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Nat Commun. 2017 Nov 20;8(1):1613. doi: 10.1038/s41467-017-01593-2.

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

Author information

1
Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, 500 Main St., Cambridge, MA, 02139, USA.
2
Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Ave., Boston, MA, 02215, USA.
3
Jerome Lipper Multiple Myeloma Center, Department of Medical Oncology, Dana-Farber Cancer Institute, 450 Brookline Ave., Boston, MA, 02215, USA.
4
Veterans Affairs Boston Healthcare System, Harvard Medical School, 150 S Huntington Ave., Boston, MA, 02130, USA.
5
Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, 500 Main St., Cambridge, MA, 02139, USA. srm@mit.edu.
6
Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA, 02139, USA. srm@mit.edu.
7
Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA, 02139, USA. srm@mit.edu.

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.

PMID:
29151572
PMCID:
PMC5694762
DOI:
10.1038/s41467-017-01593-2
[Indexed for MEDLINE]
Free PMC Article

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