bioRxivCancer BiologyPreprint
Comparative single-cell atlases reveal injury-driven tubal epithelial regeneration as a window for ovarian carcinoma initiation
Coulter Q. RalstoniD, Andrea Flesken‐NikitiniD, Dah‐Jiun FuiD, Christopher S. AsheiD, Blaine A. HarlaniD, M. Mozammal Hossain, D. Y. Wang, Anna V. YemelyanovaiD, Elisa SchmoeckeliD, Andrew K. GodwiniD, Doris Cooper Mayr, Benjamin D. CosgroveiD, Alexander Yu. NikitiniD
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Abstract
High-grade serous carcinoma (HGSC), the most lethal form of ovarian cancer, preferentially originates in the tubal epithelium (TE) of the distal uterine tube (also known as Fallopian tube or oviduct). Mouse models are widely used to study how HGSC initiates in humans; however, the extent to which mouse and human uterine tubes are comparable remains unclear. Here, we conduct cross-species single-cell transcriptomic comparative analyses and organoid assay validations to reveal conserved differentiation trajectories from bipotent progenitors to secretory or ciliated cell fates. Regional analyses of both datasets reveal enriched injury repair features in the distal human TE, where mice lack such a trend. Experimentally inducing mechanical injury to the mouse TE yields significant expansion of pre-ciliated cells compared to uninjured counterparts. Furthermore, inactivation of Trp53 and Rb1, whose pathways are commonly altered in HGSC, in regenerating pre-ciliated cells leads to rapid neoplastic transformation, implicating post-traumatic repair as a permissive window for malignant transformation. Together, our findings establish a comparative atlas of cell states between mice and humans, show that injury-associated regeneration may contribute to the known vulnerability of the fimbrial region, and raise potential concerns regarding procedures or conditions that mechanically perturb the tubal epithelium.
- organism: mouse
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- approach: deep learning
- approach: statistical model
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