CD20+ Tumor-Infiltrating Lymphocytes Have an Atypical CD27− Memory Phenotype and Together with CD8+ T Cells Promote Favorable Prognosis in Ovarian Cancer
Bibliographic record
Abstract
PURPOSE: Tumor-infiltrating lymphocytes (TIL), in particular CD8(+) T cells and CD20(+) B cells, are strongly associated with survival in ovarian cancer and other carcinomas. Although CD8(+) TIL can mediate direct cytolytic activity against tumors, the role of CD20(+) TIL is poorly understood. Here, we investigate the possible contributions of CD20(+) TIL to humoral and cellular tumor immunity. EXPERIMENTAL DESIGN: Tumor and serum specimens were obtained from patients with high-grade serous ovarian cancer. CD8(+) and CD20(+) TIL were analyzed by immunohistochemistry and flow cytometry. Immunoglobulin molecules were evaluated by DNA sequencing. Serum autoantibody responses to the tumor antigens p53 and NY-ESO-1 were measured by ELISA. RESULTS: The vast majority of CD20(+) TIL were antigen experienced, as evidenced by class-switching, somatic hypermutation, and oligoclonality, yet they failed to express the canonical memory marker CD27. CD20(+) TIL showed no correlation with serum autoantibodies to p53 or NY-ESO-1. Instead, they colocalized with activated CD8(+) TIL and expressed markers of antigen presentation, including MHC class I, MHC class II, CD40, CD80, and CD86. The presence of both CD20(+) and CD8(+) TIL correlated with increased patient survival compared with CD8(+) TIL alone. CONCLUSIONS: In high-grade serous ovarian tumors, CD20(+) TIL have an antigen-experienced but atypical CD27(-) memory B-cell phenotype. They are uncoupled from serum autoantibodies, express markers of antigen-presenting cells, and colocalize with CD8(+) T cells. We propose that the association between CD20(+) TIL and patient survival may reflect a supportive role in cytolytic immune responses.
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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 | 0.000 |
| 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) | 0.002 | 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".