Dynamic Interactions between Cancer Cells and the Embryonic Microenvironment Regulate Cell Invasion and Reveal EphB6 as a Metastasis Suppressor
Bibliographic record
Abstract
UNLABELLED: Metastatic dissemination drives the high mortality associated with melanoma. However, difficulties in visualizing in vivo cell dynamics during metastatic invasion have limited our understanding of these cell behaviors. Recent evidence has revealed that melanoma cells exploit portions of their ancestral embryonic neural crest emigration program to facilitate invasion. What remains to be determined is how embryonic microenvironmental signals influence invasive melanoma cell behavior, and whether these signals are relevant to human disease. To address these questions, we interrogated the role of the neural crest microenvironment in dictating the spatiotemporal pattern of melanoma cell invasion in the chick embryo using 2-photon time-lapse microscopy. Results reveal that both permissive and inhibitory neural crest microenvironmental signals regulate the timing and direction of melanoma invasion to coincide with the neural crest migration pattern. These cues include bidirectional signaling mediated through the ephrin family of receptor tyrosine kinases. We demonstrate that EphB6 reexpression forces metastatic melanoma cells to deviate from the canonical migration pattern observed in the chick embryo transplant model. Furthermore, EphB6-expressing melanoma cells display significantly reduced metastatic potential in a chorioallantoic membrane (CAM) metastasis assay. These data on melanoma invasion in the embryonic neural crest and CAM microenvironments identify EphB6 as a metastasis suppressor in melanoma, likely acting at the stage of intravasation. IMPLICATIONS: This article links cellular metastasis to behaviors observed in the ancestrally related embryonic neural crest and demonstrates the powerful influence of the embryonic microenvironment in regulating cell migratory behavior.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".