Mass Cytometry reveals unique phenotypic patterns associated with subclonal diversity and outcomes in multiple myeloma

Linda B. Baughn, Erik Jessen, Neeraj Sharma, Hongwei Tang, James B. Smadbeck, Mark D. Long, Kathryn Pearce, Matthew Smith, Surendra Dasari, Zohar Sachs, Michael A. Linden, Joselle Cook, A. Keith Stewart, Marta Chesi, Amit Mitra, P. Leif Bergsagel, Brian Van Ness, Shaji K. Kumar

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Multiple myeloma (MM) remains an incurable plasma cell (PC) malignancy. Although it is known that MM tumor cells display extensive intratumoral genetic heterogeneity, an integrated map of the tumor proteomic landscape has not been comprehensively evaluated. We evaluated 49 primary tumor samples from newly diagnosed or relapsed/refractory MM patients by mass cytometry (CyTOF) using 34 antibody targets to characterize the integrated landscape of single-cell cell surface and intracellular signaling proteins. We identified 13 phenotypic meta-clusters across all samples. The abundance of each phenotypic meta-cluster was compared to patient age, sex, treatment response, tumor genetic abnormalities and overall survival. Relative abundance of several of these phenotypic meta-clusters were associated with disease subtypes and clinical behavior. Increased abundance of phenotypic meta-cluster 1, characterized by elevated CD45 and reduced BCL-2 expression, was significantly associated with a favorable treatment response and improved overall survival independent of tumor genetic abnormalities or patient demographic variables. We validated this association using an unrelated gene expression dataset. This study represents the first, large-scale, single-cell protein atlas of primary MM tumors and demonstrates that subclonal protein profiling may be an important determinant of clinical behavior and outcome.

Original languageEnglish (US)
Article number84
JournalBlood cancer journal
Volume13
Issue number1
DOIs
StatePublished - Dec 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s).

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't
  • Research Support, N.I.H., Extramural

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