Abstract

Recent advances in single-cell transcriptomics are ideally placed to unravel intratumoral heterogeneity and selective resistance of cancer stem cell (SC) subpopulations to molecularly targeted cancer therapies. However, current single-cell RNA-sequencing approaches lack the sensitivity required to reliably detect somatic mutations. We developed a method that combines high-sensitivity mutation detection with whole-transcriptome analysis of the same single cell. We applied this technique to analyze more than 2,000 SCs from patients with chronic myeloid leukemia (CML) throughout the disease course, revealing heterogeneity of CML-SCs, including the identification of a subgroup of CML-SCs with a distinct molecular signature that selectively persisted during prolonged therapy. Analysis of nonleukemic SCs from patients with CML also provided new insights into cell-extrinsic disruption of hematopoiesis in CML associated with clinical outcome. Furthermore, we used this single-cell approach to identify a blast-crisis-specific SC population, which was also present in a subclone of CML-SCs during the chronic phase in a patient who subsequently developed blast crisis. This approach, which might be broadly applied to any malignancy, illustrates how single-cell analysis can identify subpopulations of therapy-resistant SCs that are not apparent through cell-population analysis.

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Acknowledgements

This work was funded by a Medical Research Council Senior Clinical Fellowship (MR/L006340/1), MRC Confidence in Concept award (MC_PC_13073) and Rosetrees Trust award (A712: Rosetrees Trust Award (A712)) to A.J.M., the MRC Molecular Haematology Unit core award (A.J.M. and S.E.W.J.; MC_UU_12009/5), a MRC programme grant to S.E.W.J. (G0801073), an international-recruitment award from the Swedish Research Council (S.E.W.J.), and grants from the Tobias Foundation (S.E.W.J.) and the Center for Innovative Medicine (CIMED) at the Karolinska Institute (S.E.W.J.). This work was also supported by the MRC-funded Oxford Consortium for Single-cell Biology (MR/M00919X/1) and the Oxford NIHR Biomedical Centre based at Oxford University Hospitals NHS Trust and University of Oxford. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, the Department of Health or the NIH. The work was also supported by an educational grant from Novartis. The authors acknowledge the contributions of the WIMM Flow Cytometry Facility, supported by the MRC HIU; MRC MHU (MC_UU_12009); NIHR Oxford BRC and John Fell Fund (131/030 and 101/517), the EPA fund (CF182 and CF170) and by the WIMM Strategic Alliance awards G0902418 and MC_UU_12025. N.A. was supported by the Oxford–Wellcome Trust Institutional Strategic Support Fund. S.M. is supported by the Finnish Cancer Institute and the Finnish Cancer Organizations.

Author information

Author notes

    • Alice Giustacchini
    •  & Supat Thongjuea

    These authors contributed equally to this work.

    • Sten Eirik W Jacobsen
    •  & Adam J Mead

    These authors jointly directed this work.

Affiliations

  1. MRC Molecular Hematology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.

    • Alice Giustacchini
    • , Supat Thongjuea
    • , Nikolaos Barkas
    • , Benjamin J Povinelli
    • , Christopher A G Booth
    • , Paul Sopp
    • , Ruggiero Norfo
    • , Alba Rodriguez-Meira
    • , Neil Ashley
    • , Lauren Jamieson
    • , Paresh Vyas
    • , Sten Eirik W Jacobsen
    •  & Adam J Mead
  2. Haemopoietic Stem Cell Biology Laboratory, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.

    • Alice Giustacchini
    • , Supat Thongjuea
    • , Nikolaos Barkas
    • , Petter S Woll
    • , Benjamin J Povinelli
    • , Christopher A G Booth
    • , Ruggiero Norfo
    • , Alba Rodriguez-Meira
    • , Neil Ashley
    • , Lauren Jamieson
    • , Sten Eirik W Jacobsen
    •  & Adam J Mead
  3. Department of Cellular Therapy, Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway.

    • Kristina Anderson
  4. Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden.

    • Åsa Segerstolpe
    • , Rickard Sandberg
    •  & Sten Eirik W Jacobsen
  5. Integrated Cardio Metabolic Center (ICMC), Karolinska Institutet, Huddinge, Sweden.

    • Åsa Segerstolpe
  6. Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden.

    • Hong Qian
    •  & Sten Eirik W Jacobsen
  7. Department of Medical Science and Division of Hematology, University Hospital, Uppsala, Sweden.

    • Ulla Olsson-Strömberg
  8. Hematology Research Unit Helsinki, Department of Clinical Chemistry and Hematology, University of Helsinki and Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland.

    • Satu Mustjoki
  9. Ludwig Institute for Cancer Research, Stockholm, Sweden.

    • Rickard Sandberg
  10. Karolinska University Hospital, Stockholm, Sweden.

    • Sten Eirik W Jacobsen
  11. NIHR Biomedical Research Centre, Churchill Hospital, Oxford, UK.

    • Adam J Mead

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Contributions

A.G. designed, performed and analyzed experiments and contributed to writing the manuscript. S.T. designed and performed bioinformatic analyses and contributed to writing the manuscript. N.B. and B.J.P. performed analyses of RNA sequencing and qPCR results. P.S.W. and P.S. were involved in FACS analysis and sorting. R.N., A.R.-M., C.A.G.B. and L.J. performed experiments. N.A. maintained single-cell facility infrastructure. P.V., S.M. and H.Q. provided infrastructure for sample banking and provided input on experimental design and analysis. K.A. performed FISH experiments. Å.S. was involved in RNA-sequencing experiments. U.O.-S. collected clinical information. R.S. provided input on RNA-sequencing experiments. A.J.M. and S.E.W.J. conceived and supervised the project, designed and analyzed experiments and wrote the manuscript.

Competing interests

A.J.M. has received honoraria and research funding from Novartis.

Corresponding authors

Correspondence to Sten Eirik W Jacobsen or Adam J Mead.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–16 and Table 12–15.

Excel files

  1. 1.

    Supplementary Table 1

    Patient demographics and characteristics.

  2. 2.

    Supplementary Table 2

    Differentially expressed genes between normal HSCs, BCRABL+ and BCR-ABL- SCs from CP-CML patients at diagnosis.

  3. 3.

    Supplementary Table 3

    Gene-sets from previous studies on CML stem and progenitor cells.

  4. 4.

    Supplementary Table 4

    Results from GSEA comparing normal HSCs to BCRABL+ SCs and BCR-ABL- SCs from CP-CML patients at diagnosis and using gene-sets from previous studies on CML stem and progenitor cells.

  5. 5.

    Supplementary Table 5

    Results from GSEA comparing normal HSCs to BCRABL+ SCs and BCR-ABL- SCs from CP-CML patients at diagnosis and using HALLMARK gene sets

  6. 6.

    Supplementary Table 6

    Results from GSEA comparing diagnostic samples from good and poor responder CML patients.

  7. 7.

    Supplementary Table 7

    Top 500 informative genes for distinguishing normal-HSCs from BCR-ABL+ SCs at diagnosis and during remission.

  8. 8.

    Supplementary Table 8

    Results from GSEA on HALLMARK gene-sets comparing remission group-A BCR-ABL+ SCs to remission group-B BCRABL+ SCs.

  9. 9.

    Supplementary Table 9

    Differentially expressed genes between normal HSCs, BCRABL+ SCs from diagnosis, remission group-A and remission group-B.

  10. 10.

    Supplementary Table 10

    Results from GSEA comparing remission group-A BCRABL+ SCs to normal HSCs and remission BCR-ABL- SCs.

  11. 11.

    Supplementary Table 11

    Differentially expressed genes between single BCRABL+ SCs falling in CP-CML cluster and BCR-ABL+ SCs falling in BC-CML cluster.

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DOI

https://doi.org/10.1038/nm.4336

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