Induction of Premature Senescence by Hsp90 Inhibition in Small Cell Lung Cancer

BACKGROUND: The molecular chaperone Hsp90 is a promising new target in cancer therapy and selective Hsp90 inhibitors are currently in clinical trials. Previously these inhibitors have been reported to induce either cell cycle arrest or cell death in cancer cells. Whether the cell cycle arrest is reversible or irreversible has not generally been assessed. Here we have examined in detail the cell cycle arrest and cell death responses of human small cell lung cancer cell lines to Hsp90 inhibition. METHODOLOGY/PRINCIPAL FINDINGS: In MTT assays, small cell lung cancer cells showed a biphasic response to the Hsp90 inhibitors geldanamycin and radicicol, with low concentrations causing proliferation arrest and high concentrations causing cell death. Assessment of Hsp90 intracellular activity using loss of client protein expression showed that geldanamycin concentrations that inhibited Hsp90 correlated closely with those causing proliferation arrest but not cell death. The proliferation arrest induced by low concentrations of geldanamycin was not reversed for a period of over thirty days following drug removal and showed features of senescence. Rare populations of variant small cell lung cancer cells could be isolated that had additional genetic alterations and no longer underwent irreversible proliferation arrest in response to Hsp90 inhibitors. CONCLUSIONS/SIGNIFICANCE: We conclude that: (1) Hsp90 inhibition primarily induces premature senescence, rather than cell death, in small cell lung cancer cells; (2) small cell lung cancer cells can bypass this senescence through further genetic alterations; (3) Hsp90 inhibitor-induced cell death in small cell lung cancer cells is due to inhibition of a target other than cytosolic Hsp90. These results have implications with regard to how these inhibitors will behave in clinical trials and for the design of future inhibitors in this class

High-Content, High-Throughput Analysis of Cell Cycle Perturbations Induced by the HSP90 Inhibitor XL888

BACKGROUND: Many proteins that are dysregulated or mutated in cancer cells rely on the molecular chaperone HSP90 for their proper folding and activity, which has led to considerable interest in HSP90 as a cancer drug target. The diverse array of HSP90 client proteins encompasses oncogenic drivers, cell cycle components, and a variety of regulatory factors, so inhibition of HSP90 perturbs multiple cellular processes, including mitogenic signaling and cell cycle control. Although many reports have investigated HSP90 inhibition in the context of the cell cycle, no large-scale studies have examined potential correlations between cell genotype and the cell cycle phenotypes of HSP90 inhibition. METHODOLOGY/PRINCIPAL FINDINGS: To address this question, we developed a novel high-content, high-throughput cell cycle assay and profiled the effects of two distinct small molecule HSP90 inhibitors (XL888 and 17-AAG [17-allylamino-17-demethoxygeldanamycin]) in a large, genetically diverse panel of cancer cell lines. The cell cycle phenotypes of both inhibitors were strikingly similar and fell into three classes: accumulation in M-phase, G2-phase, or G1-phase. Accumulation in M-phase was the most prominent phenotype and notably, was also correlated with TP53 mutant status. We additionally observed unexpected complexity in the response of the cell cycle-associated client PLK1 to HSP90 inhibition, and we suggest that inhibitor-induced PLK1 depletion may contribute to the striking metaphase arrest phenotype seen in many of the M-arrested cell lines. CONCLUSIONS/SIGNIFICANCE: Our analysis of the cell cycle phenotypes induced by HSP90 inhibition in 25 cancer cell lines revealed that the phenotypic response was highly dependent on cellular genotype as well as on the concentration of HSP90 inhibitor and the time of treatment. M-phase arrest correlated with the presence of TP53 mutations, while G2 or G1 arrest was more commonly seen in cells bearing wt TP53. We draw upon previous literature to suggest an integrated model that accounts for these varying observations