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Wizel, A.

Publications and source records attributed to Wizel, A..

2 recordsLinked to original sources

Single-cell transcriptomic analysis of HPV-related multiphenotypic sinonasal carcinoma uncovers MYB-HPV association

Human papillomavirus (HPV)-related multiphenotypic sinonasal carcinoma (HMSC) is a rare tumor that morphologically resembles high grade adenoid cystic carcinoma (ACC), yet exhibits indolent clinical behavior. Both demonstrate MYB proto-oncogene upregulation, but HMSC lacks the MYB translocation typically seen in ACC. Transcriptional changes in HMSC tumors remain uncharacterized. We performed single-cell RNA sequencing (scRNA-seq) on a human HMSC tumor and compared expression profiles with published ACC and oropharyngeal squamous cell carcinoma (OPSCC) scRNA-seq datasets. Primary malignant cells from HMSC (n=134) and ACC (n=980) clustered separately, and HMSC lacked bicellular differentiation into luminal and myoepithelial cells, distinguishing it from ACC. A greater proportion of HMSC cells expressing HPV-related genes (HPVon) expressed MYB (83% vs. 62%, p=0.022) and MYB targets (p=6.4*10-6), suggesting an HPV-MYB association. This finding was validated in HPV-positive OPSCC, with 7/10 tumors showing MYB upregulation in HPVon versus HPVoff cells (p<0.05). A 264-gene signature from HPVon HMSC cells was also associated with worse prognosis in HPV+ OPSCC (p<0.003), suggesting an alternate role for HPV that has not been well characterized. Further validation of the HPV-MYB association and prognostically relevant HPV gene signature may improve patient stratification and therapeutic strategies in HPV-related malignancies.

cancer biology↗

A novel method to accurately estimate pathway activity in single cells for clustering and differential analysis

Inferring which and how biological pathways and gene sets are changing is a key question in many studies that utilize single-cell RNA sequencing. Typically, these questions are addressed by quantifying the enrichment of known gene sets in lists of genes derived from global analysis. Here we offer SiPSiC, a new method to infer pathway activity in each cell. This allows more sensitive differential analysis and utilizing pathway scores to cluster cells and compute UMAP or other similar projections. We apply our method on datasets of COVID-19, lung adenocarcinoma and glioma, and demonstrate its utility. SiPSiC analysis is consistent with findings reported by previous analyses in many cases, but also reveals the differential activity of novel pathways, enabling us to suggest new mechanisms underlying the pathophysiology of these diseases and demonstrating SiPSiCs high accuracy and sensitivity in detecting biological function and traits. In addition, we demonstrate how it can be used to better classify cells based on activity of biological pathways instead of single genes and its ability to overcome patient specific artifacts.

bioinformatics↗