Deep learning based feature-level integration of multi-omics data for breast cancer patients survival analysis

• Type of Material: Article
• Authors: Li Tong, Georgia Institute of Technology; Jonathan Mitchel, Georgia Institute of Technology; Kevin Chatlin, Georgia Institute of Technology; May D. Wang, Emory University
• Language: English
• Date: 2020-09-15
• Publisher: BMC
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2020
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1186/s12911-020-01225-8
• Title of Journal or Parent Work: BMC MEDICAL INFORMATICS AND DECISION MAKING
• Volume: 20
• Issue: 1
• Start Page: 225
• End Page: 225
• Grant/Funding Information: The work was supported in part by grants from the National Institute of Health (NIH) under Award R01CA163256, Giglio Breast Cancer Research Fund, Petit Institute Faculty Fellow and Carol Ann and David D. Flanagan Faculty Fellow Research Fund, and Georgia Cancer Coalition Distinguished Cancer Scholar award. 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: Medical Informatics; Survival analysis; Life Sciences & Biomedicine; Science & Technology; Deep learning; Multi-omics integration; Breast Cancer
• Research Categories: Biology, Biostatistics; Health Sciences, Oncology

Abstract

Background: Breast cancer is the most prevalent and among the most deadly cancers in females. Patients with breast cancer have highly variable survival lengths, indicating a need to identify prognostic biomarkers for personalized diagnosis and treatment. With the development of new technologies such as next-generation sequencing, multi-omics information are becoming available for a more thorough evaluation of a patient's condition. In this study, we aim to improve breast cancer overall survival prediction by integrating multi-omics data (e.g., gene expression, DNA methylation, miRNA expression, and copy number variations (CNVs)). Methods: Motivated by multi-view learning, we propose a novel strategy to integrate multi-omics data for breast cancer survival prediction by applying complementary and consensus principles. The complementary principle assumes each -omics data contains modality-unique information. To preserve such information, we develop a concatenation autoencoder (ConcatAE) that concatenates the hidden features learned from each modality for integration. The consensus principle assumes that the disagreements among modalities upper bound the model errors. To get rid of the noises or discrepancies among modalities, we develop a cross-modality autoencoder (CrossAE) to maximize the agreement among modalities to achieve a modality-invariant representation. We first validate the effectiveness of our proposed models on the MNIST simulated data. We then apply these models to the TCCA breast cancer multi-omics data for overall survival prediction. Results: For breast cancer overall survival prediction, the integration of DNA methylation and miRNA expression achieves the best overall performance of 0.641 ± 0.031 with ConcatAE, and 0.63 ± 0.081 with CrossAE. Both strategies outperform baseline single-modality models using only DNA methylation (0.583 ± 0.058) or miRNA expression (0.616 ± 0.057). Conclusions: In conclusion, we achieve improved overall survival prediction performance by utilizing either the complementary or consensus information among multi-omics data. The proposed ConcatAE and CrossAE models can inspire future deep representation-based multi-omics integration techniques. We believe these novel multi-omics integration models can benefit the personalized diagnosis and treatment of breast cancer patients.