Innate and Adoptive Immune Cells Contribute to Natural Resistance to Systemic Metastasis of B16 Melanoma
Bibliographic record
Abstract
The greatest hurdle in cancer treatment is the metastasis of primary tumors to distant organs. Our knowledge on how different immune cells, in the absence of exogenous stimulation, prevent tumor metastasis in distant organs is poorly understood. Using a highly metastatic murine lung B16 melanoma cell line BL6-10, we employed naive mice that genetically lack CD4(+) or CD8(+) T cells, or are depleted of dendritic cells (DCs) or natural killer (NK) cells to understand the relative importance of these cells in metastasis prevention. Irrespective of the presence of naïve CD4(+) T, CD8(+) T, DCs, or NK cells, lungs, which act as primary site of predilection for B16 melanoma, readily developed numerous lung BL6-10 melanoma colonies. However, their absence led to B16 melanoma metastasis in variable proportions to distant organs, particularly livers, kidneys, adrenals, ovaries, and hearts. NK cells mediate prevention of BL6-10 metastasis to various organs, especially to livers. Mechanistically, CD40L signaling, a critical factor required for DC licensing and CD8(+) cytotoxic T lymphocyte (CTL) responses, was required for CD4(+) T cell-mediated prevention of systemic BL6-10 metastasis. These results suggest that the composition and functions of different immune cells in distant tissue microenvironments (distant organs other than primary sites of predilection) robustly mediate natural resistance against melanoma metastasis. Thus, harnessing these immune cells' responses in immunotherapeutics would considerably limit organ metastasis.
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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 | 0.000 |
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".