A Genetically Defined Model for Human Ovarian Cancer
Bibliographic record
Abstract
Disruptions of the p53, retinoblastoma (Rb), and RAS signaling pathways and activation of human telomerase reverse transcriptase (hTERT) are common in human ovarian cancer; however, their precise role in ovarian cancer development is not clear. We thus introduced the catalytic subunit of hTERT, the SV40 early genomic region, and the oncogenic alleles of human HRAS or KRAS into human ovarian surface epithelial cells and examined the phenotype and gene expression profile of those cells. Disruption of p53 and Rb pathway by SV40 early genomic region and hTERT immortalized but did not transform the cells. Introduction of HRAS(V12) or KRAS(V12) into the immortalized cells, however, allowed them to form s.c. tumors after injection into immunocompromised mice. Peritoneal injection of the transformed cells produced undifferentiated carcinoma or malignant mixed Mullerian tumor and developed ascites; the tumor cells are focally positive for CA125 and mesothelin. Gene expression profile analysis of transformed cells revealed elevated expression of several cytokines, including interleukin (IL)-1beta, IL-6, and IL-8, that are up-regulated by the nuclear factor-kappaB pathway, which is known to contribute to the tumor growth of naturally ovarian cancer cells. Incubation with antibodies to IL-1beta or IL-8 led to apoptosis in the ras-transformed cells and ovarian cancer cells but not in immortalized cells that had not been transformed. Thus, the transformed human ovarian surface epithelial cells recapitulated many features of natural ovarian cancer including a subtype of ovarian cancer histology, formation of ascites, CA125 expression, and nuclear factor-kappaB-mediated cytokine activation. These cells provide a novel model system to study human ovarian cancer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".