Data from Inflammation-Induced Metastatic Colonization of the Lung Is Facilitated by Hepatocyte Growth Factor-Secreting Monocyte-Derived Macrophages
Bibliographic record
Abstract
Abstract A rate-limiting step for circulating tumor cells to colonize distant organ sites is their ability to locate a microenvironmental niche that supports their survival and growth. This can be achieved by features intrinsic to the tumor cells and/or by the conditioning of a “premetastatic” niche. To determine if pulmonary inflammation promotes the latter, we initiated models for inflammatory asthma, hypersensitivity pneumonitis, or bleomycin-induced sterile inflammation before introducing tumor cells with low metastatic potential into the circulation. All types of inflammation increased the end-stage metastatic burden of the lungs 14 days after tumor cell inoculation without overtly affecting tumor extravasation. Instead, the number and size of early micrometastatic lesions found within the interstitial tissues 96 hours after tumor cell inoculation were increased in the inflamed lungs, coincident with increased tumor cell survival and the presence of nearby inflammation-induced monocyte-derived macrophages (MoDM; CD11b+CD11c+). Remarkably, the adoptive transfer of these MoDM was sufficient to increase lung metastasis in the absence of inflammation. These inflammation-induced MoDM secrete a number of growth factors and cytokines, one of which is hepatocyte growth factor (HGF), that augmented tumor cell survival under conditions of stress in vitro. Importantly, blocking HGF signaling with the cMET inhibitor capmatinib abolished inflammation-induced early micrometastatic lesion formation in vivo. These findings indicate that inflammation-induced MoDM and HGF in particular increase the efficiency of early metastatic colonization in the lung by locally preconditioning the microenvironment. Implications: Inflammation preconditions the distant site microenvironment to increase the metastatic potential of tumor cells that arrive there.
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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) | 0.006 | 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".