Impact of mechanical compressive stress on pancreatic cancer progression
Bibliographic record
Abstract
Context: Mechanical compressive stress arises during pancreatic cancer progression (PDAC). In vitro, compression forces decrease PDAC cell proliferation, increase invasiveness, and induce resistance to chemotherapies. In vivo, the importance of compression is unknown. In PDAC, increased compressive stress happens simultaneously with the second wave of genetic alterations (p53 mutations/truncations) after KRAS oncogenic mutations; it is also linked with overexpression/activation of the PI3-Kinases (PI3K) pathway. We think that compression favors selective genetic backgrounds that modify the signaling environment in cells and thus cell fate. Experimental design: We generated compressive stresses to spheroids derived from PDAC cells with KRAS G12D mutation, in which p53 R172H mutation or p53 R172H;R210* truncation are induced sequentially. Further, we applied a compressive stress to KRAS G12D ±p53 R172H /p53 R172H;R210* mutated mouse allografts using a compressive device. We also used the punch method in order to evaluate the relaxation of tumors depending on their genetic background. Results: Compression decreased the spheroid growth (<30%). However, p53 R172H mutated PDAC cells developed a resistance to compression and continued to proliferate. This mutation associated with a truncation of p53 accentuated this resistance, even bringing a proliferative advantage. A transcriptomic analysis of KRAS G12D , p53 mutated and p53 truncated spheroids under compression was performed. This analysis showed a modification in adhesion properties via plasma membrane and RTK signaling activity, mechanisms regulated by PI3K pathway. In parallel, we observed, in vivo, that the growth of KRAS G12D mutated tumors decreased by 40% under compression, whereas the size of tumors with p53 R172H;R210* truncated form was similar with or without compression. Finally, KRAS G12D tumors relaxed more easily compared to the p53 R172H and p53 R172H;R210* tumors; this was due to a greater cellular and matrix homogeneity in these tumors compared to p53 R172H and p53 R172H;R210* tumors. Conclusion: Growth under pressure can influence the progression of PDAC promoting selective genetic background and activation of oncogenic signaling pathways. These observations open the way to integrate the mechanical context in the management of patients with PDAC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".