3D microgels to quantify tumor cell properties and therapy response dynamics

被引:9
作者
Wu, Nila C. [1 ]
Cadavid, Jose L. [1 ,2 ]
Tan, Xinzhu [2 ]
Latour, Simon [2 ]
Scaini, Stefano [2 ]
Makhijani, Priya [3 ,4 ]
McGaha, Tracy L. [3 ,4 ]
Ailles, Laurie [5 ,6 ]
McGuigan, Alison P. [1 ,2 ]
机构
[1] Univ Toronto, Inst Biomed Engn, Toronto, ON M5S 3G9, Canada
[2] Univ Toronto, Dept Chem Engn & Appl Chem, Toronto, ON M5S 3E5, Canada
[3] Univ Toronto, Tumour Immunotherapy Program, Princess Margaret Canc Ctr, Univ Hlth Network, Toronto, ON M5G 2C1, Canada
[4] Univ Toronto, Dept Immunol, Toronto, ON M5G 2C1, Canada
[5] Univ Toronto, Univ Hlth Network, Princess Margaret Canc Ctr, Toronto, ON M5G 2M9, Canada
[6] Univ Toronto, Dept Med Biophys, Toronto, ON M5G 2M9, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
3D tumor model; Image -based analysis; Drug response; Growth; Regrowth; Patient-derived organoids; CANCER-ASSOCIATED FIBROBLASTS; DRUG-RESISTANCE; COLON-CANCER; IN-VITRO; MODELS; METRICS; ASSAY; MACROPHAGES; INSTABILITY; SENSITIVITY;
D O I
10.1016/j.biomaterials.2022.121417
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Tumors contain heterogeneous and dynamic populations of cells that do not all display the fast-proliferating properties that traditional chemotherapies target. There is a need therefore, to develop novel treatment strate-gies that target diverse tumor cell properties. Identifying therapy combinations is challenging however. Current approaches have relied on cell lines cultured in monolayers with treatment response being assessed using endpoint metabolic assays, which although enable large-scale throughput, do not capture tumor heterogeneity. Here, a 3D in vitro tumor model using micro-molded hydrogels (microgels), the Gels for Live Analysis of Com-partmentalized Environments (GLAnCE) platform, is adapted into a 96-well plate format (96-GLAnCE) that in-tegrates patient-derived organoids (PDOs) and is combined with longitudinal automated imaging to address these limitations. Using 96-GLAnCE, two measures of tumor aggressiveness are quantified, tumor cell growth and in situ regrowth after drug treatment, in both cell lines and PDOs. The use of longitudinal image-based readouts enables the identification of tumor cell phenotypes with cell population and subpopulation resolution that cannot be detected by standard bulk-soluble assays. 96-GLAnCE is a versatile and robust platform that combines 3D-ECM based models, PDOs, and real-time assay readouts, to provide an additional tool for pre-clinical anti -can-cer drug discovery for the identification of novel targets with translatable clinical significance.
引用
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页数:17
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