Patient-derived organoids in translational oncology and drug screening

Yang, Ruixin; Yu, Yingyan

doi:10.1016/j.canlet.2023.216180

Cited by 48 publications

(10 citation statements)

References 102 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cells of cancer organoids may acquire capabilities to sustain proliferative signaling, evade growth suppressors, resist cell death, and enable replicative immortality. Recently, this infinite proliferation potential has been proposed to support the utility of PDOs in establishing cell lines to serve as experimental models 44 , 45 .…”

Section: Discussionmentioning

confidence: 99%

A quick and reliable image-based AI algorithm for evaluating cellular senescence of gastric organoids

Yang

Kwan

et al. 2023

Cancer Biol Med

Self Cite

View full text Add to dashboard Cite

Objective: Organoids are a powerful tool with broad application prospects in biomedicine. Notably, they provide alternatives to animal models for testing potential drugs before clinical trials. However, the number of passages for which organoids maintain cellular vitality ex vivo remains unclear. Methods: Herein, we constructed 55 gastric organoids from 35 individuals, serially passaged the organoids, and captured microscopic images for phenotypic evaluation. Senescence-associated β-galactosidase (SA-β-Gal), cell diameter in suspension, and gene expression reflecting cell cycle regulation were examined. The YOLOv3 object detection algorithm integrated with a convolutional block attention module (CBAM) was used to evaluate organoid vitality. Results: SA-β-Gal staining intensity; single-cell diameter; and expression of p15, p16, p21, CCNA2, CCNE2, and LMNB1 reflected the progression of aging in organoids during passaging. The CBAM-YOLOv3 algorithm precisely evaluated aging organoids on the basis of organoid average diameter, organoid number, and number × diameter, and the findings positively correlated with SA-β-Gal staining and single-cell diameter. Organoids derived from normal gastric mucosa had limited passaging ability (passages 1–5), before aging, whereas tumor organoids showed unlimited passaging potential for more than 45 passages (511 days) without showing clear senescence. Conclusions: Given the lack of indicators for evaluating organoid growth status, we established a reliable approach for integrated analysis of phenotypic parameters that uses an artificial intelligence algorithm to indicate organoid vitality. This method enables precise evaluation of organoid status in biomedical studies and monitoring of living biobanks.

show abstract

Section: Discussionmentioning

confidence: 99%

A quick and reliable image-based AI algorithm for evaluating cellular senescence of gastric organoids

Yang

Kwan

et al. 2023

Cancer Biol Med

Self Cite

View full text Add to dashboard Cite

show abstract

“…Matrigel or other BME both are commonly used ECM protein mix, a complex mixture of different ECM proteins including laminin, fibronectin, collagen and heparin sulfate proteoglycans, which can maintain necessary cell-ECM interactions ( 30 ). As for culture medium, it usually consists of various supplements, such as B27, N -acetylcysteine, nicotinamide, noggin, WNT/R-spondins, epidermal growth factor (EGF), and fibroblast growth factor (FGF), all of which are associated with cell survival and stemness/differentiation balance ( 21 , 31 ). Therefore, understanding the function of these supplements is essential for LCO generation ( Table 2 ) ( 21 ).…”

Section: Methodology Of Lcosmentioning

confidence: 99%

Promising preclinical models for lung cancer research—lung cancer organoids: a narrative review

Chen,

Ye,

Wang

et al. 2024

Transl Lung Cancer Res

View full text Add to dashboard Cite

Background and Objective Traditional cell line models are the commonly used preclinical models for lung cancer research. However, cell lines cannot recapitulate the complex tumor heterogeneity and cannot mimic the microenvironment of human cancer. Recently, 3D multicellular in vitro self-assembled models called “organoids” have been developed at a fast pace in the field of research, which can mimic the actual primary tumor. At present, several studies have reported on protocols of lung cancer organoids (LCOs) generation, and using LCOs can provide novel insight into the basic and translational research of lung cancer. However, the establishment of the LCO models remains challenging due to the complexity of lung cancer and the immaturity of organoid technology, so it is necessary to understand the influences of different methodologies on LCO generation and review the applications and limitations of LCO models. Methods In this review, we searched the literature in the recent ten years in the field of LCOs. Key Content and Findings We summarized the methodology, the problems, and the solutions in the LCOs generation, its application and limitations, as well as proposing future challenges and perspectives. Conclusions Currently, LCOs are successfully generated via exploring the methodology by the researchers. Though there are still challenges in clinical application, LCOs are applied in some cancer studies including investigation of anti-cancer treatment response in vitro , modeling tumor immune microenvironment, and construction of organ chips, which are forging a promising path towards precision medicine.

show abstract

“…However, it has been a problem to be solved urgently: how to simulate the tumor immune microenvironment most realistically and quickly, evaluate the effect of immunotherapy, and screen out the target population that may benefit from itself clinically ( Yuki et al, 2020 ). In addition, the interaction mode between immune cells and tumor cells in TME, the mechanism of directional metastasis of tumor-producing organoids, and the screening and identification of key cell subsets and regulatory molecules that regulate the formation of the metastatic microenvironment also need the support of a reliable co-culture platform in vitro ( Yang and Yu, 2023 ).…”

Section: Application Of Organoids In Gi Tumorsmentioning

confidence: 99%

Human patient derived organoids: an emerging precision medicine model for gastrointestinal cancer research

Yan,

He,

Zhu

et al. 2024

Front. Cell Dev. Biol.

View full text Add to dashboard Cite

Gastrointestinal cancers account for approximately one-third of the total global cancer incidence and mortality with a poor prognosis. It is one of the leading causes of cancer-related deaths worldwide. Most of these diseases lack effective treatment, occurring as a result of inappropriate models to develop safe and potent therapies. As a novel preclinical model, tumor patient-derived organoids (PDOs), can be established from patients’ tumor tissue and cultured in the laboratory in 3D architectures. This 3D model can not only highly simulate and preserve key biological characteristics of the source tumor tissue in vitro but also reproduce the in vivo tumor microenvironment through co-culture. Our review provided an overview of the different in vitro models in current tumor research, the derivation of cells in PDO models, and the application of PDO model technology in gastrointestinal cancers, particularly the applications in combination with CRISPR/Cas9 gene editing technology, tumor microenvironment simulation, drug screening, drug development, and personalized medicine. It also elucidates the ethical status quo of organoid research and the current challenges encountered in clinical research, and offers a forward-looking assessment of the potential paths for clinical organoid research advancement.

show abstract

Patient-derived organoids in translational oncology and drug screening

Cited by 48 publications

References 102 publications

A quick and reliable image-based AI algorithm for evaluating cellular senescence of gastric organoids

A quick and reliable image-based AI algorithm for evaluating cellular senescence of gastric organoids

Promising preclinical models for lung cancer research—lung cancer organoids: a narrative review

Human patient derived organoids: an emerging precision medicine model for gastrointestinal cancer research

Contact Info

Product

Resources

About