The Epigenetic Influence of Tumor and Embryonic Microenvironments: How Different are They?
Bibliographic record
Abstract
The microenvironment is being increasingly recognized as a critical component in tumor progression and metastases. As such, the bi-directional signaling of extracellular mediators that promote tumor growth within the microenvironment is a focus of intense scrutiny. Interestingly, there are striking similarities between the phenotypes of aggressive tumor and embryonic stem cells, particularly with respect to specific signaling pathways underlying their intriguing plasticity. Here, we demonstrate the epigenetic influence of the hESC microenvironment on the reprogramming of aggressive melanoma cells using an innovative 3-D model. Specifically, our laboratory has previously demonstrated the redifferentiation of these melanoma cells to a more melanocyte-like phenotype (Postovit et al., Stem Cells 24(3):501-505, 2006), and now we show the loss of VE-Cadherin expression (indicative of a plastic vasculogenic phenotype) and the loss of Nodal expression (a plasticity stem cell marker) in tumor cells exposed to the hESC microenvironment. Further studies with the 3-D culture model revealed the epigenetic influence of aggressive melanoma cells on hESCs resulting in the down-regulation of plasticity markers and the emergence of phenotype-specific genes. Additional studies with the aggressive melanoma conditioned matrix microenvironment demonstrated the transdifferentiation of normal melanocytes into melanoma-like cells exhibiting a vasculogenic phenotype. Collectively, these studies have advanced our understanding of the epigenetic influence associated with the microenvironments of hESCs and aggressive melanoma cells, and shed new light on their therapeutic implications. Moreover, we have a better appreciation of the convergence of embryonic and tumorigenic signaling pathways that might stimulate further consideration of targeting Nodal in aggressive tumor cells resulting in a down-regulation of tumorigenic potential and plasticity.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".