IMP3 has a similar specificity, but a better sensitivity, intensity, and extent of reactivity in comparison with AMACR, and may be used as an alternative to AMACR, in support of the diagnosis of BE-dysplasia and EAC.
Background/Aim: Low-grade pancreatic neuroendocrine tumors (LG-PNETs) behave unpredictably. The aim of the study was to identify biomarkers that predict PNET metastasis to improve treatment selection. Patients and Methods: Five patients with primary non-metastatic LG-PNETs, six with primary LG-PNETs with synchronous or metachronous metastases (M-PNETs), and six metastatic to liver LG-PNETs (ML-PNETs) from the group of six M-PNET patients were selected. RNA data were normalized using iterative rank-order normalization. Student's t-test identified differentially-expressed genes in LG-PNETs versus M-PNETs. A 2-fold difference in expression was considered to be significant. Results were validated with an independent dataset of LG-PNETs and metastatic LG-PNETs. Results: Overall, 195 genes had a >2-fold change (in either direction). A total of 29 genes were differentially overexpressed in M-PNETs. Erythrocyte membrane protein band 4.1-like 5 (EPB41L5) had a 2.07-fold change increase in M-PNETs and the smallest p-value. EPB41L5 was not statistically different between M-PNETs and ML-PNETs. EPB41L5 differential expression between primary and metastatic LG-PNETs was confirmed by immunohistochemistry. Conclusion: These results support further investigation into whether EPB41L5 is a biomarker of PNETs with high risk for metastases. Pancreatic neuroendocrine tumors/carcinomas (PNETs/ PECAs) represent about 1-2% of all pancreatic tumors. Recently, however, it has been shown that PNETs have higher prevalence and malignant potential and result in considerable morbidity and mortality (1-4). This may be the result of increased physician awareness, increased use of advancements in imaging modalities, and the protracted clinical course of the disease (1). The oncogenic drivers responsible for the PNET metastatic phenotype have yet to be uncovered. As a result, these tumors are difficult to manage clinically because of their tendency to behave unpredictably (2). Indeed, even when considering low-grade PNETs (LG-PNETs), the presence of metastasis significantly affects patient survival and renders the tumors resistant to currently available therapies (5). Thus, the identification of a biomarker capable of identifying LG-PNETs at higher risk of metastasis may guide the clinical management of these tumors (3, 4). Published reports have shown that molecular alterations in PNETs include genomic alterations (GAs) in DNA damage repair genes MUTYH, CHEK2, and BRCA2 and inactivation of tumor suppressor genes and gene rearrangements that occur in MTAP, ARID2, SMARCA4, MLL3, CDKN2A, and SETD2 (6-12). Molecular analyses of PNETs have uncovered alterations in the pathways responsible for chromatin remodeling, DNA damage repair, activation of mammalian target of rapamycin (mTOR) signaling, and telomere maintenance. Moreover, gene expression profiling (GEP) of PNETs identified a subgroup of these tumors that are associated with hypoxia-inducible factor signaling (12). PNETs have also been reported to exhibit epithelial mesenchymal transition (EMT) b...
Histopathological classification in prostate cancer remains a challenge with high dependence on the expert practitioner. We develop a deep learning (DL) model to identify the most prominent Gleason pattern in a highly curated data cohort and validate it on an independent dataset. The histology images are partitioned in tiles (14,509) and are curated by an expert to identify individual glandular structures with assigned primary Gleason pattern grades. We use transfer learning and fine-tuning approaches to compare several deep neural network architectures that are trained on a corpus of camera images (ImageNet) and tuned with histology examples to be context appropriate for histopathological discrimination with small samples. In our study, the best DL network is able to discriminate cancer grade (GS3/4) from benign with an accuracy of 91%, F1-score of 0.91 and AUC 0.96 in a baseline test (52 patients), while the cancer grade discrimination of the GS3 from GS4 had an accuracy of 68% and AUC of 0.71 (40 patients).
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