Nagji AS, Cho SH, Liu Y, Lee JK, Jones DR. Multigene expression-based predictors for sensitivity to Vorinostat and Velcade in non-small cell lung cancer.
Mol Cancer Ther 2010;
9:2834-43. [PMID:
20713531 PMCID:
PMC2953585 DOI:
10.1158/1535-7163.mct-10-0327]
[Citation(s) in RCA: 18] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The ability to predict the efficacy of molecularly targeted therapies for non-small cell lung cancer (NSCLC) for an individual patient remains problematic. The purpose of this study was to identify, using a refined "coexpression extrapolation (COXEN)" algorithm with a continuous spectrum of drug activity, tumor biomarkers that predict drug sensitivity and therapeutic efficacy in NSCLC to Vorinostat, a histone deacetylase inhibitor, and Velcade, a proteasome inhibitor. Using our refined COXEN algorithm, biomarker prediction models were discovered and trained for Vorinostat and Velcade based on the in vitro drug activity profiles of nine NSCLC cell lines (NCI-9). Independently, a panel of 40 NSCLC cell lines (UVA-40) were treated with Vorinostat or Velcade to obtain 50% growth inhibition values. Genome-wide expression profiles for both the NCI-9 and UVA-40 cell lines were determined using the Affymetrix HG-U133A platform. Modeling generated multigene expression signatures for Vorinostat (45-gene; P = 0.002) and Velcade (15-gene; P = 0.0002), with one overlapping gene (CFLAR). Examination of Vorinostat gene ontogeny revealed a predilection for cellular replication and death, whereas that of Velcade suggested involvement in cellular development and carcinogenesis. Multivariate regression modeling of the refined COXEN scores significantly predicted the activity of combination therapy in NSCLC cells (P = 0.007). Through the refinement of the COXEN algorithm, we provide an in silico method to generate biomarkers that predict tumor sensitivity to molecularly targeted therapies. Use of this refined COXEN method has significant implications for the a priori examination of targeted therapies to more effectively streamline subsequent clinical trial design and cost.
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