Molecular Subtyping of Cancer Based on Robust Graph Neural Network and Multi-Omics Data Integration - Yin, Chaoyi; Cao, Yangkun; Sun, Peishuo; Zhang, Hengyuan; Li, Zhi; Xu, Ying; Sun, Huiyan (Frontiers in Genetics, 2022)
Accurate molecular subtypes prediction of cancer patients is significant for personalized cancer diagnosis and treatments. Large amount of multi-omics data and the advancement of data-driven methods are expected to facilitate molecular subtyping of cancer. Most existing machine learning-based methods usually classify samples according to single omics data, fail to integrate multi-omics data to learn comprehensive representations of the samples, and ignore that information transfer and aggregation among samples can better represent them and ultimately help in classification. We propose a novel framework named Multi-omics Graph Convolutional Networks (M-GCN) for molecular subtyping based on robust graph convolutional networks integrating multi-omics data. We first apply Hilbert-Schmidt independence criterion least absolute shrinkage and selection operator (HSIC LASSO) to select the molecular subtype-related transcriptomic features and then construct sample-sample similarity graph with low noise by using these features. Next, we take the selected gene expression, single nucleotide variants (SNV) and copy number variation (CNV) data as input and learn the multi-view representations of samples. On this basis, a robust variant of graph convolutional networks (GCN) model is finally developed to obtain samples’ new representation by aggregating their subgraphs. Experiment results of breast and stomach cancer demonstrate that the subtype classification performance of M-GCN is superior to other existing methods. Moreover, the identified subtype specific biomarkers are highly consistent with current clinical understanding and promising to assists accurate diagnosis and targeted drug development.