Neuroblastoma is a common pediatric cancer, where preclinical studies suggest that a mesenchymal-like gene expression program contributes to chemotherapy resistance. However, clinical outcomes remain poor, implying we need a better understanding of the relationship between patient tumor heterogeneity and preclinical models. Here, we generate single-cell RNA-seq maps of neuroblastoma cell lines, patient-derived xenograft models (PDX), and a genetically engineered mouse model (GEMM). We develop an unsupervised machine learning approach (“automatic consensus nonnegative matrix factorization” (acNMF)) to compare the gene expression programs found in preclinical models to a large cohort of patient tumors. We confirm a weakly expressed, mesenchymal-like program in otherwise adrenergic cancer cells in some pre-treated high-risk patient tumors, but this appears distinct from the presumptive drug-resistance mesenchymal programs evident in cell lines. Surprisingly, however, this weak-mesenchymal-like program is maintained in PDX and could be chemotherapy-induced in our GEMM after only 24 h, suggesting an uncharacterized therapy-escape mechanism. Collectively, our findings improve the understanding of how neuroblastoma patient tumor heterogeneity is reflected in preclinical models, provides a comprehensive integrated resource, and a generalizable set of computational methodologies for the joint analysis of clinical and pre-clinical single-cell RNA-seq datasets.

中文翻译：

---

人类神经母细胞瘤肿瘤和临床前模型的集成单细胞 RNA-seq 图谱揭示了不同的间充质样基因表达程序


神经母细胞瘤是一种常见的儿科癌症，临床前研究表明，间质样基因表达程序有助于化疗耐药。然而，临床结果仍然很差，这意味着我们需要更好地了解患者肿瘤异质性和临床前模型之间的关系。在这里，我们生成了神经母细胞瘤细胞系、患者来源的异种移植模型 (PDX) 和基因工程小鼠模型 (GEMM) 的单细胞 RNA 序列图谱。我们开发了一种无监督机器学习方法（“自动共识非负矩阵分解”（acNMF））来将临床前模型中发现的基因表达程序与大量患者肿瘤进行比较。我们在一些经过预处理的高危患者肿瘤中的其他肾上腺素能癌细胞中证实了弱表达的间充质样程序，但这似乎与细胞系中明显的推定耐药间充质程序不同。然而，令人惊讶的是，这种弱间充质样程序在 PDX 中得以维持，并且仅 24 小时后就可以在我们的 GEMM 中诱导化疗，这表明存在一种未表征的治疗逃避机制。总的来说，我们的研究结果提高了对神经母细胞瘤患者肿瘤异质性如何在临床前模型中反映的理解，为临床和临床前单细胞 RNA-seq 数据集的联合分析提供了全面的综合资源和一套通用的计算方法。