Evaluation of the Effect of Fibroblasts on Melanoma Metastasis Using a Biomimetic Co-Culture Model

Wu, Miaoben; Yu, Enxing; Yang, Kai; Huang, Yuye; Ren, Tao; Guo, Mingzhou; Yin, Jun; Xu, Kailei; Wei, Peng

doi:10.1021/acsbiomaterials.2c01186

Cited by 3 publications

(1 citation statement)

References 105 publications

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“…8 Meanwhile, researchers have realized that cell co-culture technology is vital following the success of blastocyst formation through the co-culture of human oviduct epithelial cells and embryos. 9 The cell co-culture models, 10 materialassistant 3D cell culture models 11 and cancer patientderived xenografts (PDX) models 12 have all provided essential research bases for biomedicine (Figure 1).…”

Section: Organoid and Assembloid Technologymentioning

confidence: 99%

Tumour organoids and assembloids: Patient‐derived cancer avatars for immunotherapy

Mei,

Liu,

Tian

et al. 2024

Clinical & Translational Med

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BackgroundOrganoid technology is an emerging and rapidly growing field that shows promise in studying organ development and screening therapeutic regimens. Although organoids have been proposed for a decade, concerns exist, including batch‐to‐batch variations, lack of the native microenvironment and clinical applicability.Main bodyThe concept of organoids has derived patient‐derived tumour organoids (PDTOs) for personalized drug screening and new drug discovery, mitigating the risks of medication misuse. The greater the similarity between the PDTOs and the primary tumours, the more influential the model will be. Recently, ‘tumour assembloids’ inspired by cell‐coculture technology have attracted attention to complement the current PDTO technology. High‐quality PDTOs must reassemble critical components, including multiple cell types, tumour matrix, paracrine factors, angiogenesis and microorganisms. This review begins with a brief overview of the history of organoids and PDTOs, followed by the current approaches for generating PDTOs and tumour assembloids. Personalized drug screening has been practised; however, it remains unclear whether PDTOs can predict immunotherapies, including immune drugs (e.g. immune checkpoint inhibitors) and immune cells (e.g. tumour‐infiltrating lymphocyte, T cell receptor‐engineered T cell and chimeric antigen receptor‐T cell). PDTOs, as cancer avatars of the patients, can be expanded and stored to form a biobank.ConclusionFundamental research and clinical trials are ongoing, and the intention is to use these models to replace animals. Pre‐clinical immunotherapy screening using PDTOs will be beneficial to cancer patients.Key Points The current PDTO models have not yet constructed key cellular and non‐cellular components. PDTOs should be expandable and editable. PDTOs are promising preclinical models for immunotherapy unless mature PDTOs can be established. PDTO biobanks with consensual standards are urgently needed.

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Section: Organoid and Assembloid Technologymentioning

confidence: 99%

Tumour organoids and assembloids: Patient‐derived cancer avatars for immunotherapy

Mei,

Liu,

Tian

et al. 2024

Clinical & Translational Med

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Photothermal effects of supra-CNDs@GelMA composite hydrogels under near-infrared stimulation

Duan,

Gao,

Zhang

et al. 2024

Colloids and Surfaces A: Physicochemical and Engineering Aspect

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Machine-learning-based classification of co-cultured cells from the phase images in digital holographic microscopy

Chaudhari,

Kulkarni,

Bhuyan

et al. 2024

Unconventional Optical Imaging IV

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In the tumor microenvironment, cell interactions play a crucial role in influencing t he m orphology a nd metastasis characteristics of non-cancerous and cancerous cells. Although machine learning techniques have been proven effective i n c lassifying i ndividually c ultured c ell l ines, t heir a ccuracy m ay d eteriorate i n c lassifying a co-cultured mixture of cells. In the proposed work, the optical path difference images of human dermal fibroblast and melanoma A375 cells were recorded, individually and in a mixture, using the off-axis d igital holographic microscopy setup. A dataset consisted of segmented and labeled images of both sample types. The dataset was used to train a convolutional neural network through transfer learning to extract the morphology relevant features. An XGBoost classifier trained on the extracted features is found to effectively recognize morphological changes in cells within a mixture and classify them accurately with a limited size dataset.

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Evaluation of the Effect of Fibroblasts on Melanoma Metastasis Using a Biomimetic Co-Culture Model

Cited by 3 publications

References 105 publications

Tumour organoids and assembloids: Patient‐derived cancer avatars for immunotherapy

Tumour organoids and assembloids: Patient‐derived cancer avatars for immunotherapy

Photothermal effects of supra-CNDs@GelMA composite hydrogels under near-infrared stimulation

Machine-learning-based classification of co-cultured cells from the phase images in digital holographic microscopy

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