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Time-of-day effects of cancer drugs revealed by high-throughput deep phenotyping
Carolin Ector1,2, Christoph Schmal3, Jeff Didier4
1Charité Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, Berlin, Germany.
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.
Insights
This study introduces a high-throughput method to optimize cancer drug timing by analyzing circadian rhythms. This approach identifies best treatment windows, improving efficacy and reducing side effects for better patient outcomes.
Area of Science:
- Chronobiology
- Cancer Biology
- Pharmacology
Background:
- The circadian clock regulates essential cellular functions, influencing health and disease.
- Circadian-based therapies show promise for enhancing drug efficacy and minimizing side effects.
- Identifying optimal drug administration timings remains a significant challenge.
Purpose of the Study:
- To develop a high-throughput approach for deep-phenotyping cancer cell models.
- To evaluate circadian rhythms, growth, and drug responses in cancer cells.
- To identify optimal treatment windows and responsive cell types/drug combinations.
Main Methods:
- Integration of live-imaging and data analysis techniques.
- Profiling drug sensitivities across different times of the day.
- Utilizing computational tools to identify factors influencing time-of-day drug sensitivity.
Main Results:
- A streamlined process for profiling time-dependent drug sensitivities was established.
- Optimal treatment windows and responsive cancer cell types were identified.
- Key cellular and genetic factors affecting time-of-day drug sensitivity were uncovered.
Conclusions:
- The developed versatile approach can be adapted to various biological models.
- Leveraging circadian rhythms can optimize anti-cancer drug treatments.
- This research promises improved patient outcomes and transformative therapeutic strategies.
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