Checkpoint protein expression in the tumor microenvironment defines the outcome of classical Hodgkin lymphoma patients
Bibliographic record
Abstract
Emerging evidence indicates a major impact for the tumor microenvironment (TME) and immune escape in the pathogenesis and clinical course of classical Hodgkin lymphoma (cHL). We used gene expression profiling (n = 88), CIBERSORT, and multiplex immunohistochemistry (n = 131) to characterize the immunoprofile of cHL TME and correlated the findings with survival. Gene expression analysis divided tumors into subgroups with T cell-inflamed and -noninflamed TME. Several macrophage-related genes were upregulated in samples with the non-T cell-inflamed TME, and based on the immune cell proportions, the samples clustered according to the content of T cells and macrophages. A cluster with high proportions of checkpoint protein (programmed cell death protein 1, PD-1 ligands, indoleamine 2,3 dioxygenase 1, lymphocyte-activation gene 3, and T-cell immunoglobulin and mucin domain containing protein 3) positive immune cells translated to unfavorable overall survival (OS) (5-year OS 76% vs 96%; P = .010) and remained an independent prognostic factor for OS in multivariable analysis (HR, 4.34; 95% CI, 1.05-17.91; P = .043). cHL samples with high proportions of checkpoint proteins overexpressed genes coding for cytolytic factors, proposing paradoxically that they were immunologically active. This checkpoint molecule gene signature translated to inferior survival in a validation cohort of 290 diagnostic cHL samples (P < .001) and in an expansion cohort of 84 cHL relapse samples (P = .048). Our findings demonstrate the impact of T cell- and macrophage-mediated checkpoint system on the survival of patients with cHL.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".