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

iScience (Apr 2024)

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

Journal volume & issue: Vol. 27, no. 4
p. 109388

Abstract

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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.

Published in iScience

ISSN: 2589-0042 (Online)
Publisher: Elsevier
Country of publisher: United States
LCC subjects: Science
Website: http://www.cell.com/iscience/home

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