Zhang Li, Lv Chenkai, Jin Yaqiong, Cheng Ganqi, Fu Yibao, Yuan Dongsheng, Tao Yiran, Guo Yongli, Ni Xin, Shi Tieliu
Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai, China.
Beijing Key Laboratory for Pediatric Diseases of Otolaryngology, Head and Neck Surgery, MOE Key Laboratory of Major Diseases in Children, Beijing Children's Hospital, National Center for Children's Health, Beijing Pediatric Research Institute, Capital Medical University, Beijing, China.
Front Genet. 2018 Oct 18;9:477. doi: 10.3389/fgene.2018.00477. eCollection 2018.
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 amplification was more frequently occurred in the ultra-high-risk subtype, in accordance with the overexpression of 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.
高危神经母细胞瘤是一种极具侵袭性的疾病，肿瘤生长过度且预后较差。根据预后结果对高危患者进行恰当分层对于治疗至关重要。然而，高危神经母细胞瘤仍缺乏生存分层。为填补这一空白，我们采用深度学习算法自动编码器来整合多组学数据，并将其与K均值聚类相结合，以识别出具有显著生存差异的两个亚型。通过将自动编码器与主成分分析（PCA）、iCluster和DGscore在基于多组学数据整合的分类方面进行比较，基于自动编码器的分类优于其他方法。此外，我们还通过训练机器学习分类模型在两个独立数据集中验证了该分类，并证实了其稳健性。功能分析表明，扩增在超高危亚型中更频繁发生，这与该亚型中靶点的过表达一致。总之，基于深度学习的多组学整合所识别的预后亚型不仅可以增进我们对分子机制的理解，还能帮助临床医生做出决策。
