CXCR6 by increasing retention of memory CD8 T cells in the ovarian tumor microenvironment promotes immunosurveillance and control of ovarian cancer
Bibliographic record
Abstract
Abstract Purpose Resident memory CD8 T cells owing to their ability to reside and persist in peripheral tissues, impart adaptive sentinel activity and amplify local immune response, have beneficial implications for tumor surveillance and control. The current study aims to clarify the less known chemotactic mechanisms that govern the localization, retention, and residency of memory CD8 T cells in the ovarian tumor microenvironment. Experimental Design RNA/FACS based profiling of chemokine receptor expression in CD8 + resident memory T cells in human ovarian cancer and analyze their association with survival. Analyze chemokine receptor role in anti-tumor response and control by resident memory T cells using prophylactic mice models of ovarian cancer, treated with adoptive transfer of OT1 T cells and vaccination with maraba virus-OVA to target Ovalbumin expressing tumor. Results Chemokine receptor profiling of CD8 + CD103 + resident memory TILs in ovarian cancer patients revealed high expression of CXCR6. Analysis of the TCGA ovarian cancer database revealed CXCR6 to be associated with CD103 and increased patient survival. Functional studies in mouse models of ovarian cancer revealed that CXCR6 is a marker of resident, but not circulatory tumor-specific memory CD8 T cells. Knockout of CXCR6 in tumor-specific CD8 T cells showed reduced retention in tumor tissues leading to diminished resident memory responses and poor control of ovarian cancer Conclusions CXCR6 by promoting increased retention in tumor tissues serves a critical role in resident memory T cell-mediated immunosurveillance and control of ovarian cancer. Future studies warrant exploiting CXCR6 to promote resident memory response in cancers.
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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".