IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v12y2021i1d10.1038_s41467-021-26598-w.html
   My bibliography  Save this article

Longitudinal single-cell analysis of a myeloma mouse model identifies subclonal molecular programs associated with progression

Author

Listed:
  • Danielle C. Croucher

    (University Health Network
    University of Toronto)

  • Laura M. Richards

    (University Health Network
    University of Toronto)

  • Serges P. Tsofack

    (University Health Network)

  • Daniel Waller

    (McGill University)

  • Zhihua Li

    (University Health Network)

  • Ellen Nong Wei

    (University Health Network)

  • Xian Fang Huang

    (McGill University)

  • Marta Chesi

    (Mayo Clinic)

  • P. Leif Bergsagel

    (Mayo Clinic)

  • Michael Sebag

    (McGill University)

  • Trevor J. Pugh

    (University Health Network
    University of Toronto
    Ontario Institute for Cancer Research)

  • Suzanne Trudel

    (University Health Network
    University of Toronto)

Abstract

Molecular programs that underlie precursor progression in multiple myeloma are incompletely understood. Here, we report a disease spectrum-spanning, single-cell analysis of the Vκ*MYC myeloma mouse model. Using samples obtained from mice with serologically undetectable disease, we identify malignant cells as early as 30 weeks of age and show that these tumours contain subclonal copy number variations that persist throughout progression. We detect intratumoural heterogeneity driven by transcriptional variability during active disease and show that subclonal expression programs are enriched at different times throughout early disease. We then show how one subclonal program related to GCN2 stress response is progressively activated during progression in myeloma patients. Finally, we use chemical and genetic perturbation of GCN2 in vitro to support this pathway as a therapeutic target in myeloma. These findings therefore present a model of precursor progression in Vκ*MYC mice, nominate an adaptive mechanism important for myeloma survival, and highlight the need for single-cell analyses to understand the biological underpinnings of disease progression.

Suggested Citation

  • Danielle C. Croucher & Laura M. Richards & Serges P. Tsofack & Daniel Waller & Zhihua Li & Ellen Nong Wei & Xian Fang Huang & Marta Chesi & P. Leif Bergsagel & Michael Sebag & Trevor J. Pugh & Suzanne, 2021. "Longitudinal single-cell analysis of a myeloma mouse model identifies subclonal molecular programs associated with progression," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26598-w
    DOI: 10.1038/s41467-021-26598-w
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-021-26598-w
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-021-26598-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Michael A. Chapman & Michael S. Lawrence & Jonathan J. Keats & Kristian Cibulskis & Carrie Sougnez & Anna C. Schinzel & Christina L. Harview & Jean-Philippe Brunet & Gregory J. Ahmann & Mazhar Adli & , 2011. "Initial genome sequencing and analysis of multiple myeloma," Nature, Nature, vol. 471(7339), pages 467-472, March.
    2. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Leo Rasche & Carolina Schinke & Francesco Maura & Michael A. Bauer & Cody Ashby & Shayu Deshpande & Alexandra M. Poos & Maurizio Zangari & Sharmilan Thanendrarajan & Faith E. Davies & Brian A. Walker , 2022. "The spatio-temporal evolution of multiple myeloma from baseline to relapse-refractory states," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    3. Yandan Yang & Arnold Bolomsky & Thomas Oellerich & Ping Chen & Michele Ceribelli & Björn Häupl & George W. Wright & James D. Phelan & Da Wei Huang & James W. Lord & Callie K. Winkle & Xin Yu & Jan Wis, 2022. "Oncogenic RAS commandeers amino acid sensing machinery to aberrantly activate mTORC1 in multiple myeloma," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    4. Luigi Augugliaro & Veronica Vinciotti & Ernst C. Wit, 2022. "Extending graphical models for applications: on covariates, missingness and normality," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(2), pages 241-251, June.
    5. Mark Bustoros & Shankara Anand & Romanos Sklavenitis-Pistofidis & Robert Redd & Eileen M. Boyle & Benny Zhitomirsky & Andrew J. Dunford & Yu-Tzu Tai & Selina J. Chavda & Cody Boehner & Carl Jannes Neu, 2022. "Genetic subtypes of smoldering multiple myeloma are associated with distinct pathogenic phenotypes and clinical outcomes," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    6. Yang Ni & Veerabhadran Baladandayuthapani & Marina Vannucci & Francesco C. Stingo, 2022. "Bayesian graphical models for modern biological applications," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(2), pages 197-225, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26598-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.