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Personalized drug-response prediction model for lung cancer patients using machine learning

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posted on 24.11.2020, 12:28 by Rizwan QureshiRizwan Qureshi
Lung cancer caused by mutations in the epidermal growth factor receptor (EGFR) is a major cause of cancer deaths worldwide. EGFR Tyrosine kinase inhibitors (TKIs) have been developed, and have shown increased survival rates and quality of life in clinical studies. However, drug resistance is a major issue, and treatment efficacy is lost after about an year. Therefore, predicting the response to targeted therapies for lung cancer patients is a significant research problem. In this work, we address this issue and propose a personalized model to predict the drug-response of lung cancer patients. This model uses clinical information, geometrical properties of the drug binding site, and the binding free energy of the drug-protein complex. The proposed model achieves state of the art performance with 97.5% accuracy, 100% recall, 95% precision, and 96.3% F1-score with a random forest classifier. This model can also be tested on other types of cancer and diseases, and we believe that it may help in taking optimal clinical decisions for treating patients with targeted therapies

Funding

Hong Kong Research Grants Council (Project 11200818)

City University of Hong Kong (Project 9610034)

History

Email Address of Submitting Author

engr.rizwanqureshi786@gmail.com

Submitting Author's Institution

City University of Hong Kong

Submitting Author's Country

Hong Kong