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.
Highlights
Neuroblastoma is the most common extracranial solid tumor in childhood and accounts for approximately 15% of childhood cancer mortality (Ward et al, 2014)
Several recurrently mutated genes or loci which correlated with highrisk neuroblastoma have been identified, such as ALK (Mosse et al, 2008) mutations or amplifications, PHOX2B (Brodeur et al, 1984) mutation, chromosome 1p and 11q deletions, truncating or structural variants of ATRX gene (Cheung et al, 2012; Molenaar et al, 2012), genomic rearrangements of TERT (Peifer et al, 2015; Valentijn et al, 2015)
Multi-omics data in the training data were integrated to discover a prognostic stratification of the high-risk neuroblastoma
Summary
Neuroblastoma is the most common extracranial solid tumor in childhood (mostly under the age of five) and accounts for approximately 15% of childhood cancer mortality (Ward et al, 2014). Several recurrently mutated genes or loci which correlated with highrisk neuroblastoma have been identified, such as ALK (Mosse et al, 2008) mutations or amplifications, PHOX2B (Brodeur et al, 1984) mutation, chromosome 1p and 11q deletions, truncating or structural variants of ATRX gene (Cheung et al, 2012; Molenaar et al, 2012), genomic rearrangements of TERT (Peifer et al, 2015; Valentijn et al, 2015) These genetic events cover 92% of high-risk neuroblastoma (Peifer et al, 2015)
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