High-Affinity-Mediated Viral Entry Triggers Innate Affinity Escape Resulting in Type I IFN Resistance and Impaired T Cell Immunity.

Increased receptor binding affinity may allow viruses to escape from Ab-mediated inhibition. However, how high-affinity receptor binding affects innate immune escape and T cell function is poorly understood. In this study, we used the lymphocytic choriomeningitis virus (LCMV) murine infection model system to create a mutated LCMV exhibiting higher affinity for the entry receptor α-dystroglycan (LCMV-GPH155Y). We show that high-affinity receptor binding results in increased viral entry, which is associated with type I IFN (IFN-I) resistance, whereas initial innate immune activation was not impaired during high-affinity virus infection in mice. Consequently, IFN-I resistance led to defective antiviral T cell immunity, reduced type II IFN, and prolonged viral replication in this murine model system. Taken together, we show that high-affinity receptor binding of viruses can trigger innate affinity escape including resistance to IFN-I resulting in prolonged viral replication.

SWI/SNF COMPLEX-DEFICIENT UNDIFFERENTIATED CARCINOMA OF THE PANCREAS:CLINICOPATHOLOGIC AND GENOMIC ANALYSIS.

Inactivating alterations in the SWItch/Sucrose Non-Fermentable (SWI/SNF) Chromatin Remodeling Complex subunits have been described in multiple tumor types. Recent studies focused on SMARC subunits of this complex to understand their relationship with tumor characteristics and therapeutic opportunities. To date, pancreatic cancer with these alterations has not been well-studied, although isolated cases of undifferentiated carcinomas have been reported. Herein, we screened 59 pancreatic undifferentiated carcinomas for alterations in SWI/SNF complex-related [SMARCB1 (BAF47/INI1), SMARCA4 (BRG1), SMARCA2 (BRM)] proteins and/or genes using immunohistochemistry (IHC) and/or next-generation sequencing (NGS). Cases with alterations in SWI/SNF complex-related proteins/genes were compared to cases without alterations, as well as to 96 conventional pancreatic ductal adenocarcinomas (PDAC). In all tumor groups, MMR and PD-L1 protein expression were also evaluated. Thirty of 59 (51%) undifferentiated carcinomas had a loss of SWI/SNF complex-related protein expression or gene alteration. Twenty-seven of 30 (90%) SWI/SNF-deficient undifferentiated carcinomas had rhabdoid morphology [vs. 9/29 (31%) SWI/SNF-retained undifferentiated carcinomas; p < 0.001] and all expressed cytokeratin, at least focally. Immunohistochemically, SMARCB1 protein expression was absent in 16/30 (53%) cases, SMARCA2 in 4/30 (13%), and SMARCA4 in 4/30 (13%); both SMARCB1 and SMARCA2 protein expressions were absent in 1/30 (3%). Five of 8 (62.5%) SWI/SNF-deficient undifferentiated carcinomas that displayed loss of SMARCB1 protein expression by IHC were found to have corresponding SMARCB1 deletions by NGS. Analysis of canonical driver mutations for PDAC in these cases showed KRAS (2/5) and TP53 (2/5) abnormalities. Median CPS for PD-L1 (E1L3N) was significantly higher in the undifferentiated carcinomas with/without SWI/SNF deficiency compared to the conventional PDACs (p < 0.001). SWI/SNF-deficient undifferentiated carcinomas were larger (p < 0.001) and occurred in younger patients (p < 0.001). Patients with SWI/SNF-deficient undifferentiated carcinoma had worse overall survival compared to patients with SWI/SNF-retained undifferentiated carcinoma (p = 0.004) and PDAC (p < 0.001). Our findings demonstrate that SWI/SNF-deficient pancreatic undifferentiated carcinomas are frequently characterized by rhabdoid morphology, exhibit highly aggressive behavior, and have a negative prognostic impact. The ones with SMARCB1 deletions appear to be frequently KRAS wild-type. Innovative developmental therapeutic strategies targeting this genomic basis of the SWI/SNF complex and the therapeutic implications of EZH2 inhibition (NCT03213665), SMARCA2 degrader (NCT05639751), or immunotherapy are currently under investigation.

[Histopathologic diagnosis of solid and cystic pancreatic lesions with a focus on ductal adenocarcinoma : A vademecum for daily practice]. / Histopathologische Diagnostik von soliden und zystischen Pankreasläsionen mit Hauptaugenmerk auf dem duktalen Adenokarzinom : Ein Vademecum für den Alltag.

Pancreas pathology is constantly evolving and can present various challenges for pathologists. This paper is focused on providing helpful hints for daily routine diagnostics. During histopathological analysis of pancreas biopsies, pancreatic ductal adenocarcinoma must be distinguished not only from other solid neoplasms, but especially from its mimicker, autoimmune pancreatitis. This can be achieved by a systematic workup following clear diagnostic criteria. When analyzing samples from cystic pancreatic lesions, mucin-producing neoplasms must be detected due to their role as pancreatic cancer precursors; molecular analyses can help considerably with their detection and distinction. During frozen section examination, evaluation of the pancreatic neck margin and analysis of unclear lesions of the liver are two important tasks, which are explained further in this article. A special challenge is the evaluation of neoadjuvant treated pancreatic cancer, which requires a detailed macroscopic and microscopic workup. Finally, current advances in precision oncology and emerging approaches for pancreatic cancer within this field are discussed. With the advancement of technical possibilities and their increasingly broad implementation, the classification systems in pancreatic pathology will continue to gain in complexity, but also in accuracy.

Artificial Intelligence-based Segmentation of Residual Pancreatic Cancer in Resection Specimens Following Neoadjuvant Treatment (ISGPP-2): International Improvement and Validation Study.

Neoadjuvant therapy (NAT) has become routine in patients with borderline resectable pancreatic cancer. Pathologists examine pancreatic cancer resection specimens to evaluate the effect of NAT. However, an automated scoring system to objectively quantify residual pancreatic cancer (RPC) is currently lacking. Herein, we developed and validated the first automated segmentation model using artificial intelligence techniques to objectively quantify RPC. Digitized histopathological tissue slides were included from resected pancreatic cancer specimens from 14 centers in 7 countries in Europe, North America, Australia, and Asia. Four different scanner types were used: Philips (56%), Hamamatsu (27%), 3DHistech (10%), and Leica (7%). Regions of interest were annotated and classified as cancer, non-neoplastic pancreatic ducts, and others. A U-Net model was trained to detect RPC. Validation consisted of by-scanner internal-external cross-validation. Overall, 528 unique hematoxylin and eosin (H & E) slides from 528 patients were included. In the individual Philips, Hamamatsu, 3DHistech, and Leica scanner cross-validations, mean F1 scores of 0.81 (95% CI, 0.77-0.84), 0.80 (0.78-0.83), 0.76 (0.65-0.78), and 0.71 (0.65-0.78) were achieved, respectively. In the meta-analysis of the cross-validations, the mean F1 score was 0.78 (0.71-0.84). A final model was trained on the entire data set. This ISGPP model is the first segmentation model using artificial intelligence techniques to objectively quantify RPC following NAT. The internally-externally cross-validated model in this study demonstrated robust performance in detecting RPC in specimens. The ISGPP model, now made publically available, enables automated RPC segmentation and forms the basis for objective NAT response evaluation in pancreatic cancer.