Copy Number Aberrations in Benign Serous Ovarian Tumors: A Case for Reclassification?
Bibliographic record
Abstract
PURPOSE: Serous ovarian carcinomas are the predominant epithelial ovarian cancer subtype and it has been widely believed that some or all of these may arise from precursors derived from the ovarian surface epithelium or fimbriae, although direct molecular evidence for this is limited. This study aimed to conduct copy number (CN) analysis using a series of benign and borderline serous ovarian tumors to identify underlying genomic changes that may be indicative of early events in tumorigenesis. EXPERIMENTAL DESIGN: High resolution CN analysis was conducted on DNA from the epithelial and fibroblast components of a cohort of benign (N = 39) and borderline (N = 24) serous tumors using the Affymetrix OncoScan assay and SNP6.0 arrays. RESULTS: CN aberrations were detected in the epithelium of only 2.9% (1 of 35) of serous cystadenomas and cystadenofibromas. In contrast, CN aberrations were detected in the epithelium of 67% (16 of 24) of the serous borderline tumors (SBT). Unexpectedly, CN aberrations were detected in the fibroblasts of 33% (13 of 39) of the benign serous tumors and in 15% (3 of 20) of the SBTs. Of the 16 cases with CN aberrations in the fibroblasts, 12 of these carried a gain of chromosome 12. CONCLUSIONS: Chromosome 12 trisomy has been previously identified in pure fibromas, supporting the concept that a significant proportion of benign serous tumors are in fact primary fibromas with an associated cystic mass. This is the first high resolution genomic analysis of benign serous ovarian tumors and has shown not only that the majority of benign serous tumors have no genetic evidence of epithelial neoplasia but that a significant proportion may be more accurately classified as primary fibromas.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".