Abstract 676: Leveraging TCGA gene expression data to build predictive models for cancer drug response

Personalized oncology promises to increase the success rate of cancer drug therapy by using molecular tumor profiles to determine the optimal therapeutic for an individual. 5-Fluorouracil and Gemcitabine are two Food and Drug Administration approved chemotherapeutics commonly prescribed to cancer patients. Here, we build machine learning models using gene expression data from patients’ primary tumors to predict their response, positive or negative, to these drugs. We compared several clustering and classification methods for predicting response. Of those tested, Clara was found to be the best clustering algorithm while random forest was the best classification method, with prediction accuracy up to 86%. We determined that models trained across several cancers outperform single cancer models. We also found that genes most informative for predicting drug response were enriched in well-known cancer signaling pathways, highlighting their potential significance in chemotherapy prognosis. Ultimately, such predictive models may aid oncologists with making critical treatment decisions. Citation Format: Evan Clayton. Leveraging TCGA gene expression data to build predictive models for cancer drug response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 676.