Abstract 2890: NCI60-based gene expression models for predicting chemosensitivity in canine cancer.

Abstract

Abstract Background: Personalized medicine employing treatment strategies based on molecular characteristics of patient tumors is a promising area of research. Strong experimental validation of such methods is essential before initiating clinical trials involving patients receiving personalized treatment versus standard of care. The recently developed co-expression extrapolation (COXEN) method has proven successful in predicting drug sensitivity in vitro and in vivo in human cancer. Biologic and genetic similarities make canine cancer models highly attractive for human translational research. Our purpose in this study is to explore the COXEN method's utility in a cross-species extrapolation of gene expression models (GEMs) to predict chemosensitivity in canine cancer cells from a human reference set. Materials and Methods: Microarray gene expression and drug sensitivity data for doxorubicin (DOX), vinblastine (VBL), carboplatin (CAR), paclitaxel (PTX), lomustine (CCNU), and cisplatin (CIS) was publicly available for the human NCI60 panel. Gene expression data from 15 canine osteosarcoma (COS) tumors from dogs treated with DOX, CARBO, or CIS was obtained. Microarray gene expression analysis was performed for 16 canine cancer cell lines (ACC16). Sensitivity of the ACC16 to these chemotherapeutics was calculated via Alamar Blue assays. Significance Analysis of Microarrays (SAM) with a false discovery rate of 0.1 was performed on the 12 most sensitive and resistant cell lines from the NCI60 to identify gene expression signatures predicting sensitivity for each drug. Corresponding expression data from the ACC16 was compared to the NCI60 signatures via correlation matrices to identify co-expressed genes for prediction model development using the Misclassification Penalized Posterior (MiPP) algorithm. Results: GI50 ranges for all 6 drugs in the NCI60 panel were comparable to the ranges calculated for the ACC16. COXEN analysis generated GEMs for each drug that on average were 79% accurate in predicting sensitivity in the ACC16. GEMs for VBL, PTX, and CCNU resulted in COXEN prediction scores that significantly correlated with actual GI50 data (R=0.70, 0.78, 0.70 and p=0.02, 0.01, 0.03, Spearman). COXEN GEMs based on co-expressed genes between the NCI60 and COS tumors were 73% accurate in predicting disease free interval (DFI), as opposed to GEMs based on the NCI60 and ACC16, which were 47% accurate in DFI prediction in COS patients. Conclusions: Initial results are encouraging despite the limited sample set for the use of COXEN in cross-species prediction models and for future use in veterinary clinical trials. Future and ongoing studies will expand the ACC16 panel and generate microarray data for 50 additional COS tumor samples. Incorporating dog models in these genomic strategies may provide powerful validation that is needed for the continuing advancement of personalized medicine in cancer. Citation Format: Jared S. Fowles, Ann M. Hess, Dawn L. Duval, Daniel L. Gustafson. NCI60-based gene expression models for predicting chemosensitivity in canine cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2890. doi:10.1158/1538-7445.AM2013-2890