Abstract 2630: Use of the “coexpression extrapolation” algorithm to identify tumor biomarkers that predict drug sensitivity to Vorinostat and Velcade in non-small cell lung cancer

Abstract

Abstract Background: Non-small cell lung cancer (NSCLC) is the leading cause of death from cancer in the United States. Research is currently focused on identifying novel molecularly-targeted therapies that can improve on the rather dismal results of platinum-based doublet therapies. Additionally, the inability to accurately predict the efficacy of these agents for each individual patient remains a problem with the use of molecularly-targeted therapies. The purpose of this study was to identify tumor biomarkers using the “coexpression extrapolation (COXEN)” algorithm that predict drug sensitivity to Vorinostat, a histone deacetylase inhibitor, and Velcade, a proteasome inhibitor, two molecularly-targeted agents currently in a Phase I clinical trial for NSCLC. Methods: Applying COXEN, biomarker prediction models were first discovered and trained for Vorinostat and Velcade based on in vitro drug activity profiles of the 9 NSCLC cell lines (NCI-9) in the National Cancer Institute's public database of 60 cancer cell lines. Independently, a panel of 40 NSCLC cell lines (UVA-40) was experimented with either Vorinostat or Velcade treatments in a dose-escalation schema to obtain 50% growth inhibition (GI50) values. Genome-wide expression profiles for both the NCI-9 and UVA-40 cell lines were available or obtained with Affymetrix HG-U133A GeneChips®. Prospective predictability of the two drugs' COXEN models was confirmed with 100 random splits of UVA-40 for robust biomarker selection and independent evaluation of COXEN prediction models. The final COXEN models of the two drugs were determined using all UVA-40 lines. Results: The COXEN biomarker models with 45 genes for Vorinostat and 15 genes for Velcade prospectively provided drug sensitivity prediction scores that were highly significantly associated with the drug activity GI-50 values of Vorinostat (p=0.006) and Velcade (p=0.002) on the UVA-40, respectively. Analysis of the robustness of the biomarker selection for each drug was also highly significant (p<0.005), underscoring a high fidelity of our biomarker selection and prediction modeling. Finally, review of selected biomarkers for both drugs revealed no significant overlap with only one shared biomarker (CFLAR) for both Vorinostat and Velcade, implying quite independent modes of mechanisms of the two drugs. Conclusions: Biomarker models generated through the in vitro-based COXEN algorithm were highly predictive of tumor sensitivity to Velcade and Vorinostat in NSCLC cell lines. COXEN genomic biomarker models may significantly help predict a priori the efficacy of novel targeted therapeutics Velcade and Vorinostat for NSCLC patients. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2630.