Intra‐tumoral and peripheral blood <scp>TIGIT</scp> and <scp>PD</scp>‐1 as immune biomarkers in nodular lymphocyte predominant Hodgkin lymphoma
Bibliographic record
Abstract
In classical Hodgkin lymphoma (cHL), responsiveness to immune-checkpoint blockade (ICB) is associated with specific tumor microenvironment (TME) and peripheral blood features. The role of ICB in nodular lymphocyte predominant Hodgkin lymphoma (NLPHL) is not established. To gain insights into its potential in NLPHL, we compared TME and peripheral blood signatures between HLs using an integrative multiomic analysis. A discovery/validation approach in 121 NLPHL and 114 cHL patients highlighted >2-fold enrichment in programmed cell death-1 (PD-1) and T-cell Ig and ITIM domain (TIGIT) gene expression for NLPHL versus cHL. Multiplex imaging showed marked increase in intra-tumoral protein expression of PD-1+ (and/or TIGIT+) CD4+ T-cells and PD-1+CD8+ T-cells in NLPHL compared to cHL. This included T-cells that rosetted with lymphocyte predominant (LP) and Hodgkin Reed-Sternberg (HRS) cells. In NLPHL, intra-tumoral PD-1+CD4+ T-cells frequently expressed TCF-1, a marker of heightened T-cell response to ICB. The peripheral blood signatures between HLs were also distinct, with higher levels of PD-1+TIGIT+ in TH1, TH2, and regulatory CD4+ T-cells in NLPHL versus cHL. Circulating PD-1+CD4+ had high levels of TCF-1. Notably, in both lymphomas, highly expanded populations of clonal TIGIT+PD-1+CD4+ and TIGIT+PD-1+CD8+ T-cells in the blood were also present in the TME, indicating that immune-checkpoint expressing T-cells circulated between intra-tumoral and blood compartments. In in vitro assays, ICB was capable of reducing rosette formation around LP and HRS cells, suggesting that disruption of rosetting may be a mechanism of action of ICB in HL. Overall, results indicate that further evaluation of ICB is warranted in NLPHL.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".