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Objective: Sodium glucose cotransporter 2 inhibitors (SGLT2-is) are antidiabetic drugs that improve glycemic control by limiting urinary glucose reuptake in the proximal tubule. SGLT2-is might suppress atherosclerotic processes and ameliorate the prognosis of patients with diabetes mellitus diagnosed with or at high risk of atherosclerotic cardiovascular disease (ASCVD). In this mini review, we examine the role of SGLT2-is in the development and progression of atherosclerosis throughout its spectrum, from subclinical atherosclerosis to ASCVD.Data Sources—PubMed and Google Scholar were searched for publications related to SGLT2-is and atherosclerosis. All types of articles were considered, including clinical trials, animal studies, in vitro observations, and reviews and meta-analyses. Data were examined according to their impact and clinical relevance.Synopsis of Content—We first review the underlying mechanisms of SGLT2-is on the development and progression of atherosclerosis, including favorable effects on lipid metabolism, reduction of systemic inflammation, and improvement of endothelial function. We then discuss the putative impact of SGLT2-is on the formation, composition, and stability of atherosclerotic plaque. Furthermore, we evaluate the effects of SGLT2-is in subclinical atherosclerosis assessed by carotid intima media thickness and pulse wave velocity. Subsequently, we summarize the effects of SGLT2-is in ASCVD events, including ischemic stroke, angina pectoris, myocardial infarction, revascularization, and peripheral artery disease, as well as major adverse cardiovascular events, cardiovascular mortality, heart failure, and chronic kidney disease. Moreover, we examine factors that could modify the role of SGLT2-is in atherosclerosis, including sex, age, diabetes, glycemic control, ASCVD, and SGLT2-i compounds. Additionally, we propose future directions that can improve our understanding of SGLT2-is and atherosclerosis.

Immunotherapy, including PD-1/PD-L1 agonists, has shown limited efficacy in pancreatic ductal adenocarcinoma (PDAC). We examined the PD-1/PD-L1 expression and immunoarchitectural features by automated morphometric analysis using multiplex immunofluorescence and 118 microsatellite-stable, treatment–naïve, surgically resected PDACs (study cohort). Five microsatellite-instable cases were stained in parallel (MSI cohort). Molecular analysis was additionally performed. An independent PDAC cohort (n = 226) was immunostained for PD-L1 and used as a validation cohort. PD-L1 expression on tumor cells (TC) and/or immune cells (IC) was present in 32% and 30% of the study and validation cohorts, respectively, and assigned into one of four patterns: “adaptive-1” (TC: 0, IC > 1%), “adaptive-2” (TC > 1% to < 25%, IC > 1%), “constitutive” (TC ≥ 25%, IC: 0), and “combined” (TC ≥ 25%, IC > 1%). “Constitutive” tumors were characterized by reduced numbers of all ICs and poor outcome. In contrast, “adaptive-1” tumors exhibited abundant T cells, including high counts of cytotoxic CD3+CD8+ and PD-1+CD3+CD8+ cells, but low counts of PD-L1+CD3+CD8+ cells and associated with the best outcome. “Adaptive-2” tumors displayed higher proportions of PD-L1+CD3+CD8+ T cells and tumor-associated macrophages (CD68+ and CD68+CD206+) compared with “adaptive-1” tumors. In the “combined” pattern, extensive PD-L1 expression on TCs was accompanied by increased numbers of T cells and improved overall survival. ICs were closer to PD-L1− than to PD-L1+ PDAC cells. TP53 and PIK3CA alterations tended to be more frequent in PD-L1+ tumors. The 5 MSI cases were PD-L1−. The distinct PD-1/PD-L1–associated immunoarchitectural patterns underpin the heterogeneity of the immunologic responses and might be used to inform patient outcomes and therapeutic decisions in pancreatic cancer.

ObjectivesTo identify qualitative VASARI (Visually AcceSIble Rembrandt Images) Magnetic Resonance (MR) Imaging features for differentiation of glioblastoma (GBM) and brain metastasis (BM) of different primary tumors.Materials and MethodsT1-weighted pre- and post-contrast, T2-weighted, and T2-weighted, fluid attenuated inversion recovery (FLAIR) MR images of a total of 239 lesions from 109 patients with either GBM or BM (breast cancer, non-small cell (NSCLC) adenocarcinoma, NSCLC squamous cell carcinoma, small-cell lung cancer (SCLC)) were included. A set of adapted, qualitative VASARI MR features describing tumor appearance and location was scored (binary; 1 = presence of feature, 0 = absence of feature). Exploratory data analysis was performed on binary scores using a combination of descriptive statistics (proportions with 95% binomial confidence intervals), unsupervised methods and supervised methods including multivariate feature ranking using either repeated fitting or recursive feature elimination with Support Vector Machines (SVMs).ResultsGBMs were found to involve all lobes of the cerebrum with a fronto-occipital gradient, often affected the corpus callosum (32.4%, 95% CI 19.1–49.2), and showed a strong preference for the right hemisphere (79.4%, 95% CI 63.2–89.7). BMs occurred most frequently in the frontal lobe (35.1%, 95% CI 28.9–41.9) and cerebellum (28.3%, 95% CI 22.6–34.8). The appearance of GBMs was characterized by preference for well-defined non-enhancing tumor margin (100%, 89.8–100), ependymal extension (52.9%, 36.7–68.5) and substantially less enhancing foci than BMs (44.1%, 28.9–60.6 vs. 75.1%, 68.8–80.5). Unsupervised and supervised analyses showed that GBMs are distinctively different from BMs and that this difference is driven by definition of non-enhancing tumor margin, ependymal extension and features describing laterality. Differentiation of histological subtypes of BMs was driven by the presence of well-defined enhancing and non-enhancing tumor margins and localization in the vision center. SVM models with optimal hyperparameters led to weighted F1-score of 0.865 for differentiation of GBMs from BMs and weighted F1-score of 0.326 for differentiation of BM subtypes.ConclusionVASARI MR imaging features related to definition of non-enhancing margin, ependymal extension, and tumor localization may serve as potential imaging biomarkers to differentiate GBMs from BMs.