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Integrative analysis reveals therapeutic potential of pyrvinium pamoate in Merkel cell carcinoma
Jiawen Yang, … , James A. DeCaprio, Megha Padi
Jiawen Yang, … , James A. DeCaprio, Megha Padi
Published February 11, 2025
Citation Information: J Clin Invest. 2025;135(7):e177724. https://doi.org/10.1172/JCI177724.
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Research Article Dermatology Oncology Virology

Integrative analysis reveals therapeutic potential of pyrvinium pamoate in Merkel cell carcinoma

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Abstract

Merkel Cell Carcinoma (MCC) is an aggressive neuroendocrine cutaneous malignancy arising from either ultraviolet-induced mutagenesis or Merkel cell polyomavirus (MCPyV) integration. Despite extensive research, our understanding of the molecular mechanisms driving the transition from normal cells to MCC remains limited. To address this knowledge gap, we assessed the impact of inducible MCPyV T antigens on normal human fibroblasts by performing RNA-seq. Our data uncovered changes in expression and regulation of Wnt signaling pathway members. Building on this observation, we bioinformatically evaluated various Wnt pathway perturbagens for their ability to reverse the MCC gene expression signature and identified pyrvinium pamoate, an FDA-approved anthelminthic drug known for its antitumor activity in other cancers. Leveraging transcriptomic, network, and molecular analyses, we found that pyrvinium targets multiple MCC vulnerabilities. Pyrvinium not only reverses the neuroendocrine features of MCC by modulating canonical and noncanonical Wnt signaling but also inhibits cancer cell growth by activating p53-mediated apoptosis, disrupting mitochondrial function, and inducing endoplasmic reticulum stress. Finally, we demonstrated that pyrvinium reduces tumor growth in an MCC mouse xenograft model. These findings offer a deeper understanding of the role of Wnt signaling in MCC and highlight the utility of pyrvinium as a potential treatment for MCC.

Authors

Jiawen Yang, James T. Lim, Paul Victor Santiago Raj, Marcelo G. Corona, Chen Chen, Hunain Khawaja, Qiong Pan, Gillian D. Paine-Murrieta, Rick G. Schnellmann, Denise J. Roe, Prafulla C. Gokhale, James A. DeCaprio, Megha Padi

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Figure 2

Identifying areas of active gene regulation in IMR90 cells expressing MCPyV T antigens.

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Identifying areas of active gene regulation in IMR90 cells expressing MC...
(A) Graphic workflow of regulatory network analysis. RNA-seq data was integrated with TF motif binding prior and TF protein-protein interactions to infer sample-specific regulatory networks using PANDA and LIONESS. IMR90-ER networks were grouped into 5 time periods and compared with IMR90-GFP networks from the same time period using ALPACA, to identify differential modules. (B) Sankey plot shows the dynamics of differential network communities detected by the workflow shown in A. Each vertical bar represents a differential community, with the size of the bar proportional to the number of genes in the community. Ribbons between adjacent bars represent the number of overlapping genes. Word cloud in the same color as the gene module annotates the enriched biological functions of genes inside the module (font size reflects the Padj value).

Copyright © 2025 American Society for Clinical Investigation
ISSN: 0021-9738 (print), 1558-8238 (online)

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