PolyG-DS: An ultrasensitive polyguanine tract–profiling method to detect clonal expansions and trace cell lineage
- aDepartment of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195;
- bCenter for Systems Biology, Massachusetts General Hospital, Boston, MA 02114;
- cDepartment of Radiology, Harvard Medical School, Boston, MA 02114;
- dDivision of Human Biology, Fred Hutchinson Cancer Research Center, WA 98019;
- eDivision of Clinical Research, Fred Hutchinson Cancer Research Center, WA 98019;
- fHubrecht Institute, Royal Netherlands Academy of Arts and Sciences, 3584 CT Utrecht, Netherlands;
- gDivision of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Washington Medical Center, Seattle, WA 98195;
- hDivision of Gastroenterology, Department of Medicine, University of Washington, Seattle, WA 98195;
- iDivision of Medical Oncology, Department of Medicine, University of Washington, Seattle, WA 98195
See allHide authors and affiliations
Edited by James E. Cleaver, University of California San Francisco Medical Center, San Francisco, CA, and approved May 24, 2021 (received for review November 11, 2020)

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.
Abstract
Polyguanine tracts (PolyGs) are short guanine homopolymer repeats that are prone to accumulating mutations when cells divide. This feature makes them especially suitable for cell lineage tracing, which has been exploited to detect and characterize precancerous and cancerous somatic evolution. PolyG genotyping, however, is challenging because of the inherent biochemical difficulties in amplifying and sequencing repetitive regions. To overcome this limitation, we developed PolyG-DS, a next-generation sequencing (NGS) method that combines the error-correction capabilities of duplex sequencing (DS) with enrichment of PolyG loci using CRISPR-Cas9–targeted genomic fragmentation. PolyG-DS markedly reduces technical artifacts by comparing the sequences derived from the complementary strands of each original DNA molecule. We demonstrate that PolyG-DS genotyping is accurate, reproducible, and highly sensitive, enabling the detection of low-frequency alleles (<0.01) in spike-in samples using a panel of only 19 PolyG markers. PolyG-DS replicated prior results based on PolyG fragment length analysis by capillary electrophoresis, and exhibited higher sensitivity for identifying clonal expansions in the nondysplastic colon of patients with ulcerative colitis. We illustrate the utility of this method for resolving the phylogenetic relationship among precancerous lesions in ulcerative colitis and for tracing the metastatic dissemination of ovarian cancer. PolyG-DS enables the study of tumor evolution without prior knowledge of tumor driver mutations and provides a tool to perform cost-effective and easily scalable ultra-accurate NGS-based PolyG genotyping for multiple applications in biology, genetics, and cancer research.
Footnotes
↵1Present address: Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA 92093.
↵2Present address: Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213.
↵3Present address: TwinStrand Biosciences, Seattle, WA 98121.
- ↵4To whom correspondence may be addressed. Email: rrisques{at}uw.edu.
Author contributions: Y.Z. and R.A.R. designed research; Y.Z. and D.N. performed research; B.F.K., M.Y., D.N., H.C., E.M.S., T.A.B., L.A.L., S.R.K., J.J.S., and K.N. contributed new reagents/analytic tools; Y.Z., B.F.K., T.R.S., I.-H.L., Y.T., K.N., and R.A.R. analyzed data; and Y.Z. and R.A.R. wrote the paper.
Competing interest statement: S.R.K., L.A.L., and R.A.R. are consultants and equity holders at TwinStrand Biosciences Inc. J.J.S. is an employee and equity holder at TwinStrand Biosciences Inc. S.R.K., L.A.L., R.A.R., D.N., and J.J.S. are named inventors on patents owned by the University of Washington and licensed to TwinStrand Biosciences Inc. R.A.R. is an equity holder at NanoString Technologies Inc.
This article is a PNAS Direct Submission.
This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2023373118/-/DCSupplemental.
Data Availability
Sequencing reads data have been deposited in the Sequence Read Archive (PRJNA674403). All other study data are included in the article and/or supporting information.
Published under the PNAS license.
References
- ↵
- P. C. Nowell
- ↵
- ↵
- ↵
- ↵
- ↵
- M. W. Fittall,
- P. Van Loo
- ↵
- ↵
- S. R. Kennedy,
- Y. Zhang,
- R. A. Risques
- ↵
- J. Vijg,
- X. Dong
- ↵
- ↵
- ↵
- ↵
- J. C. Whittaker et al
- ↵
- S. J. Salipante,
- M. S. Horwitz
- ↵
- K. Naxerova et al
- ↵
- K. Naxerova et al
- ↵
- ↵
- ↵
- J. J. Salk et al
- ↵
- ↵
- S. J. Salipante,
- J. M. Thompson,
- M. S. Horwitz
- ↵
- K. D. Carlson et al
- ↵
- ↵
- M. Gymrek,
- D. Golan,
- S. Rosset,
- Y. Erlich
- ↵
- G. Shin et al
- ↵
- D. Nachmanson et al
- ↵
- ↵
- ↵
- ↵
- ↵
- A.-M. Baker et al
- ↵
- K. T. Baker,
- J. J. Salk,
- T. A. Brentnall,
- R. A. Risques
- ↵
- ↵
- ↵
- ↵
- ↵
- R. R. Sokal,
- C. D. Michener
- ↵
- ↵
- T. R. Soong,
- B. E. Howitt,
- N. Horowitz,
- M. R. Nucci,
- C. P. Crum
- ↵
- ↵
- ↵
- ↵
- ↵
- ↵
- M. A. Eckert et al
- ↵
- ↵
- ↵
- ↵
- X. Gui et al
- ↵
- K. Hewitt et al
- ↵
- ↵
- ↵
- ↵
- H. Li
- ↵
- ↵
Log in using your username and password
Log in through your institution
Purchase access
Subscribers, for more details, please visit our Subscriptions FAQ.
Please click here to log into the PNAS submission website.
Citation Manager Formats
Article Classifications
- Biological Sciences
- Genetics














