Data from The Prognostic Effect of Immune Cell Infiltration Depends on Molecular Subtype in Endometrioid Ovarian Carcinomas
Bibliographic record
Abstract
AbstractPurpose: Endometrioid ovarian carcinoma (ENOC) is the second most-common type of ovarian carcinoma, comprising 10%–20% of cases. Recently, the study of ENOC has benefitted from comparisons to endometrial carcinomas including defining ENOC with four prognostic molecular subtypes. Each subtype suggests differential mechanisms of progression, although tumor-initiating events remain elusive. There is evidence that the ovarian microenvironment may be critical to early lesion establishment and progression. However, while immune infiltrates have been well studied in high-grade serous ovarian carcinoma, studies in ENOC are limited. Experimental Design: We report on 210 ENOC, with clinical follow-up and molecular subtype annotation. Using multiplex IHC and immunofluorescence, we examine the prevalence of T-cell lineage, B-cell lineage, macrophages, and populations with programmed cell death protein 1 or programmed death-ligand 1 across subtypes of ENOC. Results: Immune cell infiltrates in tumor epithelium and stroma showed higher densities in ENOC subtypes with known high mutation burden (POLEmut and MMRd). While molecular subtypes were prognostically significant, immune infiltrates were not (overall survival P > 0.2). Analysis by molecular subtype revealed that immune cell density was prognostically significant in only the no specific molecular profile (NSMP) subtype, where immune infiltrates lacking B cells (TILB minus) had inferior outcome (disease-specific survival: HR, 4.0; 95% confidence interval, 1.1–14.7; P < 0.05). Similar to endometrial carcinomas, molecular subtype stratification was generally superior to immune response in predicting outcomes. Conclusions: Subtype stratification is critical for better understanding of ENOC, in particular the distribution and prognostic significance of immune cell infiltrates. The role of B cells in the immune response within NSMP tumors warrants further study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".