Delineating the longitudinal tumor evolution using organoid models.

J Genet Genomics

CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China. Electronic address:

Published: July 2021

AI Article Synopsis

  • - Cancer evolves through genetic and epigenetic changes, and understanding this evolution can lead to better early detection and treatment strategies.
  • - Current research on tumor evolution is limited due to challenges in repeatedly sampling patient tumors, but in vitro 3D organoid culture technologies offer a promising solution by creating more realistic cancer models.
  • - The authors suggest using patient-derived organoids for experimental evolution studies to analyze clonal dynamics and evolutionary patterns, integrating population genetics and computational models to enhance our understanding of cancer mechanisms and improve therapeutic approaches.

Article 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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Source
http://dx.doi.org/10.1016/j.jgg.2021.06.010DOI Listing

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