Gain-of-function p53R175H blocks apoptosis in a precursor model of ovarian high-grade serous carcinoma
Bibliographic record
Abstract
Abstract Ovarian high-grade serous carcinoma (HGSC) is a highly lethal malignancy for which early detection is a challenge and treatment of late-stage disease is ineffective. HGSC initiation involves exfoliation of fallopian tube epithelial (FTE) cells which form multicellular clusters called spheroids that colonize and invade the ovary. HGSC contains universal mutation of the tumour suppressor gene TP53 . However, not all TP53 mutations are the same, as specific p53 missense mutants contain gain-of-function (GOF) properties that drive tumour formation. Additionally, the role of GOF p53 in spheroid-mediated spread is poorly understood. In this study, we developed and characterized an in vitro model of HGSC based on mutation of TP53 in mouse oviductal epithelial cells (OVE). We discovered increased bulk spheroid survival and increased anchorage-independent growth in OVE cells expressing the missense mutant p53 R175H compared to OVE parental and Trp53 ko cells. Transcriptomic analysis on spheroids identified decreased apoptosis signaling due to p53 R175H . Further assessment of the apoptosis pathway demonstrated decreased expression of intrinsic and extrinsic apoptosis signaling molecules due to Trp53 deletion and p53 R175H , but Caspase-3 activation was only decreased in spheroids with p53 R175H . These results highlight this model as a useful tool for discovering early HGSC transformation mechanisms and uncover a potential anti-apoptosis GOF mechanism of p53 R175H .
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".