Despite azithromycin being used in some countries to treat infections caused by Gram-negative pathogens, no resistance breakpoint forEscherichia coliexists. The aim of this study was to analyse the levels and mechanisms of azithromycin resistance inE. coli. The presence of chromosomal (rplD, rplVand23S rRNA) mutations, 10 macrolide resistance genes (MRGs) and efflux pump overexpression was determined in 343E. coliisolates. Overall, 89 (25.9%) isolates had MICs ≥ 32 mg/L to azithromycin, decreasing to 42 (12.2%) when assayed in the presence of Phe-Arg-β-Napthylamide, with 35 of these 42 possessing at least one MRG. Efflux pumps played a role in azithromycin resistance affecting the Minimal Inhibitory Concentration (MIC) levels of 91.2% isolates whereas chromosomal alterations seem to have a minimal role. At least one MRG was found in 22.7% of the isolates withmph(A) being the most commonly found gene. Themph(A) gene plays the main role in the development of azithromycin resistance and 93% of themph(A)-carrying isolates showed a MIC of 32 mg/L. In the absence of a specific resistance breakpoint our results suggest a MIC of 32 mg/L to be considered in order to detect isolates carrying mechanisms able to confer azithromycin resistance.

Background/Objectives: Sinonasal mucosal melanoma (SNMM) is a rare and aggressive melanoma subtype with a notably poor prognosis compared to cutaneous melanoma (CM). Despite advances in molecular characterization, SNMM remains underexplored, posing a clinical challenge and highlighting the need for detailed molecular profiling. This study aimed to identify the molecular features of SNMM, elucidate its clinical behavior and prognostic implications, and provide insights for improved therapeutic strategies. Methods: This retrospective study analyzed 37 primary melanoma tumors diagnosed at the Hospital Clinic of Barcelona. Gene expression was examined using 1402 immuno-oncology-related probes through next-generation sequencing. Hierarchical clustering analysis (HCA), differentially expressed genes (DEGs), gene set enrichment analysis (GSEA), and the xCell algorithm were performed. The statistical methods comprised descriptive statistics, clinical variable associations, and survival analyses. Results: HCA revealed two primary clusters. Cluster A exclusively contained CM tumors (20/24), while cluster B included all SNMMs (13/13) and some CMs (4/24). Cluster B showed a higher average age at diagnosis (p = 0.018), higher mitotic index (p = 0.0478), fewer BRAF mutations (p = 0.0017), and poorer melanoma-specific survival (p = 0.0029). Cluster B showed 602 DEGs with cell cycle pathways enriched, immune pathways diminished, lower immune scores (p < 0.0001), and higher stromal scores (p = 0.0074). Conclusions: This study revealed distinct molecular characteristics and an altered tumor microenvironment in SNMMs and certain aggressive CMs. Identifying specific genes and pathways involved in cell cycle progression and immune evasion suggests potential prognostic markers, offering new avenues for enhancing treatment strategies and improving patient survival rates.

Background/Objectives: Immune checkpoint inhibitors (ICIs) have revolutionized advanced melanoma treatment, yet many patients fail to achieve sustained clinical benefit. Several biomarkers, including tumor microenvironment (TME) signature, PD-1/PD-L1 expression, and IFN-γ signaling, have been proposed. However, robust predictive markers remain elusive. This study aimed to identify molecular markers of response by analyzing tumor and peripheral immune signatures. Methods: This study analyzed 21 advanced melanoma patients treated with ICIs. Formalin-fixed, paraffin-embedded tumors underwent RNA-sequencing targeting 1392 immuno-oncology probes. Genes significantly associated with progression-free survival (PFS) by log-rank test underwent hierarchical clustering analysis (HCA). Differential expression and xCell analyses were then performed on the resulting clusters. Cox multivariate analysis was applied to identify independent PFS predictors. Pre-treatment peripheral blood mononuclear cells were analyzed by mass cytometry, followed by FlowSOM and UMAP clustering. Results: Fifty-five genes significantly associated with PFS identified two molecular clusters via HCA. Cluster A demonstrated prolonged PFS (59.4 vs. 2.4 months, p = 0.0004), while Cluster B was characterized by downregulated IFN-γ signaling, antigen presentation pathways, and reduced immune score. Multivariate Cox analysis confirmed molecular cluster as an independent PFS predictor (p < 0.001). Mass cytometry revealed higher frequencies of circulating PD-1+ CD4+ effector memory (EM) T subpopulations among responders. Conclusions: This study highlights the potential role of molecular and immune profiling in predicting ICI response in advanced melanoma. The identification of distinct molecular clusters underscores significant TME heterogeneity, with immune-cold tumor clusters associated with poorer outcomes. Furthermore, circulating PD-1+ T subpopulations emerged as potential markers of ICI response, suggesting their value in improving patient stratification.