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DOI 10.1002/biot.70287

Медицинская библиотека · Biotechnology journal · 2026

Benchmarking Ultra-Low Attachment and Photopatterned GelMA 3D Culture Platforms for Modeling Cancer Stemness in High-Grade Serous Ovarian Cancer.

Yavuz Deren Demirel, Oguz Ferdi, Canitez Idil Su, Demir Gizem Yilmaz, Aydin Elif Merve, Kayis Merve, Giray Burak, Vatansever Dogan, Taskiran Cagatay, Sokullu Emel, Sahin Irem Durmaz

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Аннотация

High-grade serous ovarian cancer (HGSOC) exhibits pronounced intratumoral heterogeneity and chemoresistance, demanding experimental platforms that more accurately recapitulate stemness-driven relapse. Despite increasing use of three-dimensional (3D) systems in cancer research, their comparative translational performance in ovarian cancer remains unclear. Here, we systematically benchmark two scalable 3D platforms, scaffold-free ultra-low attachment (ULA) spheroids and extracellular matrix mimetic photopatterned gelatin methacryloyl (GelMA) hydrogels, using HGSOC cell lines and patient-derived primary samples. Under matched conditions, both systems enhanced cancer stem cell (CSC) associated phenotypes relative to 2D culture, including induction of stemness markers, CSC surface populations, epithelial to mesenchymal transition signatures, and drug resistance. ULA enabled long-term spheroid maturation and robust expansion of intrinsically stem-like populations, whereas GelMA photopatterning provided spatially defined microenvironmental control that promoted rapid and uniform spheroid formation and efficiently induced stem-like traits in low-stemness models. Notably, baseline SOX2 expression partially correlated with chemoresistance, supporting the biological relevance of CSC enriched platforms. Collectively, this platform level comparison establishes complementary, biotechnology driven strategies for reproducible modeling of HGSOC stemness and therapy resistance, with implications for translational drug evaluation and precision oncology.

Для цитирования: Yavuz Deren Demirel, Oguz Ferdi, Canitez Idil Su, Demir Gizem Yilmaz, Aydin Elif Merve, Kayis Merve, Giray Burak, Vatansever Dogan, Taskiran Cagatay, Sokullu Emel, Sahin Irem Durmaz. Benchmarking Ultra-Low Attachment and Photopatterned GelMA 3D Culture Platforms for Modeling Cancer Stemness in High-Grade Serous Ovarian Cancer.. Biotechnology journal. 2026. DOI: 10.1002/biot.70287.

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