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Browsing by Author "Webster, Yue W."
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Item Aurora A–Selective Inhibitor LY3295668 Leads to Dominant Mitotic Arrest, Apoptosis in Cancer Cells, and Shows Potent Preclinical Antitumor Efficacy(AACR, 2019-12) Du, Jian; Yan, Lei; Torres, Raquel; Gong, Xueqian; Bian, Huimin; Marugán, Carlos; Boehnke, Karsten; Baquero, Carmen; Hui, Yu-Hua; Chapman, Sonya C.; Yang, Yanzhu; Zeng, Yi; Bogner, Sarah M.; Foreman, Robert T.; Capen, Andrew; Donoho, Gregory P.; Van Horn, Robert D.; Barnard, Darlene S.; Dempsey, Jack A.; Beckmann, Richard P.; Marshall, Mark S.; Chio, Li-Chun; Qian, Yuewei; Webster, Yue W.; Aggarwal, Amit; Chu, Shaoyou; Bhattachar, Shobha; Stancato, Louis F.; Dowless, Michele S.; Iversen, Phillip W.; Manro, Jason R.; Walgren, Jennie L.; Halstead, Bartley W.; Dieter, Matthew Z.; Martinez, Ricardo; Bhagwat, Shripad V.; Kreklau, Emiko L.; Lallena, Maria Jose; Ye, Xiang S.; Patel, Bharvin K. R.; Reinhard, Christoph; Plowman, Gregory D.; Barda, David A.; Henry, James R.; Buchanan, Sean G.; Campbell, Robert M.; Pediatrics, School of MedicineAlthough Aurora A, B, and C kinases share high sequence similarity, especially within the kinase domain, they function distinctly in cell-cycle progression. Aurora A depletion primarily leads to mitotic spindle formation defects and consequently prometaphase arrest, whereas Aurora B/C inactivation primarily induces polyploidy from cytokinesis failure. Aurora B/C inactivation phenotypes are also epistatic to those of Aurora A, such that the concomitant inactivation of Aurora A and B, or all Aurora isoforms by nonisoform–selective Aurora inhibitors, demonstrates the Aurora B/C-dominant cytokinesis failure and polyploidy phenotypes. Several Aurora inhibitors are in clinical trials for T/B-cell lymphoma, multiple myeloma, leukemia, lung, and breast cancers. Here, we describe an Aurora A–selective inhibitor, LY3295668, which potently inhibits Aurora autophosphorylation and its kinase activity in vitro and in vivo, persistently arrests cancer cells in mitosis, and induces more profound apoptosis than Aurora B or Aurora A/B dual inhibitors without Aurora B inhibition–associated cytokinesis failure and aneuploidy. LY3295668 inhibits the growth of a broad panel of cancer cell lines, including small-cell lung and breast cancer cells. It demonstrates significant efficacy in small-cell lung cancer xenograft and patient-derived tumor preclinical models as a single agent and in combination with standard-of-care agents. LY3295668, as a highly Aurora A–selective inhibitor, may represent a preferred approach to the current pan-Aurora inhibitors as a cancer therapeutic agent.Item A Novel Open Access Web Portal for Integrating Mechanistic and Toxicogenomic Study Results(Oxford University Press, 2019-08-01) Sutherland, Jeffrey J.; Stevens, James L.; Johnson, Kamin; Elango, Navin; Webster, Yue W.; Mills, Bradley J.; Robertson, Daniel H.; Biochemistry and Molecular Biology, School of MedicineApplying toxicogenomics to improving the safety profile of drug candidates and crop protection molecules is most useful when it identifies relevant biological and mechanistic information that highlights risks and informs risk mitigation strategies. Pathway-based approaches, such as gene set enrichment analysis, integrate toxicogenomic data with known biological process and pathways. Network methods help define unknown biological processes and offer data reduction advantages. Integrating the 2 approaches would improve interpretation of toxicogenomic information. Barriers to the routine application of these methods in genome-wide transcriptomic studies include a need for "hands-on" computer programming experience, the selection of 1 or more analysis methods (eg pathway analysis methods), the sensitivity of results to algorithm parameters, and challenges in linking differential gene expression to variation in safety outcomes. To facilitate adoption and reproducibility of gene expression analysis in safety studies, we have developed Collaborative Toxicogeomics, an open-access integrated web portal using the Django web framework. The software, developed with the Python programming language, is modular, extensible and implements "best-practice" methods in computational biology. New study results are compared with over 4000 rodent liver experiments from Drug Matrix and open TG-GATEs. A unique feature of the software is the ability to integrate clinical chemistry and histopathology-derived outcomes with results from gene expression studies, leading to relevant mechanistic conclusions. We describe its application by analyzing the effects of several toxicants on liver gene expression and exemplify application to predicting toxicity study outcomes upon chronic treatment from expression changes in acute-duration studies.