Modeling high-grade serous ovarian carcinogenesis from the fallopian tube
Bibliographic record
Abstract
High-grade serous ovarian carcinoma (HGSOC) is a lethal disease for which improved screening and treatment strategies are urgently needed. Progress in these areas is impeded by our poor understanding of HGSOC pathogenesis. Most ovarian cancer research is based on the hypothesis that HGSOC arises from ovarian surface epithelial cells. However, recent studies suggest that >50% of high-grade serous carcinomas involving the ovary likely arise from fallopian tube epithelium. Therefore, limiting HGSOC research to modeling based on ovarian surface epithelium alone is inadequate. To address the need for a fallopian tube-based model of HGSOC, we have developed a system for studying human fallopian tube secretory epithelial cell (FTSEC) transformation. Our model is based on (i) immortalization of FTSECs isolated from primary samples of normal, nondiseased human fallopian tubes, (ii) transformation of FTSECs with defined genetic elements, and (iii) xenograft-based tumorigenic assays. We use our model to show that FTSECs immortalized with human telomerase reverse transcriptase (hTERT) plus SV40 large T and small T antigens are transformed by either oncogenic Ras (H-Ras(V12)) or c-Myc expression, leading to increased proliferation, clonogenicity, and anchorage-independent growth. Additionally, we demonstrate that FTSECs remain susceptible to c-Myc-mediated transformation in the absence of viral oncoproteins, by replacing SV40 large T and small T antigens with sh-p53, mutant CDK4 (CDK4(R24C)), and sh-PP2A-B56γ. Importantly, all transformed FTSECs gave rise to high-grade Müllerian carcinomas that were grossly, histologically, immunophenotypically, and genomically similar to human HGSOC. With this model, we will now be able to assess the transformative effects of specific genetic alterations on FTSECs in order to characterize their respective roles in HGSOC development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| 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 teacher head, 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".