Matrix softness regulates plasticity of tumour-repopulating cells via H3K9 demethylation and Sox2 expression
Bibliographic record
Abstract
Tumour-repopulating cells (TRCs) are a self-renewing, tumorigenic subpopulation of cancer cells critical in cancer progression. However, the underlying mechanisms of how TRCs maintain their self-renewing capability remain elusive. Here we show that relatively undifferentiated melanoma TRCs exhibit plasticity in Cdc42-mediated mechanical stiffening, histone 3 lysine residue 9 (H3K9) methylation, Sox2 expression and self-renewal capability. In contrast to differentiated melanoma cells, TRCs have a low level of H3K9 methylation that is unresponsive to matrix stiffness or applied forces. Silencing H3K9 methyltransferase G9a or SUV39h1 elevates the self-renewal capability of differentiated melanoma cells in a Sox2-dependent manner. Mechanistically, H3K9 methylation at the Sox2 promoter region inhibits Sox2 expression that is essential in maintaining self-renewal and tumorigenicity of TRCs both in vitro and in vivo. Taken together, our data suggest that 3D soft-fibrin-matrix-mediated cell softening, H3K9 demethylation and Sox2 gene expression are essential in regulating TRC self-renewal. Soft 3D gels can promote the growth of tumour-repopulating cells, a self-renewing subpopulation of cancer cells critical in cancer progression. Here, the authors investigate the mechanism behind this phenomenon and show that the histone 3 lysine residue 9 methylation and Sox2 are controlling this process.
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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".