Deep Learning-Based Multi-Omics Data Integration Reveals Two Prognostic Subtypes in High-Risk Neuroblastoma

TLDR

High‑risk neuroblastoma is an aggressive disease with poor outcomes, and current prognostic stratification is inadequate, underscoring the need for better survival subtyping. The study aims to use an Autoencoder‑based deep learning approach to integrate multi‑omics data and cluster patients into two prognostic subtypes with distinct survival outcomes. An Autoencoder was used to fuse multi‑omics data, followed by K‑means clustering, and the resulting subtypes were validated in two independent cohorts using machine‑learning classifiers. The Autoencoder‑based classification outperformed PCA, iCluster, and DGscore, was robust across independent datasets, and functional analysis linked the ultra‑high‑risk subtype to MYCN amplification and MYC/MYCN target overexpression, highlighting its clinical relevance.

Abstract

High-risk neuroblastoma is a very aggressive disease, with excessive tumor growth and poor outcomes. A proper stratification of the high-risk patients by prognostic outcome is important for treatment. However, there is still a lack of survival stratification for the high-risk neuroblastoma. To fill the gap, we adopt a deep learning algorithm, Autoencoder, to integrate multi-omics data, and combine it with K-means clustering to identify two subtypes with significant survival differences. By comparing the Autoencoder with PCA, iCluster, and DGscore about the classification based on multi-omics data integration, Autoencoder-based classification outperforms the alternative approaches. Furthermore, we also validated the classification in two independent datasets by training machine-learning classification models, and confirmed its robustness. Functional analysis revealed that MYCN amplification was more frequently occurred in the ultra-high-risk subtype, in accordance with the overexpression of MYC/MYCN targets in this subtype. In summary, prognostic subtypes identified by deep learning-based multi-omics integration could not only improve our understanding of molecular mechanism, but also help the clinicians make decisions.