CCL19⁺ fibroblasts define a proliferative niche in chronic lymphocytic leukemia
Bibliographic record
Abstract
Abstract Adaptive immune responses occur lymph nodes (LNs) in a microenvironment established by resident stromal cells. LNs are also a site of proliferation of chronic lymphocytic leukemia (CLL), a B cell cancer that alters LN structure in a stereotypic manner. To deeply characterize reactive and CLL LNs, we developed a single-cell RNA sequencing pipeline. We find that proliferation of CLL cells in proliferation centers (PCs), a CLL-specific niche, begins with transient upregulation of MYC, subsequent downregulation of which may limit CLL growth. PCs contain a distinct fibroblast population expressing CCL19 while CLL cells express the CCL19 receptor CCR7, providing a recruitment mechanism for CLL cells to PCs. Using informatic, spatial, and in situ analyses to identify ligand-receptor pairs involving PC CLL cells and nearby immune and stromal cells, we observe that PCs are enriched for macrophages expressing BAFF, the integrin αXβ2 heterodimer, and Galectin9, factors implicated in cell growth, adhesion, and immunosuppression. The most common predicted interactions in PCs involve CD74 and ligands such as MIF, and we find that CD74 blockade consistently inhibits CLL cell growth in culture. Our work highlights key features of the CLL proliferative niche and provides a roadmap for identifying vulnerabilities and new therapeutic strategies.
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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) | 0.001 | 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".