Background: Esthesioneuroblastoma (ENB) is a rare cancer deriving from the olfactory mucosa. Among the basal or neural genomic subtypes, the basal subtype is associated with poorer survival, poor differentiation, and higher levels of tumor-infiltrating immune cells (TIICs). The immune microenvironment of these ENB subtypes remains unclear. We used an established machine learning algorithm on ENB transcriptomic profiles.
Methods: The authors characterized 22 immune cell populations using the CIBERSORTx deconvolutional machine learning pipeline on RNA sequencing data from 18 ENB cases. The characterization aimed to elucidate differences in relative proportions and populations of TIICs between basal and neural ENB.
Results: No differences in age, Hyams, Dulguerov, IDH2 mutation, or PD-L1 expression were seen between basal and neural subtypes of ENB (P > 0.05). Also, no difference in median overall survival was appreciated (52.0 ± 13.1 months vs. 50.0 ± 43.2 months, P = 0.5). As a cohort, M2 macrophages were the most abundant subpopulation (14%) followed by naïve B cells (13%) and CD4 memory resting T cells (12%). No gross differences in CD20, CD4, or CD8 cells/mm2 were apparent on gross histology (P > 0.05). However, further analysis showed that activated CD4 memory T cells were significantly increased in the basal ENBs, whereas resting dendritic cells were increased in the neural ENB subtype. The TIIC profiles alone could not differentiate between basal and neural ENB, but did suggest immunoprofile differences.
Conclusions: Basal and neural subtypes display distinct TIIC involvement, which may impact their difference in outcome. These findings provide the framework for further investigation in novel immunomodulation strategies for ENB.
Keywords: Esthesioneuroblastoma; RNA deconvolution; Tumor-infiltrating immune cells.
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