Predicting developmental relationships of tumor resident and circulating T cells in ovarian cancer
Bibliographic record
Abstract
ABSTRACT Characterizing T cell populations and understanding their developmental relationships may help design more effective cancer immunotherapies. We coupled single-cell transcriptomics and T cell receptor (TCR) αβ profiling of intratumoral and peripheral T cells in ovarian cancer patients to identify transcriptional programs and infer their relationship by trajectory and TCR overlap analyses. We proposed a model of differentiation pathway from an intermediate GZMH-expressing CD8 T cell subset found in the blood and tumor that progressively reinforces the exhaustion and tissue residency programs from a CCL4 -expressing cluster towards XCL1 - and CXCL13 -expressing terminally exhausted cells. Inferred cell communication analysis suggests that interaction with CXCL13 -expressing CD4 T cells, which we refer to as Tfh-like cells, sustains the effector function of this intermediate GZMH-expressing CD8 T cell subset. Moreover, our results suggest that Tfh-like cells attract cells expressing GPR183 through the production of its ligand 7α,25 dihydroxycholesterol (7α,25-HC). Finally, we demonstrated that GPR183 is highly expressed in a subset of pre-effector GZMK -expressing CD8 T cells and plasmacytoid dendritic cells. Collectively, our results suggest that Tfh-like cells expressing IL-21 help promote antitumor immunity against ovarian tumors by coordinating the action of immune cells responsive to 7α,25-HC.
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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".