Characterization of cancer‐associated fibroblasts and their spatial architecture reveals heterogeneity and survival associations in classic Hodgkin lymphoma
Bibliographic record
Abstract
Abstract Cancer‐associated fibroblasts (CAFs) are a heterogeneous population of stromal cells, which modulate the immune system and can have both pro‐ and anti‐tumorigenic effects. In classic Hodgkin lymphoma (cHL), the role of CAFs has remained largely undefined. We applied multiplexed immunofluorescence imaging and spatial analysis on tumor samples from two independent cHL patient cohorts ( n = 131 and n = 148) to study CAFs and their interactions with Hodgkin Reed–Sternberg (HRS) and tumor microenvironment (TME) cells at the single‐cell resolution. We show that higher proportions of CAFs are associated with favorable outcomes, independent of the clinical covariables. In contrast, a subset of CD45 + immune cells with strong fibroblast‐activation protein positivity, classified as macrophages, was less abundant in nodular sclerosis subtype and associated with worse outcomes. Neighborhood analysis allowed for the identification of colocalization or regional exclusion of phenotypically defined cell types and recurrent cellular neighborhoods. Despite the positive impact of CAF proportions on survival, patients with enrichment of platelet‐derived growth factor receptor beta (PDGFRB)‐positive CAFs in the vicinity of HRS cells had worse survival in both cohorts, independent of the clinical determinants. Our findings distinguish various subsets of CAFs and macrophages impacting survival in cHL and underscore the importance of the spatial arrangements in the TME.
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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".