Transcriptomic Dichotomy in Melanoma: Proliferative Cell Cycle Shield vs. Autoimmune-like Chromatin Activation in Anti-PD-1 Response

Abstract Primary resistance to anti-PD-1 therapy in metastatic melanoma remains a clinical challenge. This study reanalyzed the GSE168204 dataset to elucidate molecular mechanisms of resistance and response, incorporating batch correction to address limitations in our prior preprint[6]. RNA-seq data from 25 melanoma biopsies (9 responders, 16 non-responders) were analyzed using DESeq2 with surrogate variable analysis[9,10]. We identified 3,247 differentially expressed genes, revealing a "cell cycle shield" signature in non-responders characterized by upregulation of CDK1, CCNB1, E2F1, and HSP90AA1 enriched for proliferation and DNA repair pathways, suggesting immune evasion through rapid tumor growth. Responders exhibited upregulation of EP300, CREBBP, FCGR2B, and histone genes enriched for chromatin organization and systemic lupus erythematosus pathways, indicating immune activation and autoimmune-like transcriptional programs. Notably, batch correction reversed the roles of EP300 and FCGR2B from non-responders to responders[6], highlighting their context-dependent functions in immune engagement. The "cell cycle shield" suggests targeting CDK1 or HSP90AA1 to overcome resistance[13,14], while the SLE signature may serve as a response biomarker reflecting immune activation states[7]. Validation in larger cohorts and experimental models is needed to translate these findings into personalized immunotherapy strategies. Competing Interest Statement The authors have declared no competing interest. Footnotes Email: basma.shabana93{at}gmail.com, drahmedzakariaanwar{at}gmail.com, surgnour{at}gmail.com, cardiologistahmedkhedr{at}gmail.com This revised manuscript incorporates significant improvements over the previous version by addressing batch effects and refining the molecular analysis of anti-PD-1 therapy resistance in metastatic melanoma. Key updates include: 1)Batch Correction Implementation: Surrogate variable analysis (SVA) was applied to the GSE168204 dataset to mitigate batch effects, resulting in a more reliable differential expression analysis. This correction reversed the roles of EP300 and FCGR2B, now upregulated in responders (R) rather than non-responders (NR), indicating their involvement in immune activation rather than immune evasion. 2)Refined Molecular Signatures: The analysis identified 3,247 differentially expressed genes, revealing a proliferation-driven "cell cycle shield" in NR (upregulated CDK1, CCNB1, E2F1, HSP90AA1) and an immune-active, autoimmune-like signature in R (upregulated EP300, CREBBP, FCGR2B, histone genes, enriched for systemic lupus erythematosus pathways). These findings replace earlier hypotheses and provide a clearer dichotomy between resistance and response mechanisms. 3)Enhanced Multi-Omics Integration: Updated transcription factor, receptor-ligand, and protein-protein interaction networks highlight context-dependent roles of shared transcription factors (SP1, NFKB1, RELA) and key hubs (E2F1, HDAC2 in NR; EP300, FOXA1 in R). Tables 1-12 were revised to reflect these new findings, with improved annotations and pathway enrichments (Tables 3-5). 4)Updated Discussion and Conclusions: The discussion was rewritten to address the reversal of EP300 and FCGR2B roles, emphasize the "cell cycle shield" and autoimmune-like signatures, and propose actionable therapeutic targets (e.g., CDK1/HSP90AA1 inhibitors) and biomarkers (SLE-like signature). Limitations were expanded to acknowledge the need for experimental validation and single-cell analysis. 5)Improved Clarity and Transparency: Methods were updated to detail SVA and DESeq2 workflows, and results were restructured for clarity with revised figures (1-16) and tables (1-12). The conclusion now emphasizes personalized immunotherapy strategies. These changes enhance the study's robustness, correct prior inaccuracies, and provide a stronger foundation for understanding anti-PD-1 resistance mechanisms in melanoma.