A Pharmacogenetic Prediction Model of Progression‐Free Survival in Breast Cancer using Genome‐Wide Genotyping Data from CALGB 40502 (Alliance). Issue 3 (1st November 2018)

• Record Type: Journal Article
• Authors: Rashkin, Sara R.; Chua, Katherina C.; Ho, Carol; Mulkey, Flora; Jiang, Chen; Mushiroda, Tasei; Kubo, Michiaki; Friedman, Paula N.; Rugo, Hope S.; McLeod, Howard L.; Ratain, Mark J.; Castillos, Francisco; Naughton, Michael; Overmoyer, Beth; Toppmeyer, Deborah; Witte, John S.; Owzar, Kouros; Kroetz, Deanna L.
• Is Part Of: Clinical pharmacology & therapeutics. Volume 105:Issue 3(2019)
• Page Start: 738
• Page End: 745
• Publication Date: 2018-11-01
• Subjects: Pharmacology -- Periodicals; Therapeutics -- Periodicals; 615.5
• DOI: 10.1002/cpt.1241
• Languages: English
• ISSNs: 0009-9236

Abstract

Abstract : Genome‐wide genotyping data are increasingly available for pharmacogenetic association studies, but application of these data for development of prediction models is limited. Prediction methods, such as elastic net regularization, have recently been applied to genetic studies but only limitedly to pharmacogenetic outcomes. An elastic net was applied to a pharmacogenetic study of progression‐free survival (PFS) of 468 patients with advanced breast cancer in a clinical trial of paclitaxel, nab‐paclitaxel, and ixabepilone. A final model included 13 single nucleotide polymorphisms (SNPs) in addition to clinical covariates (prior taxane status, hormone receptor status, disease‐free interval, and presence of visceral metastases) with an area under the curve (AUC) integrated over time of 0.81, an increase compared to an AUC of 0.64 for a model with clinical covariates alone. This model may be of value in predicting PFS with microtubule targeting agents and may inform reverse translational studies to understand differential response to these drugs.