Human embryonic stem cell microenvironment suppresses the tumorigenic phenotype of aggressive cancer cells
Bibliographic record
Abstract
Embryonic stem cells sustain a microenvironment that facilitates a balance of self-renewal and differentiation. Aggressive cancer cells, expressing a multipotent, embryonic cell-like phenotype, engage in a dynamic reciprocity with a microenvironment that promotes plasticity and tumorigenicity. However, the cancer-associated milieu lacks the appropriate regulatory mechanisms to maintain a normal cellular phenotype. Previous work from our laboratory reported that aggressive melanoma and breast carcinoma express the embryonic morphogen Nodal, which is essential for human embryonic stem cell (hESC) pluripotency. Based on the aberrant expression of this embryonic plasticity gene by tumor cells, this current study tested whether these cells could respond to regulatory cues controlling the Nodal signaling pathway, which might be sequestered within the microenvironment of hESCs, resulting in the suppression of the tumorigenic phenotype. Specifically, we discovered that metastatic tumor cells do not express the inhibitor to Nodal, Lefty, allowing them to overexpress this embryonic morphogen in an unregulated manner. However, exposure of the tumor cells to a hESC microenvironment (containing Lefty) leads to a dramatic down-regulation in their Nodal expression concomitant with a reduction in clonogenicity and tumorigenesis accompanied by an increase in apoptosis. Furthermore, this ability to suppress the tumorigenic phenotype is directly associated with the secretion of Lefty, exclusive to hESCs, because it is not detected in other stem cell types, normal cell types, or trophoblasts. The tumor-suppressive effects of the hESC microenvironment, by neutralizing the expression of Nodal in aggressive tumor cells, provide previously unexplored therapeutic modalities for cancer treatment.
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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 | 0.000 |
| 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".