Deep Learning-Based Multi-Omics Data Integration Reveals Two Prognostic Subtypes in High-Risk Neuroblastoma.
Deep Learning-Based Multi-Omics Data Integration Reveals Two Prognostic Subtypes in High-Risk Neuroblastoma.
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基于深度学习的多组学数据集成揭示了高风险神经母细胞瘤的两种预后亚型
DOI：
10.3389/fgene.2018.00477
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发表时间：
2018
影响因子：
3.7
通讯作者：
Shi T
中科院分区：
文献类型：
--
作者：
Zhang L;Lv C;Jin Y;Cheng G;Fu Y;Yuan D;Tao Y;Guo Y;Ni X;Shi T
高危神经母细胞瘤是一种非常侵袭性的疾病，肿瘤过度生长，预后差。根据预后结果对高危患者进行适当分层对治疗很重要。然而，仍然缺乏对高危神经母细胞瘤的生存分层。为了填补差距，我们采用深度学习算法Autoencoder整合多组学数据，并将其与K-means聚类联合收割机相结合，以识别两种具有显著生存差异的亚型。通过比较Autoencoder与PCA、iCluster和DGscore在多组学数据集成分类中的性能，发现Autoencoder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的数据集上验证了分类，并证实了其鲁棒性。功能分析显示，MYCN扩增更频繁地发生在超高风险亚型中，与该亚型中MYC/MYCN靶点的过表达一致。总之，通过基于深度学习的多组学整合识别预后亚型不仅可以提高我们对分子机制的理解，还可以帮助临床医生做出决策。
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.
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DOI：
10.1073/pnas.0901329106
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