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

doi:10.1016/j.isci.2024.109388

Identifying therapeutic candidates for endometriosis through a transcriptomics-based drug repositioning approach

iScience. 2024 Mar 2;27(4):109388. doi: 10.1016/j.isci.2024.109388. eCollection 2024 Apr 19.

Authors

Tomiko T Oskotsky^{1

2}, Arohee Bhoja^{1

3}, Daniel Bunis^{1

2}, Brian L Le^{1

2}, Alice S Tang^{1

2}, Idit Kosti^{1

2}, Christine Li¹, Sahar Houshdaran⁴, Sushmita Sen⁴, Júlia Vallvé-Juanico⁴, Wanxin Wang⁴, Erin Arthurs⁵, Arpita Govil⁵, Lauren Mahoney⁵, Lindsey Lang⁵, Brice Gaudilliere⁶, David K Stevenson⁷, Juan C Irwin⁴, Linda C Giudice⁴, Stacy L McAllister⁵, Marina Sirota^{1

2}

Affiliations

¹ Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA.
² Department of Pediatrics, UCSF, San Francisco, CA, USA.
³ Carnegie Mellon University, Pittsburgh, PA, USA.
⁴ Department of Obstetrics, Gynecology and Reproductive Sciences, UCSF, San Francisco, CA, USA.
⁵ Department of Gynecology and Obstetrics, Emory University, Atlanta, GA, USA.
⁶ Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, CA, USA.
⁷ Department of Pediatrics, Stanford University, Stanford, CA, USA.

Abstract

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.

Keywords: Drugs; Transcriptomics.