i-Modern: Integrated multi-omics network model identifies potential therapeutic targets in glioma by deep learning with interpretability

Xingxin Pan; Brandon Burgman; Erxi Wu; Jason H Huang; Nidhi Sahni; S Stephen Yi

doi:10.1016/j.csbj.2022.06.058

i-Modern: Integrated multi-omics network model identifies potential therapeutic targets in glioma by deep learning with interpretability

Comput Struct Biotechnol J. 2022 Jun 30:20:3511-3521. doi: 10.1016/j.csbj.2022.06.058. eCollection 2022.

Authors

Xingxin Pan¹, Brandon Burgman^{1

2}, Erxi Wu^{1

3

4

5}, Jason H Huang^{3

4}, Nidhi Sahni^{6

7

8}, S Stephen Yi^{1

4

9

10}

Affiliations

¹ Department of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA.
² Interdisciplinary Life Sciences Graduate Programs (ILSGP), College of Natural Sciences, The University of Texas at Austin, Austin, TX 78712, USA.
³ Neuroscience Institute and Department of Neurosurgery, Baylor Scott & White Health, Temple, TX 76502, USA.
⁴ Department of Surgery, Texas A & M University Health Science Center, College of Medicine, Temple, TX 76508, USA.
⁵ Department of Pharmaceutical Sciences, Texas A & M University Health Science Center, College of Pharmacy, College Station, TX 77843, USA.
⁶ Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX 77230, USA.
⁷ Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
⁸ Quantitative and Computational Biosciences Program, Baylor College of Medicine, Houston, TX 77030, USA.
⁹ Oden Institute for Computational Engineering and Sciences (ICES), The University of Texas at Austin, Austin, TX 78712, USA.
¹⁰ Department of Biomedical Engineering, Cockrell School of Engineering, The University of Texas at Austin, Austin, TX 78712, USA.

Abstract

Effective and precise classification of glioma patients for their disease risks is critical to improving early diagnosis and patient survival. In the recent past, a significant amount of multi-omics data derived from cancer patients has emerged. However, a robust framework for integrating multi-omics data types to efficiently and precisely subgroup glioma patients and predict survival prognosis is still lacking. In addition, effective therapeutic targets for treating glioma patients with poor prognoses are in dire need. To begin to resolve this difficulty, we developed i-Modern, an integrated Multi-omics deep learning network method, and optimized a sophisticated computational model in gliomas that can accurately stratify patients based on their prognosis. We built a survival-associated predictive framework integrating transcription profile, miRNA expression, somatic mutations, copy number variation (CNV), DNA methylation, and protein expression. This framework achieved promising performance in distinguishing high-risk glioma patients from those with good prognoses. Furthermore, we constructed multiple fully connected neural networks that are trained on prioritized multi-omics signatures or even only potential single-omics signatures, based on our customized scoring system. Together, the landmark multi-omics signatures we identified may serve as potential therapeutic targets in gliomas.

Keywords: Data integration; Deep learning model; Glioma; Multi-omics; Patient stratification.

Grants and funding

R35 GM133658/GM/NIGMS NIH HHS/United States