MitoTempo treatment as an approach to cure persistent viral infections?

Chronic viral infections are characterized by exhausted virus-specific T cells. Exhaustion is associated with mitochondrial dysfunction, revealing a possible target for treatment. Targeting these metabolic processes may interfere with the exhaustion process of immune cells during infection. It has been shown that the mitochondria-targeted antioxidant MitoTempo could restore hepatitis-B-virus-specific T cells in vitro. Thus, we investigated MitoTempo as a treatment option using the chronic lymphocytic choriomeningitis virus (LCMVcl13) mouse model. MitoTempo treatment of chronically LCMVcl13 infected mice resulted in a transient reduction of LCMV titer. However, no obvious restoration of functional LCMV-specific T cells was observed, beside subtle changes in phenotype of GP33- and NP205-specific T cells. However, these changes did not translate into significantly more functional responses. Our study showed a transient antiviral effect of MitoTempo, but no profound effect on exhausted T cell responses, although further studies are needed to further elucidate the mechanism and use of MitoTempo.

Next-generation lung cancer pathology: Development and validation of diagnostic and prognostic algorithms.

Non-small cell lung cancer (NSCLC) is one of the most common malignant tumors. In this study, we develop a clinically useful computational pathology platform for NSCLC that can be a foundation for multiple downstream applications and provide immediate value for patient care optimization and individualization. We train the primary multi-class tissue segmentation algorithm on a substantial, high-quality, manually annotated dataset of whole-slide images with lung adenocarcinoma and squamous cell carcinomas. We investigate two downstream applications. NSCLC subtyping algorithm is trained and validated using a large, multi-institutional (n = 6), multi-scanner (n = 5), international cohort of NSCLC cases (slides/patients 4,097/1,527). Moreover, we develop four AI-derived, fully explainable, quantitative, prognostic parameters (based on tertiary lymphoid structure and necrosis assessment) and validate them for different clinical endpoints. The computational platform enables the high-precision, quantitative analysis of H&E-stained slides. The developed prognostic parameters facilitate robust and independent risk stratification of patients with NSCLC.

Enhancing Prostate Cancer Diagnosis: Artificial Intelligence-Driven Virtual Biopsy for Optimal Magnetic Resonance Imaging-Targeted Biopsy Approach and Gleason Grading Strategy.

An optimal approach to magnetic resonance imaging fusion targeted prostate biopsy (PBx) remains unclear (number of cores, intercore distance, Gleason grading [GG] principle). The aim of this study was to develop a precise pixel-wise segmentation diagnostic artificial intelligence (AI) algorithm for tumor detection and GG as well as an algorithm for virtual prostate biopsy that are used together to systematically investigate and find an optimal approach to targeted PBx. Pixel-wise AI algorithms for tumor detection and GG were developed using a high-quality, manually annotated data set (slides n = 442) after fast-track annotation transfer into segmentation style. To this end, a virtual biopsy algorithm was developed that can perform random biopsies from tumor regions in whole-mount whole-slide images with predefined parameters. A cohort of 115 radical prostatectomy (RP) patient cases with clinically significant, magnetic resonance imaging-visible tumors (n = 121) was used for systematic studies of the optimal biopsy approach. Three expert genitourinary (GU) pathologists (Y.T., A.P., A.Q.) participated in the validation. The tumor detection algorithm (aware version sensitivity/specificity 0.99/0.90, balanced version 0.97/0.97) and GG algorithm (quadratic kappa range vs pathologists 0.77-0.78) perform on par with expert GU pathologists. In total, 65,340 virtual biopsies were performed to study different biopsy approaches with the following results: (1) 4 biopsy cores is the optimal number for a targeted PBx, (2) cumulative GG strategy is superior to using maximal Gleason score for single cores, (3) controlling for minimal intercore distance does not improve the predictive accuracy for the RP Gleason score, (4) using tertiary Gleason pattern principle (for AI tool) in cumulative GG strategy might allow better predictions of final RP Gleason score. The AI algorithm (based on cumulative GG strategy) predicted the RP Gleason score of the tumor better than 2 of the 3 expert GU pathologists. In this study, using an original approach of virtual prostate biopsy on the real cohort of patient cases, we find the optimal approach to the biopsy procedure and the subsequent GG of a targeted PBx. We publicly release 2 large data sets with associated expert pathologists' GG and our virtual biopsy algorithm.

CXCR6+CD69+ CD8+ T cells in ascites are associated with disease severity in patients with cirrhosis.

Background & Aims: Patients with advanced cirrhosis often develop hepatic decompensation, which is accompanied by systemic inflammation and may eventually lead to acute-on-chronic liver failure. One important cause of systemic hyperinflammation is a dysregulated overshooting immune response in ascites in the abdominal cavity. In this study, we analyzed the role of CD8+ T cells in the ascites immune compartment. Methods: Peripheral blood and ascites fluid were collected from 50 patients with decompensated cirrhosis. Phenotype and functional responses of CD8+ T cells were analyzed, and obtained data were compared with each other as well as with healthy controls and patients with compensated cirrhosis. Results: High-dimensional flow cytometry revealed that CD8+ T cells are abundant in the ascites of patients with cirrhosis and exhibit a chronically activated bystander phenotype with innate-like functions. Indeed, we identified distinct CXCR6+CD69+ clusters of late effector memory CD8+ T cells that were rarely found in blood and correlated with clinical parameters of disease severity. Moreover, this CD8+ T-cell population was hyperresponsive to innate cytokines and exhibited cytokine-mediated bystander activation. Interestingly, the Janus kinase (JAK) inhibitor tofacitinib was able to effectively block bystander-activated CXCR6+CD69+ CD8+ T cells and significantly suppress effector molecule production. Conclusions: The results indicate that CXCR6+CD69+ CD8+ T cells in ascites are associated with disease severity and may contribute to inflammation in patients with decompensated cirrhosis, suggesting that targeted inhibition of this immune cell subset may be a viable therapeutic option. Impact and Implications: Patients with advanced cirrhosis often develop hepatic decompensation, which is accompanied by systemic inflammation and eventually leads to acute-on-chronic liver failure. One important cause of systemic hyperinflammation is a dysregulated overshooting immune response in ascites in the abdominal cavity. In this study, we demonstrate that CXCR6+CD69+ CD8+ T cells are abundant in the ascites of patients with cirrhosis, exhibit a chronically activated bystander phenotype, and correlate with clinical parameters of disease severity. Moreover, we show that the Janus kinase (JAK) inhibitor tofacitinib can effectively block these bystander-activated CXCR6+CD69+ CD8+ T cells, suggesting that targeted inhibition of this immune cell subset may be a potential therapeutic strategy. Clinical trial number: Prospective registry: INFEKTA (DRKS00010664).

Integrative genomic analyses of European intrahepatic cholangiocarcinoma: Novel ROS1 fusion gene and PBX1 as prognostic marker.

BACKGROUND: Cholangiocarcinoma (CCA) is a fatal cancer of the bile duct with a poor prognosis owing to limited therapeutic options. The incidence of intrahepatic CCA (iCCA) is increasing worldwide, and its molecular basis is emerging. Environmental factors may contribute to regional differences in the mutation spectrum of European patients with iCCA, which are underrepresented in systematic genomic and transcriptomic studies of the disease. METHODS: We describe an integrated whole-exome sequencing and transcriptomic study of 37 iCCAs patients in Germany. RESULTS: We observed as most frequently mutated genes ARID1A (14%), IDH1, BAP1, TP53, KRAS, and ATM in 8% of patients. We identified FGFR2::BICC1 fusions in two tumours, and FGFR2::KCTD1 and TMEM106B::ROS1 as novel fusions with potential therapeutic implications in iCCA and confirmed oncogenic properties of TMEM106B::ROS1 in vitro. Using a data integration framework, we identified PBX1 as a novel central regulatory gene in iCCA. We performed extended screening by targeted sequencing of an additional 40 CCAs. In the joint analysis, IDH1 (13%), BAP1 (10%), TP53 (9%), KRAS (7%), ARID1A (7%), NF1 (5%), and ATM (5%) were the most frequently mutated genes, and we found PBX1 to show copy gain in 20% of the tumours. According to other studies, amplifications of PBX1 tend to occur in European iCCAs in contrast to liver fluke-associated Asian iCCAs. CONCLUSIONS: By analyzing an additional European cohort of iCCA patients, we found that PBX1 protein expression was a marker of poor prognosis. Overall, our findings provide insight into key molecular alterations in iCCA, reveal new targetable fusion genes, and suggest that PBX1 is a novel modulator of this disease.

Proteomic analysis of pleomorphic dermal sarcoma reveals a fibroblastic cell of origin and distinct immune evasion mechanisms.

Pleomorphic dermal sarcomas are infrequent neoplastic skin tumors, manifesting in regions of the skin exposed to ultraviolet radiation. Diagnosing the entity can be challenging and therapeutic options are limited. We analyzed 20 samples of normal healthy skin tissue (SNT), 27 malignant melanomas (MM), 20 cutaneous squamous cell carcinomas (cSCC), and 24 pleomorphic dermal sarcomas (PDS) using mass spectrometry. We explored a potential cell of origin in PDS and validated our findings using publicly available single-cell sequencing data. By correlating tumor purity (TP), inferred by both RNA- and DNA-sequencing, to protein abundance, we found that fibroblasts shared most of the proteins correlating to TP. This observation could also be made using publicly available SNT single cell sequencing data. Moreover, we studied relevant pathways of receptor/ligand (R/L) interactions. Analysis of R/L interactions revealed distinct pathways in cSCC, MM and PDS, with a prominent role of PDGFRB-PDGFD R/L interactions and upregulation of PI3K/AKT signaling pathway. By studying differentially expressed proteins between cSCC and PDS, markers such as MAP1B could differentiate between these two entities. To this end, we studied proteins associated with immunosuppression in PDS, uncovering that immunologically cold PDS cases shared a "negative regulation of interferon-gamma signaling" according to overrepresentation analysis.

Fast-Track Development and Multi-Institutional Clinical Validation of an Artificial Intelligence Algorithm for Detection of Lymph Node Metastasis in Colorectal Cancer.

Lymph node metastasis (LNM) detection can be automated using artificial intelligence (AI)-based diagnostic tools. Only limited studies have addressed this task for colorectal cancer (CRC). This study aimed to develop of a clinical-grade digital pathology tool for LNM detection in CRC using the original fast-track framework. The training cohort included 432 slides from one department. A segmentation algorithm detecting 8 relevant tissue classes was trained. The test cohorts consisted of materials from 5 pathology departments digitized by 4 different scanning systems. A high-quality, large training data set was generated within 7 days and a minimal amount of annotation work using fast-track principles. The AI tool showed very high accuracy for LNM detection in all cohorts, with sensitivity, negative predictive value, and specificity ranges of 0.980 to 1.000, 0.997 to 1.000, and 0.913 to 0.990, correspondingly. Only 5 of 14,460 analyzed test slides with tumor cells over all cohorts were classified as false negative (3/5 representing clusters of tumor cells in lymphatic vessels). A clinical-grade tool was trained in a short time using fast-track development principles and validated using the largest international, multi-institutional, multiscanner cohort of cases to date, showing very high precision for LNM detection in CRC. We are releasing a part of the test data sets to facilitate academic research.

Structure-guided design of a selective inhibitor of the methyltransferase KMT9 with cellular activity.

Inhibition of epigenetic regulators by small molecules is an attractive strategy for cancer treatment. Recently, we characterised the role of lysine methyltransferase 9 (KMT9) in prostate, lung, and colon cancer. Our observation that the enzymatic activity was required for tumour cell proliferation identified KMT9 as a potential therapeutic target. Here, we report the development of a potent and selective KMT9 inhibitor (compound 4, KMI169) with cellular activity through structure-based drug design. KMI169 functions as a bi-substrate inhibitor targeting the SAM and substrate binding pockets of KMT9 and exhibits high potency, selectivity, and cellular target engagement. KMT9 inhibition selectively downregulates target genes involved in cell cycle regulation and impairs proliferation of tumours cells including castration- and enzalutamide-resistant prostate cancer cells. KMI169 represents a valuable tool to probe cellular KMT9 functions and paves the way for the development of clinical candidate inhibitors as therapeutic options to treat malignancies such as therapy-resistant prostate cancer.

An inducible Cd79b mutation confers ibrutinib sensitivity in mouse models of Myd88-driven diffuse large B-cell lymphoma.

ABSTRACT: Diffuse large B-cell lymphoma (DLBCL) is the most common aggressive lymphoma and constitutes a highly heterogenous disease. Recent comprehensive genomic profiling revealed the identity of numerous molecularly defined DLBCL subtypes, including a cluster which is characterized by recurrent aberrations in MYD88, CD79B, and BCL2, as well as various lesions promoting a block in plasma cell differentiation, including PRDM1, TBL1XR1, and SPIB. Here, we generated a series of autochthonous mouse models to mimic this DLBCL cluster and specifically focused on the impact of Cd79b mutations in this setting. We show that canonical Cd79b immunoreceptor tyrosine-based activation motif (ITAM) mutations do not accelerate Myd88- and BCL2-driven lymphomagenesis. Cd79b-mutant murine DLBCL were enriched for IgM surface expression, reminiscent of their human counterparts. Moreover, Cd79b-mutant lymphomas displayed a robust formation of cytoplasmic signaling complexes involving MYD88, CD79B, MALT1, and BTK. These complexes were disrupted upon pharmacological BTK inhibition. The BTK inhibitor-mediated disruption of these signaling complexes translated into a selective ibrutinib sensitivity of lymphomas harboring combined Cd79b and Myd88 mutations. Altogether, this in-depth cross-species comparison provides a framework for the development of molecularly targeted therapeutic intervention strategies in DLBCL.

Risk and protective factors for postpartum depression among Polish women - a prospective study.

This prospective study conducted at a single center in 2022 aims to identify risk and protective factors for postpartum depression (PPD) in Polish women and to assess the impact of pregnancy, delivery, the postpartum period, and psychosocial factors on PPD. After delivery and 4 weeks later, 311 women filled out two questionnaires of our design related to risk factors for PPD. Immune Power Personality Questionnaire, Walsh Family Resilience Questionnaire, and Edinburg Postnatal Depression Scale were also applied. The predictors of PPD identified at two time points included: use of antidepressants, previous depressive episodes, family history of depression, risk of preterm delivery, anxiety about child's health, and breastfeeding and sleep problems. Risk factors for PPD found only after delivery were: suicidal ideation before pregnancy, stressful life events, premature rupture of the membranes, and cesarean section. Inhalation analgesia during labor reduced the PPD frequency. At 4 weeks' postpartum, regular physical activity was also predictive of PPD, while breastfeeding, financial satisfaction, and sufficient sleep duration were protective factors. PPD after delivery was negatively correlated with capacity to confide, hardiness, assertiveness, self-complexity, and communication. PPD at 4 weeks postpartum decreased belief systems, organization patterns, and communication. Two proposed self-designed questionnaires can be useful for effectively screening PPD in the Polish population.

Mouse models of diffuse large B cell lymphoma.

Diffuse large B cell lymphoma (DLBCL) is a genetically highly heterogeneous disease. Yet, to date, the vast majority of patients receive standardized frontline chemo-immune-therapy consisting of an anthracycline backbone. Using these regimens, approximately 65% of patients can be cured, whereas the remaining 35% of patients will face relapsed or refractory disease, which, even in the era of CAR-T cells, is difficult to treat. To systematically tackle this high medical need, it is important to design, generate and deploy suitable in vivo model systems that capture disease biology, heterogeneity and drug response. Recently published, large comprehensive genomic characterization studies, which defined molecular sub-groups of DLBCL, provide an ideal framework for the generation of autochthonous mouse models, as well as an ideal benchmark for cell line-derived or patient-derived mouse models of DLBCL. Here we discuss the current state of the art in the field of mouse modelling of human DLBCL, with a particular focus on disease biology and genetically defined molecular vulnerabilities, as well as potential targeting strategies.

Incidence, risk factors and outcome of acute kidney injury in critically ill COVID-19 patients in Tyrol, Austria: a prospective multicenter registry study.

INTRODUCTION: Acute kidney injury is a frequent complication in critically ill patients with and without COVID-19. The aim of this study was to evaluate the incidence of, and risk factors for, acute kidney injury and its effect on clinical outcomes of critically ill COVID-19 patients in Tyrol, Austria. METHODS: This multicenter prospective registry study included adult patients with a SARS-CoV-2 infection confirmed by polymerase chain reaction, who were treated in one of the 12 dedicated intensive care units during the COVID-19 pandemic from February 2020 until May 2022. RESULTS: In total, 1042 patients were included during the study period. The median age of the overall cohort was 66 years. Of the included patients, 267 (26%) developed acute kidney injury during their intensive care unit stay. In total, 12.3% (n = 126) required renal replacement therapy with a median duration of 9 (IQR 3-18) days. In patients with acute kidney injury the rate of invasive mechanical ventilation was significantly higher with 85% (n = 227) compared to 41% (n = 312) in the no acute kidney injury group (p < 0.001). The most important risk factors for acute kidney injury were invasive mechanical ventilation (OR = 4.19, p < 0.001), vasopressor use (OR = 3.17, p < 0.001) and chronic kidney disease (OR = 2.30, p < 0.001) in a multivariable logistic regression analysis. Hospital and intensive care unit mortality were significantly higher in patients with acute kidney injury compared to patients without acute kidney injury (Hospital mortality: 52.1% vs. 17.2%, p < 0.001, ICU-mortality: 47.2% vs. 14.7%, p < 0.001). CONCLUSION: As in non-COVID-19 patients, acute kidney injury is clearly associated with increased mortality in critically ill COVID-19 patients. Among known risk factors, invasive mechanical ventilation has been identified as an independent and strong predictor of acute kidney injury.

Collagen XVII regulates tumor growth in pancreatic cancer through interaction with the tumor microenvironment.

Expression of the gene for collagen XVII (COL17A1) in tumor tissue is positively or negatively associated with patient survival depending on cancer type. High COL17A1 expression is thus a favorable prognostic marker for breast cancer but unfavorable for pancreatic cancer. This study explored the effects of COL17A1 expression on pancreatic tumor growth and their underlying mechanisms. Analysis of published single-cell RNA-sequencing data for human pancreatic cancer tissue revealed that COL17A1 was expressed predominantly in cancer cells rather than surrounding stromal cells. Forced expression of COL17A1 did not substantially affect the proliferation rate of the mouse pancreatic cancer cell lines KPC and AK4.4 in vitro. However, in mouse homograft tumor models in which KPC or AK4.4 cells were injected into syngeneic C57BL/6 or FVB mice, respectively, COL17A1 expression promoted or suppressed tumor growth, respectively, suggesting that the effect of COL17A1 on tumor growth was influenced by the tumor microenvironment. RNA-sequencing analysis of tumor tissue revealed effects of COL17A1 on gene expression profiles (including the expression of genes related to cell proliferation, the immune response, Wnt signaling, and Hippo signaling) that differed between C57BL/6-KPC and FVB-AK4.4 tumors. Our data thus suggest that COL17A1 promotes or suppresses cancer progression in a manner dependent on the interaction of tumor cells with the tumor microenvironment.

Artificial Intelligence-Based Tool for Tumor Detection and Quantitative Tissue Analysis in Colorectal Specimens.

Digital pathology adoption allows for applying computational algorithms to routine pathology tasks. Our study aimed to develop a clinical-grade artificial intelligence (AI) tool for precise multiclass tissue segmentation in colorectal specimens (resections and biopsies) and clinically validate the tool for tumor detection in biopsy specimens. The training data set included 241 precisely manually annotated whole-slide images (WSIs) from multiple institutions. The algorithm was trained for semantic segmentation of 11 tissue classes with an additional module for biopsy WSI classification. Six case cohorts from 5 pathology departments (4 countries) were used for formal and clinical validation, digitized by 4 different scanning systems. The developed algorithm showed high precision of segmentation of different tissue classes in colorectal specimens with composite multiclass Dice score of up to 0.895 and pixel-wise tumor detection specificity and sensitivity of up to 0.958 and 0.987, respectively. In the clinical validation study on multiple external cohorts, the AI tool reached sensitivity of 1.0 and specificity of up to 0.969 for tumor detection in biopsy WSI. The AI tool analyzes most biopsy cases in less than 1 minute, allowing effective integration into clinical routine. We developed and extensively validated a highly accurate, clinical-grade tool for assistive diagnostic processing of colorectal specimens. This tool allows for quantitative deciphering of colorectal cancer tissue for development of prognostic and predictive biomarkers and personalization of oncologic care. This study is a foundation for a SemiCOL computational challenge. We open-source multiple manually annotated and weakly labeled test data sets, representing a significant contribution to the colorectal cancer computational pathology field.

An international multi-institutional validation study of the algorithm for prostate cancer detection and Gleason grading.

Pathologic examination of prostate biopsies is time consuming due to the large number of slides per case. In this retrospective study, we validate a deep learning-based classifier for prostate cancer (PCA) detection and Gleason grading (AI tool) in biopsy samples. Five external cohorts of patients with multifocal prostate biopsy were analyzed from high-volume pathology institutes. A total of 5922 H&E sections representing 7473 biopsy cores from 423 patient cases (digitized using three scanners) were assessed concerning tumor detection. Two tumor-bearing datasets (core n = 227 and 159) were graded by an international group of pathologists including expert urologic pathologists (n = 11) to validate the Gleason grading classifier. The sensitivity, specificity, and NPV for the detection of tumor-bearing biopsies was in a range of 0.971-1.000, 0.875-0.976, and 0.988-1.000, respectively, across the different test cohorts. In several biopsy slides tumor tissue was correctly detected by the AI tool that was initially missed by pathologists. Most false positive misclassifications represented lesions suspicious for carcinoma or cancer mimickers. The quadratically weighted kappa levels for Gleason grading agreement for single pathologists was 0.62-0.80 (0.77 for AI tool) and 0.64-0.76 (0.72 for AI tool) for the two grading datasets, respectively. In cases where consensus for grading was reached among pathologists, kappa levels for AI tool were 0.903 and 0.855. The PCA detection classifier showed high accuracy for PCA detection in biopsy cases during external validation, independent of the institute and scanner used. High levels of agreement for Gleason grading were indistinguishable between experienced genitourinary pathologists and the AI tool.

Predicting HPV association using deep learning and regular H&E stains allows granular stratification of oropharyngeal cancer patients.

Human Papilloma Virus (HPV)-associated oropharyngeal squamous cell cancer (OPSCC) represents an OPSCC subgroup with an overall good prognosis with a rising incidence in Western countries. Multiple lines of evidence suggest that HPV-associated tumors are not a homogeneous tumor entity, underlining the need for accurate prognostic biomarkers. In this retrospective, multi-institutional study involving 906 patients from four centers and one database, we developed a deep learning algorithm (OPSCCnet), to analyze standard H&E stains for the calculation of a patient-level score associated with prognosis, comparing it to combined HPV-DNA and p16-status. When comparing OPSCCnet to HPV-status, the algorithm showed a good overall performance with a mean area under the receiver operator curve (AUROC) = 0.83 (95% CI = 0.77-0.9) for the test cohort (n = 639), which could be increased to AUROC = 0.88 by filtering cases using a fixed threshold on the variance of the probability of the HPV-positive class - a potential surrogate marker of HPV-heterogeneity. OPSCCnet could be used as a screening tool, outperforming gold standard HPV testing (OPSCCnet: five-year survival rate: 96% [95% CI = 90-100%]; HPV testing: five-year survival rate: 80% [95% CI = 71-90%]). This could be confirmed using a multivariate analysis of a three-tier threshold (OPSCCnet: high HR = 0.15 [95% CI = 0.05-0.44], intermediate HR = 0.58 [95% CI = 0.34-0.98] p = 0.043, Cox proportional hazards model, n = 211; HPV testing: HR = 0.29 [95% CI = 0.15-0.54] p < 0.001, Cox proportional hazards model, n = 211). Collectively, our findings indicate that by analyzing standard gigapixel hematoxylin and eosin (H&E) histological whole-slide images, OPSCCnet demonstrated superior performance over p16/HPV-DNA testing in various clinical scenarios, particularly in accurately stratifying these patients.

[Hyponatremia : Etiology, diagnosis and acute therapy]. / Hyponatriämie : Ätiologie, Diagnostik und Akuttherapie.

Hyponatremia is one of the most common electrolyte disorders in emergency departments and hospitalized patients. Serum sodium concentration is controlled by osmoregulation and volume regulation. Both pathways are regulated via the release of antidiuretic hormone (ADH). Syndrome of inappropriate release of ADH (SIADH) may be caused by neoplasms or pneumonia but may also be triggered by drug use or drug abuse. Excessive fluid intake may also result in a decrease in serum sodium concentration. Rapid alteration in serum sodium concentration leads to cell swelling or cell shrinkage, which primarily causes neurological symptoms. The dynamics of development of hyponatremia and its duration are crucial. In addition to blood testing, a clinical examination and urine analysis are essential in the differential diagnosis of hyponatremia.

Intratumoral Abundance of M2-Macrophages is Associated With Unfavorable Prognosis and Markers of T-Cell Exhaustion in Small Cell Lung Cancer Patients.

Small cell lung cancer (SCLC) accounts for about 10% to 15% of lung cancer cases. Unlike non-SCLC, therapy options for SCLC are limited, reflected by a 5-year survival rate of about 7%. At the same time, the rise of immunotherapeutic approaches in cancer therapy has rationalized to account for inflammatory phenotypes in tumors. However, the composition of the inflammatory microenvironment in human SCLC is poorly understood to date. In our study, we used in-depth image analysis of virtual whole-slide-images of 45 SCLC tumors and evaluated different markers of M2-macrophages (CD163 and CD204) together with global immunologic markers (CD4, CD8, CD68, CD38, FOXP3, and CD20) and characterized their abundance intratumorally using quantitative image analysis, combined with a deep-learning model for tumor segmentation. In addition, independent scoring, blinded to the results of the computational analysis, was performed by an expert pathologist (A.Q.) of both CD163/CD204 and PD-L1. To this end, we evaluated the prognostic relevance of the abundance of these cell types to overall survival. Given a 2-tier threshold of the median of the M2 marker CD163 within the study population, there was a 12-month overall survival rate of 22% (95% CI, 10%-47%) for patients with high CD163 abundance and 41% (95% CI, 25%-68%) for patients with low CD163 counts. Patients with increased CD163 had a median overall survival of 3 months compared to 8.34 months for patients with decreased CD163 counts (P = .039), which could be confirmed by an expert pathologist (A.Q., P = .018). By analyzing cases with increased CD163 cell infiltrates, a trend for higher FOXP3 counts and PD-L1 positive cells, together with increased CD8 T-cell infiltrates, was observed, which could be confirmed using an independent cohort at the transcriptional level. Together, we showed that markers of M2 were associated with unfavorable outcome in our study cohort.

Individual Epitope-Specific CD8+ T Cell Immune Responses Are Shaped Differently during Chronic Viral Infection.

A hallmark in chronic viral infections are exhausted antigen-specific CD8+ T cell responses and the inability of the immune system to eliminate the virus. Currently, there is limited information on the variability of epitope-specific T cell exhaustion within one immune response and the relevance to the T cell receptor (TCR) repertoire. The aim of this study was a comprehensive analysis and comparison of three lymphocytic choriomeningitis virus (LCMV) epitope-specific CD8+ T cell responses (NP396, GP33 and NP205) in a chronic setting with immune intervention, e.g., immune checkpoint inhibitor (ICI) therapy, in regard to the TCR repertoire. These responses, though measured within the same mice, were individual and independent from each other. The massively exhausted NP396-specific CD8+ T cells revealed a significantly reduced TCR repertoire diversity, whereas less-exhausted GP33-specific CD8+ T cell responses were rather unaffected by chronicity in regard to their TCR repertoire diversity. NP205-specific CD8+ T cell responses showed a very special TCR repertoire with a prominent public motif of TCR clonotypes that was present in all NP205-specific responses, which separated this from NP396- and GP33-specific responses. Additionally, we showed that TCR repertoire shifts induced by ICI therapy are heterogeneous on the epitope level, by revealing profound effects in NP396-, less severe and opposed effects in NP205-, and minor effects in GP33-specific responses. Overall, our data revealed individual epitope-specific responses within one viral response that are differently affected by exhaustion and ICI therapy. These individual shapings of epitope-specific T cell responses and their TCR repertoires in an LCMV mouse model indicates important implications for focusing on epitope-specific responses in future evaluations for therapeutic approaches, e.g., for chronic hepatitis virus infections in humans.