Abstract 5784: An image based assay platform for drug screening with a 3D bioprinted vascularized lung cancer model

Abstract 3D tissue models that mimic the physiology of human tissues and organs are being developed in multi-well plate format as predictive assays for drug screening. 3D bioprinting enables the fabrication of tissues with precision and reproducibility in a 96-well plate for medium throughput drug screening. We have established robust protocols for the 3D bioprinting vascularized lung tissue models using human primary pulmonary endothelial cells constitutively expressing green fluorescent protein (GFP), pericytes, and fibroblasts mixed with a fibrin-based hydrogel. We create a vascularized lung tissue model in a ring-shaped structure that facilitates image-based quantification of vasculogenesis and angiogenesis. This bioprinted vascularized lung tissue model was used as a tumor microenvironment to grow lung cancer cells. We incorporated three immortalized lung cancer cell lines, A549, H1975, and H460, constitutively expressing red fluorescent protein (RFP) into the 3D bioprinted vascularized lung tissues. Cancer cells were added as single cells embedded in the hydrogel (1K cancer cells/well) covering the bioprinted vasculature. Fluorescence microscopy was used to quantify the growth and morphology of the cancer cells by measuring total red fluorescence intensity surface roundness and compactness of red fluorescence objects, as well as angiogenesis from the green fluorescence intensity. After 21 days of culture, the three cancer cell lines showed distinct growth morphology and migration patterns. We characterized tumor localization according to the three regions of interest: angiogenesis outwards or inwards and tumor growth inside or outside the bioprinted vasculature area. A549 and H1975 showed a strong tendency to migrate towards the bioprinted vascularized structure, growing as low compact structures. The H460 cell line formed small compact spheroid structures and did not prefer the vasculature area. We also compared cancer cell growth and morphology with and without vascular components to assess any effects of the vasculature on tumor growth. We observed that this study helps demonstrate the usefulness of the bioprinted vascularized lung tissue cancer model as an assay platform for screening for anti-cancer drugs in an assay that includes the tumor microenvironment. Keywords: 3D bioprinted, 3D cancer-vascularized lung tissue model, drug Screening, Citation Format: Fahimeh Shahabipour, Yuchi Yuchi Chen, Yen-Ting Tung, Marc Ferrer, Min Jae Song. An image based assay platform for drug screening with a 3D bioprinted vascularized lung cancer model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5784.

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Journal
Cancer Research
Published
2025-04-21
DOI
https://doi.org/10.1158/1538-7445.am2025-5784
Primary Topic
Radiomics and Machine Learning in Medical Imaging
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article
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article

Abstract 5784: An image based assay platform for drug screening with a 3D bioprinted vascularized lung cancer model

Marc Ferrer, Min Jae Song, Yu-Chi Chen, Yen‐Ting Tung et al.
Cancer Research
Radiomics and Machine Learning in Medical Imaging
article

Abstract 5784: An image based assay platform for drug screening with a 3D bioprinted vascularized lung cancer model

Marc Ferrer, Min Jae Song, Yu-Chi Chen, Yen‐Ting Tung, Fahimeh Shahabipour
article en

Abstract

Abstract 3D tissue models that mimic the physiology of human tissues and organs are being developed in multi-well plate format as predictive assays for drug screening. 3D bioprinting enables the fabrication of tissues with precision and reproducibility in a 96-well plate for medium throughput drug screening. We have established robust protocols for the 3D bioprinting vascularized lung tissue models using human primary pulmonary endothelial cells constitutively expressing green fluorescent protein (GFP), pericytes, and fibroblasts mixed with a fibrin-based hydrogel. We create a vascularized lung tissue model in a ring-shaped structure that facilitates image-based quantification of vasculogenesis and angiogenesis. This bioprinted vascularized lung tissue model was used as a tumor microenvironment to grow lung cancer cells. We incorporated three immortalized lung cancer cell lines, A549, H1975, and H460, constitutively expressing red fluorescent protein (RFP) into the 3D bioprinted vascularized lung tissues. Cancer cells were added as single cells embedded in the hydrogel (1K cancer cells/well) covering the bioprinted vasculature. Fluorescence microscopy was used to quantify the growth and morphology of the cancer cells by measuring total red fluorescence intensity surface roundness and compactness of red fluorescence objects, as well as angiogenesis from the green fluorescence intensity. After 21 days of culture, the three cancer cell lines showed distinct growth morphology and migration patterns. We characterized tumor localization according to the three regions of interest: angiogenesis outwards or inwards and tumor growth inside or outside the bioprinted vasculature area. A549 and H1975 showed a strong tendency to migrate towards the bioprinted vascularized structure, growing as low compact structures. The H460 cell line formed small compact spheroid structures and did not prefer the vasculature area. We also compared cancer cell growth and morphology with and without vascular components to assess any effects of the vasculature on tumor growth. We observed that this study helps demonstrate the usefulness of the bioprinted vascularized lung tissue cancer model as an assay platform for screening for anti-cancer drugs in an assay that includes the tumor microenvironment. Keywords: 3D bioprinted, 3D cancer-vascularized lung tissue model, drug Screening, Citation Format: Fahimeh Shahabipour, Yuchi Yuchi Chen, Yen-Ting Tung, Marc Ferrer, Min Jae Song. An image based assay platform for drug screening with a 3D bioprinted vascularized lung cancer model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5784.

Cancer ResearchVol. 85(8_Supplement_1)
National Center for Advancing Translational Sciences (US)
Openalex Percentile: Top 88%
Radiomics and Machine Learning in Medical Imaging
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