| Literature DB >> 30440885 |
Kai-Yuan Chen, Tara Srinivasan, Christopher Lin, Kuei-Ling Tung, Ziyang Gao, David S Hsu, Steven M Lipkin, Xiling Shen.
Abstract
Organoids are three-dimensional cell cultures that mimic organ functions and structures. The organoid model has been developed as a versatile in vitro platform for stem cell biology and diseases modeling. Tumor organoids are shown to share ~ 90% of genetic mutations with biopsies from same patients. However, it's not clear whether tumor organoids recapitulate the cellular heterogeneity observed in patient tumors. Here, we used single-cell RNA-Seq to investigate the transcriptomics of tumor organoids derived from human colorectal tumors, and applied machine learning methods to unbiasedly cluster subtypes in tumor organoids. Computational analysis reveals cancer heterogeneity sustained in tumor organoids, and the subtypes in organoids displayed high diversity. Furthermore, we treated the tumor organoids with a first-line cancer drug, Oxaliplatin, and investigated drug response in single-cell scale. Diversity of tumor cell populations in organoids were significantly perturbed by drug treatment. Single-cell analysis detected the depletion of chemosensitive subgroups and emergence of new drug tolerant subgroups after drug treatment. Our study suggests that the organoid model is capable of recapitulating clinical heterogeneity and its evolution in response to chemotherapy.Entities:
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Year: 2018 PMID: 30440885 PMCID: PMC6317967 DOI: 10.1109/EMBC.2018.8512784
Source DB: PubMed Journal: Annu Int Conf IEEE Eng Med Biol Soc ISSN: 2375-7477