Investigating the cell of origin and novel molecular targets in Merkel cell carcinoma: a historic misnomer
Bibliographic record
Abstract
Merkel cell carcinoma (MCC) is a highly aggressive disease with the poorest prognosis among skin cancers, originally posited to be derived from Merkel cells. Emerging evidence, however, suggests other potential origins for MCC, including hematological lineages. We utilized targeted and multi-omics approaches to explore gene expression patterns at protein and RNA levels of MCCs. Western blotting, immunofluorescence, and immunohistochemistry were performed using fresh and 92 FFPE samples of primary and metastatic MCC, and two MCC cell lines (MS-1, HaCaT). RNA sequencing of selected FFPE samples identified differentially expressed genes based on sex and Merkel cell polyomavirus (MCPyV) status. Finally, weighted gene correlation network analysis (WGCNA) and cell type enrichment analyses were employed to determine pathway and cell type enrichment, respectively. MCC patient samples heterogeneously expressed B-cell and neuroendocrine markers and novel molecular targets including BCMA, CD10, CD93, PAX5, TdT, IgA, and CD19. Transcriptome analysis demonstrated differentially expressed genes based on sex and MCPyV status. MCPyV+ tumors had significant upregulation of genes involved in immune cell function and downregulation of processes related to neuronal activity. WGCNA highlighted enrichment for pathways involved in immune function, including B-cell differentiation. Cell type enrichment analysis highlighted enrichment for multipotent stem cells, several immune cell types, and keratinocytes. Our findings support previous studies which confirm that MCC is unlikely to be derived from Merkel cells and instead from multiple or divergent cell types, including those of B-cell lineage. Our work highlights the need for a more personalized approach to diagnosis/characterization and treatment of MCCs, given the documented variability of novel potentially targetable pathways.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".