Abstract B35: High-throughput cell line profiling for identification of anticancer drug sensitivity and resistance biomarkers with OncoPredictor
Authors: C. O'Day, Y. Ovechkina, K. Marcoe, R. Keyser, K. Yoshino, P. Nguyen, J. Hnilo, R. Shively, J. Mulligan, K. Bernards, T. Lin, S. Wang, S. Sadis, D. R. Rhodes, W. Warrior
Journal: Clinical Cancer Research (2010)

Abstract Tumor derived cell lines have been in use for cancer drug profiling as evidenced by the establishment of NCI-60 panel as a drug discovery tool in early 1990s. In recent years, due to the advancement of targeted therapies in cancer, screening of larger cell panels with greater genetic heterogeneity has become very important not only to measure efficacy of the compounds but also for identifying the biomarkers that are responsible for the efficacy. Here we present data from OncoPredictorSM, a cellular screening and bioinformatics platform able to (1) evaluate multiple types of genomic biomarkers for association with in vitro drug response and (2) analyze identified biomarkers in clinical tumor populations, thereby suggesting potential drug development strategies. The cellular screening component, OncoPanel™, is comprised of a large panel of human tumor-derived cell lines from different origins with broad genetic heterogeneity providing a sensitive method of comparing proliferation or cytotoxicity (resistance or sensitivity) across genotypes. Our cell line panel consists of 240 cell lines that span a wide variety tumor tissue types including lung, breast, stomach, colon, ovary, liver, skin, kidney, bladder, prostate, pancreas, head and neck, brain, hematopoetic, and lymphoid tumors. We have mRNA expression, SNP and mutation data to characterize these cell lines. The media and culture conditions are standardized and optimized so that the genetic heterogeneity of the cell line will be responsible for the phenotypic responses obtained. We generate simultaneous data for each compound at 10 concentrations (in triplicates) resulting in precise IC50/EC50 values for analysis and comparison. Results from a case study will be presented to depict the very robust data quality including the doubling time for the cell lines. Also, sensitive and resistance data with 11 known anticancer agents including inhibitors of mTorr, ABL, MEK, PDGF, VEGF, FLT3, Aurora kinases, HSP90, EGFR, Topo II, and microtubulin disassembly will be presented using the robust high content data from these cell lines. As expected, the most sensitive cell lines against a clinical ABL inhibitor were the CML-derived cell lines. On the other hand, many of the colon, melanoma and pancreatic cell lines were sensitive to MEK inhibitor. Sensitive and resistant cells were further profiled against, mutation, expression, and SNP data to identify genes involved in the sensitive/resistant phenotypes using sophisticated bioinformatic analysis tools to identify genomic biomarker profiles and to estimate their frequency in clinical populations (data presented separately). OncoPredictor is ideally suited for prioritization of the leads, positioning of the leads against cancer types, repositioning of clinical candidates or drugs for supplemental indication, combination therapies, and for biomarker identification and characterization in clinical populations. Citation Information: Clin Cancer Res 2010;16(14 Suppl):B35.