儿童癌症与同类型成人肿瘤的区别产生的原因并不完全清楚，其基因组也不能完全解释。在本研究中，我们采用了优化的多级RNA聚类方法，为大多数儿童癌症导出分子定义。在对13,313个转录组应用该方法后，我们构建了一个儿童癌症地图，以探索与年龄相关的变化。由于具有共同起源、驱动基因或干细胞特征的原因，某些肿瘤实体有时被出人意料地分组。一些已经建立好的实体被分成了亚组，这些亚组的预测结果比当前的诊断方法更好。这些定义考虑了肿瘤内、间的异质性，具有使诊断可重复和可量化的潜力。总体而言，儿童肿瘤的转录多样性比成人肿瘤更高，维持着更高的表达灵活性。为了应用这些见解，我们设计了一个集成卷积神经网络分类器。我们证明，这个工具能够在前瞻性队列中为85%的儿童肿瘤进行诊断匹配或澄清。如果得到进一步验证，该框架可以扩展到为所有癌症类型导出分子定义。© 2023. 作者。

The causes of pediatric cancers' distinctiveness compared to adult-onset tumors of the same type are not completely clear and not fully explained by their genomes. In this study, we used an optimized multilevel RNA clustering approach to derive molecular definitions for most childhood cancers. Applying this method to 13,313 transcriptomes, we constructed a pediatric cancer atlas to explore age-associated changes. Tumor entities were sometimes unexpectedly grouped due to common lineages, drivers or stemness profiles. Some established entities were divided into subgroups that predicted outcome better than current diagnostic approaches. These definitions account for inter-tumoral and intra-tumoral heterogeneity and have the potential of enabling reproducible, quantifiable diagnostics. As a whole, childhood tumors had more transcriptional diversity than adult tumors, maintaining greater expression flexibility. To apply these insights, we designed an ensemble convolutional neural network classifier. We show that this tool was able to match or clarify the diagnosis for 85% of childhood tumors in a prospective cohort. If further validated, this framework could be extended to derive molecular definitions for all cancer types.© 2023. The Author(s).