Spatial and molecular profiling of classic Hodgkin lymphoma reveals an immunosuppressive mononuclear phagocyte network
Bibliographic record
Abstract
Abstract Although a lymph node infiltrated by classic Hodgkin lymphoma (cHL) is mostly composed of nonneoplastic immune cells, the malignant Hodgkin Reed-Sternberg cells (HRSC) successfully suppress an anti-tumor immune response, creating a cancer-permissive microenvironment. Accordingly, unleashing the dormant immune cells, for example by checkpoint inhibition, has been a central focus of recent therapeutic advances for this disease. Despite the efficacy of PD-1 blockade in relapsed cHL, a significant proportion of patients have suboptimal or non-durable responses, which may reflect HRSC and microenvironmental adaptation.Here, we profiled the global immune cell composition of normal and diseased lymph nodes by singlecell RNA sequencing, as a basis for interrogating the immediate vicinity of HRSC. We did so regionally and at cellular resolution, using spatial transcriptomics and multiplexed immunofluorescence, on fixed cHL tissue sections. It is established that tumor associated macrophages (TAMs) are associated with inferior outcomes following combination chemotherapy, but the function, interactions, and distribution of TAMs, and other mononuclear phagocytes, have not been fully explored.Our analyses revealed specific immune cells and functional states associated with HRSC. We discovered a non-random spatial organization of immunoregulatory mononuclear phagocytes (TAMs and classical monocytes) around HRSC, which express the immune checkpoints PD-L1, TIM-3, and the tryptophan-catabolizing protein IDO1. Dendritic cells (DC), key antigen presenting cells, are regionally polarized according to subtype. Specific DCs are spatially associated with the HRSC ‘neighborhood’ (cDC2), but plasmacytoid DCs and ‘activated’ DCs are excluded. These findings provide a basis for rational targeting and activation of the anti-tumor immune response in cHL.
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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) | 0.000 | 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".