Macrophages orchestrate breast cancer early dissemination and metastasis
Bibliographic record
Abstract
Abstract Cancer cell dissemination during very early stages of breast cancer proceeds through poorly understood mechanisms. Here we show, in a mouse model of HER2 + breast cancer, that a previously described sub-population of early-evolved cancer cells requires macrophages for early dissemination. Depletion of macrophages specifically during pre-malignant stages reduces early dissemination and also results in reduced metastatic burden at end stages of cancer progression. Mechanistically, we show that, in pre-malignant lesions, CCL2 produced by cancer cells and myeloid cells attracts CD206 + /Tie2 + macrophages and induces Wnt-1 upregulation that in turn downregulates E-cadherin junctions in the HER2 + early cancer cells. We also observe macrophage-containing tumor microenvironments of metastasis structures in the pre-malignant lesions that can operate as portals for intravasation. These data support a causal role for macrophages in early dissemination that affects long-term metastasis development much later in cancer progression. A pilot analysis on human specimens revealed intra-epithelial macrophages and loss of E-cadherin junctions in ductal carcinoma in situ, supporting a potential clinical relevance.
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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".