J. Mol. Pathol., Volume 6, Issue 2 (June 2025) – 4 articles

Malignant Triton Tumors (MTTs) are rare, high-grade malignant peripheral nerve sheath tumors (MPNSTs) frequently associated with Type 1 Neurofibromatosis (NF1). NF1, an autosomal dominant disorder, predisposes approximately 10% of affected individuals to developing MPNSTs, with 50% of these tumors occurring in NF1 patients, while others arise sporadically or in association with radiation exposure. MTTs predominantly affect anatomical regions rich in large nerves, such as the limbs, spinal root, and cranial nerves. Mediastinal presentations are exceedingly rare, posing significant diagnostic and therapeutic challenges. Current treatment strategies include surgical resection, chemotherapy, radiotherapy, and lung-sparing procedures for metastatic disease. Molecular studies of MPNSTs have revealed that NF1 mutations lead to dysregulation of the RAS signalling pathway, while epigenetic alterations (e.g., SUZ12/EED mutations) further contribute to tumor progression. Dysregulated phylogenetically conserved pathways, including Wnt/beta-catenin and non-canonical SHH signalling, play a role in sarcoma progression and Schwann cell transformation. Recent advances in miRNA research highlight their involvement in tumor invasion and progression, with dysregulated miRNA expression and chromatin remodeling contributing to the pathogenesis of these neoplasms. However, the distinct molecular profiles for MTTs remain incompletely understood. Further investigation of the genetic and epigenetic landscape is essential for improving our understanding and identifying potential therapies. Herein, we present a single-center retrospective case series of three patients with an intrathoracic triton tumor treated at our University Hospital between 2000 and 2024, serving as a starting point for future insights into MPNST pathobiology. Full article

Background: Navify^® Mutation Profiler (Navify MP) is a cloud-based, tertiary analysis software that provides curation, annotation, and reporting of somatic genomic alterations and biomarker signatures identified by next-generation sequencing. The Navify MP software leverages Association for Molecular Pathology/American Society of Clinical Oncology/College of American Pathologists (AMP/ASCO/CAP) Somatic Variant Classification Guidelines to provide information on detected somatic genomic variants and associated therapies according to region-specific approvals. Methods: This validation study assessed the accuracy of the Navify MP software and curation process for hematologic malignancies as compared to expert opinion. A total of 86 variants derived from hematologic malignancies (including myeloid and lymphoid leukemias, B cell lymphomas, and multiple myeloma) were used to contrive 12 VCF files. The VCFs were made up of the following classes of genomic alterations: single nucleotide variants, small insertions and deletions, fusions, and copy number alterations. Of the 86 variants, 42 were Tier IA, and 44 were non-Tier IA, based on AMP/ASCO/CAP classification. The study was performed at four sites with seven software users (molecular genetics experts). Results: Tier classification agreement between Navify MP and expert user assignment was 91.34% for Tier IA and 95.02% across all hematologic variants. The agreement on associated therapies for the Navify MP-classified Tier IA hematologic variants was 99.08%. Conclusions: Navify MP is a robust automated solution for genomic variant reporting of hematologic malignancies and remains up to date with evolving regional approvals and medical guidelines. Full article

Background/Objectives: Colorectal cancer (CRC) is a significant global health issue. The identification of methylated Septin 9 (mSEPT9) as a biomarker for CRC represents a significant advancement in cancer diagnostics. On the other hand, Fusobacterium nucleatum (FN) is one of the most studied cancer-related microbes in CRC. This study provided cohort evidence on the association of mSEPT9 with clinicopathologic characteristics and FN infection in CRC patients. Methods: Paired formalin-fixed paraffin-embedded (FFPE) tissue DNA (cancerous and adjacent non-cancer tissues) of eighty-three CRC patients was collected. Methylation-specific qPCR targeting the v2 promoter region of mSEPT9 was carried out on bisulfite-converted FFPE DNA. For FN detection, a TaqMan probe-based method targeting the 16S rRNA gene was used. The differences in mSEPT9 levels and FN expression between cancer and non-cancer tissues were evaluated. Association studies between mSEPT9 in the tumor and relative mSEPT9 levels with FN infection and available clinical data were conducted. Results: Higher mSEPT9 levels were found in the cancerous tissue compared to non-cancerous tissue (p < 0.0001). High mSEPT9 levels in the tumor were significantly associated with older patients (p < 0.001) and larger tumor size (p = 0.048) but not with other clinicopathologic variables. In double-positive patients where mSEPT9 was detected in both cancerous and non-cancerous tissue, the expression fold-change in mSEPT9, calculated using the 2^−ΔΔCT formula, was significantly higher in patients with tumor size equal to or greater than 5 cm (p = 0.042). High levels of mSEPT9 in tumor were not associated with FN infection. However, high levels of FN infection were associated with mSEPT9 (p < 0.021). Conclusions: High levels of mSEPT9 are found in CRC tumor tissue and are associated with older age and larger tumor size, while high levels of FN infection are associated with mSEPT9 in this single-center cohort study. Full article

Background: This review examines the expanding influence of artificial intelligence (AI) in the detection and management of uveal melanoma (UM). Methods: This work delves into the application of AI technologies such as machine learning (ML), deep learning (DL), and convolutional neural networks (CNNs) in various diagnostic procedures, molecular profiling, and predictive analysis. Results: The discussion underscores AI’s potential to enhance diagnostic precision and efficiency. Particular focus is placed on its role in histopathological assessments of UM, where algorithms facilitate the analysis of whole-slide images (WSIs). AI contributes to more accurate tumor classification, assists in planning treatments, and improves the prediction of the prognostic indicators and molecular characteristics of the tumor. Conclusions: Despite these promising developments, this review acknowledges existing hurdles to AI implementation, including issues with data standardization and the interpretability of AI models. It emphasizes the need for further research to fully integrate AI into clinical workflows, ultimately aiming to improve patient care and outcomes. Full article