Abstract 3828: Identification of prognostic markers in osteosarcoma using a systems biology approach.
Authors: Flores Ricardo J. , Li Yiting , Yu Alexander , Lau Serrine , Vannucci Marina , Lau Ching C. , Man Tsz-Kwong 
Published: August 2014

Abstract
Introduction
Osteosarcoma (OS) is the most common malignant bone tumor in pediatrics. The survival rate of patients with metastatic disease remains very dismal. Nevertheless, metastasis is a complex process and a single-level analysis will not likely identify its key biological determinants. In this study, we used a systems biology approach to identify common metastatic pathways that are jointly supported by both mRNA and protein levels in two distinct human metastatic OS models.
Methods
The metastatic OS models used consist of a pair of isogenic OS cell lines (low metastatic parental cell line and highly metastatic subline), namely HOS/143B and SaOS-2/LM7. We obtained mRNA expression data by microarray analysis, and captured a specific subset of N-linked glycoproteins by lectin affinity chromatography. Subsequently, we identified the glycoproteins by mass spectrometry and quantified them by spectral counting. The genomic and glycoproteomic data were integrated by topological analysis, and validated by Western blotting and reverse phase protein arrays (RPPR).
Results
Pathway analysis of the up-regulated genes and glycoproteins separately revealed pathways associated with metastasis including RAS-related G-proteins signaling and epithelial-to-mesenchymal-transition. Nonetheless, no common significant pathway was found between the two metastatic models. To address this issue, we used a topological analysis based on a “shortest path” algorithm to identify topological nodes that remained hidden from the transcriptomic and glycoproteomic analyses. Analysis of the topological nodes identified important common pathways, including “Cytoskeleton remodeling/TGF/WNT," “Development/WNT signaling," and “Cell adhesion/Chemokines and adhesion”. Up-regulation of proteins within these pathways was validated using RPPR. To determine if we could utilize the candidates identified as circulating prognostic biomarkers, we used Luminex bead assays to quantify a set of cytokine/chemokine candidates in the plasma samples from a cohort of OS patients. The analysis showed that expression of candidate molecules such as: TGFA (HR 14.3, p-value &lt;0.05), EGF (HR 5.6, p-value &lt;0.05), and IL-10 (HR 5.0, p-value &lt;0.05) at initial diagnosis correlated with poor survival and were independent of metastasis at diagnosis. We will also use Multiple Reaction Monitoring to investigate the candidate biomarkers in other common pathways to correlate with tumor progression, metastases, and survival.
Conclusions
In this study, we used a systems biology approach by integrating genomic and proteomic data to identify key and common metastatic mechanisms in OS. Since WNT signaling and chemokines have been previously implicated in OS and other tumors, further characterization and validation in human samples of these common pathways may lead to novel prognostic biomarkers and therapeutic strategies for curing metastatic OS.
Citation Format: Ricardo J. Flores, Yiting Li, Alexander Yu, Serrine Lau, Marina Vannucci, Ching C. Lau, Tsz-Kwong Man. Identification of prognostic markers in osteosarcoma using a systems biology approach. [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 3828. doi:10.1158/1538-7445.AM2013-3828