CDKPredict: a clinicogenomic model for risk-adapted therapeutic intervention in breast cancer

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, P=1.5x10-17). The model retained robust prognostication across clinically relevant subgroups and biomarker-selected populations and outperformed the current escalation criterion of treatment-free interval ≤12 months. Notably, CDKPredict demonstrated strong clinical and biological plausibility, with high-risk tumors featuring genomic and transcriptomic programs mechanistically linked to CDK4/6i+ET resistance, and higher-risk strata progressively enriched for actionable alterations. Finally, we developed CDKPredict-S, a pragmatic and scalable model requiring a restricted number of features intended for resource-variable settings, which was validated across two independent external cohorts totaling 1,019 patients and made publicly available. Collectively, CDKPredict provides a foundation for the prospective evaluation of risk-adapted, biomarker-informed therapeutic strategies.