ArticleResearch square2026
CDKPredict: a clinicogenomic model for risk-adapted therapeutic intervention in breast cancer.
Article in Research square, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
36 authors.
Funding
Abstract
Wide variation in outcomes is observed for patients with hormone-receptor-positive metastatic breast cancer (MBC) receiving first-line CDK4/6 inhibitors plus endocrine therapy (CDK4/6i+ET). Although front-line escalation strategies are beginning to enter clinical practice, assigning patients to intensified or de-intensified approaches remains challenging due to several mechanisms of resistance spanning tumor-intrinsic and host factors. To address this, we developed CDKPredict, an interpretable machine-learning model integrating baseline clinicopathologic and genomic features to predict outcomes from first-line CDK4/6i+ET. The model was trained on a densely annotated cohort of 1,264 patients and validated in a large multi-institutional external cohort (n=564, time-dependent AUC=0.74), stratifying patients into low-, intermediate-, and high-risk groups (median progression-free survival: 29.8, 14.7, and 8.0 months, respectively; hazard ratio [high vs low]=3.85,
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.