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Volume 48 Issue 7
Jul.  2021
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Article Navigation >  Journal of Genetics and Genomics  > 2021  >  48(7): 560-570

Delineating the longitudinal tumor evolution using organoid models

doi: 10.1016/j.jgg.2021.06.010
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    a CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;

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    b CAS Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China;

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    c Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China

Funds:

This work was supported by the grants from the Natural Science Foundation of Guangdong (2021B1515020042 to Z.H.) and SIAT Innovation Program for Excellent Young Researchers (E1G053 to Z.L.) and China Postdoctoral Science Foundation (2021M693303 to Z.L.). We thank Kasper Karlsson for constructive comments.

  • Received Date: 2021-04-12
  • Accepted Date: 2021-06-16
  • Rev Recd Date: 2021-06-11
  • Publish Date: 2021-07-20
    Abstract

  • Cancer is an evolutionary process fueled by genetic or epigenetic alterations in the genome. Understanding the evolutionary dynamics that are operative at different stages of tumor progression might inform effective strategies in early detection, diagnosis, and treatment of cancer. However, our understanding on the dynamics of tumor evolution through time is very limited since it is usually impossible to sample patient tumors repeatedly. The recent advances in in vitro 3D organoid culture technologies have opened new avenues for the development of more realistic human cancer models that mimic many in vivo biological characteristics in human tumors. Here, we review recent progresses and challenges in cancer genomic evolution studies and advantages of using tumor organoids to study cancer evolution. We propose to establish an experimental evolution model based on continuous passages of patient-derived organoids and longitudinal sampling to study clonal dynamics and evolutionary patterns over time. Development and integration of population genetic theories and computational models into time-course genomic data in tumor organoids will help to pinpoint the key cellular mechanisms underlying cancer evolutionary dynamics, thus providing novel insights on therapeutic strategies for highly dynamic and heterogeneous tumors.

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    1. Lu, B., Curtius, K., Graham, T.A. et al. CNETML: maximum likelihood inference of phylogeny from copy number profiles of multiple samples. Genome Biology, 2023, 24(1): 144. doi:10.1186/s13059-023-02983-0
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    3. Niu, Y., Kang, H., Wang, P. et al. WHY INCREASE OF HETEROGENEITY SIGNALS PRE-DETERIORATION DURING TUMOR PROGRESSION: A UNIFIED MATHEMATICAL MODEL. SIAM Journal on Applied Mathematics, 2023, 83(2): 791-815. doi:10.1137/22M1497729
    4. Xu, R., Chen, R., Tu, C. et al. 3D Models of Sarcomas: The Next-generation Tool for Personalized Medicine. 2023. doi:10.1007/s43657-023-00111-3
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    6. Guan, X., Huang, S. Advances in the application of 3D tumor models in precision oncology and drug screening. Frontiers in Bioengineering and Biotechnology, 2022. doi:10.3389/fbioe.2022.1021966
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