A New Perspective on Precision Medicine: The Power of Digital Organoids

Abstract

Precision medicine is a personalized medical model based on the individual's genome, phenotype, and lifestyle that provides tailored treatment plans for patients. In this context, tumor organoids, a 3-dimensional preclinical model based on patient-derived tumor cell self-organization, combined with digital analysis methods, such as high-throughput sequencing and image processing technology, can be used to analyze the genome, transcriptome, and cellular heterogeneity of tumors, so as to accurately track and assess the growth process, genetic characteristics, and drug responsiveness of tumor organoids, thereby facilitating the implementation of precision medicine. This interdisciplinary approach is expected to promote the innovation of cancer diagnosis and enhance personalized treatment. In this review, the characteristics and culture methods of tumor organoids are summarized, and the application of multi-omics, such as bioinformatics and artificial intelligence, and the digital methods of organoids in precision medicine research are discussed. Finally, this review explores the main causes and potential solutions for the bottleneck in the clinical translation of digital tumor organoids, proposes the prospects of multidisciplinary cooperation and clinical transformation to narrow the gap between laboratory and clinical settings, and provides references for research and development in this field.

Fig. 1.

Multi-omics data in organoids [78]. Copyright 2023, Bai et al.

Fig. 2.

Application of single-cell sequencing in organoids. (A) Comprehensive analysis of tumor and paired adjacent normal tissue-derived organoids by single-cell RNA-seq [86]. Copyright 2022, Wang et al. (B) Flowchart of single-cell transcriptome analysis of intratumor heterogeneity and drug resistance in hepatobiliary tumor organoids [89]. Copyright 2021, Zhao et al.

Fig. 3.

Common machine learning (A) types and (B) algorithms [78]. Copyright 2023, Bai et al.

Fig. 4.

Application of AI/machine learning in organoids. (A) OrgaQuant automatically locates and quantifies the size distribution of human intestinal organoids in light field images [109]. Copyright 2019, Kassis et al. (B) Colorectal and bladder organoid models were analyzed by machine learning to predict patients’ anticancer drug efficacy [103]. Copyright 2020, Kong et al. (C) High-speed live-cell interferometry imaging organoids at single-organoid resolution [101]. Copyright 2023, Tebon et al.

Fig. 5.

The application of digital tumor organoids in precision medicine. (A) Multi-omics analysis of hepatobiliary tumor organoids was used to identify neoantigen peptides for individual immunotherapy [120]. Copyright 2022, Wang et al. (B) Flowchart for the development of molecular biomarkers of drug resistance to predict survival in CRC patients based on patient-derived tumor organoids [122]. Copyright 2022, Chen et al. (C) Integrated imaging, computer vision, and machine learning to track and analyze the dynamic response of organoids to drugs [126]. Copyright 2021, Spiller et al. (D) Design tailored synergistic multidrug combinations for patients through multi-omics characterization and mathematical model prediction of patient-derived organoids [128]. Copyright 2023, Ramzy et al.

Fig. 6.

The workflow of digital tumor organoid analysis from culture to clinical application.