Abstract 2896: Genomic determinants and signature of MET-targeted therapy in glioblastoma.

Proceedings: AACR 104th Annual Meeting 2013; Apr 6-10, 2013; Washington, DC Glioblastoma (GBM) is one of the most devastating cancers, because the intrinsic capability of single tumor cells to invade normal brain impedes surgical eradication, predictably resulting in early local recurrence and death. The goal of this study is to develop therapeutic strategies for treating GBM patients by identifying and targeting the molecular mechanisms of GBM invasiveness through inhibiting MET signaling pathway. Overexpression of hepatocyte growth factor (HGF) and its receptor MET are associated with poor prognosis of GBM. Genomic analysis of primary GBM tumors indicates that MET is overexpressed in tumors of the mesenchymal phenotype, which is more invasive and results in short patient survival. More recently, MET activation was found to contribute to recurrence in GBM patients after bevacizumab treatment, further highlighting the utility of blocking the MET pathway in this disease. With MET inhibitors’ entering clinical trials, there is increased need to find the molecular determinants of MET sensitivity. Because GBM is a heterogeneous disease in which drug response in the individual patient can be regulated by different mechanisms, approaches for understanding and predicting treatment outcomes based on the molecular features of individual tumor will be of a high value. Based on our previous finding that HGF-autocrine activation predicts sensitivity to MET inhibition, we have developed a two-step methodology using microarray technology and bioinformatics analysis to identify and test genomic determinants in order to effectively deploy MET-targeted therapy in GBM patients. By analyzing the data sets from The Cancer Genome Atlas Network (TCGA) and those from GBM xenograft models sensitive and insensitive to MET inhibitors, a 25-gene signature highly associated with HGF-autocrine activation is identified. When performing validation analysis, the signature scored 40 human GBM xenograft models by HGF expression level, providing a good rationale for further testing its predictive value in identifying tumors sensitive to MET inhibitors. We conclude that HGF-autocrine activation can result in oncogene addiction to MET signaling in GBM. Via a unique panel of signature genes we may be able to identify a subset of patients vulnerable to anti-MET drugs. Citation Format: Qian Xie, Jennifer Johnson, Maria Libera Ascierto, Liang Kang, Robert Bradley, Sandeep Mittal, Kyle Furge, Michael Briggs, Kirk Tanner, Michael E. Berens, Francesco M. Marincola, George F. Vande Woude. Genomic determinants and signature of MET-targeted therapy in glioblastoma. [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 2896. doi:10.1158/1538-7445.AM2013-2896