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Cancer subclone detection based on DNA copy number in single-cell and spatial omic sequencing data

Nature Methods volume 22, pages 1846–1856 (2025) Cite this article

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Abstract

Somatic mutations such as copy number alterations accumulate during cancer progression, driving intratumor heterogeneity that impacts therapy effectiveness. Understanding the characteristics and spatial distribution of genetically distinct subclones is essential for unraveling tumor evolution and improving cancer treatment. Here we present Clonalscope, a subclone detection method using copy number profiles, applicable to spatial transcriptomics and single-cell sequencing data. Clonalscope implements a nested Chinese Restaurant Process to identify de novo tumor subclones, which can incorporate prior information from matched bulk DNA sequencing data for improved subclone detection and malignant cell labeling. On single-cell RNA sequencing and single-cell assay for transposase-accessible chromatin using sequencing data from gastrointestinal tumors, Clonalscope successfully labeled malignant cells and identified genetically different subclones with thorough validations. On spatial transcriptomics data from various primary and metastasized tumors, Clonalscope labeled malignant spots, traced subclones and identified spatially segregated subclones with distinct differentiation levels and expression of genes associated with drug resistance and survival.

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Fig. 1: Overview of subclone detection with Clonalscope.
The alternative text for this image may have been generated using AI.
Fig. 2: Subclone detection and malignant cell labeling for scRNA-seq data validated by matched scDNA-seq data.
The alternative text for this image may have been generated using AI.
Fig. 3: Subclone detection based on coverage and allelic ratio in the scATAC–seq data from the SNU601 gastric cancer cell line.
The alternative text for this image may have been generated using AI.
Fig. 4: Subclone detection and malignant spot labeling in ST data.
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Fig. 5: Subclone tracing on ST data.
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Fig. 6: Subclone detection and differential gene analysis on a CRC sample.
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Data availability

The patient scRNA-seq and WGS data generated for this study are available under database of Genotypes and Phenotypes (dbGAP) identifier phs001818. Data are available with dbGaP authorized access for health/medical/biomedical purposes. The other patient scRNA-seq data were obtained from dbGAP under accession phs001818 (refs. 31,51). The patient scDNA-seq data were from dbGAP under accession phs001711 (ref. 9) and accession phs001818 (ref. 51). For the SNU601 sample, scDNA-seq data and scATAC–seq data were retrieved from the Sequence Read Archive under accession PRJNA598203 (ref. 60) and accession PRJNA674903 (ref. 9), respectively. For P9962-23049B and P9962-23050D, the ST data and WGS were uploaded to dbGAP identifier phs001818. For the V11Y04-378-A1 sample, the ST data were uploaded to GSE284061. For the mouse Slide-seq and Slide-DNA-seq30, the data were retrieved from PRJNA768453 and https://singlecell.broadinstitute.org/single_cell/study/SCP1278. The ST and WGS data of the SCC P6 were provided and retrieved from GSE144240 (ref. 23). For ST-colon 1 and ST-liver 1 (ref. 32), the data were retrieved from OEP001756 and http://www.cancerdiversity.asia/scCRLM. The three ST datasets of breast cancer were retrieved from 10x Genomics datasets as in Supplementary Table 3.

Code availability

Clonalscope is available via GitHub at https://github.com/seasoncloud/Clonalscope.

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Acknowledgements

This work is supported by the National Institutes of Health (P01HG00205ESH to B.T.L., S.M.G. and H.P.J. and 5R01-HG006137-07 and 1U2CCA233285-01 to C.-Y.W. and N.R.Z.). Additional support to H.P.J. came from the Research Scholar Grant (RSG-13-297-01-TBG) from the American Cancer Society, the Clayville Foundation and the Gastric Cancer Foundation.

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Author notes

  1. These authors contributed equally: Chi-Yun Wu, Jiazhen Rong.

Authors and Affiliations

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

    Chi-Yun Wu, Jiazhen Rong & Nancy R. Zhang

  2. Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, USA

    Chi-Yun Wu, Jiazhen Rong, Paul R. Hess, Sijia Huang & Nancy R. Zhang

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

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

  4. Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Paul R. Hess

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

    Billy T. Lau

  6. Department of Electrical Engineering, Stanford University, Stanford, CA, USA

    Hanlee P. Ji

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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. C.-Y.W. and J.R. conducted the data analyses. A.S. performed all related sample preparation and sequencing. J.R. and S.H. helped with benchmarking and improvement of the analysis pipeline. P.R.H. helped with pathology annotation and data interpretation. B.T.L. helped in data interpretation. S.M.G. performed data preprocessing and coordinated data transfer. H.P.J. advised on all experiments and data collection. C.-Y.W., J.R. and N.R.Z. wrote the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Nancy R. Zhang.

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The authors declare no competing interests.

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Nature Methods thanks Chenfei Wang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Lin Tang, in collaboration with the Nature Methods team.

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Extended data

Extended Data Fig. 1 Subclone detection and differential gene analysis an invasive ductal carcinoma sample.

a. Subclones detected from Clonalscope on the ST dataset from an invasive ductal carcinoma sample and paired spot-level pathologist annotation. b. The mean copy number profiles across the cells assigned to the three spatially segregated subclones (bottom), and the density plots of four regions with differences in the copy number states for at least one of the three subclones (top). c, d. show the top 6 highly expressed genes for tumor subclones 2 and 3. The color scale indicates log-normalized UMI counts for each gene.

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Wu, CY., Rong, J., Sathe, A. et al. Cancer subclone detection based on DNA copy number in single-cell and spatial omic sequencing data. Nat Methods 22, 1846–1856 (2025). https://doi.org/10.1038/s41592-025-02773-5

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