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Time-of-day effects of cancer drugs revealed by high-throughput deep phenotyping

Author

Listed:
  • Carolin Ector

    (Charité – Universitätsmedizin Berlin
    Humboldt-Universität zu Berlin)

  • Christoph Schmal

    (Humboldt-Universität zu Berlin)

  • Jeff Didier

    (University of Luxembourg)

  • Sébastien De Landtsheer

    (University of Luxembourg)

  • Anna-Marie Finger

    (Charité – Universitätsmedizin Berlin
    San Francisco)

  • Francesca Müller-Marquardt

    (Charité – Universitätsmedizin Berlin
    University of Montpellier)

  • Johannes H. Schulte

    (Charité – Universitätsmedizin Berlin
    Universitätsklinikum Tübingen)

  • Thomas Sauter

    (University of Luxembourg)

  • Ulrich Keilholz

    (Charité – Universitätsmedizin Berlin
    German Cancer Consortium (DKTK))

  • Hanspeter Herzel

    (Humboldt-Universität zu Berlin
    Charité – Universitätsmedizin Berlin)

  • Achim Kramer

    (Charité – Universitätsmedizin Berlin)

  • Adrián E. Granada

    (Charité – Universitätsmedizin Berlin
    German Cancer Consortium (DKTK))

Abstract

The circadian clock, a fundamental biological regulator, governs essential cellular processes in health and disease. Circadian-based therapeutic strategies are increasingly gaining recognition as promising avenues. Aligning drug administration with the circadian rhythm can enhance treatment efficacy and minimize side effects. Yet, uncovering the optimal treatment timings remains challenging, limiting their widespread adoption. In this work, we introduce a high-throughput approach integrating live-imaging and data analysis techniques to deep-phenotype cancer cell models, evaluating their circadian rhythms, growth, and drug responses. We devise a streamlined process for profiling drug sensitivities across different times of the day, identifying optimal treatment windows and responsive cell types and drug combinations. Finally, we implement multiple computational tools to uncover cellular and genetic factors shaping time-of-day drug sensitivity. Our versatile approach is adaptable to various biological models, facilitating its broad application and relevance. Ultimately, this research leverages circadian rhythms to optimize anti-cancer drug treatments, promising improved outcomes and transformative treatment strategies.

Suggested Citation

  • Carolin Ector & Christoph Schmal & Jeff Didier & Sébastien De Landtsheer & Anna-Marie Finger & Francesca Müller-Marquardt & Johannes H. Schulte & Thomas Sauter & Ulrich Keilholz & Hanspeter Herzel & A, 2024. "Time-of-day effects of cancer drugs revealed by high-throughput deep phenotyping," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51611-3
    DOI: 10.1038/s41467-024-51611-3
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    References listed on IDEAS

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    2. Jordi Barretina & Giordano Caponigro & Nicolas Stransky & Kavitha Venkatesan & Adam A. Margolin & Sungjoon Kim & Christopher J. Wilson & Joseph Lehár & Gregory V. Kryukov & Dmitriy Sonkin & Anupama Re, 2012. "The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity," Nature, Nature, vol. 483(7391), pages 603-607, March.
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