A Novel Recurrent Neural Network Framework for Prediction and Treatment of Oncogenic Mutation Progression

Despite significant medical advancements, cancer remains the second leading cause of death in the US, causing over 600,000 deaths per year. One emerging field, pathway analysis, is promising but still relies on manually derived wet lab data, which is time-consuming to acquire. This work proposes an efficient, effective, end-to-end framework for Artificial Intelligence (AI)-based pathway analysis that predicts both cancer severity and mutation progression in order to recommend possible treatments. The proposed technique involves a novel combination of time-series machine learning models and pathway analysis. First, mutation sequences were isolated from The Cancer Genome Atlas (TCGA) Database. Then, a novel preprocessing algorithm was used to filter key mutations by mutation frequency. This data was fed into a Recurrent Neural Network (RNN) that predicted cancer severity. The model probabilistically used the RNN predictions, information from the preprocessing algorithm, and multiple drug-target databases to predict future mutations and recommend possible treatments. This framework achieved robust results and Receiver Operating Characteristic (ROC) curves (a key statistical metric) with accuracies greater than 60%, similar to existing cancer diagnostics. In addition, preprocessing played a key role in isolating a few hundred key driver mutations per cancer stage, consistent with current research. Heatmaps based on predicted gene frequency were also generated, highlighting key mutations in each cancer. Overall, this work is the first to propose an efficient, cost-effective end-to-end framework for projecting cancer prognosis and providing possible treatments without relying on expensive, time-consuming wet lab work.