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SWIFT-seq enables comprehensive single-cell transcriptomic profiling of circulating tumor cells in multiple myeloma and its precursors

Nature Cancer volume 6, pages 1595–1611 (2025)Cite this article

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Abstract

Multiple myeloma is a bone marrow (BM) plasma cell malignancy preceded by precursor conditions. BM biopsies are conducted infrequently and can yield inconclusive results due to technical limitations. Profiling circulating tumor cells (CTCs) may enable noninvasive routine clinical assessments but remains challenging. Here, to address this, we describe a single-cell sequencing workflow to interrogate few tumor cells (SWIFT-seq), and employ single-cell RNA sequencing and B cell receptor sequencing on paired BM and CTCs from 101 patients and healthy donors. We establish a sequencing-based CTC enumeration strategy and develop a CTC classifier to infer cytogenetic abnormalities. Additionally, we leverage expression profiling to measure tumor proliferative index in CTCs, and demonstrate that clonal dynamics can be captured in CTCs. Last, we propose a circulatory dynamics model whereby tumor burden, proliferation, cytogenetics and a circulatory capacity signature influence CTC burden. Overall, SWIFT-seq may advance blood-based myeloma diagnostics, surveillance and prognostication, and reveal biological mechanisms of tumor dissemination.

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Fig. 1: scRNA-seq coupled with scBCR-seq efficiently enumerates CTCs from the PB of patients with MGUS/SMM/MM and captures prognostically relevant differences in tumor burden.
Fig. 2: scRNA-seq can be used to infer tumor cytogenetics and high-risk copy number abnormalities that associate with elevated CTC levels.
Fig. 3: scRNA-seq can measure the proliferative index in BM and CTCs and demonstrates that tumor cycling associates with CTC burden.
Fig. 4: scRNA-seq of CTCs identifies a transcriptional signature of circulatory capacity that is associated with CTC burden, cytogenetics and significantly shorter overall survival.
Fig. 5: CTCs capture the genomic complexity and clonal dynamics of tumor cells in the BM.
Fig. 6: scRNA-seq of CTCs provides clinical and biological insights for patients with MM and its precursor conditions.

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Data availability

Data generated for this study were deposited in a controlled-access repository (dbGaP) in accordance with privacy requirements set forth in the informed consent forms signed by study participants. Access to this dataset can be obtained by registering the investigator’s institution in eRA Commons, establishing an eRA Commons account for the investigator and submitting a Data Access Request through the dbGaP Authorized Access website; following authorization by the requesting institution’s signing official and review by NIH staff, the request may be approved for data download. Single-cell RNA and BCR sequencing raw and processed data generated for this study are deposited in dbGaP under accession code phs003855.v1.p1. Research-level whole-genome sequencing data used in this study are deposited in dbGaP under accession codes phs003084.v1.p1 and phs003846.v1.p1 (refs. 11,38). Processed bulk RNA-seq data and metadata for the CoMMpass cohort were downloaded from the MMRF Researcher Gateway (IA22 release; https://research.themmrf.org/). Source data are provided with this paper.

Code availability

Code used for downstream analysis can be found on GitHub at https://github.com/romanos-sp/Nat-Can-2025-SWIFT-seq.

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Acknowledgements

We thank the patients who participate in the PCROWD and PROMISE research studies. We thank S. Belkin (Broad Institute of MIT and Harvard) for assistance with data management, Designs that Cell for illustration support and J. Keats for assisting in the identification of relevant metadata for the CoMMpass cohort. E.D.L. is supported by a Helen Gurley Brown Foundation Award and the International Myeloma Society. R.S.-P. is supported by the Multiple Myeloma Research Foundation Research Fellowship Award, the International Waldenstrom’s Macroglobulinemia Foundation’s (IWMF) Robert A. Kyle Award, the Dana-Farber Cancer Institute’s Center for Early Detection and Interception of Blood Cancers Award, the Claudia Adams-Barr Award for Innovative Basic Cancer Research, and the FNIH. We acknowledge funding support for this study from the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation (awarded to I.M.G.), the Multiple Myeloma Research Foundation (MMRF) (awarded to I.M.G.), the National Institutes of Health (grant nos. R35CA263817 and U01CA271492 awarded to I.M.G., and R21 CA256644 and K22 CA251648 awarded to C.R.M.) and the Stand Up To Cancer Dream Team Research Grant (grant no. SU2C-AACR-DT-28-18 awarded to C.R.M., I.M.G. and G.G.). Stand Up To Cancer is a division of the Entertainment Industry Foundation. The indicated SU2C grant is administered by the American Association for Cancer Research, the scientific partner of Stand Up To Cancer. Opinions, interpretations, conclusions and recommendations are those of the authors and are not necessarily endorsed by Stand Up To Cancer, the Entertainment Industry Foundation or the American Association for Cancer Research.

Author information

Author notes

  1. These authors contributed equally: Elizabeth D. Lightbody, Romanos Sklavenitis-Pistofidis.

  2. These authors jointly supervised this work: Irene M. Ghobrial, Gad Getz.

Authors and Affiliations

  1. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Elizabeth D. Lightbody, Romanos Sklavenitis-Pistofidis, Michael P. Agius, Ankit K. Dutta, Hadley Barr, Jean-Baptiste Alberge, Nang Kham Su, Cody J. Boehner, Michelle P. Aranha, Mahshid Rahmat, Yoshinobu Konishi, Laura Hevenor, Katherine Towle, Erica Horowitz, Jacqueline Perry, Maya Davis, Christian J. Cea-Curry, Grace Fleming, Daniel Heilpern-Mallory, Habib El-Khoury, Annie Cowan, Catherine R. Marinac & Irene M. Ghobrial

  2. Harvard Medical School, Boston, MA, USA

    Elizabeth D. Lightbody, Romanos Sklavenitis-Pistofidis, Michael P. Agius, Ankit K. Dutta, Hadley Barr, Jean-Baptiste Alberge, Nang Kham Su, Cody J. Boehner, Michelle P. Aranha, Mahshid Rahmat, Yoshinobu Konishi, Laura Hevenor, Katherine Towle, Erica Horowitz, Jacqueline Perry, Maya Davis, Christian J. Cea-Curry, Grace Fleming, Daniel Heilpern-Mallory, Habib El-Khoury, Annie Cowan, John E. Ready, Catherine R. Marinac, Gad Getz & Irene M. Ghobrial

  3. Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, USA

    Elizabeth D. Lightbody, Romanos Sklavenitis-Pistofidis, Ting Wu, Junko Tsuji, Danielle T. Firer, Michael P. Agius, Ankit K. Dutta, Sungjae Kim, Jean-Baptiste Alberge, Tim Coorens, Nicholas J. Haradhvala, Nang Kham Su, Cody J. Boehner, Michelle P. Aranha, Mahshid Rahmat, Yoshinobu Konishi, Michael E. Vinyard, Daniel Heilpern-Mallory, Habib El-Khoury, Gad Getz & Irene M. Ghobrial

  4. Department of Microbiology and Immunology, Dalhousie University, Halifax, Nova Scotia, Canada

    Sarah Nersesian

  5. Department of Orthopaedic Surgery, Brigham and Women’s Hospital, Boston, MA, USA

    Kelly A. Walsh & John E. Ready

  6. Center for Early Detection and Interception of Blood Cancers, Dana-Farber Cancer Institute, Boston, MA, USA

    Catherine R. Marinac & Irene M. Ghobrial

  7. Krantz Family Center for Cancer Research and Department of Pathology, Massachusetts General Hospital, Boston, MA, USA

    Gad Getz

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Contributions

T.W., J.T. and D.T.F. are equally contributing second authors in this paper. E.D.L., R.S.-P., G.G. and I.M.G. conceived of and designed the research study. E.D.L., A.K.D., H.B., N.K.S., C.J.B., L.H., K.T., E.H., M.D., K.A.W., C.J.C.-C., G.F., D.H.-M., A.C., J.E.R., C.R.M. and I.M.G. enrolled patients and/or acquired clinical samples. E.D.L., M.P. Agius, A.K.D., H.B., N.K.S. and C.J.B. performed research experiments. E.D.L., R.S.-P., D.T.F., J.T., T.W., M.P. Agius, A.K.D., H.B., S.K., J.-B.A., M.R. and Y.K. acquired the sequencing data. E.D.L., R.S.-P., A.K.D., H.B., E.H., J.P., M.D., K.A.W., A.C., J.E.R., H.E.-K. and I.M.G. acquired clinical data and/or provided patient care. E.D.L., R.S.-P., D.T.F., J.T., T.W., S.K., T.C., J.-B.A., M.P. Aranha and M.E.V. analyzed the data. R.S.-P., J.T., T.C., J.-B.A., N.J.H., S.N. and G.G. provided guidance in data analysis. E.D.L., R.S.-P., G.G. and I.M.G. drafted the paper. All authors reviewed, edited and approved the paper.

Corresponding authors

Correspondence to Gad Getz or Irene M. Ghobrial.

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Competing interests

J.T., D.T.F., T.W., M.P. Agius, A.K.D., H.B., S.K., J.-B.A., T.C., S.N., N.K.S., C.J.B., M.P. Aranha, M.R., Y.K., L.H., K.T., E.H., J.P., M.D., K.A.W., C.J.C.-C., G.F., M.E.V., D.H.-M., H.E.-K., A.C., J.E.R. and C.R.M. declare no competing interests. R.S.-P. is a co-founder, equity holder and consultant for PreDICTA Biosciences, a precision oncology company integrating multiomics and liquid biopsies to develop diagnostic and therapeutic products. N.J.H. is a consultant for Constellation Pharmaceuticals. G.G. is an inventor on patent applications filed by the Broad Institute related to MSMuTect, MSMutSig, POLYSOLVER, SignatureAnalyzer-GPU, MSIDetect and MinumuMM-seq. G.G. receives research funds from IBM, Pharmacyclics and Ultima Genomics, and is a founder of, consultant for and holds privately held equity in Scorpion Therapeutics; he is also a founder of and holds privately held equity in PreDICTA Biosciences. I.M.G. has a consulting or advisory role with AbbVie, Adaptive, Amgen, Aptitude Health, Bristol Myers Squibb, GlaxoSmithKline, Huron Consulting, Janssen, Menarini Silicon Biosystems, Oncopeptides, Pfizer, Sanofi, Sognef, Takeda, The Binding Site and Window Therapeutics; has received speaker fees from Vor Biopharma and Veeva Systems, Inc.; is a co-founder, equity holder and consultant for PreDICTA Biosciences; and her spouse is the CMO and equity holder of Disc Medicine. E.D.L., R.S.-P., G.G. and I.M.G. have submitted a patent application related to this work.

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Nature Cancer thanks P. Leif Bergsagel, Jérôme Moreaux and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 SWIFT-seq identifies rare CTCs that reflect BM biology and burden.

a) Heatmap of V(D)JC gene (y-axis) usage in all tumors (n = 75) (x-axis). Tiles are colored orange when the gene is used in the corresponding tumor’s clonotype. b) UMAP embedding of plasma cells (n = 1,292,479) colored by patient ID (top) or disease stage (bottom). c) Heatmap of correlation coefficients between BM tumor cells and CTCs in patients with at least 30 tumor cells in each compartment (n = 51). Correlation coefficients were computed using two-sided Spearman’s tests. d) Scatter plot of BM biopsy (BMBx) plasma cell infiltration (%) (x-axis) and the proportion of CTCs out of all plasma cells (y-axis) in patients (n = 71). For patients with two tumors (n = 3), secondary tumors were excluded. Two patients with MGUS had no BM infiltration information. A line (black) was fit using the “lm” method (95% confidence interval in light orange). Patients were colored by disease stage. The correlation coefficient and p-value were computed using a two-sided Pearson’s test (r = 0.56, p = 3.4e-07).

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Extended Data Fig. 2 SWIFT-seq detects cytogenetic abnormalities that influence CTC burden in CoMMpass cohort.

a) Barplots of the proportion of patients with MGUS (n = 14), SMM (n = 41), and NDMM (n = 18) who were unclassified by FISH (negative results, no results due to insufficient number of plasma cells in the clinical sample, or positive results but without an IgH translocation or HRD). Error bars represent 95% confidence intervals. b) Barplots of the number of patients who were positive via FISH and/or scRNA-seq for Amp1q (both: n = 13; FISH only, n = 2; scRNA-seq only, n = 3), Del13q (both: n = 5; FISH only, n = 1; scRNA-seq only, n = 2), and Del17p (both: n = 4; FISH only, n = 0; scRNA-seq only, n = 0) (Positive on both: green; positive on FISH: red; position on scRNA-seq: blue). Patients with FISH failure due to insufficient plasma cells detected in the clinical sample were removed. Patients who were not tested for a particular abnormality by FISH were not considered in the count for that particular abnormality. c) Boxplots, violin plots, and scatter plots of the proportion of plasma cells in the PB (y-axis), as assessed through flow cytometry, in CoMMpass patients with HRD (n = 365), t(11;14) (n = 142), t(4;14) (n = 84), t(14;16) (n = 26), and t(14;20) (n = 8) (x-axis). Patients from these 5 cytogenetic classes with available CTC data were included (n = 625). Violin outline width represents density. Box: 1st quartile, median, 3rd quartile; whiskers: +/− 1.5*IQR. The Q-values for the comparisons between patients were as follows; HRD and t(11;14) Trx, q = 0.00011; HRD and t(4;14) Trx, q = 0.0036; HRD and t(14;16) Trx, q = 3.3e-10. P-values were computed using two-sided Wilcoxon’s rank-sum tests and adjusted using the Benjamini-Hochberg approach. d, e) Boxplots, violin plots, and scatter plots of the proportion of plasma cells in the PB (y-axis), as assessed through flow cytometry in CoMMpass patients with or without Amp1q (D; Present, n = 274; Absent, n = 426) and Del13q (E; Present, n = 344; Absent, n = 356) (x-axis). A total of 700 patients with available Amp1q/Del13q status and CTC data were included. Violin outline width represents density. Box: 1st quartile, median, 3rd quartile; whiskers: +/− 1.5*IQR. The p-values for the comparisons between patients were as follows; Amp1q Absent and Amp1q Present, p = 5.3e-05; Del13q Absent and Del13q Present, p = 0.0011. P-values were computed using two-sided Wilcoxon’s rank-sum tests.

Source data

Extended Data Fig. 3 CTC proliferative index positively correlates with BM tumor cells.

Scatter plot of the proportion of cycling BM tumor cells (out of all BM tumor cells) (x-axis) and the proportion of cycling CTCs (out of all CTCs) (y-axis). Patients with at least 100 CTCs were included (n = 43). Outlier patients were visualized in red triangles (n = 4) with error bars corresponding to the 95% confidence interval. A dark orange line was fit using the “lm” method (95% confidence interval in light orange). The correlation coefficient and p-value were computed on the data excluding outliers using a two-sided Pearson’s test (r = 0.53, p = 0.00055).

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Lightbody, E.D., Sklavenitis-Pistofidis, R., Wu, T. et al. SWIFT-seq enables comprehensive single-cell transcriptomic profiling of circulating tumor cells in multiple myeloma and its precursors. Nat Cancer 6, 1595–1611 (2025). https://doi.org/10.1038/s43018-025-01006-0

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