Survival Following Chemotherapy in Ovarian Clear Cell Carcinoma Is Not Associated with Pathological Misclassification of Tumor Histotype
Bibliographic record
Abstract
Abstract Purpose: Although ovarian clear cell carcinomas (OCCC) are commonly resistant to platinum-based chemotherapy, good clinical outcomes are observed in a subset of patients. The explanation for this is unknown but may be due to misclassification of high-grade serous ovarian cancer (HGSOC) as OCCC or mixed histology. Experimental Design: To discover potential biomarkers of survival benefit following platinum-based chemotherapy, we ascertained a cohort of 68 Japanese and Australian patients in whom progression-free survival (PFS) and overall survival (OS) could be assessed. We performed IHC reclassification of tumors, and targeted sequencing and immunohistochemistry of known driver genes. Exome sequencing was performed in 10 patients who had either unusually long survival (N = 5) or had a very short time to progression (N = 5). Results: The majority of mixed OCCC (N = 6, 85.7%) and a small proportion of pure OCCC (N = 3, 4.9%) were reclassified as likely HGSOC. However, the PFS and OS of patients with misclassified samples were similar to that of patients with pathologically validated OCCC. Absent HNF1B expression was significantly correlated with longer PFS and OS (P = 0.0194 and 0.0395, respectively). Mutations in ARID1A, PIK3CA, PPP2R1A, and TP53 were frequent, but did not explain length of PFS and OS. An exploratory exome analysis of patients with favorable and unfavorable outcomes did not identify novel outcome-associated driver mutations. Conclusions: Survival benefit following chemotherapy in OCCC was not associated with pathological misclassification of tumor histotype. HNF1B loss may help identify the subset of patients with OCCC with a more favorable outcome.
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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 | 0.003 |
| 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".