Model Development of CDK4/6 Predicted Efficacy in Patients With Hormone Receptor-Positive, Human Epidermal Growth Factor Receptor 2-Negative Advanced or Metastatic Breast Cancer.

ABSTRACT

PURPOSE: Three cyclin-dependent kinase 4/6 inhibitors (CDKIs) are approved by the US Food and Drug Administration for the treatment of patients with hormone receptor-positive, human epidermal growth factor receptor 2-negative advanced or metastatic breast cancer in combination with hormonal therapy (HT). We hypothesized that on an individual basis, efficacy outcomes and adverse event (AE) development can be predicted using baseline patient and tumor characteristics. METHODS: Individual-level data from seven randomized controlled trials submitted to the US Food and Drug Administration for new or supplemental marketing applications of CDKIs were pooled. Progression-free survival (PFS), overall survival (OS), and AE prediction models were developed for specific treatment regimens (HT v HT plus CDKI). An individual's characteristics were used in all models simultaneously to create a group of predicted outcomes that are comparable across treatment settings. RESULTS: Accuracy of the PFS and OS prediction models for HT were 66% and 64%, respectively, with the strongest predictors being menopausal status and therapy line. The corresponding AE prediction models resulted in an average area under the curve of 0.613. Accuracy of the PFS and OS prediction models for HT plus CDKI were 62% and 63%, respectively, with the strongest predictors being histologic grade for both. The corresponding AE prediction models resulted in an average area under the curve of 0.639. CONCLUSION: This exploratory analysis demonstrated that models of efficacy outcomes and AE development can be developed using baseline patient and tumor characteristics. Comparison of paired models can inform treatment selection for individuals on the basis of the patient's personalized goals and concerns. Although use of CDKIs is standard of care in the first- or second-line setting, this model provides prognostic information that may inform individual treatment decisions.