Jeremy Mason1,2,3, Yutao Gong1, Laleh Amiri-Kordestani1, Suparna Wedam1, Jennifer J Gao4, Tatiana M Prowell1, Harpreet Singh1, Anup Amatya5, Shenghui Tang5, Richard Pazdur1,4, Peter Kuhn2, Gideon M Blumenthal4, Julia A Beaver1,4. 1. Office of Oncologic Diseases, US Food and Drug Administration, Silver Spring, MD. 2. Convergent Science Institute in Cancer, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA. 3. USC Institute of Urology, Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA. 4. Oncology Center of Excellence, US Food and Drug Administration, Silver Spring, MD. 5. Office of Biostatistics (DB5), US Food and Drug Administration, Silver Spring, MD.
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.
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.
Authors: Jennifer J Gao; Joyce Cheng; Erik Bloomquist; Jacquelyn Sanchez; Suparna B Wedam; Harpreet Singh; Laleh Amiri-Kordestani; Amna Ibrahim; Rajeshwari Sridhara; Kirsten B Goldberg; Marc R Theoret; Paul G Kluetz; Gideon M Blumenthal; Richard Pazdur; Julia A Beaver; Tatiana M Prowell Journal: Lancet Oncol Date: 2019-12-16 Impact factor: 41.316
Authors: Richard S Finn; John P Crown; Istvan Lang; Katalin Boer; Igor M Bondarenko; Sergey O Kulyk; Johannes Ettl; Ravindranath Patel; Tamas Pinter; Marcus Schmidt; Yaroslav Shparyk; Anu R Thummala; Nataliya L Voytko; Camilla Fowst; Xin Huang; Sindy T Kim; Sophia Randolph; Dennis J Slamon Journal: Lancet Oncol Date: 2014-12-16 Impact factor: 41.316
Authors: Dennis J Slamon; Patrick Neven; Stephen Chia; Peter A Fasching; Michelino De Laurentiis; Seock-Ah Im; Katarina Petrakova; Giulia Val Bianchi; Francisco J Esteva; Miguel Martín; Arnd Nusch; Gabe S Sonke; Luis De la Cruz-Merino; J Thaddeus Beck; Xavier Pivot; Gena Vidam; Yingbo Wang; Karen Rodriguez Lorenc; Michelle Miller; Tetiana Taran; Guy Jerusalem Journal: J Clin Oncol Date: 2018-06-03 Impact factor: 44.544