Project description:Epithelial, non-glandular sinonasal cancers (SNCs) is a rare disease, with a global dismal prognosis. There are no recognized targeted treatments and the knowledge of molecular mechanisms involved in the resistance to available therapies is limited. Dissecting the heterogeneity of paranasal sinus cancersSNCs and providing valuable information on the biology of the malignancy is eagerly needed to improve therapeutic approaches.
Project description:Single cell RNA sequencing analyses of human sinonasal tissue samples of chronic rhinosinusitus patients clustered the sinus cell into 13 types. Two cell types (monocyte/Macrophage and dendritic cells) demonstrated over-lapping expression of MPEG1, BATF3, IRF4, CD86, and HLA-DRA.
Project description:In mouse peritoneal and other serous cavities, the transcription factor Gata6 drives the identity of the major cavity resident population of macrophages, with a smaller subset of cavity-resident macrophages dependent on the transcription factor Irf4. Here we showed that GATA6+ macrophages in the human peritoneum were rare, regardless of age. Instead, more human peritoneal macrophages aligned with mouse CD206+ LYVE1+ cavity macrophages that represent a differentiation stage just preceding expression of Gata6. Low abundance of CD206+ macrophages was retained in C57BL/6J mice fed a high-fat diet or in wild-captured mice, suggesting that differences between serous cavity-resident macrophages in humans and mice were not environmental. Irf4-dependent mouse serous cavity macrophages aligned closely with human CD1c+CD14+CD64+ peritoneal cells that, in turn, resembled human peritoneal CD1c+CD14-CD64- cDC2. Thus, major populations of serous cavity-resident mononuclear phagocytes in humans and mice shared common features but the proportions of different macrophage differentiation stages greatly differ between the two species and DC2-like cells were especially prominent in humans.
Project description:The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, whereas tumors that are driven by SMARCB1-deficiency and tumors that represent previously misclassified adenoid cystic carcinomas are highly aggressive. Our findings have the potential to dramatically improve the diagnostic classification of sinonasal tumors and will fundamentally change the current perception of SNUCs.