Stefano Landi

Decoding the role of mesothelin in tumor dynamics and targeted treatment innovations

Abstract Mesothelin (MSLN) is among the most studied cancer-related antigens, and it is extensively studied as a therapeutic target for the treatment of various malignancies, including pleural mesothelioma, pancreatic ductal adenocarcinoma, and ovarian cancer. However, despite the development of many MSLN-targeting strategies, such as antibody–drug conjugates (ADC), bispecific antibodies, and CAR-T cells, clinical responses have remained limited, underscoring the need for a deeper understanding of MSLN biology. Over the past decades, many studies have highlighted a link between MSLN and cancer progression and its association with specific features within the tumor microenvironment (TME). More recently, mechanistic evidence has emerged showing the involvement of MSLN in the establishment of key malignant features, such as the epithelial-to-mesenchymal transition (EMT) and matrix metalloproteinase 7-mediated remodeling of the extracellular matrix (ECM). Furthermore, these studies also show a direct role for MSLN in the immunosuppressive polarization of the TME through the interaction with CD206 macrophage receptors (leading to an M2-like polarization) and by promoting the transition of mesothelial cells into specific cancer-associated fibroblasts (CAFs). This review synthesizes current evidence on MSLN transcriptional regulation and its functional implications in invasion, metastasis, and immune evasion. We also summarize ongoing therapeutic strategies targeting MSLN and discuss how TME-driven resistance mechanisms are shaping the next generation of MSLN-directed therapies. By integrating molecular insights with translational perspectives, this work provides a comprehensive overview of MSLN biology and its emerging therapeutic relevance in cancer.

Transcriptomic Profiling of the Tumor Microenvironment in High-Grade Serous Carcinoma: A Pilot Study of Morphologic and Molecular Distinctions Between Classic and SET Patterns

High-grade serous carcinoma (HGSC) of the ovary is characterized by two major histological patterns: a classic papillary/micropapillary architecture and a solid pseudo-endometrioid transitional (SET) variant. We investigated whether the distinct morphologic subtypes are underpinned by transcriptomic differences in the tumor microenvironment (TME). We profiled 21 HGSC tumors (7 SET, 14 classic) using a 770-gene NanoString PanCancer Progression panel. Differential expression analysis revealed ~20 genes with significantly different expression (>4-fold, adjusted p < 0.01) between SET and classic tumors. Unsupervised clustering partially separated SET and classic tumors, suggesting that global gene expression patterns correlate with histologic subtype. SET tumors exhibited upregulation of cell-cycle and epithelial genes (e.g., PTTG1, TRAIL, HER3) and downregulation of genes involved in epithelial–mesenchymal transition (EMT), extracellular matrix (ECM) organization, and angiogenesis (e.g., TWIST2, FGF2, decorin) relative to classic tumors. Notably, PTTG1 and TRAIL were upregulated ~6–9-fold in SET tumors, whereas TWIST2 was ~7-fold downregulated, consistent with reduced EMT in SET tumors. Pathway analysis indicated that SET tumors appear to have an immune-active, stroma-poor microenvironment, in line with an “immunoreactive” phenotype, whereas classic tumors showed a mesenchymal, stroma-rich profile. These molecular distinctions could have diagnostic utility and may inform therapeutic stratification, with key dysregulated genes (e.g., HER3, TRAIL, FGF2) representing potential prognostic or predictive biomarkers. For example, high HER3 expression in SET tumors might predict sensitivity to ERBB3/PI3K inhibitors, whereas stromal factors (e.g., FGF2) enriched in classic HGSC could be targeted with microenvironment-modulating therapies. These preliminary findings require validation before translation into pathology practice via immunohistochemical (IHC) assays (e.g., for HER3 or TRAIL), potentially enabling improved classification and personalized treatment of HGSC. We report effect sizes as log2 fold change with 95% confidence intervals and emphasize FDR-adjusted q-values. Given the small sample size and the absence of outcome data (OS/PFS/PFI), results are preliminary and hypothesis-generating. Orthogonal protein-level validation and replication in larger, independent cohorts are required before any translational inference.