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    Please cite this article in press as: Huang et al., A Systems Pharmacology Approach Uncovers Wogonoside as an Angiogenesis Inhibitor of Triple-Negative Breast Cancer by Targeting Hedgehog Signaling, Cell Chemical Biology (2019),
    significantly higher intratumoral VEGF levels and shorter recur-rence-free survival (Linderholm et al., 2009). So far, 11 anti-VEGF drugs were approved for multiple cancer types, either alone or in combination with various cytotoxic chemotherapies or targeted therapies (Zirlik and Duyster, 2018). For example, anti-VEGF monoclonal antibody (mAb) bevacizumab has improved the response rate in several clinical trials (Earl et al., 2015; von Minckwitz et al., 2012; von Minckwitz et al., 2014a). However, antibody-based antiangiogenic therapies have a limited effect on overall survival of patients with cancer, and inhibition of the VEGF signaling pathway is not effective in most cancer types. Thus, development of antiangiogenic thera-pies, especially small molecules with low toxicity, is a pressing need for highly targeted therapies in TNBC. Although identifica-tion of agents (e.g., angiogenesis inhibitors) with ideal pharma-cokinetics/pharmacodynamics is important, traditional experi-mental approaches are costly and often show lack efficacy in vivo.
    The Hedgehog signaling pathway plays a crucial role in cell proliferation and differentiation by regulating vascular formation in early embryonic development (Pak and Segal, 2016). The binding of Hedgehog ligands to Patched, a 12-pass transmem-brane protein inhibiting membrane translocation of Smoothened (SMO), results in the activation of glioma-associated oncogene homolog proteins (Gli), a key protein regulating cellular cycle and apoptosis (Hui and Angers, 2011). Recently, regulation of the mesenchymal marker expression and epithelial-mesen-chymal transition of TNBC Doxorubicin showed that the expression of SMO and Gli1 is significantly elevated in TNBC (Tao et al., 2011). In addition, increased blood vessel density in breast can-cer is observed upon activation of the Hedgehog signaling pathway (Harris et al., 2012), and Gli enhances the vascular endothelial growth factor A (VEGFA) gene promoter resulting in the upregulation of VEGFA in breast cancer cells (Cao et al., 2012). Thus, Hedgehog signaling inhibitors present opportunities for potential therapeutic strategies in TNBC.
    Recent advances in systems biology and omics technologies have enabled the development of in silico network-based (Cheng et al., 2012b, 2018, 2019a), genomics-based (Jahchan et al., 2013; Jin et al., 2012; Lee et al., 2012), and systems pharma-cology-based approaches to drug discovery (Fang et al., 2017a). In this study, we applied a systems pharmacology-based methodology, in order to identify consistent, therapeutic agents to treat TNBC. Using this integrated computational and experimental approach, we predicted wogonoside, a bioactive flavonoid extracted from the root of Scutellaria baicalensis Georgi, as an effective angiogenesis inhibitor in TNBC. We experimentally validated the prediction, and, critically, then via a network-based approach predicted and tested its mechanism in TNBC, showing that wogonoside blocks Hedgehog signaling in vitro and in vivo.
    Systems Pharmacology-Based Prediction of Anti-TNBC Agents from Natural Products
    To identify potential TNBC therapeutic agents, we employed a systems pharmacology approach that quantifies the therapeu-tic potential of natural products for TNBC by integrating the
    known drug-target interactions, as well as disease genomic and genetic profiles. We hypothesized that a natural promis-cuous (‘‘multi-target’’) drug is a candidate to treat TNBC if its targets are more likely to be functional gene products (proteins) of TNBC (Fang et al., 2017c; Jiang et al., 2018). The null hypothesis asserts that natural products randomly target TNBC functional proteins across the human proteome. A permutation testing was performed to calculate the statis-tical significance of a natural product to be prioritized in treat-ing TNBC. Then, the nominal p values from the permutation tests were corrected as adjusted p values (q) using the Benja-mini-Hochberg approach (Benjamini and Hochberg, 1995). Subsequently, a Z score (z) was calculated for each natural product to be prioritized in treating TNBC during permutation testing: