Commonly Occurring Cell Subsets in High-Grade Serous Ovarian Tumors Identified by Single-Cell Mass Cytometry
Bibliographic record
Abstract
We have performed an in-depth single-cell phenotypic characterization of high-grade serous ovarian cancer (HGSOC) by multiparametric mass cytometry (CyTOF). Using a CyTOF antibody panel to interrogate features of HGSOC biology, combined with unsupervised computational analysis, we identified noteworthy cell types co-occurring across the tumors. In addition to a dominant cell subset, each tumor harbored rarer cell phenotypes. One such group co-expressed E-cadherin and vimentin (EV), suggesting their potential role in epithelial mesenchymal transition, which was substantiated by pairwise correlation analyses. Furthermore, tumors from patients with poorer outcome had an increased frequency of another rare cell type that co-expressed vimentin, HE4, and cMyc. These poorer-outcome tumors also populated more cell phenotypes, as quantified by Simpson's diversity index. Thus, despite the recognized genomic complexity of the disease, the specific cell phenotypes uncovered here offer a focus for therapeutic intervention and disease monitoring.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".