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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.

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Fig. 1: Overview of allele-specific copy number estimation of single cells with Alleloscope.
Fig. 2: Validation of Alleloscope’s results on the P5931 gastric cancer sample using linked-read sequencing data.
Fig. 3: Across multiple cancer types, Alleloscope detects loss-of-heterozygosity events and multiallelic CNAs, delineating complex subclonal structure that are invisible to total copy number analysis.
Fig. 4: Alleloscope multiomic analysis of scATAC-seq data of a BCC sample (SU008, ref. 25).
Fig. 5: Alleloscope analysis of scDNA-seq and scATAC-seq data reveals complex subclonal heterogeneity in the SNU601 gastric cancer cell line.
Fig. 6: Integrative analysis of allele-specific copy number and chromatin accessibility for SNU601 ATAC-seq data.

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 

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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.

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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.

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

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Supplementary Figs. 1–18, Tables 1, 2 and 4, Results and Methods.

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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

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