Lung-Derived Factors Mediate Breast Cancer Cell Migration through CD44 Receptor-Ligand Interactions in a Novel Ex Vivo System for Analysis of Organ-Specific Soluble Proteins
Bibliographic record
Abstract
Breast cancer preferentially metastasizes to lung, lymph node, liver, bone, and brain. However, it is unclear whether properties of cancer cells, properties of organ microenvironments, or a combination of both is responsible for this observed organ tropism. We hypothesized that breast cancer cells exhibit distinctive migration/growth patterns in organ microenvironments that mirror common clinical sites of breast cancer metastasis and that receptor-ligand interactions between breast cancer cells and soluble organ-derived factors mediate this behavior. Using an ex vivo model system composed of organ-conditioned media (CM), human breast cancer cells (MDA-MB-231,MDA-MB-468, SUM149, and SUM159) displayed cell line-specific and organ-specific patterns of migration/proliferation that corresponded to their in vivo metastatic behavior. Notably, exposure to lung-CM increased migration of all cell lines and increased proliferation in two of four lines (P < .05). Several cluster of differentiation (CD) 44 ligands including osteopontin (OPN) and L-selectin (SELL) were identified in lung-CM by protein arrays. Immunodepletion of SELL decreased migration of MDA-MB-231 cells, whereas depletion of OPN decreased both migration and proliferation. Pretreatment of cells with a CD44-blocking antibody abrogated migration effects (P < .05). "Stemlike" breast cancer cells with high aldehyde dehydrogenase and CD44 (ALDH(hi)CD44(+)) responded in a distinct chemotactic manner toward organ-CM, preferentially migrating toward lung-CM through CD44 receptor-ligand interactions (P < .05). In contrast, organ-specific changes in migration were not observed for ALDH(low)CD44(-) cells. Our data suggest that interactions between CD44(+) breast cancer cells and soluble factors present in the lung microenvironment may play an important role in determining organotropic metastatic behavior.
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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".