a-robust-discovery-platform-for-identification-novel-mediators

A Robust Discovery Platform for the Identification of Novel Mediators of Melanoma Metastasis

Metastasis is a complex process, requiring cells to overcome barriers that are only incompletely modeled by in vitro assays. A systematic workflow was ...

NYU Grossman School of Medicine

Perlmutter Cancer Center

Melanoma is the most lethal skin malignancy, driven by genetic and epigenetic alterations in the complex tumour microenvironment. While large-scale molecular profiling of melanoma has identified molecular signatures associated with melanoma progression, comprehensive systems-level modeling remains elusive. This study builds up predictive gene network models of molecular alterations in primary melanoma by integrating large-scale bulk-based multi-omic and single-cell transcriptomic data. Incorporating clinical, epigenetic, and proteomic data into these networks reveals key subnetworks, cell types, and regulators underlying melanoma progression. Tumors with high immune infiltrates are found to be associated with good prognosis, presumably due to induced CD8+ T-cell cytotoxicity, via MYO1F-mediated M1-polarization of macrophages. Seventeen key drivers of the gene subnetworks associated with poor prognosis, including the transcription factor ZNF180, are tested for their pro-tumorigenic effects in vitro. The anti-tumor effect of silencing ZNF180 is further validated using in vivo xenografts. Experimentally validated targets of ZNF180 are enriched in the ZNF180 centered network and the known pathways such as melanoma cell maintenance and immune cell infiltration. The transcriptional networks and their critical regulators provide insights into the molecular mechanisms of melanomagenesis and pave the way for developing therapeutic strategies for melanoma.

Network models of primary melanoma microenvironments identify key melanoma regulators underlying prognosis.

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer death in the United States. Glycans, such as carbohydrate antigen 19-9, are biomarkers of PDAC and are emerging as important modulators of cancer phenotypes. Herein, we used a systems-based approach integrating glycomic analysis of the well-established KC mouse, which models early events in transformation, and analysis of samples from human pancreatic cancer patients to identify glycans with potential roles in cancer formation. We observed both common and distinct patterns of glycosylation in pancreatic cancer across species. Common alterations included increased levels of α-2,3-sialic acid and α-2,6-sialic acid, bisecting GlcNAc and poly-N-acetyllactosamine. However, core fucose, which was increased in human PDAC, was not seen in the mouse, indicating that not all human glycomic changes are observed in the KC mouse model. In silico analysis of bulk and single-cell sequencing data identified ST6 beta-galactoside alpha-2,6-sialyltransferase 1, which underlies α-2,6-sialic acid, as overexpressed in human PDAC, concordant with histological data showing higher levels of this enzyme at the earliest stages. To test whether ST6 beta-galactoside alpha-2,6-sialyltransferase 1 promotes pancreatic cancer, we created a novel mouse in which a pancreas-specific genetic deletion of this enzyme overlays the KC mouse model. The analysis of our new model showed delayed cancer formation and a significant reduction in fibrosis. Our results highlight the importance of a strategic systems approach to identifying glycans whose functions can be modeled in mouse, a crucial step in the development of therapeutics targeting glycosylation in pancreatic cancer.

Article	Year
Network models of primary melanoma microenvironments identify key melanoma regulators underlying prognosis. Nature communications\| PubMed ID: 33619278	2021
Integrated Systems Analysis of the Murine and Human Pancreatic Cancer Glycomes Reveals a Tumor-Promoting Role for ST6GAL1. Molecular & cellular proteomics : MCP\| PubMed ID: 34634466	2021

Praveen Agrawal

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