Molecular subtyping of endometrial carcinoma cell lines uncovers subtype-specific targetable vulnerabilities
Bibliographic record
Abstract
Endometrial carcinoma (EC), the most common gynecologic cancer type in developed countries, encompasses four molecular subtypes (POLEmut, MMRd, p53abn, and NSMP) that have prognostic values and guide treatment decisions. Additionally, dual loss of ARID1A and ARID1B (referred to as ARID1A/B) characterizes a significant portion of dedifferentiated/undifferentiated EC (DD/UDEC), a rare but highly aggressive subtype of EC. To advance the translational research for ECs, we analyzed the genomic features of a panel of 39 EC cell lines, leading to the identification of cell lines representing each of these EC molecular subtype. Histologic and immunohistochemical analyses of xenografted tumors from these cell lines confirmed their resemblance of cognate primary EC molecular subtypes. Further investigation of the publicly available genome-wide CRISPR screen data for EC cell lines identified multiple specific genetic dependencies in MMRd, p53abn, and ARID1A/B-dual deficient EC cell lines. Particularly, ARID1A/B-dual deficient DD/UDEC cells selectively rely on mitochondrial oxidative phosphorylation in vitro and in vivo. Therefore, through molecular subtyping of EC cell lines and subsequent characterization of molecular subtype-specific genetic dependencies, our study provides a framework that guides the utility of the EC cell line models for accelerating translational research in EC.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".