Human CXCR5 <sup>+</sup> PD‐1 <sup>+</sup> CD8 T cells in healthy individuals and patients with hematologic malignancies
Bibliographic record
Abstract
Abstract Immune checkpoint blockade (ICB) has revolutionized cancer therapy, but varying response rates illustrate the need for biomarkers of response. Studies in mice have identified a subset of CD8 T cells that is essential for response to PD‐1 ICB. These CD8 T cells co‐express CXCR5, PD‐1 and Tcf1, and provide effector T cells upon PD‐1 ICB. It is unknown whether similar T cells play a role in PD‐1 ICB in humans. We studied human peripheral blood and lymph nodes (LNs) for the frequency, phenotype, and functionality of CXCR5 + PD‐1 + CD8 T cells. We find that CXCR5 + PD‐1 + CD8 T cells are memory‐like cells, express Tcf1, and lack expression of effector molecules. CXCR5 + PD‐1 + CD8 T cells produce cytokines upon stimulation, but have limited proliferative capacity. We studied patients with hematologic malignancies with varying response rates to PD‐1 ICB. Specifically in chronic lymphocytic leukemia, in which PD‐1 ICB does not induce clinical responses, CXCR5 + PD‐1 + CD8 T cells show loss of the memory phenotype and increased effector differentiation. In conclusion, we identified CXCR5 + PD‐1 + CD8 T cells in human peripheral blood and LN, which could play a similar role during PD‐1 ICB. Future studies should analyze CXCR5 + PD‐1 + CD8 T cells during PD‐1 ICB and their importance for therapeutic response.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".