MYADM Activates RhoA-Mediated Ameboid Migration to Drive Metastasis
Bibliographic record
Abstract
Metastasis, the leading cause of cancer-related mortality, remains the most critical challenge in cancer treatment. Cancer cells can adopt amoeboid migration to facilitate metastasis, highlighting the need to elucidate the molecular pathways regulating the amoeboid migration phenotype. In this study, we identified that MYADM, a transmembrane protein expressed during myeloid cell maturation, enabled cancer cells to acquire amoeboid migration plasticity, promoting metastasis and contributing to poor patient outcomes. Cancer cells exploited MYADM-mediated adhesion and migration to mimic leukocyte trafficking. By interacting with RhoGDI, MYADM activated RhoA-mediated leukocyte trafficking-associated gene enrichment, invasiveness, membrane blebbing, and anoikis resistance. MYADM modulated chromatin accessibility-regulatory genes, influencing intermediate filament cytoskeleton dynamics of cancer cells and tumor tissues. MYADM loss in cancer cells triggered chromatin accessibility-driven death signaling, blocking metastasis, which was not observed in monocytes. These findings position MYADM as a potential therapeutic target to disrupt metastasis, offering avenues for clinical intervention. SIGNIFICANCE: Targeting MYADM, which interacts with RhoGDI to induce RhoA-driven amoeboid migration plasticity and chromatin dynamics, is a strategy to block metastatic progression that selectively impairs cancer cell survival.
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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".