Single-cell Profiles and Prognostic Impact of Tumor-Infiltrating Lymphocytes Coexpressing CD39, CD103, and PD-1 in Ovarian Cancer
Bibliographic record
Abstract
Abstract Purpose: Tumor-infiltrating lymphocytes (TIL) are strongly associated with survival in most cancers; however, the tumor-reactive subset that drives this prognostic effect remains poorly defined. CD39, CD103, and PD-1 have been independently proposed as markers of tumor-reactive CD8+ TIL in various cancers. We evaluated the phenotype, clonality, and prognostic significance of TIL expressing various combinations of these markers in high-grade serous ovarian cancer (HGSC), a malignancy in need of more effective immunotherapeutic approaches. Experimental Design: Expression of CD39, CD103, PD-1, and other immune markers was assessed by high-dimensional flow cytometry, single-cell sequencing, and multiplex immunofluorescence of primary and matched pre/post-chemotherapy HGSC specimens. Results: Coexpression of CD39, CD103, and PD-1 (“triple-positive” phenotype) demarcated subsets of CD8+ TIL and CD4+ regulatory T cells (Treg) with a highly activated/exhausted phenotype. Triple-positive CD8+ TIL exhibited reduced T-cell receptor (TCR) diversity and expressed genes involved in both cytolytic and humoral immunity. Triple-positive Tregs exhibited higher TCR diversity and a tumor-resident phenotype. Triple-positive TIL showed superior prognostic impact relative to TIL expressing other combinations of these markers. TIGIT was uniquely upregulated on triple-positive CD8+ effector cells relative to their CD4+ Treg counterparts. Conclusions: Coexpression of CD39, CD103, and PD-1 demarcates highly activated CD8+ and CD4+ TIL with inferred roles in cytolytic, humoral, and regulatory immune functions. Triple-positive TIL demonstrate exceptional prognostic significance and express compelling targets for combination immunotherapy, including PD-1, CD39, and TIGIT.
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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) | 0.001 | 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".