⚡ Quick Summary
A recent study utilized machine learning to analyze comprehensive genomic profiling (CGP) data from 60,655 cancer patients in Japan, identifying key patient characteristics that predict the success of genome-matched therapies. The findings suggest that expedited CGP could significantly enhance targeted therapy outcomes for patients.
🔍 Key Details
- 📊 Dataset: 60,655 patients from a national database in Japan
- 🧩 Features used: Patient demographics, cancer type, and clinical characteristics
- ⚙️ Technology: eXtreme Gradient Boosting (XGBoost) and SHAP algorithm
- 🏆 Performance: Best model AUC of 0.819 for overall cancer population
🔑 Key Takeaways
- 📊 CGP identified genome-matched therapies in 18.5% of cases.
- 💡 Cancer type was the most significant predictor of therapy success.
- 👩🔬 Age, presence of liver metastasis, and number of metastatic sites also influenced outcomes.
- 🏆 AYA patients had a lower identification rate of 13.3% for genome-matched therapies.
- 🌍 Study period: June 2019 to November 2023.
- 🔍 SHAP analysis provided insights into clinical features affecting predictions.
- 🚀 Expedited CGP is recommended for patients fitting the identified profiles.
📚 Background
Comprehensive genomic profiling (CGP) is a powerful tool in cancer precision medicine, yet its adoption is limited due to the high costs and low probability of identifying actionable mutations. Understanding which patients are most likely to benefit from CGP is essential for improving its effectiveness and efficiency in clinical settings.
🗒️ Study
This nationwide retrospective study analyzed CGP data from 99.7% of patients who underwent testing in Japan. The researchers employed machine learning models to predict the identification of genome-matched therapies, focusing on various cancer types and the adolescent and young adult (AYA) demographic.
📈 Results
Out of the 60,655 patients analyzed, CGP successfully identified at least one genome-matched therapy in 11,227 cases (18.5%). The eXtreme Gradient Boosting model achieved an impressive AUC of 0.819, indicating strong predictive capability. Notably, cancer type emerged as the most critical predictor, with breast and lung cancers showing positive associations, while pancreatic cancer was a negative predictor.
🌍 Impact and Implications
The implications of this study are profound for the field of oncology. By identifying specific patient characteristics that correlate with successful therapy identification, healthcare providers can prioritize CGP for those most likely to benefit. This could lead to earlier interventions and improved patient outcomes, ultimately enhancing the landscape of cancer treatment.
🔮 Conclusion
This study highlights the transformative potential of machine learning in cancer precision medicine. By leveraging CGP data, we can better identify patients who will benefit from targeted therapies, paving the way for more personalized and effective treatment strategies. Continued research in this area is essential to further refine these predictive models and improve patient care.
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A machine learning-based analysis of nationwide cancer comprehensive genomic profiling data across cancer types to identify features associated with recommendation of genome-matched therapy.
Abstract
BACKGROUND: The low probability of identifying druggable mutations through comprehensive genomic profiling (CGP) and its financial and time costs hinder its widespread adoption. To enhance the effectiveness and efficiency of cancer precision medicine, it is critical to identify patient characteristics that are most likely to benefit from CGP.
PATIENTS AND METHODS: This nationwide retrospective study employed machine learning models to predict the identification of genome-matched therapies by CGP, utilizing a national database covering 99.7% of the patients who underwent CGP in Japan from June 2019 to November 2023. Prediction models were constructed for the overall cancer population, specific cancer types, and adolescent and young adult (AYA) group. The SHapley Additive exPlanations (SHAP) algorithm was applied to elucidate clinical features contributing to model predictions.
RESULTS: This study included 60 655 patients [mean age (standard deviation), 60.8 years (14.5 years); 50.1% males]. CGP identified at least one genome-matched therapy in 11 227 cases (18.5%). The best prediction model was eXtreme Gradient Boosting (XGBoost) with an area under the receiver operating characteristic curve of 0.819. Cancer type was the most important predictor (negative for pancreas and positive for breast and lung), followed by the age, presence of liver metastasis, and number of metastatic sites. Analysis of cancer type-specific models identified several organ-specific features, including the sex, interval between the cancer diagnosis and CGP, sampling site, and CGP panel. Among 3455 AYA patients, genome-matched therapies were identified in 459 patients (13.3%). The AYA-specific model achieved an area under the receiver operating characteristic curve of 0.768, with bone tumor identified as a negative predictor in addition to those identified in the overall cancer population model.
CONCLUSION: Several factors predicting the identification of genome-matched therapies through CGP were identified for the overall cancer population and cancer type-specific subpopulations. Expedited CGP is recommended for patients who match the identified profile to facilitate early targeted therapy.
Author: [‘Ikushima H’, ‘Watanabe K’, ‘Shinozaki-Ushiku A’, ‘Oda K’, ‘Kage H’]
Journal: ESMO Open
Citation: Ikushima H, et al. A machine learning-based analysis of nationwide cancer comprehensive genomic profiling data across cancer types to identify features associated with recommendation of genome-matched therapy. A machine learning-based analysis of nationwide cancer comprehensive genomic profiling data across cancer types to identify features associated with recommendation of genome-matched therapy. 2024; 9:103998. doi: 10.1016/j.esmoop.2024.103998