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Determining therapeutic susceptibility in multiple myeloma by single-cell mass accumulation

Author

Listed:
  • Arif E. Cetin

    (Massachusetts Institute of Technology)

  • Mark M. Stevens

    (Massachusetts Institute of Technology
    Harvard Medical School)

  • Nicholas L. Calistri

    (Massachusetts Institute of Technology)

  • Mariateresa Fulciniti

    (Dana-Farber Cancer Institute)

  • Selim Olcum

    (Massachusetts Institute of Technology)

  • Robert J. Kimmerling

    (Massachusetts Institute of Technology)

  • Nikhil C. Munshi

    (Dana-Farber Cancer Institute
    Harvard Medical School)

  • Scott R. Manalis

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

Abstract

Multiple myeloma (MM) has benefited from significant advancements in treatment that have improved outcomes and reduced morbidity. However, the disease remains incurable and is characterized by high rates of drug resistance and relapse. Consequently, methods to select the most efficacious therapy are of great interest. Here we utilize a functional assay to assess the ex vivo drug sensitivity of single multiple myeloma cells based on measuring their mass accumulation rate (MAR). We show that MAR accurately and rapidly defines therapeutic susceptibility across human multiple myeloma cell lines to a gamut of standard-of-care therapies. Finally, we demonstrate that our MAR assay, without the need for extended culture ex vivo, correctly defines the response of nine patients to standard-of-care drugs according to their clinical diagnoses. This data highlights the MAR assay in both research and clinical applications as a promising tool for predicting therapeutic response using clinical samples.

Suggested Citation

  • Arif E. Cetin & Mark M. Stevens & Nicholas L. Calistri & Mariateresa Fulciniti & Selim Olcum & Robert J. Kimmerling & Nikhil C. Munshi & Scott R. Manalis, 2017. "Determining therapeutic susceptibility in multiple myeloma by single-cell mass accumulation," Nature Communications, Nature, vol. 8(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-01593-2
    DOI: 10.1038/s41467-017-01593-2
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    Cited by:

    1. L. Mathur & B. Szalai & N. H. Du & R. Utharala & M. Ballinger & J. J. M. Landry & M. Ryckelynck & V. Benes & J. Saez-Rodriguez & C. A. Merten, 2022. "Combi-seq for multiplexed transcriptome-based profiling of drug combinations using deterministic barcoding in single-cell droplets," Nature Communications, Nature, vol. 13(1), pages 1-15, December.

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