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

• Type of Material: Article
• Authors: Jonathan Mitchel, Georgia Institute of Technology; Kevin Chatlin, Georgia Institute of Technology; Li Tong, Georgia Institute of Technology; Dongmei Wang, Emory University
• Language: English
• Date: 2019-01-01
• Publisher: IEEE
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © Copyright 2019 IEEE - All rights reserved.
• Final Published Version (URL): http://dx.doi.org/10.1109/BIBM47256.2019.8983243
• Title of Journal or Parent Work: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
• Volume: 2019
• Start Page: 1573
• End Page: 1580
• Grant/Funding Information: The work was supported in part by grants from the National Center for Advancing Translational Sciences of the National Institute of Health (NIH) under Award UL1TR000454, the National Science Foundation EAGER Award NSF1651360, Children’s Healthcare of Atlanta and Georgia Tech Partnership Grant, Giglio Breast Cancer Research Fund, and Carol Ann and David D. Flanagan Faculty Fellow Research Fund.; This work was also supported in part by the scholarship from China Scholarship Council (CSC) under the Grant CSC NO. 201406010343. The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
• Keywords: Multi-Omics; Breast Cancer; Mathematical & Computational Biology; Biochemistry & Molecular Biology; Biomarker Identification; Life Sciences & Biomedicine; Science & Technology; Overall Survival; Biochemical Research Methods; Decision-Level Integration
• Research Categories: Biology, Biostatistics; Chemistry, Biochemistry; Health Sciences, Oncology; Biology, Molecular

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.