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Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology
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Accelerating precision exercise medicine in cancer patients using pooled individual patient data: POLARIS experience
Laurien M Buffart1,2, Marlou-Floor Kenkhuis1, Robert U Newton2
1Department of Medical BioSciences, Radboud University Medical Center, Nijmegen, The Netherlands.
JNCI Cancer Spectrum
|August 12, 2025
Summary
Precision exercise medicine for cancer patients shows benefits in fitness, fatigue, and mood. Exercise effects vary by patient characteristics and intervention details, guiding personalized rehabilitation strategies.
Area of Science:
- Exercise oncology
- Cancer rehabilitation
- Precision medicine
Background:
- Numerous exercise oncology trials inform current recommendations for cancer patients.
- Exercise medicine for cancer can be tailored in type, dose, schedule, and timing.
- Precision exercise medicine requires understanding individual patient responses to interventions.
Purpose of the Study:
- Highlight the value of pooled individual patient data (IPD) analyses.
- Summarize findings from pooled IPD analyses on exercise effects in cancer patients.
- Provide guidance for advancing precision exercise medicine in oncology.
Main Methods:
- Utilized the Predicting OptimaL cAncer RehabIlitation and Supportive care (POLARIS) study infrastructure.
- Performed pooled analyses of IPD from multiple randomized controlled trials.
- Included IPD from 52 exercise trials in the current POLARIS database.
Main Results:
- Exercise interventions benefit physical fitness, fatigue, quality of life, cognition, sleep, anxiety, and depression in cancer patients.
- Exercise effects are modified by patient characteristics (e.g., baseline values, age, marital status, education).
- Intervention characteristics (e.g., supervision, specificity) also influence exercise effects.
Conclusions:
- Pooled IPD analyses are valuable for advancing precision exercise medicine in cancer care.
- Future research should focus on understudied populations, clinical outcomes, biomarkers, and machine learning for personalized treatment.
- Tailoring exercise interventions based on individual patient and intervention characteristics is key for optimal cancer rehabilitation.
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