Abstract 2179: Gene expression models for predicting doxorubicin response in canine osteosarcoma

Abstract

Abstract Purpose: The use of gene expression models for predicting individual response to cancer therapy is a promising area of research for improving outcome. Osteosarcoma is the most common primary bone cancer in dogs, with an average 1 year survival after treatment. The co-expression extrapolation (COXEN) method has been established as a gene expression method for accurately predicting drug sensitivity in human cancer. Our purpose is to explore the ability of COXEN for the intra- or interspecies extrapolation of cancer cell line data for prediction of chemosensitivity in canine osteosarcoma patients. Methods: Gene expression and drug sensitivity data to doxorubicin (DOX) for the human NCI60 cell line panel were publicly available. Corresponding data was obtained and generated for the canine ACC29 cell line panel and canine osteosarcoma tumors via cell viability assays, hospital record search, and microarray gene expression analysis. Differentially expressed genes (DEGs) were identified in reference sets by Significance Analysis of Microarrays or t-test analysis between sensitive and resistant samples to DOX. Probe matching between species was done based on highest target sequence homology or by collapsing probes to gene level by selecting probes with maximum variance between samples. Hierarchical clustering was done using the CIMminer program. DEGs are filtered for genes sharing strong co-expression between reference and co-expression datasets, followed by prediction model building using the Misclassification Penalized Posterior (MiPP) algorithm. Results: The NCI60 and ACC29 panels are similarly sensitive to DOX with log GI50 molar ranges of -8.3 to -5.7 and -8.2 to -5.9, respectively. Samples from ACC29 and COS16 clustered according to DOX sensitivity 67 and 100% correctly when DEGs from NCI60 were used. NCI60-trained models using DEGs co-expressed with the ACC29 were 83% accurate in predicting sensitivity to DOX in ACC29 (p = 0.019, binomial). NCI60-trained models using DEGs co-expressed with COS49 were 67% accurate in predicting response to DOX in COS49 (p = 0.049, binomial, p = 0.045, Log Rank test). Models with NCI60 DEGs but co-expressed and built on COS16 were 73% accurate in COS33 (p = 0.026, binomial, p = 0.001, Log Rank test). ACC29-trained models using DEGs co-expressed with COS49 were 73% accurate in COS49 (p = 0.008, binomial). Models with ACC29 or ACCosteo DEGs but co-expressed and built on COS16 were each 70% accurate in COS33 (p = 0.047, binomial, p = 0.071 and 0.071, Log Rank test). Conclusions: Both human and canine COXEN models were able to accurately predict response of canine osteosarcoma patients to DOX. The best performing model involved identifying DEGs from human cell lines, co-expressing and building the model with dog tumors, suggesting that genomic strategies in canine oncology could benefit from the wealth of available human genomic data, and could provide validation for personalized medicine in human cancer. Citation Format: Jared S. Fowles, Kristen C. Brown, Ann M. Hess, Dawn L. Duval, Daniel L. Gustafson. Gene expression models for predicting doxorubicin response in canine osteosarcoma. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2179. doi:10.1158/1538-7445.AM2015-2179