Tumor-Draining Lymph Node Reconstruction Promotes B Cell Activation During E0771 Mouse Breast Cancer Growth
Bibliographic record
Abstract
Lymph node metastasis is associated with tumor aggressiveness and poor prognosis in patients. Despite its significance in cancer progression, how immune cells in the tumor-draining lymph node (TDLN) participate in cancer immune regulation remains poorly understood. It has been reported that both anti-tumor and exhausted tumor-specific T cells can be induced in the TDLNs; however, B cell activation and maturation in the TDLN has received far less attention. In our studies using C57BL/6 mouse syngeneic E0771 breast cancer or B16F10 melanoma cell lines, tumor-associated antigens were found colocalized with the follicular dendritic cells (FDCs) in the germinal centers (GCs), where antigen-specific B cell maturation occurs. LN conduits and the subcapsular sinus (SCS) macrophages are two major routes of antigen trafficking to FDCs. Tumor growth induced LN conduit expansion in the B cell zone and disrupted the SCS macrophage layer, facilitating both the entry of tumor-associated antigens into the B cell zone and access to FDCs located in the GCs. Regional delivery of clodronate liposome specifically depleted SCS macrophages in the TDLN, increasing GC formation, and promoting tumor growth. Our study suggests that TDLN reconstruction creates a niche that favors B cell activation and maturation during tumor growth.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".