A Translational Pipeline for Overall Survival Prediction of Breast Cancer Patients by Decision-Level Integration of Multi-Omics Data

• Authors: Jonathan Mitchel, Georgia Institute of Technology; Kevin Chatlin, Georgia Institute of Technology; Li Tong, Georgia Institute of Technology; Dongmei Wang, Emory University
• Journal Title: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
• Volume: Volume 2019
• Publisher: IEEE | 2019-01-01, Pages 1573-1580
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1109/BIBM47256.2019.8983243
• Subjects: Health Sciences, Oncology; Biology, Molecular; Chemistry, Biochemistry; Biology, Biostatistics
• Keywords: Science & Technology; Life Sciences & Biomedicine; Biochemical Research Methods; Mathematical & Computational Biology; Biochemistry & Molecular Biology; Breast Cancer; Overall Survival; Multi-Omics; Decision-Level Integration; Biomarker Identification

Abstract

Breast cancer is the most prevalent and among the most deadly cancers in females. Patients with breast cancer have highly variable survival rates, indicating a need to identify prognostic biomarkers. By integrating multi-omics data (e.g., gene expression, DNA methylation, miRNA expression, and copy number variations (CNVs)), it is likely to improve the accuracy of patient survival predictions compared to prediction using single modality data. Therefore, we propose to develop a machine learning pipeline using decision-level integration of multi-omics tumor data from The Cancer Genome Atlas (TCGA) to predict the overall survival of breast cancer patients. With multi-omics data consisting of gene expression, methylation, miRNA expression, and CNVs, the top-performing model predicted survival with an accuracy of 85% and area under the curve (AUC) of 87%. Furthermore, the model was able to identify which modalities best contributed to prediction performance, identifying methylation, miRNA, and gene expression as the best integrated classification combination. Our method not only recapitulated several breast cancer-specific prognostic biomarkers that were previously reported in the literature but also yielded several novel biomarkers. Further analysis of these biomarkers could lend insight into the molecular mechanisms that lead to poor survival.