Molecular stratification of clear cell ovarian carcinomas
Bibliographic record
Abstract
Background and aims Advanced-stage CCOC has an exceptionally poor outcome and exhibits insensitivity to chemotherapy. With a lack of informative, quantitative prognostic and phenotypic markers defined, CCOC is poorly positioned to make use of emerging targeted therapies and the rise in personalized medicine. We aim to stratify biological subtypes of CCOC based on recurrent somatic alterations and prognostic biomarkers, ultimately developing a clinically relevant molecular classifier that will allow for patient-centered treatment decisions and outcome predictions, based on quantitative molecular patterns. Materials A total of 215 formalin-fixed paraffin-embedded tissue blocks with cases from Tübingen (n = 46), Vancouver, Canada (n = 126), Munich (n = 18), Vienna, Austria (n = 18), and Friedrichshafen (n = 7) was assembled. Methods Immunohistochemical (IHC) biomarker markers including NapsinA, WT1, HNF1B, p53, ARID1A, PMS2, MSH6, p16, IGFBP3, PTEN, CCNE1, PR, and CD8 were assessed in tissue microarray format. Hotspot cancer gene mutations, in genes previously reported to be altered in clear cell or related endometriosis-associated malignancies, were tested using a modified tailed-amplicon sequencing strategy. We tested for hotspot alterations in PIK3CA, PIK3R1, KRAS, POLE, CTNNB1, and TERT. Results and
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".