Clinical translation of patient-derived tumour organoids- bottlenecks and strategies.
Malia Alexandra FooMingliang YouShing Leng ChanGautam SethiGlenn K BonneyWei-Peng YongEdward Kai-Hua ChowEliza Li Shan FongLing-Zhi WangBoon-Cher GohPublished in: Biomarker research (2022)
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.
Keyphrases
- artificial intelligence
- clinical trial
- machine learning
- high resolution
- randomized controlled trial
- single cell
- deep learning
- gene expression
- healthcare
- big data
- stem cells
- oxidative stress
- young adults
- dna methylation
- cell proliferation
- high throughput
- cell death
- cell cycle arrest
- open label
- signaling pathway
- endoplasmic reticulum stress
- fluorescence imaging
- childhood cancer