Improved overall survival in patients with high-grade serous ovarian cancer is associated with CD16a+ immunologic neighborhoods containing NK cells, T cells and macrophages
Bibliographic record
Abstract
Background For patients with high grade serous carcinoma of the ovary (HGSC), survival rates have remained static for the last half century. Despite the presence of tumor mutations and infiltration of immune cells, existing immunotherapies have achieved little success against HGSC. These observations highlight a gap in the understanding of how the immune system functions and interacts within HGSC tumors. Methods We analyzed duplicate core samples from 939 patients with HGSC to understand patterns of immune cell infiltration, localization, and associations with clinical features. We used high-parameter immunohistochemical/Opal multiplex, digital pathology, computational biology, and multivariate analysis to identify immune cell subsets and their associations with HGSC tumors. Results We defined six patterns of cellular infiltration by spatially restricted unsupervised clustering of cell subsets. Each pattern was represented to some extent in most patient samples, but their specific distributions differed. Overall (OS) and progression-free survival (PFS) corresponded with higher infiltration of CD16a+ cells, and their co-localization with macrophages, T cells, NK cells, in one of six cellular neighborhoods that we defined with our spatial assessment. Conclusions Immune cell neighborhoods containing CD16a+ cells are associated with improved OS and PFS for patients with HGSC. Patterns of immunologic neighborhoods differentiate patient outcomes, and could inform future, more precise approaches to treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".