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Cell facilitation promotes growth and survival under drug pressure in breast cancer

Author

Listed:
  • Rena Emond

    (Beckman Research Institute, City of Hope National Medical Center)

  • Jason I. Griffiths

    (Beckman Research Institute, City of Hope National Medical Center)

  • Vince Kornél Grolmusz

    (Beckman Research Institute, City of Hope National Medical Center)

  • Aritro Nath

    (Beckman Research Institute, City of Hope National Medical Center)

  • Jinfeng Chen

    (Beckman Research Institute, City of Hope National Medical Center)

  • Eric F. Medina

    (Beckman Research Institute, City of Hope National Medical Center)

  • Rachel S. Sousa

    (University of Utah
    University of California)

  • Timothy Synold

    (Beckman Research Institute, City of Hope National Medical Center)

  • Frederick R. Adler

    (University of Utah
    University of Utah)

  • Andrea H. Bild

    (Beckman Research Institute, City of Hope National Medical Center)

Abstract

The interplay of positive and negative interactions between drug-sensitive and resistant cells influences the effectiveness of treatment in heterogeneous cancer cell populations. Here, we study interactions between estrogen receptor-positive breast cancer cell lineages that are sensitive and resistant to ribociclib-induced cyclin-dependent kinase 4 and 6 (CDK4/6) inhibition. In mono- and coculture, we find that sensitive cells grow and compete more effectively in the absence of treatment. During treatment with ribociclib, sensitive cells survive and proliferate better when grown together with resistant cells than when grown in monoculture, termed facilitation in ecology. Molecular, protein, and genomic analyses show that resistant cells increase metabolism and production of estradiol, a highly active estrogen metabolite, and increase estrogen signaling in sensitive cells to promote facilitation in coculture. Adding estradiol in monoculture provides sensitive cells with increased resistance to therapy and cancels facilitation in coculture. Under partial inhibition of estrogen signaling through low-dose endocrine therapy, estradiol supplied by resistant cells facilitates sensitive cell growth. However, a more complete blockade of estrogen signaling, through higher-dose endocrine therapy, diminished the facilitative growth of sensitive cells. Mathematical modeling quantifies the strength of competition and facilitation during CDK4/6 inhibition and predicts that blocking facilitation has the potential to control both resistant and sensitive cancer cell populations and inhibit the emergence of a refractory population during cell cycle therapy.

Suggested Citation

  • Rena Emond & Jason I. Griffiths & Vince Kornél Grolmusz & Aritro Nath & Jinfeng Chen & Eric F. Medina & Rachel S. Sousa & Timothy Synold & Frederick R. Adler & Andrea H. Bild, 2023. "Cell facilitation promotes growth and survival under drug pressure in breast cancer," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39242-6
    DOI: 10.1038/s41467-023-39242-6
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    References listed on IDEAS

    as
    1. Qunyan Yu & Yan Geng & Piotr Sicinski, 2001. "Specific protection against breast cancers by cyclin D1 ablation," Nature, Nature, vol. 411(6841), pages 1017-1021, June.
    2. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    3. Suha Naffar-Abu Amara & Hendrik J. Kuiken & Laura M. Selfors & Timothy Butler & Marco L. Leung & Cheuk T. Leung & Elaine P. Kuhn & Teodora Kolarova & Carina Hage & Kripa Ganesh & Richard Panayiotou & , 2020. "Transient commensal clonal interactions can drive tumor metastasis," Nature Communications, Nature, vol. 11(1), pages 1-17, December.
    4. Allison S. Cleary & Travis L. Leonard & Shelley A. Gestl & Edward J. Gunther, 2014. "Tumour cell heterogeneity maintained by cooperating subclones in Wnt-driven mammary cancers," Nature, Nature, vol. 508(7494), pages 113-117, April.
    5. Samuel W. Brady & Jasmine A. McQuerry & Yi Qiao & Stephen R. Piccolo & Gajendra Shrestha & David F. Jenkins & Ryan M. Layer & Brent S. Pedersen & Ryan H. Miller & Amanda Esch & Sara R. Selitsky & Joel, 2017. "Combating subclonal evolution of resistant cancer phenotypes," Nature Communications, Nature, vol. 8(1), pages 1-15, December.
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