Abstract A30: Interspecies applications of gene expression-based prediction of chemosensitivity

Introduction: Treatment of cancer based on molecular characteristics of individual tumors is a promising area of research that can lead to improved outcomes. Validation of these new genomic methods is crucial for their success in future clinical trials. The co-expression extrapolation (COXEN) method has been shown to extrapolate data from a reference dataset to accurately predict drug sensitivity in vitro and in vivo in human cancer. Canine cancer models have great translational potential because of their biologic and genetic similarities with humans. Implementing genomic strategies across species allows dogs to take advantage of the wealth of available human data, and humans to take advantage of data from veterinary clinical trials. Our purpose is to explore the ability of COXEN for interspecies extrapolation of human cancer cell line data to generate gene expression models for the prediction of chemosensitivity in canine cancer cell lines and tumors. Methods: Publicly available gene expression and drug sensitivity data for the human NCI60 panel was obtained for doxorubicin (DOX), vinblastine (VBL), carboplatin (CAR), paclitaxel (PTX), lomustine (LOM), and cisplatin (CIS). Gene expression data was publicly obtained or generated via microarray gene expression analysis after RNA extraction of archived tumor samples for canine osteosarcoma (COS49) or from 16 canine cancer cell lines (ACC16). Alamar blue assays were performed to calculate drug sensitivity in the ACC16. Gene signatures predictive of drug sensitivity were identified by comparing the 12 least and most sensitive cell lines from the NCI60 via Significance Analysis of Microarrays (SAM). Human probesets were matched with canine orthologs according to highest sequence homology. A subset of genes from the signatures that shared strong co-expression with a second dataset (ACC16 or COS49) was identified through correlation matrices followed by percentile cutoffs from a permutation-based null distribution. The co-expressed genes were used in the Missclassification Penalized Posterior (MiPP) algorithm to generate models using the NCI60 panel with or without the second dataset for external cross-validation. Drug sensitivity in cell lines or disease free interval (DFI) in tumor patients was predicted from final models. Results: NCI60 models with co-expressed genes with the ACC16 were on average 66% accurate in predicting sensitivity to 6 drugs when built on the NCI60 panel alone (significant by binomial test for LOM (P = 0.0327)) and 82% accurate when using the ACC16 for external cross-validation (significant by binomial test for DOX, CAR, and LOM (P = 0.0461, 0.0327, 0.0005), and by spearman rank correlation for VBL, PTX, and LOM (P = 0.0202, 0.0105, 0.0306)). NCI60 models with co-expressed genes with the COS49 were 70% and 50% accurate for DOX and CAR when built on NCI60 panel and 75% and 66% accurate when using the COS49 for external cross-validation (significant by binomial test for DOX (P = 0.0414)). Conclusions: The results show the potential of the COXEN method in translating known human in vitro chemosensitivity data to both canine cancer cells and tumors. NCI60 models that used canine datasets for external cross-validation were more accurate in most cases, suggesting the need for a canine component during the model building process. Future expansion of the canine cancer panels may lead to improvement with accuracy and robustness in predicting chemosensitivity in dogs. Likewise, incorporating canine models with these genomic strategies in the veterinary clinical trial setting could provide needed validation for personalized medicine in human cancer. Citation Format: Jared S. Fowles, Ann M. Hess, Dawn L. Duval, Douglas H. Thamm, Daniel L. Gustafson. Interspecies applications of gene expression-based prediction of chemosensitivity. [abstract]. In: Proceedings of the AACR Special Conference: The Translational Impact of Model Organisms in Cancer; Nov 5-8, 2013; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2014;12(11 Suppl):Abstract nr A30.