Inhibition of Metastatic Outgrowth from Single Dormant Tumor Cells by Targeting the Cytoskeleton
Bibliographic record
Abstract
Metastatic breast cancer may emerge from latent tumor cells that remain dormant at disseminated sites for many years. Identifying mechanisms regulating the switch from dormancy to proliferative metastatic growth has been elusive due to the lack of experimental models of tumor cell dormancy. We characterized the in vitro growth characteristics of cells that exhibit either dormant (D2.0R, MCF-7, and K7M2AS1.46) or proliferative (D2A1, MDA-MB-231, and K7M2) metastatic behavior in vivo. Although these cells proliferate readily in two-dimensional culture, we show that when grown in three-dimensional matrix, distinct growth properties of the cells were revealed that correlate to their dormant or proliferative behavior at metastatic sites in vivo. In three-dimensional culture, cells with dormant behavior in vivo remained cell cycle arrested with elevated nuclear expression of p16 and p27. The transition from quiescence to proliferation of D2A1 cells was dependent on fibronectin production and signaling through integrin beta1, leading to cytoskeletal reorganization with filamentous actin (F-actin) stress fiber formation. We show that phosphorylation of myosin light chain (MLC) by MLC kinase (MLCK) through integrin beta1 is required for actin stress fiber formation and proliferative growth. Inhibition of integrin beta1 or MLCK prevents transition from a quiescent to proliferative state in vitro. Inhibition of MLCK significantly reduces metastatic outgrowth in vivo. These studies show that the switch from dormancy to metastatic growth may be regulated, in part, through epigenetic signaling from the microenvironment, leading to changes in the cytoskeletal architecture of dormant cells. Targeting this process may provide therapeutic strategies for inhibition of the dormant-to-proliferative metastatic switch.
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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) | 0.001 | 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".