An Ensemble Learning Approach for Cancer Drug Prediction

Abstract

Predicting the response to a particular drug for specific cancer, despite known genetic mutations, still remains a huge challenge in modern oncology and precision medicine. Today, prescribing a drug for a cancer patient is based on a doctor’s analysis of various articles and previous clinical trials; it is an extremely time-consuming process. We developed a machine learning classifier to automatically predict a drug given a carcinogenic gene mutation profile. Using the Breast Invasive Carcinoma Dataset from The Cancer Genome Atlas (TCGA), the method first selects features from mutated genes and then applies K-Fold, Decision Tree, Random Forest and Ensemble Learning classifiers to predict best drugs. Ensemble Learning yielded prediction accuracy of 66% on the test set in predicting the correct drug. To validate that the model is general-purpose, Lung Adenocarcinoma (LUAD) data and Colorectal Adenocarcinoma (COADREAD) data from TCGA was trained and tested, yielding prediction accuracies 50% and 66% respectively. The resulting accuracy indicates a direct correlation between prediction accuracy and cancer data size. More importantly, the results of LUAD and COADREAD show that the implemented model is general purpose as it is able to achieve similar results across multiple cancer types. We further verified the validity of the model by implementing it on patients with unclear recovery status from the COADREAD dataset. In every case, the model predicted a drug that was administered to each patient. This method will offer oncologists significant time-saving compared to their current approach of extensive background research, and offers personalized patient care for cancer patients.