Temporal progression of metastasis in lung: cell survival, dormancy, and location dependence of metastatic inefficiency.
Bibliographic record
Abstract
Cancer metastasis is an inefficient process. The steps in metastasis responsible for this inefficiency and how metastatic inefficiency can vary in different locations within an organ remain poorly understood. B16F10 cells were injected to target mouse lung, and at sequential times thereafter we quantified in lung the time course of: (a) overall cell survival and metastatic development; and (b) local cell survival and growth with respect to the lung surface and specific interior structures. We found high rates of initial survival of cells trapped in the lung circulation, extravasation into lung tissue, and subsequent survival of extravasated solitary cells (74% at day 3) before metastasis formation. However, at the time of initial replication of metastatic cells a major loss of cells occurred. Although only a small proportion of injected cells started to form metastases, most of these developed into macroscopic tumors. Solitary cells found at later times were dormant. Thus, overall metastatic inefficiency was largely due to postextravasation events affecting solitary cells. Regionally within the lung, cells and metastases were randomly distributed to day 4, but by day 10 preferential tumor growth was found along the lung surface and around arterial and venous vessels. Thus, trapping and early growth of injected cells was unaffected by location within the lung, whereas subsequent metastatic growth was enhanced in specific microenvironments. This study: (a) quantifies early temporal and spatial progression of metastasis in lung; (b) documents persistence of solitary dormant cells; and (c) shows that metastatic inefficiency depends on the initiation of growth in a subset of extravasated cells, whereas continued growth of metastases occurs preferentially in specific tissue environments.
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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".