{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"Research Square"}],"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T15:36:32Z","timestamp":1793288192297,"version":"4.5.0"},"posted":{"date-parts":[[2026,9,29]]},"group-title":"In Review","reference-count":0,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,6,28]]},"abstract":"<title>Abstract<\/title>\n                <p>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 \u226412 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.<\/p>","DOI":"10.21203\/rs.3.rs-10176871\/v1","type":"posted-content","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T02:58:18Z","timestamp":1790650698000},"source":"Crossref","is-referenced-by-count":0,"title":["CDKPredict: a clinicogenomic model for risk-adapted therapeutic intervention in breast cancer"],"prefix":"10.21203","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1026-3923","authenticated-orcid":false,"given":"Luca","family":"Boscolo-Bielo","sequence":"first","affiliation":[{"name":"Memorial Sloan Kettering Cancer Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2990-4893","authenticated-orcid":false,"given":"Francisco","family":"Sanchez-Vega","sequence":"additional","affiliation":[{"name":"Memorial Sloan Kettering Cancer 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