Conditional c-MYC activation in catecholaminergic cells drives distinct neuroendocrine tumors: neuroblastoma vs somatostatinoma.

The MYC proto-oncogenes (c-MYC, MYCN , MYCL ) are among the most deregulated oncogenic drivers in human malignancies including high-risk neuroblastoma, 50% of which are MYCN -amplified. Genetically engineered mouse models (GEMMs) based on the MYCN transgene have greatly expanded the understanding of neuroblastoma biology and are powerful tools for testing new therapies. However, a lack of c-MYC-driven GEMMs has hampered the ability to better understand mechanisms of neuroblastoma oncogenesis and therapy development given that c-MYC is also an important driver of many high-risk neuroblastomas. In this study, we report two transgenic murine neuroendocrine models driven by conditional c-MYC induction in tyrosine hydroxylase (Th) and dopamine ß-hydroxylase (Dbh)-expressing cells. c-MYC induction in Th-expressing cells leads to a preponderance of Pdx1 + somatostatinomas, a type of pancreatic neuroendocrine tumor (PNET), resembling human somatostatinoma with highly expressed gene signatures of δ cells and potassium channels. In contrast, c-MYC induction in Dbh-expressing cells leads to onset of neuroblastomas, showing a better transforming capacity than MYCN in a comparable C57BL/6 genetic background. The c-MYC murine neuroblastoma tumors recapitulate the pathologic and genetic features of human neuroblastoma, express GD2, and respond to anti-GD2 immunotherapy. This model also responds to DFMO, an FDA-approved inhibitor targeting ODC1, which is a known MYC transcriptional target. Thus, establishing c-MYC-overexpressing GEMMs resulted in different but related tumor types depending on the targeted cell and provide useful tools for testing immunotherapies and targeted therapies for these diseases.

An integrated single-cell RNA-seq map of human neuroblastoma tumors and preclinical models uncovers divergent mesenchymal-like gene expression programs.

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.

An integrated single-cell RNA-seq map of human neuroblastoma tumors and preclinical models uncovers divergent mesenchymal-like gene expression programs.

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 generated single-cell RNA-seq maps of neuroblastoma cell lines, patient-derived xenograft models (PDX), and a genetically engineered mouse model (GEMM). We developed 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 confirmed 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 was maintained in PDX and could be chemotherapy-induced in our GEMM after only 24 hours, 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.