Antisense Suppression of Pygopus2 Results in Growth Arrest of Epithelial Ovarian Cancer
Bibliographic record
Abstract
PURPOSE: The Pygopus proteins are critical elements of the canonical Wnt/beta-catenin transcriptional complex. In epithelial ovarian cancer, constitutively active Wnt signaling is restricted to one (endometrioid) tumor subtype. The purpose of this study was to determine the level of expression and growth requirements of human Pygopus2 (hPygo2) protein in epithelial ovarian cancer. EXPERIMENTAL DESIGN: Expression and subcellular localization of hPygo2 was determined in epithelial ovarian cancer cell lines and tumors using Northern blot, immunoblot, and immunofluorescence. Immunohistochemistry was done on 125 archived patient epithelial ovarian cancer tumors representing all epithelial ovarian cancer subtypes. T-cell factor-dependent transcription levels were determined in epithelial ovarian cancer cells using TOPflash/FOPflash in vivo assays. Phosphorothioated antisense oligonucleotides were transfected into cell lines and growth assayed by cell counting, anchorage-independent colony formation on soft agar, and xenografting into severe combined immunodeficient mice. RESULTS: All six epithelial ovarian cancer cell lines and 82% of the patient samples overexpressed nuclear hPygo2 compared with control cells and benign disease. Depletion of hPygo2 by antisense oligonucleotides in both Wnt-active (TOV-112D) and Wnt-inactive serous (OVCAR-3, SKOV-3) and clear cell (TOV-21G) carcinoma cell lines halted growth, assessed using tissue culture, anchorage-independent, and xenograft assays. CONCLUSIONS: hPygo2 is unexpectedly widely expressed in, and required in the absence of, Wnt signaling for malignant growth of epithelial ovarian cancer, the deadliest gynecologic malignancy. These findings strongly suggest that inhibition of hPygo2 may be of therapeutic benefit for treating this disease.
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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 | 0.000 |
| 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".