1. An integrated single-cell RNA-seq map of human neuroblastoma tumors and preclinical models uncovers divergent mesenchymal-like gene expression programs.
- Author
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Chapple RH, Liu X, Natarajan S, Alexander MIM, Kim Y, Patel AG, LaFlamme CW, Pan M, Wright WC, Lee HM, Zhang Y, Lu M, Koo SC, Long C, Harper J, Savage C, Johnson MD, Confer T, Akers WJ, Dyer MA, Sheppard H, Easton J, and Geeleher P
- Subjects
- Humans, Animals, Mice, Cell Line, Tumor, Gene Expression Regulation, Neoplastic, Drug Resistance, Neoplasm genetics, Transcriptome, Single-Cell Gene Expression Analysis, Neuroblastoma genetics, Neuroblastoma pathology, Single-Cell Analysis methods, RNA-Seq
- Abstract
Background: 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., Results: 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., Conclusions: 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., (© 2024. The Author(s).)
- Published
- 2024
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