Targeting GLP-1 receptor trafficking to improve agonist efficacy

Glucagon-like peptide-1 receptor (GLP-1R) activation promotes insulin secretion from pancreatic beta cells, causes weight loss, and is an important pharmacological target in type 2 diabetes (T2D). Like other G protein-coupled receptors, the GLP-1R undergoes agonist-mediated endocytosis, but the functional and therapeutic consequences of modulating GLP-1R endocytic trafficking have not been clearly defined. Here, we investigate a series of biased GLP-1R agonists with variable propensities for GLP-1R internalization and recycling. Compared to a panel of FDA-approved GLP-1 mimetics, compounds that retain GLP-1R at the plasma membrane produce greater long-term insulin release, which is dependent on a reduction in β-arrestin recruitment and faster agonist dissociation rates. Such molecules elicit glycemic benefits in mice without concomitant increases in signs of nausea, a common side effect of GLP-1 therapies. Our study identifies a set of agents with specific GLP-1R trafficking profiles and the potential for greater efficacy and tolerability as T2D treatments

TOP2A Amplification and Overexpression in Hepatocellular Carcinoma Tissues

Hepatocellular carcinoma (HCC) is the leading cause of cancer death in men worldwide owing to limited insights into pathogenesis and unsatisfactory efficacy of current therapies. HER2 and TOP2A genes are coamplified in breast and some other cancers. In this study, we investigated gene aberrations of HER2 and TOP2A and protein expressions of HER2, TOP2A, Ki-67, and p53 in tumor and matched nontumor tissues, as well as their associations with clinicopathological features. Gene aberrations were evaluated by FISH and protein expressions by IHC. Neither HER2 overexpression nor HER2 gene amplification was observed in both tumor tissues and matched nontumor tissues. By contrast, TOP2A overexpression was detected in 72.5% of tumor tissues but not detected in matched nontumor tissues. However, TOP2A gene amplification was not observed in both tumor and matched nontumor tissues. TOP2A overexpression was significantly associated with HCC tumor tissues (P < 0.001), hepatitis B surface antigen (HBsAg) in the serum (P = 0.004), and Ki-67 (P = 0.038) but not with age, tumor size, alpha-fetoprotein, TP53, and copy number of TOP2A gene and chromosome 17 centromere. In conclusion, TOP2A overexpression in HCC was not secondary to gene amplification. In addition, neither HER2 amplification nor overexpression could be used as prognostic and predictive marker in HCC

PseudoCell: Hard Negative Mining as Pseudo Labeling for Deep Learning-Based Centroblast Cell Detection

Patch classification models based on deep learning have been utilized in whole-slide images (WSI) of H&E-stained tissue samples to assist pathologists in grading follicular lymphoma patients. However, these approaches still require pathologists to manually identify centroblast cells and provide refined labels for optimal performance. To address this, we propose PseudoCell, an object detection framework to automate centroblast detection in WSI (source code is available at https://github.com/IoBT-VISTEC/PseudoCell.git). This framework incorporates centroblast labels from pathologists and combines them with pseudo-negative labels obtained from undersampled false-positive predictions using the cell's morphological features. By employing PseudoCell, pathologists' workload can be reduced as it accurately narrows down the areas requiring their attention during examining tissue. Depending on the confidence threshold, PseudoCell can eliminate 58.18-99.35% of non-centroblasts tissue areas on WSI. This study presents a practical centroblast prescreening method that does not require pathologists' refined labels for improvement. Detailed guidance on the practical implementation of PseudoCell is provided in the discussion section