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Integration of multiple lineage measurements from the same cell reconstructs parallel tumor evolution.

Authors :
Kester L
de Barbanson B
Lyubimova A
Chen LT
van der Schrier V
Alemany A
Mooijman D
Peterson-Maduro J
Drost J
de Ridder J
van Oudenaarden A
Source :
Cell genomics [Cell Genom] 2022 Feb 09; Vol. 2 (2), pp. 100096. Date of Electronic Publication: 2022 Feb 09 (Print Publication: 2022).
Publication Year :
2022

Abstract

Organoid evolution models complemented with integrated single-cell sequencing technology provide a powerful platform to characterize intra-tumor heterogeneity (ITH) and tumor evolution. Here, we conduct a parallel evolution experiment to mimic the tumor evolution process by evolving a colon cancer organoid model over 100 generations, spanning 6 months in time. We use single-cell whole-genome sequencing (WGS) in combination with viral lineage tracing at 12 time points to simultaneously monitor clone size, CNV states, SNV states, and viral lineage barcodes for 1,641 single cells. We integrate these measurements to construct clonal evolution trees with high resolution. We characterize the order of events in which chromosomal aberrations occur and identify aberrations that recur multiple times within the same tumor sub-population. We observe recurrent sequential loss of chromosome 4 after loss of chromosome 18 in four unique tumor clones. SNVs and CNVs identified in our organoid experiments are also frequently reported in colorectal carcinoma samples, and out of 334 patients with chromosome 18 loss in a Memorial Sloan Kettering colorectal cancer cohort, 99 (29.6%) also harbor chromosome 4 loss. Our study reconstructs tumor evolution in a colon cancer organoid model at high resolution, demonstrating an approach to identify potentially clinically relevant genomic aberrations in tumor evolution.<br />Competing Interests: The authors declare no competing financial interests. J.d.R. is founder of Cyclomics B.V.<br /> (© 2022 The Authors.)

Details

Language :
English
ISSN :
2666-979X
Volume :
2
Issue :
2
Database :
MEDLINE
Journal :
Cell genomics
Publication Type :
Academic Journal
Accession number :
36778661
Full Text :
https://doi.org/10.1016/j.xgen.2022.100096