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.

An AI-assisted tool for efficient prostate cancer diagnosis in low-grade and low-volume cases.

Pathologists diagnose prostate cancer by core needle biopsy. In low-grade and low-volume cases, they look for a few malignant glands out of hundreds within a core. They may miss a few malignant glands, resulting in repeat biopsies or missed therapeutic opportunities. This study developed a multi-resolution deep-learning pipeline to assist pathologists in detecting malignant glands in core needle biopsies of low-grade and low-volume cases. Analyzing a gland at multiple resolutions, our model exploited morphology and neighborhood information, which were crucial in prostate gland classification. We developed and tested our pipeline on the slides of a local cohort of 99 patients in Singapore. Besides, we made the images publicly available, becoming the first digital histopathology dataset of patients of Asian ancestry with prostatic carcinoma. Our multi-resolution classification model achieved an area under the receiver operating characteristic curve (AUROC) value of 0.992 (95% confidence interval [CI]: 0.985-0.997) in the external validation study, showing the generalizability of our multi-resolution approach.

A bio-functional polymer that prevents retinal scarring through modulation of NRF2 signalling pathway.

One common cause of vision loss after retinal detachment surgery is the formation of proliferative and contractile fibrocellular membranes. This aberrant wound healing process is mediated by epithelial-mesenchymal transition (EMT) and hyper-proliferation of retinal pigment epithelial (RPE) cells. Current treatment relies primarily on surgical removal of these membranes. Here, we demonstrate that a bio-functional polymer by itself is able to prevent retinal scarring in an experimental rabbit model of proliferative vitreoretinopathy. This is mediated primarily via clathrin-dependent internalisation of polymeric micelles, downstream suppression of canonical EMT transcription factors, reduction of RPE cell hyper-proliferation and migration. Nuclear factor erythroid 2-related factor 2 signalling pathway was identified in a genome-wide transcriptomic profiling as a key sensor and effector. This study highlights the potential of using synthetic bio-functional polymer to modulate RPE cellular behaviour and offers a potential therapy for retinal scarring prevention.

Tumor-adjacent tissue co-expression profile analysis reveals pro-oncogenic ribosomal gene signature for prognosis of resectable hepatocellular carcinoma.

Currently, molecular markers are not used when determining the prognosis and treatment strategy for patients with hepatocellular carcinoma (HCC). In the present study, we proposed that the identification of common pro-oncogenic pathways in primary tumors (PT) and adjacent non-malignant tissues (AT) typically used to predict HCC patient risks may result in HCC biomarker discovery. We examined the genome-wide mRNA expression profiles of paired PT and AT samples from 321 HCC patients. The workflow integrated differentially expressed gene selection, gene ontology enrichment, computational classification, survival predictions, image analysis and experimental validation methods. We developed a 24-ribosomal gene-based HCC classifier (RGC), which is prognostically significant in both PT and AT. The RGC gene overexpression in PT was associated with a poor prognosis in the training (hazard ratio = 8.2, P = 9.4 × 10-6 ) and cross-cohort validation (hazard ratio = 2.63, P = 0.004) datasets. The multivariate survival analysis demonstrated the significant and independent prognostic value of the RGC. The RGC displayed a significant prognostic value in AT of the training (hazard ratio = 5.0, P = 0.03) and cross-validation (hazard ratio = 1.9, P = 0.03) HCC groups, confirming the accuracy and robustness of the RGC. Our experimental and bioinformatics analyses suggested a key role for c-MYC in the pro-oncogenic pattern of ribosomal biogenesis co-regulation in PT and AT. Microarray, quantitative RT-PCR and quantitative immunohistochemical studies of the PT showed that DKK1 in PT is the perspective biomarker for poor HCC outcomes. The common co-transcriptional pattern of ribosome biogenesis genes in PT and AT from HCC patients suggests a new scalable prognostic system, as supported by the model of tumor-like metabolic redirection/assimilation in non-malignant AT. The RGC, comprising 24 ribosomal genes, is introduced as a robust and reproducible prognostic model for stratifying HCC patient risks. The adjacent non-malignant liver tissue alone, or in combination with HCC tissue biopsy, could be an important target for developing predictive and monitoring strategies, as well as evidence-based therapeutic interventions, that aim to reduce the risk of post-surgery relapse in HCC patients.

Automated Renal Cancer Grading Using Nuclear Pleomorphic Patterns.

PURPOSE: Nuclear pleomorphic patterns are essential for Fuhrman grading of clear cell renal cell carcinoma (ccRCC). Manual observation of renal histopathologic slides may lead to subjective and inconsistent assessment between pathologists. An automated, image-based system that classifies ccRCC slides by quantifying nuclear pleomorphic patterns in an objective and consistent interpretable fashion can aid pathologists in histopathologic assessment. METHODS: In the current study, histopathologic tissue slides of 59 patients with ccRCC who underwent surgery at Singapore General Hospital were assembled retrospectively. An automated image classification pipeline detects and analyzes prominent nucleoli in ccRCC images to classify them as either low (Fuhrman grade 1 and 2) or high (Fuhrman grade 3 and 4). The pipeline uses machine learning and image pixel intensity-based feature extraction techniques for nuclear analysis. We trained classification systems that concurrently analyze different permutations of multiple prominent nucleoli image patches. RESULTS: Given the parameters for feature combination and extraction, we present experimental results across various configurations for the classification of a given ccRCC histopathologic image. We also demonstrate that the image score used by the pipeline, termed fraction value, is correlated ( R = 0.59) with an existing multigene assay-based scoring system that has previously been demonstrated to be a strong indicator of prognosis in patients with ccRCC. CONCLUSION: The current method provides an objective and fully automated way by which to process pathologic slides. The correlation study with a multigene assay-based scoring system also allows us to provide quantitative interpretation for already established nuclear pleomorphic patterns in ccRCC. This method can be extended to other cancers whose corresponding grading systems use nuclear pattern information.

Gland segmentation in prostate histopathological images.

Glandular structural features are important for the tumor pathologist in the assessment of cancer malignancy of prostate tissue slides. The varying shapes and sizes of glands combined with the tedious manual observation task can result in inaccurate assessment. There are also discrepancies and low-level agreement among pathologists, especially in cases of Gleason pattern 3 and pattern 4 prostate adenocarcinoma. An automated gland segmentation system can highlight various glandular shapes and structures for further analysis by the pathologist. These objective highlighted patterns can help reduce the assessment variability. We propose an automated gland segmentation system. Forty-three hematoxylin and eosin-stained images were acquired from prostate cancer tissue slides and were manually annotated for gland, lumen, periacinar retraction clefting, and stroma regions. Our automated gland segmentation system was trained using these manual annotations. It identifies these regions using a combination of pixel and object-level classifiers by incorporating local and spatial information for consolidating pixel-level classification results into object-level segmentation. Experimental results show that our method outperforms various texture and gland structure-based gland segmentation algorithms in the literature. Our method has good performance and can be a promising tool to help decrease interobserver variability among pathologists.

Automated image based prominent nucleoli detection.

INTRODUCTION: Nucleolar changes in cancer cells are one of the cytologic features important to the tumor pathologist in cancer assessments of tissue biopsies. However, inter-observer variability and the manual approach to this work hamper the accuracy of the assessment by pathologists. In this paper, we propose a computational method for prominent nucleoli pattern detection. MATERIALS AND METHODS: Thirty-five hematoxylin and eosin stained images were acquired from prostate cancer, breast cancer, renal clear cell cancer and renal papillary cell cancer tissues. Prostate cancer images were used for the development of a computer-based automated prominent nucleoli pattern detector built on a cascade farm. An ensemble of approximately 1000 cascades was constructed by permuting different combinations of classifiers such as support vector machines, eXclusive component analysis, boosting, and logistic regression. The output of cascades was then combined using the RankBoost algorithm. The output of our prominent nucleoli pattern detector is a ranked set of detected image patches of patterns of prominent nucleoli. RESULTS: The mean number of detected prominent nucleoli patterns in the top 100 ranked detected objects was 58 in the prostate cancer dataset, 68 in the breast cancer dataset, 86 in the renal clear cell cancer dataset, and 76 in the renal papillary cell cancer dataset. The proposed cascade farm performs twice as good as the use of a single cascade proposed in the seminal paper by Viola and Jones. For comparison, a naive algorithm that randomly chooses a pixel as a nucleoli pattern would detect five correct patterns in the first 100 ranked objects. CONCLUSIONS: Detection of sparse nucleoli patterns in a large background of highly variable tissue patterns is a difficult challenge our method has overcome. This study developed an accurate prominent nucleoli pattern detector with the potential to be used in the clinical settings.

An atypical presentation of obsessive compulsive disorder with difficulty in hearing.

Obsessive compulsive disorder (OCD) is a common psychiatric disorder which is easily recognized. However, sometimes patients of OCD present in such an atypical or bizarre way that their problem comes to notice as being a psychiatric disorder after multiple consultations in different specialties. We are reporting a case of a man who had first sought opinion in the Department of Ear, Nose and Throat (ENT) for hearing impairment. He was then referred to a neurologist and a general physician for evaluation of neurological cause of his symptom. As no pathology related to ENT or neurology could be detected, he was referred to the Department of Psychiatry. The patient's chief complaints were difficulty in hearing and inability to understand at once. He could be diagnosed as a case of OCD after meticulous evaluation and studying his response to treatment. There was significant improvement in all the presenting symptoms over a period of 6 weeks on 60 mg of fluoxetine.