Markers of T Cell Infiltration and Function Associate with Favorable Outcome in Vascularized High-Grade Serous Ovarian Carcinoma
Bibliographic record
Abstract
BACKGROUND: When T cells infiltrate the tumor environment they encounter a myriad of metabolic stressors including hypoxia. Overcoming the limitations imposed by an inadequate tumor vasculature that contributes to these stressors may be a crucial step to immune cells mounting an effective anti-tumor response. We sought to determine whether the functional capacity of tumor infiltrating lymphocytes (TIL) could be influenced by the tumor vasculature and correlated this with survival in patients with ovarian cancer. METHODOLOGY AND PRINCIPAL FINDINGS: In 196 high-grade serous ovarian tumors, we confirmed that the tumor vascularity as measured by the marker CD31 was associated with improved patient disease-specific survival. We also found that tumors positive for markers of TIL (CD8, CD4 and forkhead box P3 (FoxP3)) and T cell function (granzyme B and T-cell restricted intracellular antigen-1 (TIA-1)) correlated significantly with elevated vascularity. In vitro, hypoxic CD8 T cells showed reduced cytolytic activity, secreted less effector cytokines and upregulated autophagy. Survival analysis revealed that patients had a significant improvement in disease-specific survival when FoxP3 expressing cells were present in CD31-high tumors compared to patients with FoxP3 expressing cells in CD31-low tumors [HR: 2.314 (95% CI 1.049-5.106); p = 0.0377]. Patients with high vascular endothelial growth factor (VEGF) expressing tumors containing granzyme B positive cells had improved survival compared to patients with granzyme B positive cells in VEGF-low tumors [HR: 2.522 (95% CI 1.097-5.799); p = 0.0294]. SIGNIFICANCE: Overall, this data provides a rationale for developing strategies aimed at improving the adaptability and function of TIL to hypoxic tumor conditions.
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 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 | 0.000 | 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".