Single-cell landscape of functionally cured chronic hepatitis B patients reveals activation of innate and altered CD4-CTL-driven adaptive immunity.

BACKGROUND & AIMS: Hepatitis B surface antigen (HBsAg) loss or functional cure (FC) is considered the optimal therapeutic outcome for patients with chronic hepatitis B (CHB). However, the immune-pathological biomarkers and underlying mechanisms of FC remain unclear. In this study we comprehensively interrogate disease-associated cell states identified within intrahepatic tissue and matched PBMCs (peripheral blood mononuclear cells) from patients with CHB or after FC, at the resolution of single cells, to provide novel insights into putative mechanisms underlying FC. METHODS: We combined single-cell transcriptomics (single-cell RNA sequencing) with multiparametric flow cytometry-based immune phenotyping, and multiplexed immunofluorescence to elucidate the immunopathological cell states associated with CHB vs. FC. RESULTS: We found that the intrahepatic environment in CHB and FC displays specific cell identities and molecular signatures that are distinct from those found in matched PBMCs. FC is associated with the emergence of an altered adaptive immune response marked by CD4 cytotoxic T lymphocytes, and an activated innate response represented by liver-resident natural killer cells, specific Kupffer cell subtypes and marginated neutrophils. Surprisingly, we found MHC class II-expressing hepatocytes in patients achieving FC, as well as low but persistent levels of covalently closed circular DNA and pregenomic RNA, which may play an important role in FC. CONCLUSIONS: Our study provides conceptually novel insights into the immuno-pathological control of HBV cure, and opens exciting new avenues for clinical management, biomarker discovery and therapeutic development. We believe that the discoveries from this study, as it relates to the activation of an innate and altered immune response that may facilitate sustained, low-grade inflammation, may have broader implications in the resolution of chronic viral hepatitis. IMPACT AND IMPLICATIONS: This study dissects the immuno-pathological cell states associated with functionally cured chronic hepatitis B (defined by the loss of HBV surface antigen or HBsAg). We identified the sustained presence of very low viral load, accessory antigen-presenting hepatocytes, adaptive-memory-like natural killer cells, and the emergence of helper CD4 T cells with cytotoxic or effector-like signatures associated with functional cure, suggesting previously unsuspected alterations in the adaptive immune response, as well as a key role for the innate immune response in achieving or maintaining functional cure. Overall, the insights generated from this study may provide new avenues for the development of alternative therapies as well as patient surveillance for better clinical management of chronic hepatitis B.

A comprehensive AI model development framework for consistent Gleason grading.

BACKGROUND: Artificial Intelligence(AI)-based solutions for Gleason grading hold promise for pathologists, while image quality inconsistency, continuous data integration needs, and limited generalizability hinder their adoption and scalability. METHODS: We present a comprehensive digital pathology workflow for AI-assisted Gleason grading. It incorporates A!MagQC (image quality control), A!HistoClouds (cloud-based annotation), Pathologist-AI Interaction (PAI) for continuous model improvement, Trained on Akoya-scanned images only, the model utilizes color augmentation and image appearance migration to address scanner variations. We evaluate it on Whole Slide Images (WSI) from another five scanners and conduct validations with pathologists to assess AI efficacy and PAI. RESULTS: Our model achieves an average F1 score of 0.80 on annotations and 0.71 Quadratic Weighted Kappa on WSIs for Akoya-scanned images. Applying our generalization solution increases the average F1 score for Gleason pattern detection from 0.73 to 0.88 on images from other scanners. The model accelerates Gleason scoring time by 43% while maintaining accuracy. Additionally, PAI improve annotation efficiency by 2.5 times and led to further improvements in model performance. CONCLUSIONS: This pipeline represents a notable advancement in AI-assisted Gleason grading for improved consistency, accuracy, and efficiency. Unlike previous methods limited by scanner specificity, our model achieves outstanding performance across diverse scanners. This improvement paves the way for its seamless integration into clinical workflows.

Gleason grading is a well-accepted diagnostic standard to assess the severity of prostate cancer in patients' tissue samples, based on how abnormal the cells in their prostate tumor look under a microscope. This process can be complex and time-consuming. We explore how artificial intelligence (AI) can help pathologists perform Gleason grading more efficiently and consistently. We build an AI-based system which automatically checks image quality, standardizes the appearance of images from different equipment, learns from pathologists' feedback, and constantly improves model performance. Testing shows that our approach achieves consistent results across different equipment and improves efficiency of the grading process. With further testing and implementation in the clinic, our approach could potentially improve prostate cancer diagnosis and management.