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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
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    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. 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.
    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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