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Record W4296351412 · doi:10.3389/fcell.2022.932510

Prostate cancer cells of increasing metastatic potential exhibit diverse contractile forces, cell stiffness, and motility in a microenvironment stiffness-dependent manner

2022· article· en· W4296351412 on OpenAlexafffund
Clayton Molter, Eliana F. Muszynski, Yuanyuan Tao, Tanisha Trivedi, Anna Clouvel, Allen J. Ehrlicher

Bibliographic record

VenueFrontiers in Cell and Developmental Biology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsMcGill University Health CentreMcGill University
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaInstitut de Valorisation des DonnéesMcGill UniversityCanada Excellence Research Chairs, Government of CanadaProstate Cancer CanadaCanada Research Chairs
KeywordsDU145ContractilityLNCaPTumor microenvironmentMetastasisCancer cellProstate cancerMotilityChemistryCellCancer researchCancerBiologyCell biologyMedicineInternal medicineEndocrinologyBiochemistry

Abstract

fetched live from OpenAlex

During metastasis, all cancer types must migrate through crowded multicellular environments. Simultaneously, cancers appear to change their biophysical properties. Indeed, cell softening and increased contractility are emerging as seemingly ubiquitous biomarkers of metastatic progression which may facilitate metastasis. Cell stiffness and contractility are also influenced by the microenvironment. Stiffer matrices resembling the tumor microenvironment cause metastatic cells to contract more strongly, further promoting contractile tumorigenic phenotypes. Prostate cancer (PCa), however, appears to deviate from these common cancer biophysics trends; aggressive metastatic PCa cells appear stiffer, rather than softer, to their lowly metastatic PCa counterparts. Although metastatic PCa cells have been reported to be more contractile than healthy cells, how cell contractility changes with increasing PCa metastatic potential has remained unknown. Here, we characterize the biophysical changes of PCa cells of various metastatic potential as a function of microenvironment stiffness. Using a panel of progressively increasing metastatic potential cell lines (22RV1, LNCaP, DU145, and PC3), we quantified their contractility using traction force microscopy (TFM), and measured their cortical stiffness using optical magnetic twisting cytometry (OMTC) and their motility using time-lapse microscopy. We found that PCa contractility, cell stiffness, and motility do not universally scale with metastatic potential. Rather, PCa cells of various metastatic efficiencies exhibit unique biophysical responses that are differentially influenced by substrate stiffness. Despite this biophysical diversity, this work concludes that mechanical microenvironment is a key determinant in the biophysical response of PCa with variable metastatic potentials. The mechanics-oriented focus and methodology of the study is unique and complementary to conventional biochemical and genetic strategies typically used to understand this disease, and thus may usher in new perspectives and approaches.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.698

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.207
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations50
Published2022
Admission routes2
Has abstractyes

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