Integrative single-cell analysis of allele-specific copy number alterations and chromatin accessibility in cancer thumbnail

Integrative single-cell analysis of allele-specific copy number alterations and chromatin accessibility in cancer

Abstract

Cancer progression is driven by both somatic copy number aberrations (CNAs) and chromatin remodeling, yet little is known about the interplay between these two classes of events in shaping the clonal diversity of cancers. We present Alleloscope, a method for allele-specific copy number estimation that can be applied to single-cell DNA- and/or transposase-accessible chromatin-sequencing (scDNA-seq, ATAC-seq) data, enabling combined analysis of allele-specific copy number and chromatin accessibility. On scDNA-seq data from gastric, colorectal and breast cancer samples, with validation using matched linked-read sequencing, Alleloscope finds pervasive occurrence of highly complex, multiallelic CNAs, in which cells that carry varying allelic configurations adding to the same total copy number coevolve within a tumor. On scATAC-seq from two basal cell carcinoma samples and a gastric cancer cell line, Alleloscope detected multiallelic copy number events and copy-neutral loss-of-heterozygosity, enabling dissection of the contributions of chromosomal instability and chromatin remodeling to tumor evolution.

Access options

Subscribe to Journal

Get full journal access for 1 year

$59.00

only $4.92 per issue

All prices are NET prices.

VAT will be added later in the checkout.

Tax calculation will be finalised during checkout.

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Data availability

The patient scDNA-seq and linked-read sequencing data generated for this study are available under dbGAP identifier phs001711. The scATAC-seq dataset is available in the National Institute of Health’s Sequence Read Archive (SRA) repository under accession PRJNA674903. There are no restrictions on data availability or use. The other patient scDNA-seq data were obtained from dbGAP under accession phs001818.v3.p1 (ref. 27) and phs001711 (ref. 12). The cell line scDNA-seq dataset was from the SRA under accession PRJNA498809. The public scATAC-seq data and WES data were obtained from the SRA under accession PRJNA532774 (ref. 25) and PRJNA533341 (ref. 31).

Code availability

Alleloscope is available on GitHub at https://github.com/seasoncloud/Alleloscope and as a compute capsule on Code Ocean (https://doi.org/10.24433/CO.2295856.v1).

References

  1. 1.

    Baylin, S. B. & Jones, P. A. A decade of exploring the cancer epigenome—biological and translational implications. Nat. Rev. Cancer 11, 726–734 (2011).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  2. 2.

    Sandoval, J. & Esteller, M. Cancer epigenomics: beyond genomics. Curr Opin Genet. Dev. 22, 50–55 (2012).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  3. 3.

    Greaves, M. & Maley, C. C. Clonal evolution in cancer. Nature 481, 306–313 (2012).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  4. 4.

    Burrell, R. A., McGranahan, N., Bartek, J. & Swanton, C. The causes and consequences of genetic heterogeneity in cancer evolution. Nature 501, 338–345 (2013).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  5. 5.

    Chen, H., Bell, J. M., Zavala, N. A., Ji, H. P. & Zhang, N. R. Allele-specific copy number profiling by next-generation DNA sequencing. Nucleic Acids Res. 43, e23 (2015).

    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar
     

  6. 6.

    Favero, F. et al. Sequenza: allele-specific copy number and mutation profiles from tumor sequencing data. Ann. Oncol. 26, 64–70 (2015).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  7. 7.

    Ha, G. et al. TITAN: inference of copy number architectures in clonal cell populations from tumor whole-genome sequence data. Genome Res. 24, 1881–1893 (2014).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  8. 8.

    Shen, R. & Seshan, V. E. FACETS: allele-specific copy number and clonal heterogeneity analysis tool for high-throughput DNA sequencing. Nucleic Acids Res. 44, e131 (2016).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  9. 9.

    Jamal-Hanjani, M. et al. Tracking the evolution of non-small-cell lung cancer. N. Engl. J. Med. 376, 2109–2121 (2017).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  10. 10.

    Zaccaria, S. & Raphael, B. J. Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL. Nat. Biotechnol. 39, 207–214 (2020).

    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar
     

  11. 11.

    Van Loo, P. et al. Allele-specific copy number analysis of tumors. Proc. Natl Acad. Sci. USA 107, 16910–16915 (2010).

    Article 

    Google Scholar
     

  12. 12.

    Andor, N. et al. Joint single cell DNA-seq and RNA-seq of gastric cancer cell lines reveals rules of in vitro evolution. NAR Genom. Bioinform. 2, lqaa016 (2020).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  13. 13.

    Bakker, B. et al. Single-cell sequencing reveals karyotype heterogeneity in murine and human malignancies. Genome Biol. 17, 115 (2016).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  14. 14.

    Garvin, T. et al. Interactive analysis and assessment of single-cell copy-number variations. Nat. Methods 12, 1058–1060 (2015).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  15. 15.

    Kim, C. et al. Chemoresistance evolution in triple-negative breast cancer delineated by single-cell sequencing. Cell 173, 879–893 e813 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  16. 16.

    Laks, E. et al. Clonal decomposition and DNA replication states defined by scaled single-cell genome sequencing. Cell 179, 1207–1221 e1222 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  17. 17.

    Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  18. 18.

    Velazquez-Villarreal, E. I. et al. Single-cell sequencing of genomic DNA resolves sub-clonal heterogeneity in a melanoma cell line. Commun. Biol. 3, 318 (2020).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  19. 19.

    Wang, Y. et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature 512, 155–160 (2014).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  20. 20.

    Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  21. 21.

    Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  22. 22.

    Corces, M. R. et al. The chromatin accessibility landscape of primary human cancers. Science 362, eaav1898 (2018).

  23. 23.

    Granja, J. M. et al. Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia. Nat. Biotechnol. 37, 1458–1465 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  24. 24.

    Litzenburger, U. M. et al. Single-cell epigenomic variability reveals functional cancer heterogeneity. Genome Biol. 18, 15 (2017).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  25. 25.

    Satpathy, A. T. et al. Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion. Nat. Biotechnol. 37, 925–936 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  26. 26.

    Schep, A. N., Wu, B., Buenrostro, J. D. & Greenleaf, W. J. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat. Methods 14, 975–978 (2017).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  27. 27.

    Sathe, A. et al. The cellular genomic diversity, regulatory states and networking of the metastatic colorectal cancer microenvironment. Preprint at bioRxiv https://doi.org/10.1101/2020.09.01.273672 (2020).

  28. 28.

    Bell, J. M. et al. Chromosome-scale mega-haplotypes enable digital karyotyping of cancer aneuploidy. Nucleic Acids Res. 45, e162 (2017).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  29. 29.

    Greer, S. U. et al. Linked read sequencing resolves complex genomic rearrangements in gastric cancer metastases. Genome Med. 9, 57 (2017).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  30. 30.

    Zheng, G. X. et al. Haplotyping germline and cancer genomes with high-throughput linked-read sequencing. Nat. Biotechnol. 34, 303–311 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  31. 31.

    Yost, K. E. et al. Clonal replacement of tumor-specific T cells following PD-1 blockade. Nat. Med. 25, 1251–1259 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  32. 32.

    Yu, J. et al. REC8 functions as a tumor suppressor and is epigenetically downregulated in gastric cancer, especially in EBV-positive subtype. Oncogene 36, 182–193 (2017).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  33. 33.

    McFarlane, R. J. & Wakeman, J. A. Meiosis-like functions in oncogenesis: a new view of cancer. Cancer Res. 77, 5712–5716 (2017).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  34. 34.

    Aqeilan, R. I. et al. Loss of WWOX expression in gastric carcinoma. Clin. Cancer Res. 10, 3053–3058 (2004).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  35. 35.

    Baryla, I., Styczen-Binkowska, E. & Bednarek, A. K. Alteration of WWOX in human cancer: a clinical view. Exp. Biol. Med. 240, 305–314 (2015).

    CAS 
    Article 

    Google Scholar
     

  36. 36.

    Watkins, T. B. K. et al. Pervasive chromosomal instability and karyotype order in tumour evolution. Nature 587, 126–132 (2020).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  37. 37.

    Luo, C. et al. Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex. Science 357, 600–604 (2017).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  38. 38.

    Gupta, I. et al. Single-cell isoform RNA sequencing characterizes isoforms in thousands of cerebellar cells. Nat. Biotechnol. 36, 1197–1202 (2018).

  39. 39.

    Lebrigand, K., Magnone, V., Barbry, P. & Waldmann, R. High throughput error corrected Nanopore single cell transcriptome sequencing. Nat. Commun. 11, 4025 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  40. 40.

    Singh, M. et al. High-throughput targeted long-read single cell sequencing reveals the clonal and transcriptional landscape of lymphocytes. Nat. Commun. 10, 3120 (2019).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  41. 41.

    Zhu, C., Preissl, S. & Ren, B. Single-cell multimodal omics: the power of many. Nat. Methods 17, 11–14 (2020).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  42. 42.

    Poplin, R. et al. Scaling accurate genetic variant discovery to tens of thousands of samples. Preprint at BioRxiv, 201178 (2018).

  43. 43.

    Benjamin, D. et al. Calling somatic SNVs and indels with mutect2. Preprint at bioRxiv https://doi.org/10.1101/861054 (2019).

  44. 44.

    Wang, R., Lin, D. Y. & Jiang, Y. SCOPE: a normalization and copy-number estimation method for single-cell DNA sequencing. Cell Syst. 10, 445–452 e446 (2020).

    PubMed 
    Article 
    CAS 

    Google Scholar
     

  45. 45.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  46. 46.

    McKenna, A. et al. The genome analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  47. 47.

    Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  48. 48.

    McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426 (2018).

  49. 49.

    Yu, W., Uzun, Y., Zhu, Q., Chen, C. & Tan, K. scATAC-pro: a comprehensive workbench for single-cell chromatin accessibility sequencing data. Genome Biol. 21, 94 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

Download references

Acknowledgements

The work is supported by the National Institutes of Health (grant nos. P01HG00205ESH to B.T.L., S.M.G. and H.P.J., 5R01-HG006137-07 and 1U2CCA233285-01 to C-Y.W. and to N.R.Z., 1R35HG011292-01 to B.T.L.). Additional support to H.P.J. came from the Research Scholar grant no. RSG-13-297-01-TBG from the American Cancer Society, Clayville Foundation and the Gastric Cancer Foundation. Additional support to N.R.Z. came from 1R01GM125301-01, 1P01CA210944-01 and The Mark Foundation for Cancer Research.

Author information

Affiliations

  1. Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, USA

    Chi-Yun Wu & Nancy R. Zhang

  2. Department of Statistics, University of Pennsylvania, Philadelphia, PA, USA

    Chi-Yun Wu & Nancy R. Zhang

  3. Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA

    Billy T. Lau, Heon Seok Kim, Anuja Sathe, Susan M. Grimes & Hanlee P. Ji

  4. Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA

    Billy T. Lau & Hanlee P. Ji

Contributions

C.-Y.W. and N.R.Z. conceived the computational methods and designed the study with help from H.P.J. C.-Y.W. developed and implemented the computational methods and conducted all data analyses. B.T.L. helped with data interpretation. B.T.L., H.S.K. and A.S. performed all related sample preparation and sequencing. S.M.G. performed data preprocessing and coordinated data transfer. H.P.J. advised all experiments and data collection. C.-Y.W., N.R.Z. and H.P.J. wrote the paper. All authors read and approved the final draft.

Corresponding authors

Correspondence to
Hanlee P. Ji or Nancy R. Zhang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Biotechnology thanks Stephen Chanock and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wu, CY., Lau, B.T., Kim, H.S. et al. Integrative single-cell analysis of allele-specific copy number alterations and chromatin accessibility in cancer.
Nat Biotechnol (2021). https://doi.org/10.1038/s41587-021-00911-w

Download citation

Read More

Leave a Reply

Your email address will not be published. Required fields are marked *