A Rare Multipotent Peg-like Epithelial Cell is a Candidate Cell-of-Origin for High-Grade Serous Ovarian Cancer
Bibliographic record
Abstract
Abstract To illuminate the origins of high-grade serous ovarian cancer (HGSOC), the most lethal and common form of ovarian cancer, we have created a comprehensive living organoid biobank of human fallopian tube tissue, which is thought to be the origin of this cancer. Through optimized culture protocols and integrated multi-omic profiling—including single-cell RNA sequencing, chromatin accessibility (ATAC) analysis, proteomics, and secretomics—we assembled the largest molecular atlas of the fallopian tube epithelium to date. This resource revealed diverse epithelial lineages and regulatory networks, including a rare, multipotent epithelial subpopulation with hybrid epithelial–mesenchymal features. Spatially localized to the basal epithelium and resembling mesonephric developmental precursors, these cells exhibit transcriptomic and proteomic similarities to the mesenchyme-like subtype of HGSOC, implicating them as potential cells-of-origin. Their molecular identity is preserved in organoid models, enabling future mechanistic and translational studies. This resource, which advances fundamental understanding of epithelial hierarchy and cancer susceptibility, provides a platform to inform early detection and prevention strategies for aggressive forms of ovarian cancer. Highlights Establishment of a clinically annotated fallopian tube organoid biobank enables delineation of epithelial lineage hierarchies and differentiation capacity. Multi-omics integration defines robust, lineage-specific transcriptional and regulatory networks in the fallopian tube epithelium. A rare basal epithelial subpopulation with mesenchymal features aligns with a mesenchyme-like subtype of high-grade serous ovarian cancer. Rare basal ‘peg’ cells exhibit fetal mesonephric developmental transcriptional programs and are maintained ex-vivo in fallopian tube organoids.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".