@article{f5ca7dab184d42038178bb44c915edf8,
title = "Systematic identification of druggable epithelial-stromal crosstalk signaling networks in ovarian cancer",
abstract = "Background: Bulk tumor tissue samples are used for generating gene expression profiles in most research studies, making it difficult to decipher the stroma-cancer crosstalk networks. In the present study, we describe the use of microdissected transcriptome profiles for the identification of cancer-stroma crosstalk networks with prognostic value, which presents a unique opportunity for developing new treatment strategies for ovarian cancer. Methods: Transcriptome profiles from microdissected ovarian cancer-associated fibroblasts (CAFs) and ovarian cancer cells from patients with high-grade serous ovarian cancer (n = 70) were used as input data for the computational systems biology program CCCExplorer to uncover crosstalk networks between various cell types within the tumor microenvironment. The crosstalk analysis results were subsequently used for discovery of new indications for old drugs in ovarian cancer by computational ranking of candidate agents. Survival analysis was performed on ovarian tumor-bearing Dicer/Pten double-knockout mice treated with calcitriol, a US Food and Drug Administration-approved agent that suppresses the Smad signaling cascade, or vehicle control (9-11 mice per group). All statistical tests were two-sided. Results: Activation of TGF-β-dependent and TGF-β-independent Smad signaling was identified in a particular subtype of CAFs and was associated with poor patient survival (patients with higher levels of Smad-regulated gene expression by CAFs: median overall survival = 15 months, 95% confidence interval [CI] = 12.7 to 17.3 months; vs patients with lower levels of Smad-regulated gene expression: median overall survival = 26 months, 95% CI = 15.9 to 36.1 months, P = .02). In addition, the activated Smad signaling identified in CAFs was found to be targeted by repositioning calcitriol. Calcitriol suppressed Smad signaling in CAFs, inhibited tumor progression in mice, and prolonged the median survival duration of ovarian cancer-bearing mice from 36 to 48 weeks (P = .04). Conclusions: Our findings suggest the feasibility of using novel multicellular systems biology modeling to identify and repurpose known drugs targeting cancer-stroma crosstalk networks, potentially leading to faster and more effective cures for cancers.",
author = "Yeung, {Tsz Lun} and Jianting Sheng and Leung, {Cecilia S.} and Fuhai Li and Jaeyeon Kim and Ho, {Samuel Y.} and Matzuk, {Martin M.} and Lu, {Karen H.} and Wong, {Stephen T.C.} and Mok, {Samuel C.}",
note = "Funding Information: Health and Human Services; by W81XWH-17-1-0126, W81XWH-17-1-0146, and W81XWH-16-1-0038 from the Ovarian Cancer Research Program, Department of Defense; by the Gilder Foundation; by grants RP100094 and RP110532 from the Cancer Prevention and Research Institute of Texas (CPRIT); by CPRIT Core Facility Support Award RP160805; and by funding support from Mr. Carl L. Norton, the Anne and John J. Sie Foundation, the Mary K. Chapman Foundation, the Ovarian Cancer Research Fund, the Ting Tsung and Wei Fong Chao Center for Bioinformatics Research and Imaging for Neurosciences (BRAIN), Cancer Fighters of Houston, and the John S. Dunn Research Foundation. Funding Information: This study was supported in part by grants R01CA133057, R01CA142832, RC4CA156551, U01188388, U54CA151668, U54CA149196, and UH2 TR000943; by The University of Texas MD Anderson Cancer Center Ovarian Cancer Specialized Program of Research Excellence (SPORE) grant P50CA083639; by The University of Texas MD Anderson Cancer Center Uterine SPORE grant P50CA098258; by MD Anderson's Cancer Center Support Grant P30CA016672 from the National Institutes of Health; by the US Department of Health and Human Services; by W81XWH-17-1-0126, W81XWH-17-1-0146, and W81XWH-16-1-0038 from the Ovarian Cancer Research Program, Department of Defense; by the Gilder Foundation; by grants RP100094 and RP110532 from the Cancer Prevention and Research Institute of Texas (CPRIT); by CPRIT Core Facility Support Award RP160805; and by funding support from Mr. Carl L. Norton, the Anne and John J. Sie Foundation, the Mary K. Chapman Foundation, the Ovarian Cancer Research Fund, the Ting Tsung and Wei Fong Chao Center for Bioinformatics Research and Imaging for Neurosciences (BRAIN), Cancer Fighters of Houston, and the John S. Dunn Research Foundation. Funding Information: This study was supported in part by grants R01CA133057, R01CA142832, RC4CA156551, U01188388, U54CA151668, U54CA149196, and UH2 TR000943; by The University of Texas MD Anderson Cancer Center Ovarian Cancer Specialized Program of Research Excellence (SPORE) grant P50CA083639; by The University of Texas MD Anderson Cancer Center Uterine SPORE grant P50CA098258; by MD Anderson{\textquoteright}s Cancer Center Support Grant P30CA016672 from the National Institutes of Health; by the US Department of Publisher Copyright: {\textcopyright} The Author(s) 2018.",
year = "2019",
month = mar,
day = "1",
doi = "10.1093/jnci/djy097",
language = "English",
volume = "111",
pages = "272--282",
journal = "Journal of the National Cancer Institute",
issn = "0027-8874",
number = "3",
}