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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".