Identifying therapeutic candidates for endometriosis through a transcriptomics-based drug repositioning approach
Tomiko T. Oskotsky,
Arohee Bhoja,
Daniel Bunis,
Brian L. Le,
Alice S. Tang,
Idit Kosti,
Christine Li,
Sahar Houshdaran,
Sushmita Sen,
Júlia Vallvé-Juanico,
Wanxin Wang,
Erin Arthurs,
Arpita Govil,
Lauren Mahoney,
Lindsey Lang,
Brice Gaudilliere,
David K. Stevenson,
Juan C. Irwin,
Linda C. Giudice,
Stacy L. McAllister,
Marina Sirota
Affiliations
Tomiko T. Oskotsky
Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA; Department of Pediatrics, UCSF, San Francisco, CA, USA
Arohee Bhoja
Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA; Carnegie Mellon University, Pittsburgh, PA, USA
Daniel Bunis
Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA; Department of Pediatrics, UCSF, San Francisco, CA, USA
Brian L. Le
Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA; Department of Pediatrics, UCSF, San Francisco, CA, USA
Alice S. Tang
Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA; Department of Pediatrics, UCSF, San Francisco, CA, USA
Idit Kosti
Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA; Department of Pediatrics, UCSF, San Francisco, CA, USA
Christine Li
Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA
Sahar Houshdaran
Department of Obstetrics, Gynecology and Reproductive Sciences, UCSF, San Francisco, CA, USA
Sushmita Sen
Department of Obstetrics, Gynecology and Reproductive Sciences, UCSF, San Francisco, CA, USA
Júlia Vallvé-Juanico
Department of Obstetrics, Gynecology and Reproductive Sciences, UCSF, San Francisco, CA, USA
Wanxin Wang
Department of Obstetrics, Gynecology and Reproductive Sciences, UCSF, San Francisco, CA, USA
Erin Arthurs
Department of Gynecology and Obstetrics, Emory University, Atlanta, GA, USA
Arpita Govil
Department of Gynecology and Obstetrics, Emory University, Atlanta, GA, USA
Lauren Mahoney
Department of Gynecology and Obstetrics, Emory University, Atlanta, GA, USA
Lindsey Lang
Department of Gynecology and Obstetrics, Emory University, Atlanta, GA, USA
Brice Gaudilliere
Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, CA, USA
David K. Stevenson
Department of Pediatrics, Stanford University, Stanford, CA, USA
Juan C. Irwin
Department of Obstetrics, Gynecology and Reproductive Sciences, UCSF, San Francisco, CA, USA
Linda C. Giudice
Department of Obstetrics, Gynecology and Reproductive Sciences, UCSF, San Francisco, CA, USA
Stacy L. McAllister
Department of Gynecology and Obstetrics, Emory University, Atlanta, GA, USA
Marina Sirota
Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA; Department of Pediatrics, UCSF, San Francisco, CA, USA; Corresponding author
Summary: Existing medical treatments for endometriosis-related pain are often ineffective, underscoring the need for new therapeutic strategies. In this study, we applied a computational drug repurposing pipeline to stratified and unstratified disease signatures based on endometrial gene expression data to identify potential therapeutics from existing drugs, based on expression reversal. Of 3,131 unique genes differentially expressed by at least one of six endometriosis signatures, only 308 (9.8%) were in common; however, 221 out of 299 drugs identified, (73.9%) were shared. We selected fenoprofen, an uncommonly prescribed NSAID that was the top therapeutic candidate for further investigation. When testing fenoprofen in an established rat model of endometriosis, fenoprofen successfully alleviated endometriosis-associated vaginal hyperalgesia, a surrogate marker for endometriosis-related pain. These findings validate fenoprofen as a therapeutic that could be utilized more frequently for endometriosis and suggest the utility of the aforementioned computational drug repurposing approach for endometriosis.
