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

Lightbody ED*, Sklavenitis-Pistofidis R*, Wu T, Tsuji J, Firer DT, Agius MP, Dutta AK, Barr H, Kim S, Alberge JB, Nersesian S, Coorens T, Haradhvala NJ, Su NK, Boehner CJ, Aranha MP, Rahmat M, Konishi Y, Hevenor L, Towle K, Horowitz E, Perry J, Davis M, Walsh KA, Cea-Curry CJ, Fleming G, Vinyard ME, Heilpern-Mallory D, El-Khoury H, Cowan A, Ready JE, Marinac CR, Getz G#^, Ghobrial IM#^
Nat Cancer (2025)

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.

Getz Lab Authors
* First Authors
# Senior Authors
^ Corresponding Authors
Pubmed
DOI
Dimensions
(# of citations)
Altmetrics
Share
tweet