From Spheroids to Tumor-on-a-Chip for Cancer Modeling and Therapeutic Testing

The high failure rate of anticancer drugs in clinical trials highlights the need for preclinical models that accurately reproduce the structural, biochemical, and mechanical complexity of human tumors. Conventional two-dimensional cultures and animal models often lack the physiological complexity required to predict clinical outcomes, driving the development of three-dimensional systems that better emulate the tumor microenvironment. Among these, microfluidic-based spheroid models have emerged as powerful tools for cancer research and drug screening. By integrating 3D spheroids with microfluidics, these platforms allow precise control of nutrient flow, oxygen gradients, shear stress, and interstitial pressure, while supporting co-culture with stromal, immune, and endothelial cells. Such systems enable the investigation of drug response, angiogenesis, metastasis, and immune interactions under dynamic and physiologically relevant conditions. This review summarizes recent advances in microfluidic spheroid models for cancer, covering both carcinomas and sarcomas, with an emphasis on device design, biomaterial integration, and translational validation. Key challenges remain, including technical complexity, scalability constraints, and the absence of standardized protocols. Overall, the merger of microfluidic technology with 3D spheroid culture provides a promising pathway toward predictive, ethical, and personalized preclinical testing, bridging the gap between in vitro modeling and clinical oncology.

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Publication Details

Journal
Micromachines
Published
2025-11-27
DOI
https://doi.org/10.3390/mi16121343
Citations
1
Primary Topic
3D Printing in Biomedical Research
Type
article
Field-Weighted Citation Impact
0.67

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article

From Spheroids to Tumor-on-a-Chip for Cancer Modeling and Therapeutic Testing

Maria Veronica Lipreri, Nicola Baldini, Sofia Avnet, Massimo Totaro
1 citations
Micromachines
3D Printing in Biomedical Research
0.67
article

From Spheroids to Tumor-on-a-Chip for Cancer Modeling and Therapeutic Testing

Maria Veronica Lipreri, Nicola Baldini, Sofia Avnet, Massimo Totaro
article en
1 citations

Abstract

The high failure rate of anticancer drugs in clinical trials highlights the need for preclinical models that accurately reproduce the structural, biochemical, and mechanical complexity of human tumors. Conventional two-dimensional cultures and animal models often lack the physiological complexity required to predict clinical outcomes, driving the development of three-dimensional systems that better emulate the tumor microenvironment. Among these, microfluidic-based spheroid models have emerged as powerful tools for cancer research and drug screening. By integrating 3D spheroids with microfluidics, these platforms allow precise control of nutrient flow, oxygen gradients, shear stress, and interstitial pressure, while supporting co-culture with stromal, immune, and endothelial cells. Such systems enable the investigation of drug response, angiogenesis, metastasis, and immune interactions under dynamic and physiologically relevant conditions. This review summarizes recent advances in microfluidic spheroid models for cancer, covering both carcinomas and sarcomas, with an emphasis on device design, biomaterial integration, and translational validation. Key challenges remain, including technical complexity, scalability constraints, and the absence of standardized protocols. Overall, the merger of microfluidic technology with 3D spheroid culture provides a promising pathway toward predictive, ethical, and personalized preclinical testing, bridging the gap between in vitro modeling and clinical oncology.

MicromachinesVol. 16(12)
Istituto Ortopedico Rizzoli (IT), University of Bologna (IT)
European Commission, Associazione Italiana per la Ricerca sul Cancro
Openalex Percentile: Top 24%
3D Printing in Biomedical Research
0.67
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From Spheroids to Tumor-on-a-Chip for Cancer Modeling and Therapeutic Testing — Maria Veronica Lipreri, Nicola Baldini, et al. · Micromachines (2025) | TGRS Research Map | TGRS