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

• Authors: Evan A. Clayton, Georgia Institute of Technology; Toyya A. Pujol, Georgia Institute of Technology; John F. McDonald, Georgia Institute of Technology; Peng Qiu, Emory University
• Journal Title: BMC Bioinformatics
• Volume: Volume 21, Number Suppl 14
• Publisher: BMC | 2020-09-30, Pages 364-364
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1186/s12859-020-03690-4
• Subjects: Biology, Genetics; Health Sciences, Oncology
• Keywords: Drug response; Machine learning; Personalized oncology; Predictive models; Antineoplastic Agents; Area Under Curve; Cluster Analysis; Databases, Genetic; Deoxycytidine; Fluorouracil; Gene Expression Regulation, Neoplastic; Humans; Machine Learning; Neoplasms; ROC Curve

Abstract

Background: Machine learning has been utilized to predict cancer drug response from multi-omics data generated from sensitivities of cancer cell lines to different therapeutic compounds. Here, we build machine learning models using gene expression data from patients' primary tumor tissues to predict whether a patient will respond positively or negatively to two chemotherapeutics: 5-Fluorouracil and Gemcitabine. Results: We focused on 5-Fluorouracil and Gemcitabine because based on our exclusion criteria, they provide the largest numbers of patients within TCGA. Normalized gene expression data were clustered and used as the input features for the study. We used matching clinical trial data to ascertain the response of these patients via multiple classification methods. Multiple clustering and classification methods were compared for prediction accuracy of drug response. Clara and random forest were found to be the best clustering and classification methods, respectively. The results show our models predict with up to 86% accuracy; despite the study's limitation of sample size. We also found the genes most informative for predicting drug response were enriched in well-known cancer signaling pathways and highlighted their potential significance in chemotherapy prognosis. Conclusions: Primary tumor gene expression is a good predictor of cancer drug response. Investment in larger datasets containing both patient gene expression and drug response is needed to support future work of machine learning models. Ultimately, such predictive models may aid oncologists with making critical treatment decisions.