Organoid-Guided Synthetic Micropeptide Therapeutics to Target Ovarian Cancer Stem Cells

Ovarian cancer remains the most lethal gynecological malignancy, largely due to high recurrence rates and the development of therapy-resistant disease. Cancer stem cells (CSCs) contribute to treatment failure by promoting chemoresistance through signaling pathways such as Wnt/β-catenin, Hedgehog, and Notch, as well as through dynamic interactions with the tumor microenvironment (TME). Ovarian CSC (OCSC)-associated biomarkers, including CD44, CD24, CD133, and ALDH, have expanded opportunities for specific tumor characterization and biomarker identification. Effective targeting of OCSCs requires experimental models that preserve cellular plasticity and hierarchical organization. Development of synthetic matrices and chemically defined media has improved the reproducibility and scalability of three-dimensional (3D) organoid cultures for preclinical drug testing. CSC-derived organoids, established by enriching cells positive for CSC markers directly from patient tumor material, provide a 3D platform for studying OCSC-associated phenotypes and therapeutic responses. This review examines current understanding of ovarian CSC biology and the development of OCSC-derived 3D organoid models that can recapitulate cellular architecture and patient-specific drug responses. These 3D organoid models provide a guided platform for evaluating emerging therapeutics, such as micropeptides encoded by short open reading frames or engineered from functional tumor-suppressor protein domains selected to target OCSCs. Their small size enables modulation of intracellular signaling and highly specific protein–protein interactions. We further review micropeptide stabilization strategies and tumor-targeted delivery systems designed to enhance bioavailability. This review highlights how integrating patient-specific OCSC-derived organoid models with engineered micropeptides may accelerate the development of personalized treatment strategies for ovarian cancer while improving the preclinical evaluation of novel therapies.