Prediction of Drug Potency and Latent Relation Analysis in Precision Cancer Treatment

Abstract

As cancer treatments are gaining momentum, in a bid to improve drug potency, doctors are looking towards precision cancer medicine. Here the drug prescriptions are tailored to the patients gene changes. In this paper, the aim is to automate the task of drug selection, by predicting the clinical outcome of using a particular drug on a combination of the patients gene, variant and cancer disease type. While the main idea behind precision cancer treatment is to identify drugs suitable to each patients unique case, it is justifiable for to assume that there exists a predictive pattern in these prescriptions. We propose to implement this prediction using three machine learning models, the Support Vector Machine, the Random Forest Classifier and the Deep Neural Network. The models yielded promising results of over 90% accuracy and over 95% ROC-AUC score. This positive outcome affirms the assumption that there exists a predictive pattern in precision treatments, that could be extrapolated to help automate such tasks. We further analyzed the data set and identified latent relations between drug, cancer disease, target gene and gene variant. This exploration uncovered some significant patterns where it can be observed how a particular drug has had successful results in treating a particular cancer and targeting specific gene variants.