Impact of Mechanical and Architectural Signals in the Tumor Microenvironment on Melanoma
Bibliographic record
Abstract
Compared to sun-exposed melanomas, acral melanomas are genetically diverse and occur in areas with low sun exposure and high mechanical loads. During metastatic growth, melanomas invade from the epidermis to the dermis layers through dense tumor stroma and are exposed to fibrillar collagen architectures and mechanical stresses. However, the role of these signals during acral melanoma pathogenesis is not well understood. In this study, a novel 3D in vitro platform comprising heterogeneous, bundled collagen architectures recapitulates mechanical and architectural signals from the melanoma tumor microenvironment. YUSEEP patient-derived human acral melanoma and B16F10 mouse melanoma single cells and spheroids are embedded in collagen or bundled collagen hydrogels and mechanically compressed to quantitatively profile cellular responses to these cues, including viability, DNA damage and repair, proliferation, invasion, and nuclear and cellular morphologies. Spatial confinement of cells in a microfluidic platform, solid mechanics simulations, and pharmacological inhibition studies lend further mechanistic insights into these cues. Results reveal mechanical compression induces DNA damage and repair, while interactions with bundled collagen promote a malignant, protrusive phenotype. The findings further suggest that actin polymerization and contractility inhibitors may rescue DNA damage and mitigate malignancy upon compression, thereby potentially paving the way for novel therapeutic targets against acral melanomas.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".