bioRxivCancer Biology
Clinical and molecular characterization of virus-positive and virus-negative Merkel cell carcinoma
G. J. Starrett, M. Thakuria, T. Chen, C. Marcelus, J. Cheng, J. Nomburg, A. R. Thorner, M. K. Slevin, W. Powers, R. T. Burns, C. Perry, A. Piris, F. C. Kuo, G. Rabinowits, A. Giobbie-Hurder, L. E. MacConaill, J. A. DeCaprio
Abstract
Merkel cell carcinoma (MCC) is a highly aggressive neuroendocrine carcinoma of the skin mediated by the integration of Merkel cell polyomavirus (MCPyV) and expression of viral T antigens or by ultraviolet induced damage to the tumor genome from excessive sunlight exposure. An increasing number of deep sequencing studies of MCC have identified significant differences between the number and types of point mutations, copy number alterations, and structural variants between virus-positive and virus-negative tumors. In this study, we assembled a cohort of 71 MCC patients and performed deep sequencing with OncoPanel, a next-generation sequencing assay targeting over 400 cancer-associated genes. To improve the accuracy and sensitivity for virus detection compared to traditional PCR and IHC methods, we developed a hybrid capture baitset against the entire MCPyV genome. The viral baitset identified integration junctions in the tumor genome and generated assemblies that strongly support a model of a hybrid, virus-host, circular DNA intermediate during integration that promotes focal amplification of host DNA. Using the clear delineation between virus-positive and virus-negative tumors from this method, we identified recurrent somatic alterations common across MCC and alterations specific to each class of tumor, associated with differences in overall survival. Comparing the molecular and clinical data from these patients revealed a surprising association of immunosuppression with virus-negative MCC and significantly shortened overall survival. These results demonstrate the value of high-confidence virus detection for identifying clinically important features in MCC that impact patient outcome.
- organism: human
- assay: whole-exome sequencing
- research focus: variant calling
- research focus: differential expression
- approach: deep learning
- approach: statistical model
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