Beyond one-size-fits-all: single-cell transcriptomic signatures predict drug efficacy and reveal responder subgroups in endometriosis

Pérez-Moraga R, Raúl Pérez-Moraga, Cemsel Bafligil, Harden S, Sarah Harden, García-Martín S, Santiago García-Martín, María José Jiménez-Santos, Jiménez-Santos MJ, Tiniakou I, Ioanna Tiniakou, Sophie Ribeiro-Volturo, Ribeiro-Volturo S, Gies L, Line Gies, Zaballos MTP, María Teresa Pérez Zaballos, Cristina Fernández-Molina, Fernández-Molina C
In: bioRxiv · 2025 · doi:10.64898/2025.12.09.693218 · W7114928105
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Single-cell transcriptomics identified drug candidates and patient subgroups by revealing drug efficacy predictions and cell-type-specific vulnerabilities in endometriosis tissues.

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This study employed single-cell RNA sequencing on eutopic and ectopic tissues to analyze therapeutic heterogeneity in endometriosis using a machine learning-based drug response model. The researchers identified stromal, endothelial, and stem cell populations as key targets, revealing that histone deacetylase and tubulin polymerization inhibitors could revert disease-associated transcriptional states in specific patient subgroups. A major finding was that transcriptomic signatures distinguishing responders from non-responders were conserved between tissue types, suggesting that accessible eutopic biopsies may suffice for predicting treatment efficacy. This paper is centrally about endometriosis — specifically the use of single-cell transcriptomics to stratify patients and identify precision non-hormonal therapies for the condition.

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Abstract

Abstract Endometriosis affects ∼10% of reproductive-age women, yet targeted non-hormonal therapies remain unavailable, and treatment response is highly variable. Here, we apply a single-cell framework to resolve therapeutic heterogeneity at a resolution previously unattained in drug development efforts. Using scRNA-seq profiles from eutopic and ectopic tissues, combined with a machine learning-based drug response model, we identified compounds predicted to revert disease-associated transcriptional states and map cell-type-specific vulnerabilities across patients and tissues. Our analysis revealed pronounced tissue-specific and inter-patient heterogeneity in predicted responses. Stromal, endothelial, and stem cell populations emerged as the dominant therapeutic targets, collectively revealing selective sensitivity to two recurrent drug classes, histone deacetylase and tubulin polymerisation inhibitors. Transcriptomic comparison of predicted responders and non-responders to these drugs pointed to conserved molecular programmes involving extracellular matrix remodelling, angiogenesis, and proliferative activation. These signatures were shared between eutopic and ectopic stromal compartments, supporting the feasibility of assessing therapeutic response using readily accessible eutopic tissue. Our findings show that this single-cell framework can dissect therapeutic heterogeneity in endometriosis, support the development of precision non-hormonal therapies and identify responder subgroups relevant for patient stratification. Together, these results highlight that underlying molecular diversity in endometriosis necessitates therapeutic approaches beyond a one-size-fits-all model.
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Abstract Endometriosis affects ∼10% of reproductive-age women, yet targeted non-hormonal therapies remain unavailable, and treatment response is highly variable. Here, we apply a single-cell framework to resolve therapeutic heterogeneity at a resolution previously unattained in drug development efforts. Using scRNA-seq profiles from eutopic and ectopic tissues, combined with a machine learning-based drug response model, we identified compounds predicted to revert disease-associated transcriptional states and map cell-type-specific vulnerabilities across patients and tissues. Our analysis revealed pronounced tissue-specific and inter-patient heterogeneity in predicted responses. Stromal, endothelial, and stem cell populations emerged as the dominant therapeutic targets, collectively revealing selective sensitivity to two recurrent drug classes, histone deacetylase and tubulin polymerisation inhibitors. Transcriptomic comparison of predicted responders and non-responders to these drugs pointed to conserved molecular programmes involving extracellular matrix remodelling, angiogenesis, and proliferative activation. These signatures were shared between eutopic and ectopic stromal compartments, supporting the feasibility of assessing therapeutic response using readily accessible eutopic tissue. Our findings show that this single-cell framework can dissect therapeutic heterogeneity in endometriosis, support the development of precision non-hormonal therapies and identify responder subgroups relevant for patient stratification. Together, these results highlight that underlying molecular diversity in endometriosis necessitates therapeutic approaches beyond a one-size-fits-all model. Competing Interest Statement R.P.M., C.B., S.H., I.T., S.R.V., L.G. and C.F.M. are employees of endogene.bio. M.T.P.Z. is the Chief Executive Officer of endogene.bio. Footnotes ↵# These authors jointly supervised this work.

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Condition tags

endometriosis

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Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

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