Intra- and interspecies gene expression models for predicting drug response in canine osteosarcoma

BMC Bioinformatics

Research Article

Kristen C. Brown¹  Jared S. Fowles²  Dawn L. Duval²  Daniel L. Gustafson³  Ann M. Hess⁴ 

[1] Cell and Molecular Biology Program, Department of Biology, Colorado State University, Fort Collins, CO, USA;Cell and Molecular Biology Program, Department of Clinical Sciences, Colorado State University, Fort Collins, CO, USA;Flint Animal Cancer Center, Veterinary Medical Center, Colorado State University, Fort Collins, CO, USA;Cell and Molecular Biology Program, Department of Clinical Sciences, Colorado State University, Fort Collins, CO, USA;Flint Animal Cancer Center, Veterinary Medical Center, Colorado State University, Fort Collins, CO, USA;Shipley University Chair in Comparative Oncology, Flint Animal Cancer Center, Room 246, Colorado State University VMC, 300 West Drake Road, 80523-1620, Fort Collins, CO, USA;Department of Statistics, Colorado State University, Fort Collins, CO, USA;

关键词: Osteosarcoma;    Canine;    Human;    COXEN;    Gene expression modeling;   

DOI  :  10.1186/s12859-016-0942-8

 received in 2015-08-03, accepted in 2016-02-10,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundGenomics-based predictors of drug response have the potential to improve outcomes associated with cancer therapy. Osteosarcoma (OS), the most common primary bone cancer in dogs, is commonly treated with adjuvant doxorubicin or carboplatin following amputation of the affected limb. We evaluated the use of gene-expression based models built in an intra- or interspecies manner to predict chemosensitivity and treatment outcome in canine OS. Models were built and evaluated using microarray gene expression and drug sensitivity data from human and canine cancer cell lines, and canine OS tumor datasets. The “COXEN” method was utilized to filter gene signatures between human and dog datasets based on strong co-expression patterns. Models were built using linear discriminant analysis via the misclassification penalized posterior algorithm.ResultsThe best doxorubicin model involved genes identified in human lines that were co-expressed and trained on canine OS tumor data, which accurately predicted clinical outcome in 73 % of dogs (p = 0.0262, binomial). The best carboplatin model utilized canine lines for gene identification and model training, with canine OS tumor data for co-expression. Dogs whose treatment matched our predictions had significantly better clinical outcomes than those that didn’t (p = 0.0006, Log Rank), and this predictor significantly associated with longer disease free intervals in a Cox multivariate analysis (hazard ratio = 0.3102, p = 0.0124).ConclusionsOur data show that intra- and interspecies gene expression models can successfully predict response in canine OS, which may improve outcome in dogs and serve as pre-clinical validation for similar methods in human cancer research.

【 授权许可】

CC BY   
© Fowles et al. 2016