Geometric graph neural networks on multi-omics data to predict cancer survival outcomes

Zhu, Jiening, Oh, Jung Hun, Simhal, Anish K, Elkin, Rena, Norton, Larry, Deasy, Joseph O, Tannenbaum, Allen (September 2023) Geometric graph neural networks on multi-omics data to predict cancer survival outcomes. Computers in Biology and Medicine, 163. p. 107117. ISSN 0010-4825

DOI: 10.1016/j.compbiomed.2023.107117


The advance of sequencing technologies has enabled a thorough molecular characterization of the genome in human cancers. To improve patient prognosis predictions and subsequent treatment strategies, it is imperative to develop advanced computational methods to analyze large-scale, high-dimensional genomic data. However, traditional machine learning methods face a challenge in handling the high-dimensional, low-sample size problem that is shown in most genomic data sets. To address this, our group has developed geometric network analysis techniques on multi-omics data in connection with prior biological knowledge derived from protein-protein interactions (PPIs) or pathways. Geometric features obtained from the genomic network, such as Ollivier-Ricci curvature and the invariant measure of the associated Markov chain, have been shown to be predictive of survival outcomes in various cancers. In this study, we propose a novel supervised deep learning method called geometric graph neural network (GGNN) that incorporates such geometric features into deep learning for enhanced predictive power and interpretability. More specifically, we utilize a state-of-the-art graph neural network with sparse connections between the hidden layers based on known biology of the PPI network and pathway information. Geometric features along with multi-omics data are then incorporated into the corresponding layers. The proposed approach utilizes a local-global principle in such a manner that highly predictive features are selected at the front layers and fed directly to the last layer for multivariable Cox proportional-hazards regression modeling. The method was applied to multi-omics data from the CoMMpass study of multiple myeloma and ten major cancers in The Cancer Genome Atlas (TCGA). In most experiments, our method showed superior predictive performance compared to other alternative methods.

Item Type: Paper
Subjects: bioinformatics
diseases & disorders > cancer
diseases & disorders
bioinformatics > genomics and proteomics
diseases & disorders > neoplasms
bioinformatics > computational biology > algorithms
bioinformatics > computational biology
bioinformatics > computational biology > algorithms > machine learning
organs, tissues, organelles, cell types and functions > tissues types and functions > neural networks
CSHL Authors:
Communities: CSHL labs > Wigler lab
SWORD Depositor: CSHL Elements
Depositing User: CSHL Elements
Date: September 2023
Date Deposited: 22 Sep 2023 15:02
Last Modified: 11 Jan 2024 15:14
PMCID: PMC10638676
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