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  5. PolyG-DS: An ultrasensitive polyguanine tract–profiling method to detect clonal expansions and trace cell lineage

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Article
en
2021

PolyG-DS: An ultrasensitive polyguanine tract–profiling method to detect clonal expansions and trace cell lineage

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0 Files

en
2021
Vol 118 (31)
Vol. 118
DOI: 10.1073/pnas.2023373118

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Hans Clevers
Hans Clevers

Utrecht University

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Yuezheng Zhang
Brendan F. Kohrn
Ming Yang
+27 more

Abstract

Significance The ability to detect precancerous clones and reconstruct cancer evolution is important for early cancer detection and improving prevention and treatment strategies. We present PolyG-DS, a sequencing method that combines the unique properties of polyguanine tracts (PolyGs) for cell lineage tracing with ultrahighaccuracy duplex sequencing (DS). PolyG-DS enables accurate and reproducible PolyG genotyping, providing high sensitivity for the detection of low-frequency alleles in mixed populations. This translates into an improved ability to identify clonal expansions within normal tissue, with potential application to detect cancer progression in preneoplastic diseases such as ulcerative colitis. Because PolyG-DS is driver mutation agnostic, it provides a universal, cost-effective approach for assessing tumor evolution across cancer types.

How to cite this publication

Yuezheng Zhang, Brendan F. Kohrn, Ming Yang, Daniela Nachmanson, T. Rinda Soong, I-Hsiu Lee, Yong Tao, Hans Clevers, Elizabeth M. Swisher, Teresa A. Brentnall, Lawrence A. Loeb, Scott R. Kennedy, Jesse J. Salk, Kamila Naxerova, Rosa Ana Risques, Yuezheng Zhang, Brendan F. Kohrn, Ming Yang, Daniela Nachmanson, T. Rinda Soong, I-Hsiu Lee, Yong Tao, Hans Clevers, Elizabeth M. Swisher, Teresa A. Brentnall, Lawrence A. Loeb, Scott R. Kennedy, Jesse J. Salk, Kamila Naxerova, Rosa Ana Risques (2021). PolyG-DS: An ultrasensitive polyguanine tract–profiling method to detect clonal expansions and trace cell lineage. , 118(31), DOI: https://doi.org/10.1073/pnas.2023373118.

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Publication Details

Type

Article

Year

2021

Authors

30

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1073/pnas.2023373118

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