neighbor (KNN) classifier trained on a dataset comprising 2436 patients with MBC. Patients were categorized into predicted Stage IV<subscript>aggressive</subscript> (pStage IV<subscript>aggressive</subscript>, ≥5 predicted CTCs) or predicted Stage IV<subscript>indolent</subscript> (pStage IV<subscript>indolent</subscript>, <5 predicted CTCs). Prognosis was tested in terms of progression-free-survival (PFS) and overall survival (OS) through Cox regression. Results Patients classified as predicted pStage IV<subscript>aggressive</subscript> and predicted pStage Stage IV<subscript>indolent</subscript> were, respectively, 183 (28%) and 461 (72%). After multivariable Cox regression, predicted CTCs were confirmed as independently associated with prognosis in terms of OS, together with ECOG performance status, liver involvement, bone-only disease, and treatment arm. Patients in the pStage Stage IV<subscript>indolent</subscript> subgroup treated with abemaciclib experienced the best prognosis both in terms of PFS and OS. The treatment effect of abemaciclib on OS was then explored through subgroup analysis, showing a consistent benefit across all subgroups. Conclusion This study is the first analysis of CTCs modeling for stage IV disease stratification. These results show the need to expand biomarker profiling in combination with CTCs stratification for improved biomarker-driven drug development. [ABSTRACT FROM AUTHOR]
- Subjects :
- STATISTICS
DISEASE progression
CONFIDENCE intervals
CYTOMETRY
LOG-rank test
MULTIVARIATE analysis
MACHINE learning
RETROSPECTIVE studies
TUMOR classification
RANDOMIZED controlled trials
HYPOTHESIS
RESEARCH funding
DESCRIPTIVE statistics
KAPLAN-Meier estimator
CELL lines
TUMOR markers
DATA analysis software
HORMONE receptor positive breast cancer
Subjects
Details
- Language :
- English
- ISSN :
- 10837159
- Volume :
- 29
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Oncologist
- Publication Type :
- Academic Journal
- Accession number :
- 175259573
- Full Text :
- https://doi.org/10.1093/oncolo/oyad293