Endocrine Pathology最新文献

Although anaplastic thyroid carcinomas (ATCs) typically arise from papillary thyroid carcinomas (PTCs), follicular thyroid carcinomas (FTCs) can also progress to ATCs; however, histologically confirmed FTC-derived ATCs are relatively uncommon and remain poorly characterized. To clarify this phenomenon, we analyzed eight FTC-derived ATCs and compared them with 11 PTC-derived ATCs. Whole-exome sequencing (WES) was conducted on the differentiated thyroid carcinoma (DTC) and ATC components within the same tumors to examine mutational profiles; three additional cases underwent FoundationOne® testing. The demographic features were similar between FTC- and PTC-derived ATCs. Histologically, spindle-cell morphology was more common in FTC-derived ATCs (3/8), whereas PTC-derived ATCs exhibited squamoid (5/11) and giant cell features (5/11), including osteoclast-like cells. WES was successfully performed on both the ATC and FTC components in seven of the eight FTC-derived ATCs. Common alterations included TERT promoter (5/7), NRAS (4/7), and HRAS (2/7) mutations in both components. TP53 mutations were observed only in the ATC component (5/7). EIF1AX mutations co-occurred with TERT and HRAS mutations in two cases. PTEN mutations were found in two FTCs with solid/trabecular patterns but were absent in the corresponding ATC components. One tumor harbored a DGCR8 p.E518K mutation that was retained during progression. By contrast, PTC-derived ATCs consistently showed concurrent BRAF and TERT promoter mutations (11/11). Immunohistochemistry for BRAF V600E, RAS Q61R, p53, PTEN, and MTAP showed high concordance with the corresponding mutation status. These findings revealed significant histological and genetic differences between FTC- and PTC-derived ATCs, providing new insights into the molecular basis of FTC dedifferentiation into ATC.

Mixed neuroendocrine-nonneuroendocrine neoplasms (MiNEN) are usually highly aggressive tumors characterized by marked histological heterogeneity, most commonly represented by mixed adenocarcinoma and poorly differentiated neuroendocrine carcinoma (NEC). However, beyond morphological observations, the biological basis and implications of this heterogeneity remain incompletely understood. In this study, we combined component-specific next-generation sequencing and spatial transcriptomics to investigate three mixed adenocarcinoma-NEC cases from different anatomical sites (ileocecal, ovarian, gastric), tracing tumor progression from precursor lesions to invasive NEC. Genomic analyses revealed a shared trunk of driver mutations across all tumor compartments, confirming their clonal origin, while also uncovering additional compartment-specific alterations. Spatial transcriptomics, together with gene set enrichment analysis (GSEA), revealed distinct transcriptional profiles aligned with histologically annotated compartments (e.g., adenocarcinoma, NEC, precursor). In NECs, GSEA consistently showed downregulation of immune-related pathways and upregulation of proliferation-associated pathways compared to non-neuroendocrine tumor areas. Moreover, distinct transcriptomic subclusters were identified within morphologically homogeneous NEC regions in two of the three cases. These subclusters exhibited significant differences in immune regulation, proliferation signaling, and cell-cycle control, and were associated with divergent predicted chemotherapy-response signatures, suggesting clinically relevant implications for treatment sensitivity and resistance. In summary, our findings indicate that despite a shared clonal origin, MiNEN develop distinct genetic and transcriptomic features across tumor compartments. The inconsistent presence of transcriptomic subclusters within morphologically similar regions underscores the complexity of intratumoral heterogeneity in these aggressive neoplasms. By connecting morphological and molecular layers of tumor architecture, spatial profiling may aid in translating biological complexity into more targeted clinical strategies.

Neuroendocrine tumors (NETs) of the prostate gland represent a distinct entity within the spectrum of prostate cancer, characterized by neuroendocrine differentiation on morphology and unique clinical behavior. Despite their clinical significance, there remains a lack of consensus regarding their diagnosis, classification, immunohistochemical evaluation, and management. A comprehensive understanding of the molecular and clinical heterogeneity of prostate NETs is essential for developing tailored treatment strategies and improving patient outcomes. Over the years, there has been a continuous effort to refine the terminology and classification of neuroendocrine tumors of the prostate. The current WHO 2022 classification scheme for genitourinary neuroendocrine neoplasms provides a standardized framework. However, given the substantial clinical and molecular heterogeneity of prostate NETs, there is an emerging need for an organ-specific classification system that better captures the biological and clinical diversity of these tumors. In this review, we propose a novel classification system for neuroendocrine neoplasms (NENs) of the prostate that integrates both histomorphology and clinical context. With the growing role of liquid biopsy and molecular biomarkers, there is a shift towards more precise, real-time detection of disease progression and treatment resistance. This review highlights the importance of a more nuanced, biologically and clinically informed approach to the diagnosis and management of prostate NETs. A dedicated classification system, combined with advancements in precision genomics and targeted therapies, holds significant promise for improving outcomes in patients with these rare and challenging tumors.

Fine needle aspiration (FNA) and fine needle biopsy (FNB) procedures are increasingly employed in the diagnostic work-up of pancreatic masses. These procedures represent a challenge for pathologists who have to adapt to handling specimens of limited cellularity. In several cases, FNA and FNB specimens are the only available material, as many pancreatic neoplasms are surgically unresectable at the time of the initial diagnosis. In the present review paper, the diagnosis of pancreatic neuroendocrine neoplasms in limited cellularity specimens is presented using a morphology-based approach. The aim is to provide a practical guide for pathologists to select the most appropriate ancillary techniques to be used for the diagnostic work-up, while conserving precious material. The integration of morphology, immunohistochemistry, and molecular biology will be discussed to provide the reader with practical tools to solve the main differential diagnostic problems encountered in routine practice when working with cytological samples or small biopsies.

The clinical behavior of well-differentiated pancreatic neuroendocrine tumors (PanNETs) is difficult to predict. In order to define, more accurately, prognosticators for patients with a surgically resected PanNET, the pathologic features and Ki-67 immunolabeling indexes of PanNETs resected from 904 consecutive patients at an academic tertiary care hospital were correlated with patient outcome. The mean patient age at surgery was 56.6 years (SD 14.0), 477 were male (52.8%), and 7882 person-years of follow-up were obtained (mean 8.8 years, SD 6.5). The 10-year survival was 81% (95% CI: 77,86%) for patients with G1 PanNETs (Ki-67 <3%), 68% (95% CI: 61,76%) for patients with G2a PanNETs (Ki-67 3 - <10%), 44% (95% CI: 29,66%) for patients with G2b PanNETs (Ki-67 of 10%- ≤20%), and 23% (95% CI: 8,61%) for patients with G3 PanNETs. Vascular invasion (HR 3.0, p <0.0001), tumor size ≥ 2 cm (HR 2.88, p <0.0001), perineural invasion (HR 2.42, p<0.0001), and positive margins (HR 2.18, p <0.0001) were associated with worse overall survival. Insulinoma (HR 0.34, p=3e-04), sclerosing variant (HR 0.47, p=0.05), and cystic variant (HR 0.61, p=0.05) were associated with improved overall survival. T, N and M stages were all statistically significant classifiers of overall survival. Similar associations were found with respect to disease relapse. There was a significant (P<0.001) increase in the proportion of patients diagnosed with stage I vs stage IV disease over time. This study supports the classification of PanNETs into four grades (G1, G2a, G2b, and G3) based on Ki-67 labeling, which allows a more accurate prognostic assessments of patients.

We studied transcription factors and hormones expressed by duodenal neuroendocrine cells in a consecutively diagnosed series of 53 patients with well-differentiated duodenal NETs. There were 30 men; the mean age was 65 years (33 to 81). The study included biopsies (n = 18), endoscopic mucosal resections (n = 19), and surgical resections (n = 16). Three patients had multifocal disease; two had MEN1. Two patients had neurofibromatosis. Metastases were identified in 15/23 patients with biopsied lymph nodes. PAX6 was expressed in 85%, followed by CDX2 in 65%; ARX was expressed in 33%, and no tumors expressed PAX4. The commonest hormone expressed was gastrin; 23 (43%) had diffuse expression, and 12 (23%) had focal reactivity. Pancreatic polypeptide was diffuse and strong in 17 tumors (32%) classified as PP cell NETs; another 3 tumors had focal staining (total n = 20, 38%). Serotonin was identified only focally in 14 tumors (26%). Somatostatin was positive in 13 tumors (25%), 3 classical D cell tumors and 10 tumors with focal positivity. PYY was expressed in 10 tumors (19%), diffusely in 1 and focally in 9. CCK was identified in 6 tumors (11%), diffusely in 1 and focally in 5. Staining for glucagon/GLPs, insulin, and motilin was completely negative in all tumors. Thirty tumors (57%) expressed more than one hormone; gastrin was the most frequent. In 2 composite gangliocytoma/NETs (CoGNETs), the NET component expressed PP, and both NET and ganglion cells expressed ARX. These data identify a broad spectrum of duodenal NETs including novel cell types and a high incidence of plurihormonality.

The recent introduction of the term non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) marked a pivotal shift in the classification of encapsulated follicular variant of papillary thyroid carcinoma (EFVPTC) lacking invasive features. While its reclassification from the "malignant" to "low-risk neoplasm" category significantly reduced overtreatment, its histopathological diagnosis remains challenging due to overlapping features with other thyroid lesions and inter-observer variability. Artificial intelligence (AI) overcomes such key limitations of histopathological evaluation, ensuring a robust and efficient diagnostic process. While preliminary studies are promising, AI models capable of efficiently distinguishing NIFTP from other benign and malignant thyroid entities are yet to be developed. We devised an innovative AI-based three-stage hierarchical pipeline that systematically evaluates architectural patterns and nuclear features. The prioritized models were trained using 154,498 patches, derived from 134 sections prepared from 125 thyroid nodules, representing follicular nodular disease (FND), follicular adenoma, dominant nodule in FND, invasive EFVPTC (IEFVPTC), and classic and infiltrative follicular subtypes of PTC. External validation revealed good accuracy at the overall, patient-wise, and class-wise levels. However, it showed limitations in the differential diagnosis of NIFTP from IEFVPTC-an expected challenge due to overlapping nuclear features and the absence of incorporating the assessment of the tumor capsule for invasive characteristics. While the novel approach and the algorithm show promise in transforming histopathological NIFTP diagnostics, further improvements and rigorous validations are necessary before considering its application in real-world clinical settings.

The reticulin framework, composed mainly of type III collagen, is an essential structural component of biological tissues. Reticulin stains, particularly silver-based methods, enable detailed visualization of reticulin framework alterations, which have been proven to be quick, low-cost, and reliable solutions for highlighting quantitative and qualitative changes of reticulin framework and have been variably associated with neoplastic and non-neoplastic conditions. This review provides an updated overview of reticulin stain applications and reticulin framework assessment in endocrine and neuroendocrine neoplasms, including those of the pituitary, parathyroid, adrenal, and other neuroendocrine systems. In pituitary neuroendocrine tumors, reticulin framework loss serves as a distinguishing feature between normal and neoplastic adenohypophysis. Parathyroid neoplasms, including adenomas, atypical tumors, and carcinomas, exhibit varying degrees of reticulin framework disruption, which may aid in differential diagnosis. Similarly, in adrenocortical neoplasms, reticulin framework evaluation plays a crucial role in malignancy assessment, as defined in the reticulin algorithm, which incorporates reticulin framework alterations alongside three Weiss criteria: necrosis, high mitotic count (> 5/10 mm²), and venous invasion. Moreover, specific reticulin framework patterns help to distinguish the different morphological subtypes of bilateral macronodular adrenocortical disease. Pheochromocytomas and paragangliomas display a range of reticulin framework patterns which might be related to the genetic background of the tumor. Finally, different neuroendocrine neoplasms exhibit variable reticulin framework integrity, with a more significant disruption observed in high-grade carcinomas. Advancements in digital pathology and artificial intelligence offer promising avenues for automated reticulin framework quantification, enhancing diagnostic precision and prognostic assessments. The integration of computational approaches may further improve the clinical utility of reticulin framework evaluation in endocrine pathology.