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Modular micro-physiological human tumor/tissue models based on decellularized tissue for improved preclinical testing

Johanna Kühnemundt1, Heidi Leifeld1, Florian Scherg1

  • 1Department Tissue Engineering and Regenerative Medicine, University Hospital Würzburg, Würzburg, Germany.

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|December 14, 2020
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Summary
This summary is machine-generated.

Researchers developed advanced 3D tumor models using decellularized tissue for more accurate drug testing. These human tumor models improve preclinical testing and reduce animal use.

Keywords:
bioreactor culturecombinatorial drug predictionsimmunotherapies modelinginvasivenessmodular tumor tissue models

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Area of Science:

  • Biomedical Engineering
  • Cancer Research
  • Tissue Engineering

Background:

  • Traditional 2D cell cultures and animal models show limitations in predicting drug efficacy due to high attrition rates.
  • There is a critical need for more predictive in vitro models of human tumors for preclinical drug development.
  • Previous work demonstrated 3D models on decellularized tissue matrices offer improved predictivity and chemoresistance.

Purpose of the Study:

  • To develop and validate modular 3D human tumor models with tissue-like features for enhanced preclinical drug testing.
  • To investigate the utility of these 3D models for drug target prediction, mode-of-action analysis, and immune-oncology studies.
  • To establish a scalable and cost-effective platform for generating multiple 3D tumor models from a single biological source.

Main Methods:

  • Utilizing decellularized porcine intestine to create a tissue matrix supporting 3D tumor models for breast, lung, colorectal cancer (CRC), and leukemia.
  • Culturing cells in dynamic bioreactors to promote growth within the matrix's diverse niches (monolayer, crypts, deeper layers).
  • Integrating computational (in silico) drug target prediction with in vitro validation, and enriching models with human endothelial cells (hECs) and immune cells for complex assays.

Main Results:

  • Gene expression analysis revealed significant changes in proliferation, apoptosis, and stemness markers in 3D versus 2D cultures.
  • In silico prediction identified an additive effect of metformin and gefitinib for lung cancer, which was successfully validated in vitro.
  • 3D models demonstrated enhanced investigation of immune cell transmigration and revealed lower basal apoptosis rates with peripheral blood mononuclear cells (PBMCs) compared to 2D models.

Conclusions:

  • Modular 3D human tumor models recapitulate tissue architecture and physiological conditions, offering superior predictivity for preclinical drug screening.
  • These models facilitate the study of drug mechanisms, immune interactions, and cellular responses, paving the way for reduced reliance on animal testing.
  • The platform provides a scalable method for generating diverse cancer models, supporting personalized medicine approaches and accelerating drug discovery.