小脑母细胞瘤（MB）是最常见的儿童恶性脑肿瘤之一，由四个不同的分子组（WNT、SHH、3组、4组）组成，是一种异质性疾病。每个分子组可以进一步细分为独特的分子亚组，而每个亚组都具有不同的遗传和临床特征。例如，非WNT/非SHH的MB（3/4组）可以分子上细分为8个不同的临床相关肿瘤亚组。这些SGS MB的进一步分子分层/总结将允许将患者分配到与风险相关的治疗方案中。在这里，我们对574例非WNT/非SHH MB进行了DNA和RNA分析，并分析了整个队列和8个SGS MB中不同分子模式的临床意义，旨在开发这些肿瘤的最佳风险分层策略。多基因分析揭示了每个分子亚组内高度特异性与生存相关的基因，这些基因在该非WNT/非SHH MB队列中具有最小的亚组间重叠。这些亚组特异性和具有预后意义的基因与可能潜在SGS MB临床分子多样性和肿瘤驱动机制相关的通路有关。通过结合每个SGS MB内的与生存相关的基因，鉴定出适合于其最佳风险分层的不同元基因集合。定义的亚组特异性元基因集在为每个SGS MB生成的多元模型中是独立变量，并在完全非重叠验证队列的非WNT/非SHH MB（n = 377）中证实了其预后价值。总之，目前的结果表明，在风险分层模型中整合转录组数据可能会改善每个非WNT/非SHH SGS MB的预后预测。鉴定的亚组特异性基因表达签名可能与临床实施相关，并且生存相关的元基因组可以用于进一步的SGS MB风险分层。未来的研究应该旨在验证这些基于转录组的SGS MB亚型在前瞻性临床试验中的预后作用。© 2023。作者，独家授权Springer-Verlag GmbH Germany的一部分Springer Nature。

Medulloblastoma (MB), one of the most common malignant pediatric brain tumor, is a heterogenous disease comprised of four distinct molecular groups (WNT, SHH, Group 3, Group 4). Each of these groups can be further subdivided into second-generation MB (SGS MB) molecular subgroups, each with distinct genetic and clinical characteristics. For instance, non-WNT/non-SHH MB (Group 3/4) can be subdivided molecularly into eight distinct and clinically relevant tumor subgroups. A further molecular stratification/summarization of these SGS MB would allow for the assignment of patients to risk-associated treatment protocols. Here, we performed DNA- and RNA-based analysis of 574 non-WNT/non-SHH MB and analyzed the clinical significance of various molecular patterns within the entire cohort and the eight SGS MB, with the aim to develop an optimal risk stratification of these tumors. Multigene analysis disclosed several survival-associated genes highly specific for each molecular subgroup within this non-WNT/non-SHH MB cohort with minimal inter-subgroup overlap. These subgroup-specific and prognostically relevant genes were associated with pathways that could underlie SGS MB clinical-molecular diversity and tumor-driving mechanisms. By combining survival-associated genes within each SGS MB, distinct metagene sets being appropriate for their optimal risk stratification were identified. Defined subgroup-specific metagene sets were independent variables in the multivariate models generated for each SGS MB and their prognostic value was confirmed in a completely non-overlapping validation cohort of non-WNT/non-SHH MB (n = 377). In summary, the current results indicate that the integration of transcriptome data in risk stratification models may improve outcome prediction for each non-WNT/non-SHH SGS MB. Identified subgroup-specific gene expression signatures could be relevant for clinical implementation and survival-associated metagene sets could be adopted for further SGS MB risk stratification. Future studies should aim at validating the prognostic role of these transcriptome-based SGS MB subtypes in prospective clinical trials.© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.