Main Manuscript for Cell migration simulator-based biomarkers for glioblastoma.
| العنوان: | Main Manuscript for Cell migration simulator-based biomarkers for glioblastoma. |
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| المؤلفون: | Hou J; Department of Biomedical Engineering, University of Minnesota - Twin Cities., McMahon M; Department of Biomedical Engineering, University of Minnesota - Twin Cities., Sarkaria JN; Department of Radiation Oncology, Mayo Clinic Rochester., Chen CC; Department of Neurosurgery, University of Minnesota - Twin Cities., Odde DJ; Department of Biomedical Engineering, University of Minnesota - Twin Cities. |
| المصدر: | BioRxiv : the preprint server for biology [bioRxiv] 2023 Feb 24. Date of Electronic Publication: 2023 Feb 24. |
| نوع المنشور: | Preprint |
| اللغة: | English |
| بيانات الدورية: | Country of Publication: United States NLM ID: 101680187 Publication Model: Electronic Cited Medium: Internet NLM ISO Abbreviation: bioRxiv Subsets: PubMed not MEDLINE |
| مستخلص: | Glioblastoma is the most aggressive malignant brain tumor with poor survival due to its invasive nature driven by cell migration, with unclear linkage to transcriptomic information. Here, we applied a physics-based motor-clutch model, a cell migration simulator (CMS), to parameterize the migration of glioblastoma cells and define physical biomarkers on a patient-by-patient basis. We reduced the 11-dimensional parameter space of the CMS into 3D to identify three principal physical parameters that govern cell migration: motor number - describing myosin II activity, clutch number - describing adhesion level, and F-actin polymerization rate. Experimentally, we found that glioblastoma patient-derived (xenograft) (PD(X)) cell lines across mesenchymal (MES), proneural (PN), classical (CL) subtypes and two institutions (N=13 patients) had optimal motility and traction force on stiffnesses around 9.3kPa, with otherwise heterogeneous and uncorrelated motility, traction, and F-actin flow. By contrast, with the CMS parameterization, we found glioblastoma cells consistently had balanced motor/clutch ratios to enable effective migration, and that MES cells had higher actin polymerization rates resulting in higher motility. The CMS also predicted differential sensitivity to cytoskeletal drugs between patients. Finally, we identified 11 genes that correlated with the physical parameters, suggesting that transcriptomic data alone could potentially predict the mechanics and speed of glioblastoma cell migration. Overall, we describe a general physics-based framework for parameterizing individual glioblastoma patients and connecting to clinical transcriptomic data, that can potentially be used to develop patient-specific anti-migratory therapeutic strategies generally. Competing Interests: Competing Interest Statement: There is no conflict of interest. |
| فهرسة مساهمة: | Keywords: Biological Sciences; biophysical modeling; biophysics and computational biology and medical sciences; cell migration; glioblastoma subtypes; motor-clutch model; patient derived cell lines |
| تواريخ الأحداث: | Date Created: 20230303 Latest Revision: 20231019 |
| رمز التحديث: | 20240628 |
| مُعرف محوري في PubMed: | PMC9980090 |
| DOI: | 10.1101/2023.02.24.529880 |
| PMID: | 36865270 |
| قاعدة البيانات: | MEDLINE |
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