Attention-based GCN Integrates Multi-omics Data for Breast Cancer Subtype Classification and Patient-specific Gene Marker Identification

Abstract

AbstractBreast cancer is a heterogeneous disease and can be divided into several subtypes with unique prognostic and molecular characteristics. Classification of breast cancer subtypes plays an important role in the precision treatment and prognosis of breast cancer. Benefitting from the relation-aware ability of graph convolution network (GCN), we presented a multi-omics integrative method, attention-based GCN, for breast cancer molecular subtype classification using mRNA expression, copy number variation, and DNA methylation multi-omics data. Several attention mechanisms were performed and all exhibited effectiveness in integrating heterogeneous data. Column-wise attention-based GCN outperformed the other baseline methods, achieving AUC of 0.9816, ACC of 0.8743 and MCC of 0.8151 in 5-fold cross validation. Besides, Layer-wise Relevance Propagation (LRP) algorithm was used for interpretation of model decision and could identify patient-specific important biomarkers which were reported to be related to the occurrence and development of breast cancer. Our results highlighted the effectiveness of GCN and attention mechanisms in multi-omics integrative analysis and implement of LRP algorithm could provide biologically reasonable insights into model decision.Author SummaryIdentification of molecular subtype is essential to understanding the pathogenesis of cancer and advancing the development of cancer precision treatment. We have developed a graph convolution network architecture to predict the molecular subtype of breast cancer patients based on multi-omics data. The major difference between our work and other methods is that we have offered a new view for multi-omics data integration with the assistance of graph convolution network. Besides, we adopted an interpretability method to explain the model decision and prioritize the genes for biomarker discovery.