bioRxivCancer Biology
Cell lineage as a predictor of immune response in neuroblastoma
S. Sengupta, S. Das, A. Crespo, B. Miller, B. Sharma, S. Zhang, R. Dries, H. Huang, M. Krajewska, D. N. Debruyne, L. Soriano, M. A. Cohen, R. Versteeg, R. Jaenisch, S. Spranger, J. Lieberman, R. E. George
Abstract
Immunotherapy for patients with neuroblastoma has met with limited success, partly due to an incomplete understanding of the mechanisms underlying immune responsiveness in this clinically and genetically heterogenic tumor. Here, we undertook an unbiased analysis using dimension reduction and UMAP visualization of transcriptional signatures derived from 498 primary neuroblastoma tumors. Four distinct clusters based on differentially expressed genes emerged, of which one, representing about 30% and comprising mainly of MYCN-nonamplified tumors, was notable for the high expression of genes associated with both immune response activation and suppression. This capacity to elicit a productive immune response resided exclusively in tumors with dominant populations of undifferentiated, neural crest-like or mesenchymal cells; by contrast, tumors comprising primarily of committed, adrenergic neuron-like cells were less immunogenic. Mesenchymal neuroblastoma cells were enriched for innate and adaptive immune gene signatures, demonstrated engagement with cytotoxic T and natural killer cells, and induced immune cell infiltration in an immunocompetent mouse model. Transcriptional or targeted therapy-induced reprogramming of adrenergic cells to the mesenchymal state led to reactivation of tumor cell-intrinsic immune genes. Key immune response genes in adrenergic tumor cells were found to be epigenetically silenced by the PRC2 complex, and such repression could be relieved by either mesenchymal cell state reprogramming or EZH2 inhibition, leading to increased activation of natural killer cells by the tumor cells. These data identify cell lineage as a major determinant of the immunogenic potential in neuroblastoma that could be used to stratify patients who are most likely to benefit from immunotherapy.
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- approach: statistical model
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