Tumor microenvironment differences between diagnostic and relapsed classic Hodgkin lymphoma revealed by scRNA-seq
Bibliographic record
Abstract
ABSTRACT: Classical Hodgkin lymphoma (CHL) is characterized by a complex tumor microenvironment (TME) that supports disease progression. Although immune cell recruitment by Hodgkin and Reed-Sternberg (HRS) cells is well documented, the role of nonmalignant B cells in relapse remains unclear. Using single-cell RNA sequencing (scRNA-seq) on paired diagnostic and relapsed CHL samples, we identified distinct shifts in B-cell populations, particularly an enrichment of naïve B cells and a reduction of memory B cells in early-relapse CHL compared to late-relapse and newly diagnosed CHL. The enrichment of naïve B cells in early relapse biopsies was confirmed in independent validation cohorts using scRNA-seq and immunohistochemistry. Notably, naïve B cells in early-relapse samples exhibited high expression of LGALS9, an immunosuppressive gene encoding galectin-9, which binds to HAVCR2 (T-cell immunoglobulin and mucin domain-containing protein 3 [TIM-3]) on regulatory T cells (Tregs). Cell-cell interaction analysis revealed the importance of interactions between LGALS9+ naïve B cells and HAVCR2+ Tregs in the early-relapse setting. Spatial analysis by imaging mass cytometry confirmed close proximity of galectin-9-positive naïve B cells with TIM-3+ CD4+ T cells and HRS cells, pointing to their role in shaping an immunosuppressive niche. Our findings highlight a previously unrecognized population of galectin-9-positive naïve B cells with immunoregulatory potential in early-relapse CHL and provide new insights into the spatial and transcriptional architecture of the relapsed TME in 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".