6533b85dfe1ef96bd12bdfa0
RESEARCH PRODUCT
Digital Image Analysis Applied to Tumor Cell Proliferation, Aggressiveness, and Migration-Related Protein Synthesis in Neuroblastoma 3D Models
Amparo López-carrascoAmparo López-carrascoJosep SamitierRebeca Burgos-panaderoRebeca Burgos-panaderoAndrea García-lizarribarEzequiel MonferrerEzequiel MonferrerRosa NogueraRosa NogueraSabina SanegreSamuel NavarroSamuel NavarroSusana Martín-vañóSusana Martín-vañósubject
Stromal cellSchwann cellBiology3D cancer modelingvitronectinCatalysisArticleInorganic Chemistrylcsh:ChemistryNeuroblastomaCell MovementNeuroblastomaCell Line TumorProtein biosynthesismedicineImage Processing Computer-AssistedHumansPhysical and Theoretical ChemistryMolecular Biologylcsh:QH301-705.5SpectroscopyCell ProliferationCell growthOrganic ChemistryCancerGeneral Medicinemedicine.diseaseDOCK8Computer Science ApplicationsNeoplasm Proteinsmedicine.anatomical_structurelcsh:Biology (General)lcsh:QD1-999Protein BiosynthesisCancer researchbiology.proteinKANK1preclinical therapeutic studiesVitronectinDock8Ki67description
Patient-derived cancer 3D models are a promising tool that will revolutionize personalized cancer therapy but that require previous knowledge of optimal cell growth conditions and the most advantageous parameters to evaluate biomimetic relevance and monitor therapy efficacy. This study aims to establish general guidelines on 3D model characterization phenomena, focusing on neuroblastoma. We generated gelatin-based scaffolds with different stiffness and performed SK-N-BE(2) and SH-SY5Y aggressive neuroblastoma cell cultures, also performing co-cultures with mouse stromal Schwann cell line (SW10). Model characterization by digital image analysis at different time points revealed that cell proliferation, vitronectin production, and migration-related gene expression depend on growing conditions and are specific to the tumor cell line. Morphometric data show that 3D in vitro models can help generate optimal patient-derived cancer models, by creating, identifying, and choosing patterns of clinically relevant artificial microenvironments to predict patient tumor cell behavior and therapeutic responses.
year | journal | country | edition | language |
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2020-11-17 | International Journal of Molecular Sciences |