Abstract
Gynecological cancers, notably breast and ovarian cancers, exhibit significant heterogeneity, complicating treatment strategies and impacting patient outcomes. Traditional cancer models often fail to capture the complexity and diversity of these tumors. Induced pluripotent stem cells (iPSCs) offer a promising alternative for modeling cancer due to their ability to differentiate into various cell types. This study aims to establish and characterize iPSC-derived models of breast and ovarian cancers to explore their heterogeneity and therapeutic responses. We cultured iPSCs and differentiated them into breast and ovarian cancer cell lineages using lineage-specific protocols. Differentiation was confirmed by the expression of specific markers (CK14, CK18 for breast cancer; CA125, CK7 for ovarian cancer). We generated tumor spheroids from the differentiated cells and assessed their morphology, size, and viability. Functional assays revealed significant differences in invasive and migratory capabilities between the two cancer models. Gene and protein expression analyses highlighted the upregulation of BRCA1 and BRCA2 in breast cancer models and higher TP53 expression in ovarian cancer models. Proliferation assays demonstrated variability in drug sensitivity, with breast cancer spheroids showing higher sensitivity to trastuzumab and ovarian cancer spheroids to olaparib. Apoptosis assays indicated higher basal and treatment-induced apoptotic activity in ovarian cancer spheroids. Angiogenesis potential, assessed using HUVEC tube formation assays, was greater in ovarian cancer models. Our results validate the use of iPSC-derived models for studying gynecological cancer heterogeneity. These models accurately reflect the molecular and functional diversity observed in patient tumors, providing a robust platform for investigating cancer biology and evaluating therapeutic strategies. Future research should expand these models to include additional gynecological cancers, incorporate patient-derived iPSCs for personalized medicine, and utilize multi-omics approaches to further understand cancer heterogeneity and improve treatment outcomes.
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