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