⚡ Quick Summary
This study utilized machine learning to identify distinct phenogroups among women with metastatic breast cancer, revealing significant variability in cardiorespiratory fitness (CRF) impairment and response to aerobic exercise training. The findings suggest that understanding these phenogroups could enhance cardiovascular risk stratification and inform targeted exercise interventions.
🔍 Key Details
- 👩🔬 Participants: 64 women with metastatic breast cancer
- 🗓️ Duration: 12 weeks of structured aerobic training
- 🔍 Analysis Method: Unsupervised hierarchical cluster analyses
- 📊 Variables Assessed: 120 candidate baseline variables, 32 representative variables identified
🔑 Key Takeaways
- 📈 CRF Range: Baseline CRF varied from 10.2 to 38.8 mL O2·kg-1·min-1.
- 🔄 CRF Response: Response to training ranged from -15.7 to 4.1 mL O2·kg-1·min-1.
- 👥 Phenogroups Identified: Two distinct phenogroups with unique baseline characteristics.
- 💔 Phenogroup 2: Showed blunted CRF response compared to phenogroup 1 (-1.94 ± 3.80 vs 0.70 ± 2.22 mL O2·kg-1·min-1).
- 📉 Health Metrics: Phenogroup 2 had lower cardiac function and poorer patient-reported outcomes.
- 🔍 Implications: Findings could guide targeted exercise interventions for cancer patients.
📚 Background
Cardiorespiratory fitness (CRF) is a crucial indicator of health, particularly in patients undergoing treatment for cancer. However, the extent of CRF impairment and the effectiveness of exercise interventions can vary significantly among individuals. This study aimed to leverage machine learning to better understand these variations and identify patients at high risk for poor CRF outcomes.
🗒️ Study
Conducted with 64 women diagnosed with metastatic breast cancer, this study randomly assigned participants to either a structured aerobic training program or a control group. The researchers employed unsupervised hierarchical cluster analyses to categorize patients into distinct phenogroups based on their baseline characteristics and CRF metrics.
📈 Results
The analysis revealed two phenogroups, with phenogroup 2 exhibiting significantly poorer baseline health metrics, including lower cardiac function and CRF. Notably, the CRF response to aerobic training was markedly blunted in phenogroup 2, indicating that these patients may require tailored interventions to improve their fitness levels.
🌍 Impact and Implications
The identification of CRF phenogroups has profound implications for the management of metastatic breast cancer. By understanding the unique characteristics of each group, healthcare providers can better stratify cardiovascular risks and design targeted exercise interventions that cater to the specific needs of patients, ultimately improving their overall health outcomes.
🔮 Conclusion
This study highlights the potential of machine learning in enhancing our understanding of CRF among cancer patients. By identifying distinct phenogroups, we can pave the way for more personalized and effective exercise interventions, improving the quality of life for those battling cancer. Continued research in this area is essential for optimizing patient care and outcomes.
💬 Your comments
What are your thoughts on the role of machine learning in cancer care? We invite you to share your insights and engage in a discussion! 💬 Leave your comments below or connect with us on social media:
Machine Learning-Driven Phenogrouping and Cardiorespiratory Fitness Response in Metastatic Breast Cancer.
Abstract
PURPOSE: The magnitude of cardiorespiratory fitness (CRF) impairment during anticancer treatment and CRF response to aerobic exercise training (AT) are highly variable. The aim of this ancillary analysis was to leverage machine learning approaches to identify patients at high risk of impaired CRF and poor CRF response to AT.
METHODS: We evaluated heterogeneity in CRF among 64 women with metastatic breast cancer randomly assigned to 12 weeks of highly structured AT (n = 33) or control (n = 31). Unsupervised hierarchical cluster analyses were used to identify representative variables from multidimensional prerandomization (baseline) data, and to categorize patients into mutually exclusive subgroups (ie, phenogroups). Logistic and linear regression evaluated the association between phenogroups and impaired CRF (ie, ≤16 mL O2·kg-1·min-1) and CRF response.
RESULTS: Baseline CRF ranged from 10.2 to 38.8 mL O2·kg-1·min-1; CRF response ranged from -15.7 to 4.1 mL O2·kg-1·min-1. Of the n = 120 candidate baseline variables, n = 32 representative variables were identified. Patients were categorized into two phenogroups. Compared with phenogroup 1 (n = 27), phenogroup 2 (n = 37) contained a higher number of patients with none or >three lines of previous anticancer therapy for metastatic disease and had lower resting left ventricular systolic and diastolic function, cardiac output reserve, hematocrit, lymphocyte count, patient-reported outcomes, and CRF (P < .05) at baseline. Among patients allocated to AT (phenogroup 1, n = 12; 44%; phenogroup 2, n = 21; 57%), CRF response (-1.94 ± 3.80 mL O2·kg-1·min-1 v 0.70 ± 2.22 mL O2·kg-1·min-1) was blunted in phenogroup 2 compared with phenogroup 1.
CONCLUSION: Phenotypic clustering identified two subgroups with unique baseline characteristics and CRF outcomes. The identification of CRF phenogroups could help improve cardiovascular risk stratification and guide investigation of targeted exercise interventions among patients with cancer.
Author: [‘Novo RT’, ‘Thomas SM’, ‘Khouri MG’, ‘Alenezi F’, ‘Herndon JE’, ‘Michalski M’, ‘Collins K’, ‘Nilsen T’, ‘Edvardsen E’, ‘Jones LW’, ‘Scott JM’]
Journal: JCO Clin Cancer Inform
Citation: Novo RT, et al. Machine Learning-Driven Phenogrouping and Cardiorespiratory Fitness Response in Metastatic Breast Cancer. Machine Learning-Driven Phenogrouping and Cardiorespiratory Fitness Response in Metastatic Breast Cancer. 2024; 8:e2400031. doi: 10.1200/CCI.24.00031