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Open Access Highly Accessed Research article

Expression profiling in canine osteosarcoma: identification of biomarkers and pathways associated with outcome

Liza E O'Donoghue1, Andrey A Ptitsyn2, Debra A Kamstock1, Janet Siebert3, Russell S Thomas4 and Dawn L Duval1*

Author Affiliations

1 Department of Clinical Sciences, Colorado State University, Fort Collins, Colorado, USA

2 Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado, USA

3 CytoAnalytics, Analytical Services, Denver, Colorado, USA

4 The Hamner Institutes for Health Sciences, Research Triangle Park, North Carolina, USA

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BMC Cancer 2010, 10:506  doi:10.1186/1471-2407-10-506

Published: 22 September 2010

Abstract

Background

Osteosarcoma (OSA) spontaneously arises in the appendicular skeleton of large breed dogs and shares many physiological and molecular biological characteristics with human OSA. The standard treatment for OSA in both species is amputation or limb-sparing surgery, followed by chemotherapy. Unfortunately, OSA is an aggressive cancer with a high metastatic rate. Characterization of OSA with regard to its metastatic potential and chemotherapeutic resistance will improve both prognostic capabilities and treatment modalities.

Methods

We analyzed archived primary OSA tissue from dogs treated with limb amputation followed by doxorubicin or platinum-based drug chemotherapy. Samples were selected from two groups: dogs with disease free intervals (DFI) of less than 100 days (n = 8) and greater than 300 days (n = 7). Gene expression was assessed with Affymetrix Canine 2.0 microarrays and analyzed with a two-tailed t-test. A subset of genes was confirmed using qRT-PCR and used in classification analysis to predict prognosis. Systems-based gene ontology analysis was conducted on genes selected using a standard J5 metric. The genes identified using this approach were converted to their human homologues and assigned to functional pathways using the GeneGo MetaCore platform.

Results

Potential biomarkers were identified using gene expression microarray analysis and 11 differentially expressed (p < 0.05) genes were validated with qRT-PCR (n = 10/group). Statistical classification models using the qRT-PCR profiles predicted patient outcomes with 100% accuracy in the training set and up to 90% accuracy upon stratified cross validation. Pathway analysis revealed alterations in pathways associated with oxidative phosphorylation, hedgehog and parathyroid hormone signaling, cAMP/Protein Kinase A (PKA) signaling, immune responses, cytoskeletal remodeling and focal adhesion.

Conclusions

This profiling study has identified potential new biomarkers to predict patient outcome in OSA and new pathways that may be targeted for therapeutic intervention.