Abstract 2711: Graphical method to predict drug response using genomic biomarkers with nonlinear interaction effect
Authors: Tanaka Noriko , Jeong Jong-Hyeon , Gavin Patrick G. , Pogue-Geile Katherine L. , Paik Soonmyung , Costantino Joseph P. 
Published: August 2012

Abstract
Background Building predictive models using genomic markers has become a frequent objective of health researchers. Most markers have been selected based on linear dose-response assumption, ie, patients with higher/lower value of certain markers receive the benefit from a drug and patients with lower/higher do not. However, we now see many markers indicating non-linear treatment-marker interaction effects. Bonetti et al (Stat Med, 2000) developed the subpopulation treatment effect pattern plot (STEPP) method which can address such nonlinear interaction effects between treatment and continuous covariates. Here we explore an extension of the STEPP methodology to build a prediction model for treatment benefit when there is more than one genetic/genomic pathway that is related to treatment response. To illustrate our method, we used gene expression data from the NSABP B-31 study. Trastuzumab is a targeted antibody therapy for HER2 protein overexpressed in a subset of breast cancer. Recent studies have indicated that response to trastuzumab may depend not only on HER2 expression but also on other factors including estrogen receptor (ER). The current version of STEPP does not allow examination of multiple markers on treatment response. Methods Patients were divided into 2 groups: validation set and discovery set. Genes related to one of the 2 pathways described above were selected based on the correlation coefficient between HER2 and ER gene expression values. Principal component scores were calculated from the expression values of the selected genes. The STEPP method was extended to Three-Dimensional STEPP (TDSTEPP) to determine if the treatment effect was affected by first and second principal components. Treatment effect was assessed by the hazard ratio (HR) in subpopulations. Patients were then categorized into 3 groups (no benefit, moderate benefit, substantial benefit) based on the estimated and smoothed HR from the TDSTEPP. All the above processes were undertaken using the discovery set first and were validated using the validation set. Results The TDSTEPP showed that treatment effect could be modified by 2 significant pathways in a nonlinear fashion. Then we applied Support Vector Machines to categorize patients into 1 of the 3 treatment benefit groups and validated the categorizations. Conclusion The visualization procedure presented here is useful to assess situations where several dependent genomic pathways could be associated with drug effect. The validated results for prediction of trastuzmub effect in NSABP B-31 suggest that our method also has the potential utility of providing accurate models to predict drug responses. Supported by U10-CA-12027, -37377, -69651, -69974, and Genentech, Inc. This project is funded in part under a grant with the Pennsylvania Department of Health. The department specifically disclaims responsibility for any analysis, interpretations, or conclusions.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 2711. doi:1538-7445.AM2012-2711