Beyond one-size-fits-all: single-cell transcriptomic signatures predict drug efficacy and reveal responder subgroups in endometriosis
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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.
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These results suggest that the core transcriptomic signature of the drug response can be recapitulated in stromal cells from the eutopic endometrium of endometriosis patients, supporting their use as a minimally invasive source for evaluating candidate therapeutics for the disease.
Thank you for doing this work! Utilizing single-cell datasets for in silico drug prediction is a pragmatic way to help us better understand and treat endometriosis.
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230 showed concordant direction of change (hypergeometric test, p = 4.43 × 10⁻³³; Jaccard index = 0.252). Notably, 381 downregulated genes displayed a highly significant overlap across tissues
Would it be possible to include these gene lists in a supplemental table?
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These populations displayed strong enrichment for epithelial-mesenchymal transition (EMT) and hypoxia pathways (Figure 2, Table 1), in line with the invasive and fibrotic features of endometriotic lesions previously reported
I'm curious if your analyses were able to provide any new information about specific EMT-related factors that are enriched in these cell types. What EMT genes were consistently identified? Were there enrichment signatures you found that bulk analyses may have missed?
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