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ABSTRACT: Genomic characterization of Sinonasal Undifferentiated Carcinoma

PROVIDER: PRJNA668767 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR12812021_1.fastq.gz	Fastqsanger.gz
	SRR12812021_2.fastq.gz	Fastqsanger.gz

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Project description:Molecular Characterization of Sinonasal Undifferentiated Carcinoma

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Project description:Genomic alterations driving tumorigenesis in sinonasal malignancies remain largely unexplored. Here, we perform an in vivo loss-of-function screen using a pooled custom single-guide library delivered to the sinonasal cavity by adeno-associated virus vector to identify cancer driver genes across diverse sinonasal malignancies. This approach yielded sinonasal malignancies with diverse histologies, including sinonasal squamous cell carcinoma, adenocarcinoma, poorly differentiated sinonasal carcinoma, and sinonasal neuroendocrine tumors characteristic of olfactory neuroblastoma. Surprisingly, rather than observing distinct sgRNA profiles across sinonasal tumor subtypes, common recurrent mutations were identified in Nf1 (79%), Rasa1 (74%), and Trp53 (68%) across malignancies with distinct histologies. Utilizing an orthogonal approach, we confirmed that Nf1/Trp53 were required for sinonasal tumorigenesis. Given that loss-of-function in NF1 and RASA1 may lead to increased Ras activity and downstream MEK signaling, we tested small molecule targeting of the RAS-MAPK pathway in sinonasal malignancies. Indeed, both tumor cell lines derived from our loss-of-function approach as well as from human sinonasal malignancies displayed significant sensitivity to MEK inhibition in standard in vitro culture and organoid models. These findings demonstrate that loss of NF1 and RASA1-mediated Ras-GAP activity leads to Ras activation and downstream MEK signaling which is a potential common target throughout major sinonasal tumor subtypes.

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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.

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