Implementation of the NCI-60 Human Tumor Cell Line Panel to Screen 2260 Cancer Drug Combinations to Generate >3 Million Data Points Used to Populate a Large Matrix of Anti-Neoplastic Agent Combinations (ALMANAC) Database

Animal and clinical studies demonstrate that cancer drug combinations (DCs) are more effective than single agents. However, it is difficult to predict which DCs will be more efficacious than individual drugs. Systematic DC high-throughput screening (HTS) of 100 approved drugs in the National Cancer Institute’s panel of 60 cancer cell lines (NCI-60) produced data to help select DCs for further consideration. We miniaturized growth inhibition assays into 384-well format, increased the fetal bovine serum amount to 10%, lengthened compound exposure to 72 h, and used a homogeneous detection reagent. We determined the growth inhibition 50% values of individual drugs across 60 cell lines, selected drug concentrations for 4 × 4 DC matrices (DCMs), created DCM master and replica daughter plate sets, implemented the HTS, quality control reviewed the data, and analyzed the results. A total of 2620 DCMs were screened in 60 cancer cell lines to generate 3.04 million data points for the NCI ALMANAC (A Large Matrix of Anti-Neoplastic Agent Combinations) database. We confirmed in vitro a synergistic drug interaction flagged in the DC HTS between the vinca-alkaloid microtubule assembly inhibitor vinorelbine (Navelbine) tartrate and the epidermal growth factor-receptor tyrosine kinase inhibitor gefitinib (Iressa) in the SK-MEL-5 melanoma cell line. Seventy-five percent of the DCs examined in the screen are not currently in the clinical trials database. Selected synergistic drug interactions flagged in the DC HTS described herein were subsequently confirmed by the NCI in vitro, evaluated mechanistically, and were shown to have greater than single-agent efficacy in mouse xenograft human cancer models. Enrollment is open for two clinical trials for DCs that were identified in the DC HTS. The NCI ALMANAC database therefore constitutes a valuable resource for selecting promising DCs for confirmation, mechanistic studies, and clinical translation.