4.6 Article

Novel HTS Strategy Identifies TRAIL-Sensitizing Compounds Acting Specifically Through the Caspase-8 Apoptotic Axis

Journal

PLOS ONE
Volume 5, Issue 10, Pages -

Publisher

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0013375

Keywords

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Funding

  1. National Institutes of Health

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Tumor Necrosis Factor-Related Apoptosis-Inducing Ligand (TRAIL) is potentially a very important therapeutic as it shows selectivity for inducing apoptosis in cancer cells whilst normal cells are refractory. TRAIL binding to its cognate receptors, Death Receptors-4 and -5, leads to recruitment of caspase-8 and classical activation of downstream effector caspases, leading to apoptosis. As with many drugs however, TRAIL's usefulness is limited by resistance, either innate or acquired. We describe here the development of a novel 384-well high-throughput screening (HTS) strategy for identifying potential TRAIL-sensitizing agents that act solely in a caspase-8 dependent manner. By utilizing a TRAIL resistant cell line lacking caspase-8 (NB7) compared to the same cells reconstituted with the wild-type protein, or with a catalytically inactive point mutant of caspase-8, we are able to identify compounds that act specifically through the caspase-8 axis, rather than through general toxicity. In addition, false positive hits can easily be weeded out in this assay due to their activity in cells lacking caspase-8-inducible activity. Screening of the library of pharmacologically active compounds (LOPAC) was performed as both proof-of-concept and to discover potential unknown TRAIL sensitizers whose mechanism is caspase-8 mediated. We identified known TRAIL sensitizers from the library and identified new compounds that appear to sensitize specifically through caspase-8. In sum, we demonstrate proof-of-concept and discovery of novel compounds with a screening strategy optimized for the detection of caspase-8 pathway-specific TRAIL sensitizers. This screen was performed in the 384-well format, but could easily be further miniaturized, allows easy identification of artifactual false positives, and is highly scalable to accommodate diverse libraries.

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