Reprogramming multipotent tumor cells with the embryonic neural crest microenvironment
Bibliographic record
Abstract
The embryonic microenvironment is an important source of signals that program multipotent cells to adopt a particular fate and migratory path, yet its potential to reprogram and restrict multipotent tumor cell fate and invasion is unrealized. Aggressive tumor cells share many characteristics with multipotent, invasive embryonic progenitors, contributing to the paradigm of tumor cell plasticity. In the vertebrate embryo, multiple cell types originate from a highly invasive cell population called the neural crest. The neural crest and the embryonic microenvironments they migrate through represent an excellent model system to study cell diversification during embryogenesis and phenotype determination. Recent exciting studies of tumor cells transplanted into various embryo models, including the neural crest rich chick microenvironment, have revealed the potential to control and revert the metastatic phenotype, suggesting further work may help to identify new targets for therapeutic intervention derived from a convergence of tumorigenic and embryonic signals. In this mini-review, we summarize markers that are common to the neural crest and highly aggressive human melanoma cells. We highlight advances in our understanding of tumor cell behaviors and plasticity studied within the chick neural crest rich microenvironment. In so doing, we honor the tremendous contributions of Professor Elizabeth D. Hay toward this important interface of developmental and cancer biology.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".