Stiffness of pancreatic cancer cells is associated with increased invasive potential
Bibliographic record
Abstract
Metastasis is a fundamentally physical process in which cells are required to deform through narrow gaps as they invade surrounding tissues and transit to distant sites. In many cancers, more invasive cells are more deformable than less invasive cells, but the extent to which mechanical phenotype, or mechanotype, can predict disease aggressiveness in pancreatic ductal adenocarcinoma (PDAC) remains unclear. Here we investigate the invasive potential and mechanical properties of immortalized PDAC cell lines derived from primary tumors and a secondary metastatic site, as well as noncancerous pancreatic ductal cells. To investigate how invasive behavior is associated with cell mechanotype, we flow cells through micron-scale pores using parallel microfiltration and microfluidic deformability cytometry; these results show that the ability of PDAC cells to passively transit through pores is only weakly correlated with their invasive potential. We also measure the Young's modulus of pancreatic ductal cells using atomic force microscopy, which reveals that there is a strong association between cell stiffness and invasive potential in PDAC cells. To determine the molecular origins of the variability in mechanotype across our PDAC cell lines, we analyze RNAseq data for genes that are known to regulate cell mechanotype. Our results show that vimentin, actin, and lamin A are among the most differentially expressed mechanoregulating genes across our panel of PDAC cell lines, as well as a cohort of 38 additional PDAC cell lines. We confirm levels of these proteins across our cell panel using immunoblotting, and find that levels of lamin A increase with both invasive potential and Young's modulus. Taken together, we find that stiffer PDAC cells are more invasive than more compliant cells, which challenges the paradigm that decreased cell stiffness is a hallmark of metastatic potential.
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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".