BACKGROUND Neuroblastoma is a heterogeneous disease with diverse clinical outcomes. Current risk group models require improvement as patients within the same risk group can still show variable prognosis. Recently collected genome-wide datasets provide opportunities to infer neuroblastoma subtypes in a more unified way. Within this context, data integration is critical as different molecular characteristics can contain complementary signals. To this end, we utilized the genomic datasets available for the SEQC cohort patients to develop supervised and unsupervised models that can predict disease prognosis. RESULTS Our supervised model trained on the SEQC cohort can accurately predict overall survival and event-free survival profiles of patients in two independent cohorts. We also performed extensive experiments to assess the prediction accuracy of high risk patients and patients without MYCN amplification. Our results from this part suggest that clinical endpoints can be predicted accurately across multiple cohorts. To explore the data in an unsupervised manner, we used an integrative clustering strategy named multi-view kernel k-means (MVKKM) that can effectively integrate multiple high-dimensional datasets with varying weights. We observed that integrating different gene expression datasets results in a better patient stratification compared to using these datasets individually. Also, our identified subgroups provide a better Cox regression model fit compared to the existing risk group definitions. CONCLUSION Altogether, our results indicate that integration of multiple genomic characterizations enables the discovery of subtypes that improve over existing definitions of risk groups. Effective prediction of survival times will have a direct impact on choosing the right therapies for patients. REVIEWERS This article was reviewed by Susmita Datta, Wenzhong Xiao and Ziv Shkedy.

中文翻译：

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通过基因组数据整合预测神经母细胞瘤的临床结果。

背景技术神经母细胞瘤是一种具有多种临床结果的异质性疾病。当前的风险组模型需要改进，因为同一风险组中的患者仍可以显示出不同的预后。最近收集的全基因组数据集提供了以更统一的方式推断神经母细胞瘤亚型的机会。在这种情况下，数据集成至关重要，因为不同的分子特征可以包含互补信号。为此，我们利用了SEQC队列患者可用的基因组数据集来开发可以预测疾病预后的有监督和无监督的模型。结果我们在SEQC队列中训练的监督模型可以准确预测两个独立队列中患者的总体生存和无事件生存情况。我们还进行了广泛的实验，以评估高危患者和无MYCN扩增患者的预测准确性。我们从这一部分得出的结果表明，可以在多个队列中准确预测临床终点。为了以无人监督的方式浏览数据，我们使用了一种称为多视图核k均值（MVKKM）的集成聚类策略，该策略可以有效地集成具有不同权重的多个高维数据集。我们观察到，与单独使用这些数据集相比，整合不同的基因表达数据集可导致更好的患者分层。此外，与现有风险组定义相比，我们确定的亚组提供了更好的Cox回归模型拟合度。结论总而言之，我们的结果表明，多种基因组特征的整合使发现亚型的风险类型得以改善。有效预测生存时间将直接影响为患者选择正确的治疗方法。审阅者本文由Susmita Datta，肖文忠和Ziv Shkedy审阅。