Sequence diversity of hepatitis D virus in Mongolia.

Introduction: The Hepatitis Delta Virus (HDV) is a defective, single-stranded RNA virusoid encoding for a single protein, the Hepatitis Delta Antigen (HDAg), which requires the hepatitis B virus (HBV) envelope protein (HBsAg) for its transmission. Currently, hepatitis D is the most aggressive form of viral hepatitis and treatment options are limited. Worldwide 12 million people are chronically infected with HDV being at high risk for progression to cirrhosis and development of liver cancer. Objectives: Although it is well established that Mongolia is the country with the highest prevalence of HDV infections, the information on the molecular epidemiology and factors contributing to HDV sequence diversity are largely unclear. The aim of the study was to characterize the sequence diversity of HDV in rural areas from Mongolia and to determine the extent of HLA class I-associated selection pressure. Patients and methods: From the HepMongolia cohort from rural areas in Mongolia, 451 HBsAg-positive individuals were selected and anti-HDV, HDV-RNA and the sequence of the large HDAg was determined. For all individuals the HLA class I locus was genotyped. Residues under selection pressure in the presence of individual HLA class I types were identified with the recently published analysis tool HAMdetector. Results: Of 431 HBsAg positive patients, 281 were anti-HDV positive (65%), and HDV-RNA could be detected in 207 of 281 (74%) of patients. The complete large HDAg was successfully sequenced from 131 samples. Phylogenetic analysis revealed that all Mongolian HDV isolates belong to genotype 1, however, they separate into several different clusters without clear regional association. In turn, from phylogeny there is strong evidence for recent local transmission events. Importantly, we found multiple residues with strong support for HLA class I-associated selection pressure consistent with a functional CD8+ T cell response directed against HDV. Conclusion: HDV isolates from Mongolia are highly diverse. The molecular epidemiology suggests circulation of multiple subtypes and provides evidence for ongoing recent transmissions.

Dissecting drivers of immune activation in chronic HIV-1 infection.

BACKGROUND: Immune activation is a significant contributor to HIV pathogenesis and disease progression. In virally-suppressed individuals on ART, low-level immune activation has been linked to several non-infectious comorbid diseases. However, studies have not been systematically performed in sub-Saharan Africa and thus the impact of demographics, ART and regional endemic co-infections on immune activation is not known. We therefore comprehensively evaluated in a large multinational African cohort markers for immune activation and its distribution in various settings. METHODS: 2747 specimens from 2240 people living with HIV (PLWH) and 477 without HIV from the observational African Cohort Study (AFRICOS) were analyzed for 13 immune parameters. Samples were collected along with medical history, sociodemographic and comorbidity data at 12 HIV clinics across 5 programs in Uganda, Kenya, Tanzania and Nigeria. Data were analyzed with univariate and multivariate methods such as random forests and principal component analysis. FINDINGS: Immune activation was markedly different between PLWH with detectable viral loads, and individuals without HIV across sites. Among viremic PLWH, we found that all immune parameters were significantly correlated with viral load except for IFN-α. The overall inflammatory profile was distinct between men and women living with HIV, in individuals off ART and with HIV viremia. We observed stronger differences in the immune activation profile with increasing viremia. Using machine learning methods, we found that geographic differences contributed to unique inflammatory profiles. We also found that among PLWH, age and the presence of infectious and/or noninfectious comorbidities showed distinct inflammatory patterns, and biomarkers may be used to predict the presence of some comorbidities. INTERPRETATION: Our findings show that chronic immune activation in HIV-1 infection is influenced by HIV viral load, sex, age, region and ART use. These predictors, as well as associations among some biomarkers and coinfections, influence biomarkers associated with noncommunicable diseases. FUNDING: This work was supported by the President's Emergency Plan for AIDS Relief via a cooperative agreement between the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., and the U.S. Department of Defense [W81XWH-11-2-0174, W81XWH-18-2-0040]. The investigators have adhered to the policies for protection of human subjects as prescribed in AR 70-25. This article was prepared while Michael A. Eller was employed at Henry M. Jackson Foundation for the Advancement of Military Medicine for the U.S. Military HIV Research Program. The views expressed are those of the authors and should not be construed to represent the positions of the US Army or the Department of Defense. The opinions expressed in this article are the author's own, and do not reflect the view of the National Institutes of Health, the U.S. Department of Health and Human Services, or the U.S. government.

HAMdetector: a Bayesian regression model that integrates information to detect HLA-associated mutations.

MOTIVATION: A key process in anti-viral adaptive immunity is that the human leukocyte antigen (HLA) system presents epitopes as major histocompatibility complex I (MHC I) protein-peptide complexes on cell surfaces and in this way alerts CD8+ cytotoxic T-lymphocytes (CTLs). This pathway exerts strong selection pressure on viruses, favoring viral mutants that escape recognition by the HLA/CTL system. Naturally, such immune escape mutations often emerge in highly variable viruses, e.g. HIV or HBV, as HLA-associated mutations (HAMs), specific to the hosts MHC I proteins. The reliable identification of HAMs is not only important for understanding viral genomes and their evolution, but it also impacts the development of broadly effective anti-viral treatments and vaccines against variable viruses. By their very nature, HAMs are amenable to detection by statistical methods in paired sequence/HLA data. However, HLA alleles are very polymorphic in the human host population which makes the available data relatively sparse and noisy. Under these circumstances, one way to optimize HAM detection is to integrate all relevant information in a coherent model. Bayesian inference offers a principled approach to achieve this. RESULTS: We present a new Bayesian regression model for the detection of HAMs that integrates a sparsity-inducing prior, epitope predictions and phylogenetic bias assessment, and that yields easily interpretable quantitative information on HAM candidates. The model predicts experimentally confirmed HAMs as having high posterior probabilities, and it performs well in comparison to state-of-the-art models for several datasets from individuals infected with HBV, HDV and HIV. AVAILABILITY AND IMPLEMENTATION: The source code of this software is available at https://github.com/HAMdetector/Escape.jl under a permissive MIT license. The data underlying this article were provided by permission. Data will be shared on request to the corresponding author with permission of the respective co-authors. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Clinical and molecular characteristics associated with response to therapeutic PD-1/PD-L1 inhibition in advanced Merkel cell carcinoma.

BACKGROUND: Based on its viral-associated or UV-associated carcinogenesis, Merkel cell carcinoma (MCC) is a highly immunogenic skin cancer. Thus, clinically evident MCC occurs either in immuno-compromised patients or based on tumor-intrinsic immune escape mechanisms. This notion may explain that although advanced MCC can be effectively restrained by treatment with PD-1/PD-L1 immune checkpoint inhibitors (ICIs), a considerable percentage of patients does not benefit from ICI therapy. Biomarkers predicting ICI treatment response are currently not available. METHODS: The present multicenter retrospective study investigated clinical and molecular characteristics in 114 patients with unresectable MCC at baseline before treatment with ICI for their association with therapy response (best overall response, BOR). In a subset of 21 patients, pretreatment tumor tissue was analyzed for activation, differentiation and spatial distribution of tumor infiltrating lymphocytes (TIL). RESULTS: Of the 114 patients, n=74 (65%) achieved disease control (BOR=complete response/partial response/stable disease) on ICI. A Bayesian cumulative ordinal regression model revealed absence of immunosuppression and a limited number of tumor-involved organ systems was highly associated with a favorable therapy response. Unimpaired overall performance status, high age, normal serum lactate dehydrogenase and normal serum C reactive protein were moderately associated with disease control. While neither tumor Merkel cell polyomavirus nor tumor PD-L1 status showed a correlation with therapy response, treatment with anti-PD-1 antibodies was associated with a higher probability of disease control than treatment with anti-PD-L1 antibodies. Multiplexed immunohistochemistry demonstrated the predominance of CD8+ effector and central memory T cells (TCM) in close proximity to tumor cells in patients with a favorable therapy response. CONCLUSIONS: Our findings indicate the absence of immunosuppression, a limited number of tumor-affected organs, and a predominance of CD8+ TCM among TIL, as baseline parameters associated with a favorable response to PD-1/PD-L1 ICI therapy of advanced MCC. These factors should be considered when making treatment decisions in MCC patients.

Predominance of Central Memory T Cells with High T-Cell Receptor Repertoire Diversity is Associated with Response to PD-1/PD-L1 Inhibition in Merkel Cell Carcinoma.

PURPOSE: Merkel cell carcinoma (MCC) is an aggressive neuroendocrine skin cancer, which can be effectively controlled by immunotherapy with PD-1/PD-L1 checkpoint inhibitors. However, a significant proportion of patients are characterized by primary therapy resistance. Predictive biomarkers for response to immunotherapy are lacking. EXPERIMENTAL DESIGN: We applied Bayesian inference analyses on 41 patients with MCC testing various clinical and biomolecular characteristics to predict treatment response. Further, we performed a comprehensive analysis of tumor tissue-based immunologic parameters including multiplexed immunofluorescence for T-cell activation and differentiation markers, expression of immune-related genes and T-cell receptor (TCR) repertoire analyses in 18 patients, seven objective responders, and 11 nonresponders. RESULTS: Bayesian inference analyses demonstrated that among currently discussed biomarkers only unimpaired overall performance status and absence of immunosuppression were associated with response to therapy. However, in responders, a predominance of central memory T cells and expression of genes associated with lymphocyte attraction and activation was evident. In addition, TCR repertoire usage of tumor-infiltrating lymphocytes (TILs) demonstrated low T-cell clonality, but high TCR diversity in responding patients. In nonresponders, terminally differentiated effector T cells with a constrained TCR repertoire prevailed. Sequential analyses of tumor tissue obtained during immunotherapy revealed a more pronounced and diverse clonal expansion of TILs in responders indicating an impaired proliferative capacity among TILs of nonresponders upon checkpoint blockade. CONCLUSIONS: Our explorative study identified new tumor tissue-based molecular characteristics associated with response to anti-PD-1/PD-L1 therapy in MCC. These observations warrant further investigations in larger patient cohorts to confirm their potential value as predictive markers.