Journal of Pathology Clinical Research最新文献

The classification of multifocal lung adenocarcinomas (MLAs), including multiple primary lung adenocarcinomas (MPLAs) and intrapulmonary metastases (IPMs), has great clinical significance in staging and treatment determination. However, the application of molecular approaches in pN0M0 MLA diagnosis has not been well investigated. Here, we performed next-generation sequencing (NGS) analysis in 45 pN0M0 MLA patients (101 lesion pairs) who were initially diagnosed as having MPLA by comprehensive histologic assessment (CHA). Five additional patients with intrathoracic metastases were used as positive controls, while 197 patients with unifocal lung adenocarcinomas (425 random lesion pairs) were used as negative controls. By utilizing a predefined NGS criterion, all IPMs in the positive control group could be accurately classified, whereas 13 lesion pairs (3.1%) in the negative control cohort were misdiagnosed as IPMs. Additionally, 14 IPM lesion pairs were diagnosed in the study group, with at least 7 misdiagnoses. We thus developed a refined algorithm, incorporating both NGS and histologic results, that could correctly diagnose all the known MPLAs and IPMs. In particular, all IPMs identified by the refined algorithm were diagnosed to be IPMs or suspected IPMs by CHA reassessment. The refined algorithm-diagnosed MPLAs patients also had significantly better progression-free survival than the refined algorithm-diagnosed IPMs (p < 0.0001), which is superior to conventional NGS or CHA diagnoses. Overall, we developed an NGS-based algorithm that could accurately distinguish IPMs from MPLAs in MLA patients. Our results demonstrate a promising clinical utility of NGS to complement traditional CHA-based MLA diagnosis and help determine patient staging and treatment.

Tropomyosin receptor kinase B (TrkB), a transmembrane receptor protein, has been found to play a pivotal role in neural development. This protein is encoded by the neurotrophic receptor tyrosine kinase 2 (NTRK2) gene, and its abnormal activation caused by NTRK2 overexpression or fusion can contribute to tumour initiation, progression, and resistance to therapeutics in multiple types of neurogenic tumours. Targeted therapies for this mechanism have been designed and developed in preclinical and clinical studies, including selective TrkB inhibitors and pan-TRK inhibitors. This review describes the gene structure, biological function, abnormal TrkB activation mechanism, and current-related targeted therapies in neurogenic tumours.

The standardized preanalytical code (SPREC) aggregates warm ischemia (WIT), cold ischemia (CIT), and fixation times (FIT) in a precise format. Despite its growing importance underpinned by the European in vitro diagnostics regulation or broad preanalytical programs by the National Institutes of Health, little is known about its empirical occurrence in biobanked surgical specimen. In several steps, the Tissue Bank Bern achieved a fully informative SPREC code with insights from 10,555 CIT, 4,740 WIT, and 3,121 FIT values. During process optimization according to LEAN six sigma principles, we identified a dual role of the SPREC code as a sample characteristic and a traceable process parameter. With this preanalytical study, we outlined real-life data in a variety of organs with specific differences in WIT, CIT, and FIT values. Furthermore, our FIT data indicate the potential to adapt the SPREC fixation toward concrete paraffin-embedding time points and to extend its categories beyond 72 h due to weekend delays. Additionally, we identified dependencies of preanalytical variables from workload, daytime, and clinics that were actionable with LEAN process management. Thus, streamlined biobanking workflows during the day were significantly resilient to workload peaks, diminishing the turnaround times of native tissue processing (i.e. CIT) from 74.6 to 46.1 min under heavily stressed conditions. In conclusion, there are surgery-specific preanalytics that are surgico-pathologically limited even under process optimization, which might affect biomarker transfer from one entity to another. Beyond sample characteristics, SPREC coding is highly beneficial for tissue banks and Institutes of Pathology to track WIT, CIT, and FIT for process optimization and monitoring measurements.