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Single cell characterization of myeloma and its precursor conditions reveals transcriptional signatures of early tumorigenesis

Author

Listed:
  • Rebecca Boiarsky

    (Broad Institute of MIT and Harvard
    CSAIL and IMES, Massachusetts Institute of Technology)

  • Nicholas J. Haradhvala

    (Broad Institute of MIT and Harvard
    Harvard Graduate Program in Biophysics)

  • Jean-Baptiste Alberge

    (Broad Institute of MIT and Harvard
    Dana-Farber Cancer Institute
    Harvard Medical School)

  • Romanos Sklavenitis-Pistofidis

    (Broad Institute of MIT and Harvard
    Dana-Farber Cancer Institute
    Harvard Medical School)

  • Tarek H. Mouhieddine

    (Icahn School of Medicine at Mount Sinai)

  • Oksana Zavidij

    (Constellation Pharmaceuticals a MorphoSys Company)

  • Ming-Chieh Shih

    (CSAIL and IMES, Massachusetts Institute of Technology)

  • Danielle Firer

    (Broad Institute of MIT and Harvard)

  • Mendy Miller

    (Broad Institute of MIT and Harvard)

  • Habib El-Khoury

    (Dana-Farber Cancer Institute)

  • Shankara K. Anand

    (Broad Institute of MIT and Harvard)

  • François Aguet

    (Broad Institute of MIT and Harvard)

  • David Sontag

    (Broad Institute of MIT and Harvard
    CSAIL and IMES, Massachusetts Institute of Technology)

  • Irene M. Ghobrial

    (Broad Institute of MIT and Harvard
    Dana-Farber Cancer Institute
    Harvard Medical School)

  • Gad Getz

    (Broad Institute of MIT and Harvard
    Harvard Medical School
    Massachusetts General Hospital)

Abstract

Multiple myeloma is a plasma cell malignancy almost always preceded by precursor conditions, but low tumor burden of these early stages has hindered the study of their molecular programs through bulk sequencing technologies. Here, we generate and analyze single cell RNA-sequencing of plasma cells from 26 patients at varying disease stages and 9 healthy donors. In silico dissection and comparison of normal and transformed plasma cells from the same bone marrow biopsy enables discovery of patient-specific transcriptional changes. Using Non-Negative Matrix Factorization, we discover 15 gene expression signatures which represent transcriptional modules relevant to myeloma biology, and identify a signature that is uniformly lost in abnormal cells across disease stages. Finally, we demonstrate that tumors contain heterogeneous subpopulations expressing distinct transcriptional patterns. Our findings characterize transcriptomic alterations present at the earliest stages of myeloma, providing insight into the molecular underpinnings of disease initiation.

Suggested Citation

  • Rebecca Boiarsky & Nicholas J. Haradhvala & Jean-Baptiste Alberge & Romanos Sklavenitis-Pistofidis & Tarek H. Mouhieddine & Oksana Zavidij & Ming-Chieh Shih & Danielle Firer & Mendy Miller & Habib El-, 2022. "Single cell characterization of myeloma and its precursor conditions reveals transcriptional signatures of early tumorigenesis," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33944-z
    DOI: 10.1038/s41467-022-33944-z
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    References listed on IDEAS

    as
    1. Niccolò Bolli & Francesco Maura & Stephane Minvielle & Dominik Gloznik & Raphael Szalat & Anthony Fullam & Inigo Martincorena & Kevin J. Dawson & Mehmet Kemal Samur & Jorge Zamora & Patrick Tarpey & H, 2018. "Genomic patterns of progression in smoldering multiple myeloma," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    2. Thomas Nerreter & Sebastian Letschert & Ralph Götz & Sören Doose & Sophia Danhof & Hermann Einsele & Markus Sauer & Michael Hudecek, 2019. "Super-resolution microscopy reveals ultra-low CD19 expression on myeloma cells that triggers elimination by CD19 CAR-T," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    3. Bénedith Oben & Guy Froyen & Kylee H. Maclachlan & Daniel Leongamornlert & Federico Abascal & Binbin Zheng-Lin & Venkata Yellapantula & Andriy Derkach & Ellen Geerdens & Benjamin T. Diamond & Ingrid A, 2021. "Whole-genome sequencing reveals progressive versus stable myeloma precursor conditions as two distinct entities," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
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    Cited by:

    1. Aleksandrina Goeva & Michael-John Dolan & Judy Luu & Eric Garcia & Rebecca Boiarsky & Rajat M. Gupta & Evan Macosko, 2024. "HiDDEN: a machine learning method for detection of disease-relevant populations in case-control single-cell transcriptomics data," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    2. Travis S. Johnson & Parvathi Sudha & Enze Liu & Nathan Becker & Sylvia Robertson & Patrick Blaney & Gareth Morgan & Vivek S. Chopra & Cedric Santos & Michael Nixon & Kun Huang & Attaya Suvannasankha &, 2024. "1q amplification and PHF19 expressing high-risk cells are associated with relapsed/refractory multiple myeloma," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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