AI Article Synopsis
- Multiple 3D tumour organoid models, combined with multi-omics and AI, enhance preclinical drug development and precision medicine by preserving the genetic diversity of tumours.
- Although these models show promise in research, their application in clinical settings faces challenges like difficult tissue acquisition, low establishment rates, and slow processing times.
- Proposed solutions, such as using liquid biopsies and automated platforms, aim to streamline processes for patient-derived tumour organoids (PDTOs) in clinical oncology, potentially leading to better outcomes for cancer patients.
Article 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.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8908618 | PMC |
| http://dx.doi.org/10.1186/s40364-022-00356-6 | DOI Listing |
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