Affiliations
1Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore.
2Hangzhou Cancer Institute, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Hangzhou, 31002, China.
3Affiliated Hangzhou Cancer Hospital, Zhejiang University School of Medicine, Hangzhou, 31002, China.
4Department of Surgery, National University Hospital, Singapore, Singapore.
5NUS Centre for Cancer Research (N2CR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
6Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
7Department of Haematology-Oncology, National University Hospital, National University Health System, Singapore, Singapore.
8Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore.
9Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore. csiwl@nus.edu.sg.
10NUS Centre for Cancer Research (N2CR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. csiwl@nus.edu.sg.
11Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. csiwl@nus.edu.sg.
12Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore. phcgbc@nus.edu.sg.
13NUS Centre for Cancer Research (N2CR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. phcgbc@nus.edu.sg.
14Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. phcgbc@nus.edu.sg.
15Department of Haematology-Oncology, National University Hospital, National University Health System, Singapore, Singapore. phcgbc@nus.edu.sg.
Abstract
Multiple three-dimensional (3D) tumour organoid models assisted by multi-omics and Artificial Intelligence (AI) have contributed greatly to preclinical drug development and precision medicine. The intrinsic ability to maintain genetic and phenotypic heterogeneity of tumours allows for the reconciliation of shortcomings in traditional cancer models. While their utility in preclinical studies have been well established, little progress has been made in translational research and clinical trials. In this review, we identify the major bottlenecks preventing patient-derived tumour organoids (PDTOs) from being used in clinical setting. Unsuitable methods of tissue acquisition, disparities in establishment rates and a lengthy timeline are the limiting factors for use of PDTOs in clinical application. Potential strategies to overcome this include liquid biopsies via circulating tumour cells (CTCs), an automated organoid platform and optical metabolic imaging (OMI). These proposed solutions accelerate and optimize the workflow of a clinical organoid drug screening. As such, PDTOs have the potential for potential applications in clinical oncology to improve patient outcomes. If remarkable progress is made, cancer patients can finally benefit from this revolutionary technology.
Keywords: Medicine; Organoid; Precision; Three-Dimensional (3D); Tumour.
© 2022. The Author(s).
PMID: 35272694 PMCID: PMC8908618 DOI: 10.1186/s40364-022-00356-6