Strategies to improve atrioventricular synchrony in patients with a Micra AV leadless pacemaker.

The second generation of transcatheter pacing systems, called Micra AV, can provide atrioventricular (AV) synchronous pacing via a new pacing algorithm relying on sensing mechanical atrial contraction. Several novel programming parameters were introduced to enable AV synchronous pacing, including an A3 window and A4 window as well as a conduction mode switch and an activity mode switch. In addition to several automated features, manual programming optimization of some of the novel parameters is key to improving AV synchrony. A solid knowledge of the features and their programming is essential for electrophysiologists implanting or following patients with Micra AV devices. Differences in programming optimization might partially explain the high variability of AV synchrony published in real-world data reports. This article reviews the key programming parameters of Micra AV. Subsequently, optimal programming recommendations for defined patient profiles are presented. Those were established by consensus within an expert panel comprised of 11 European electrophysiologists from high-volume Micra AV centres. The patient profiles were (1) high degree AV block and slow sinus rhythm; (2) high degree AV block and fast sinus rhythm; and (3) intermittent AV block. The panel recommended to evaluate the mechanical atrial activity on transthoracic echocardiography prior to implant. It was also agreed that Auto A3 Threshold and Tracking Check should be turned off in all patients, AV conduction mode switch should be turned off in all patients with high degree AV block, and the lower rate should be programmed to 50 bpm with exceptions based on individual clinical assessment. Future studies will be useful to evaluate the strength of those recommendations to improve the AV synchrony.

A Neurofilament-L reporter cell line for the quantification of early neuronal differentiation: A Bioassay for neurotrophic activities.

Background: Neurotrophic activity constitutes a crucial factor in the recovery from neurological injuries and is impaired in neurodegenerative disorders. Preclinical studies of neurotrophic factors to improve outcome of neurodegenerative diseases have yielded promising results. However, due to the complexity of these therapies, the clinical translation of this approach was so far not successful and more feasible treatments with neurotrophic activity may be promising alternatives. Therefore, highly sensitive and robust assays for compound screening are required. New method: Nerve growth factor is known to induce Neurofilament-L (NF-L) expression in a rat pheochromocytoma cell line (PC12 cells) during early neuronal differentiation. We generated and characterized an enhanced green fluorescent protein (EGFP)-NF-L reporter PC12 cell line for the development of a cell-based assay (designated Neurofilament-L Bioassay) that allows straightforward quantification of early neuronal differentiation based on NF-L expression. Results: Using Cerebrolysin® as a role model for a pharmacological compound that stimulates neurotrophic activity in the central nervous system, the Neurofilament-L Bioassay was proved to be a robust, specific, and reproducible method. Comparison with existing methods: It was already shown that NF-L expression correlates with neurite outgrowth in PC12 cells. Currently, quantification of neurite outgrowth is the most commonly used method to evaluate neuronal differentiation in PC12 cells, an approach that is time-consuming and of high variability. Conclusions: This work describes the development of an EGFP-NF-L reporter PC12 cell-based assay as a robust and reproducible tool for "high throughput" compound screening for neurotrophic activity.

Treatment with Cerebrolysin Prolongs Lifespan in a Mouse Model of Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy.

Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is a rare familial neurological disorder caused by mutations in the NOTCH3 gene and characterized by migraine attacks, depressive episodes, lacunar strokes, dementia, and premature death. Since there is no therapy for CADASIL the authors investigate whether the multi-modal neuropeptide drug Cerebrolysin may improve outcome in a murine CADASIL model. Twelve-month-old NOTCH3R169C mutant mice (n=176) are treated for nine weeks with Cerebrolysin or Vehicle and histopathological and functional outcomes are evaluated within the subsequent ten months. Cerebrolysin treatment improves spatial memory and overall health, reduces epigenetic aging, and prolongs lifespan, however, CADASIL-specific white matter vacuolization is not affected. On the molecular level Cerebrolysin treatment increases expression of Calcitonin Gene-Related Peptide (CGRP) and Silent Information Regulator Two (Sir2)-like protein 6 (SIRT6), decreases expression of Insulin-like Growth Factor 1 (IGF-1), and normalizes the expression of neurovascular laminin. In summary, Cerebrolysin fosters longevity and healthy aging without specifically affecting CADASIL pathology. Hence, Cerebrolysin may serve a therapeutic option for CADASIL and other disorders characterized by accelerated aging.

Polymorphisms in genes of melatonin biosynthesis and signaling support the light-at-night hypothesis for breast cancer.

Light-at-night triggers the decline of pineal gland melatonin biosynthesis and secretion and is an IARC-classified probable breast-cancer risk factor. We applied a large-scale molecular epidemiology approach to shed light on the putative role of melatonin in breast cancer. We investigated associations between breast-cancer risk and polymorphisms at genes of melatonin biosynthesis/signaling using a study population of 44,405 women from the Breast Cancer Association Consortium (22,992 cases, 21,413 population-based controls). Genotype data of 97 candidate single nucleotide polymorphisms (SNPs) at 18 defined gene regions were investigated for breast-cancer risk effects. We calculated adjusted odds ratios (ORs) and 95% confidence intervals (CI) by logistic regression for the main-effect analysis as well as stratified analyses by estrogen- and progesterone-receptor (ER, PR) status. SNP-SNP interactions were analyzed via a two-step procedure based on logic regression. The Bayesian false-discovery probability (BFDP) was used for all analyses to account for multiple testing. Noteworthy associations (BFDP < 0.8) included 10 linked SNPs in tryptophan hydroxylase 2 (TPH2) (e.g. rs1386492: OR = 1.07, 95% CI 1.02-1.12), and a SNP in the mitogen-activated protein kinase 8 (MAPK8) (rs10857561: OR = 1.11, 95% CI 1.04-1.18). The SNP-SNP interaction analysis revealed noteworthy interaction terms with TPH2- and MAPK-related SNPs (e.g. rs1386483R ∧ rs1473473D ∧ rs3729931D: OR = 1.20, 95% CI 1.09-1.32). In line with the light-at-night hypothesis that links shift work with elevated breast-cancer risks our results point to SNPs in TPH2 and MAPK-genes that may impact the intricate network of circadian regulation.

Novel drug transporter substrates identification: An innovative approach based on metabolomic profiling, in silico ligand screening and biological validation.

Solute carrier (SLC) transport proteins are fundamental for the translocation of endogenous compounds and drugs across membranes, thus playing a critical role in disease susceptibility and drug response. Because only a limited number of transporter substrates are currently known, the function of a large number of SLC transporters is elusive. Here, we describe the proof-of-concept of a novel strategy to identify SLC transporter substrates exemplarily for the proton-coupled peptide transporter (PEPT) 2 (SLC15A2) and multidrug and toxin extrusion (MATE) 1 transporter (SLC47A1), which are important renal transporters of drug reabsorption and excretion, respectively. By combining metabolomic profiling of mice with genetically-disrupted transporters, in silico ligand screening and in vitro transport studies for experimental validation, we identified nucleobases and nucleoside-derived anticancer and antiviral agents (flucytosine, cytarabine, gemcitabine, capecitabine) as novel drug substrates of the MATE1 transporter. Our data confirms the successful applicability of this new approach for the identification of transporter substrates in general, which may prove particularly relevant in drug research.

First long-term outcome data for the MicraVR™ transcatheter pacing system: data from the largest prospective German cohort.

AIMS: The MicraVR™ transcatheter pacing system (TPS) has been implemented into clinical routine for several years. The primary recipients are patients in need for VVI pacing due to bradycardia in the setting of atrial fibrillation (AF). Implantation safety and acute success have been proven in controlled studies and registries. So far only few long-term real-life data on TPS exist. We report indication, procedure and outcome data from two high-volume implanting German centers. METHODS: Between 2016 and 2019, 188 (of 303) patients were included. During follow-up (FU), TPS interrogation was performed after 4 weeks and thereafter every 6 months. RESULTS: Indication for TPS implantation in 159/188 (85%) patients was permanent or intermittent AV block III° in the setting of atrial fibrillation. The mean procedure duration was 50 min [35.0-70.0]. The average acute values after system release were: thresholds: 0.5V [0.38-0.74]/0.24ms; R-wave sensing: 10.0mV [8.1-13.5]; impedance: 650 Ohm [550-783]; RV-pacing demand: 16.9% [0.9-75.9]; and battery status: 3.15 V [3.12-3.16]. During FU of 723.4 ± 597.9 days, neither pacemaker failure nor infections were reported. Long-term FU revealed: thresholds: 0.5V [0.38-0.63]/0.24 ms; sensing: 12.3mV [8.9-17.2]; impedance: 570 Ohm [488-633]; RV-pacing demand: 87.1% [29.5-98.6]; and battery status 3.02 V [3.0-3.1]. Forty-three patients died from not-device-related causes. CONCLUSION: This to date largest German long-term dataset for MicraVR™ TPS implantation revealed stable device parameter. Foremost, battery longevity seems to fulfill predicted values despite a significant increase in RV-pacing demand over time and even in patients with consecutive AV-node ablation. Of note, no infections or system failure were observed.

Platelet miRNAs: differential expression in coronary artery disease and associations with course of left ventricular systolic function.

BACKGROUND: MicroRNAs are paramount in post transcriptional gene regulation. We investigated platelet miRNAs in patients with CAD and examined potential associations with course of left ventricular ejection fraction (LVEF%). MATERIALS AND METHODS: In a first cohort, 62 MiRNAs were measured in platelets of 100 patients suffering from CAD. Expression profiles of individuals with chronic coronary syndrome (CCS) and MI were compared (CCS n = 67, MI n = 33). Also, associations between miRNA profiles and change in left ventricular ejection fraction (LVEF%) were investigated. In a second cohort of patients suffering from CCS (n = 10), MI (n = 11) or no CAD (n = 13), we measured miRNA expression in platelets, platelet supernatant and serum. This was carried out before and after in vitro platelet activation with CRP. RESULTS: Platelet miRNAs 103a-3p and 155-5p demonstrated higher expression in patients with CCS then in individuals with MI. Furthermore, multiple miRNAs were significantly higher expressed in matched controls compared to MI patients. 8 miRNAs showed higher expression in patients with improving LVEF% after a 1-year follow-up. In our second cohort, we found higher concentrations of 6 miRNAs in the platelet supernatant of patients with CCS, MI and no CAD after in vitro platelet activation. Most of these miRNAs showed a higher abundance in serum of MI patients as compared to CCS. CONCLUSION: Several miRNAs show higher expression in platelets of CCS compared to MI. After in vitro platelet activation, a release of multiple miRNAs out of the thrombocyte was observed. Furthermore, upregulation of serum miRNAs was found in MI patients when compared to CCS patients and individuals without CAD. Hence, platelets could present a source of upregulated circulating miRNAs in MI and additionally affect course of LVEF%.

A leadless pacemaker in the real-world setting: Patient profile and performance over time.

Background: While prior Micra trials demonstrated a high implant success rate and favorable safety and efficacy results, changes in implant populations and safety over time is not well studied. The objective of this analysis was to report the performance of Micra in European and Middle Eastern patients and compare to the Micra Investigational Device Exemption (IDE) and Micra Post Approval Registry (PAR) studies. Methods: The prospective, single-arm Micra Acute Performance European and Middle Eastern (MAP EMEA) registry was designed to further study the performance of Micra in patients from EMEA. The primary endpoint was to characterize acute (30-day) major complications. Electrical performance was analyzed. The major complication rate through 12 months was compared with the IDE and PAR studies. Results: The MAP EMEA cohort (n = 928 patients) had an implant success rate of 99.9% and were followed for an average of 9.7 ± 6.5 months. Compared to prior studies, MAP EMEA patients were more likely to have undergone dialysis and have a condition which precluded the use of a transvenous pacemaker (p < .001). Within 30 days of implantation, the MAP EMEA cohort had a major complication rate of 2.59%. Mean pacing thresholds were low and stable through 12 months (0.61 ± 0.40 V at 0.24 ms at implant and 12 months). Through 12 months post-implantation, the major complication rate for MAP EMEA was not significantly different from IDE (p = .56) or PAR (p = .79). Conclusion: Despite patient differences over time, the Micra leadless pacemaker was implanted with a high success rate and low complication rate, in-line with prior reports.

Cross-Ancestry Genome-Wide Association Study Defines the Extended CYP2D6 Locus as the Principal Genetic Determinant of Endoxifen Plasma Concentrations.

The therapeutic efficacy of tamoxifen is predominantly mediated by its active metabolites 4-hydroxy-tamoxifen and endoxifen, whose formation is catalyzed by the polymorphic cytochrome P450 2D6 (CYP2D6). Yet, known CYP2D6 polymorphisms only partially determine metabolite concentrations in vivo. We performed the first cross-ancestry genome-wide association study with well-characterized patients of European, Middle-Eastern, and Asian descent (n = 497) to identify genetic factors impacting active and parent metabolite formation. Genome-wide significant variants were functionally evaluated in an independent liver cohort (n = 149) and in silico. Metabolite prediction models were validated in two independent European breast cancer cohorts (n = 287, n = 189). Within a single 1-megabase (Mb) region of chromosome 22q13 encompassing the CYP2D6 gene, 589 variants were significantly associated with tamoxifen metabolite concentrations, particularly endoxifen and metabolic ratio (MR) endoxifen/N-desmethyltamoxifen (minimal P = 5.4E-35 and 2.5E-65, respectively). Previously suggested other loci were not confirmed. Functional analyses revealed 66% of associated, mostly intergenic variants to be significantly correlated with hepatic CYP2D6 activity or expression (ρ = 0.35 to -0.52), and six hotspot regions in the extended 22q13 locus impacting gene regulatory function. Machine learning models based on hotspot variants (n = 12) plus CYP2D6 activity score (AS) increased the explained variability (~ 9%) compared with AS alone, explaining up to 49% (median R2 ) and 72% of the variability in endoxifen and MR endoxifen/N-desmethyltamoxifen, respectively. Our findings suggest that the extended CYP2D6 locus at 22q13 is the principal genetic determinant of endoxifen plasma concentration. Long-distance haplotypes connecting CYP2D6 with adjacent regulatory sites and nongenetic factors may account for the unexplained portion of variability.

Development and Implementation of the Data Science Learning Platform for Research Physician.

Data analysis and their application are the unavoidable factors in the activities analyses in health care. Unfortunately, the acquisition of data from large available medical databases is a complex process and requires deep knowledge of computer science and especially knowledge of tools for data management. According to the European General Data Protection Regulation, the problem becomes much more complex. Recognizing these problems and difficulties, we have developed a Data Science Learning Platform (DSLP) that primarily targets practitioners and researchers but also the computer science students. Using our proposed tool chain together with the developed graphical user interface, data scientists and research physicians will be able to use available medical databases, apply and analyze different anonymization methods, analyze data according to the patient's risk and quickly formulate new studies to target a disease in a complex data model. This article presents a clinical research discovery toolbox that implements and demonstrates tools for data anonymization, patient data visualization, NLP-tools for guideline search and data science learning tools.

From Unit to Dose: A Machine Learning Approach for Precise Prediction of Hemoglobin and Iron Content in Individual Packed Red Blood Cell Units.

Transfusion of packed red blood cells (pRBCs) saves lives, but iron overload limits survival of chronically transfused patients. Quality control methods, which involve entering pRBC units and removing them from the blood supply, reveal that hemoglobin (38.5-79.9 g) and heme iron (133.42-276.89 mg) vary substantially between pRBCs. Yet, neither hemoglobin nor iron content can be quantified for individual clinically used pRBCs leading to rules of thumb for pRBC transfusions. Keeping their integrity, the authors seek to predict hemoglobin/iron content of any given pRBC unit applying eight machine learning models on 6,058 pRBCs. Based on thirteen features routinely collected during blood donation, production and quality control testing, the model with best trade-off between performance and complexity in hemoglobin/iron content prediction is identified. Validation of this model in an independent cohort of 2637 pRBCs confirms an adjusted R2 > 0.9 corresponding to a mean absolute prediction error of ≤1.43 g hemoglobin/4.96 mg iron (associated standard deviation: ≤1.13 g hemoglobin/3.92 mg iron). Such unprecedented precise prediction enables reliable pRBC dosing per pharmaceutically active agent, and monitoring iron uptake in patients and individual iron loss in donors. The model is implemented in a free open source web application to facilitate clinical application.

Clinical and Experimental Evidence for a Strain-Based Classification of Left Bundle Branch Block-Induced Cardiac Remodeling.

BACKGROUND: Septal strain patterns measured by echocardiography reflect the severity of left bundle branch block (LBBB)-induced left ventricular (LV) dysfunction. We investigated whether these LBBB strain stages predicted the response to cardiac resynchronization therapy in an observational study and developed a sheep model of LBBB-induced cardiomyopathy. METHODS: The clinical study enrolled cardiac resynchronization therapy patients who underwent echocardiographic examination with speckle-tracking strain analysis before cardiac resynchronization therapy implant. In an experimental sheep model with pacing-induced dyssynchrony, LV remodeling and strain were assessed at baseline, at 8 and 16 weeks. Septal strain curves were classified into 5 patterns (LBBB-0 to LBBB-4). RESULTS: The clinical study involved 250 patients (age 65 [58; 72] years; 79% men; 89% LBBB) with a median LV ejection fraction of 25 [21; 30]%. Across the stages, cardiac resynchronization therapy resulted in a gradual volumetric response, ranging from no response in LBBB-0 patients (ΔLV end-systolic volume 0 [-12; 15]%) to super-response in LBBB-4 patients (ΔLV end-systolic volume -44 [-64; -18]%) (P<0.001). LBBB-0 patients had a less favorable long-term outcome compared with those in stage LBBB≥1 (log-rank P=0.003). In 13 sheep, acute right ventricular pacing resulted in LBBB-1 (23%) and LBBB-2 (77%) patterns. Over the course of 8-16 weeks, continued pacing resulted in progressive LBBB-induced dysfunction, coincident with a transition to advanced strain patterns (92% LBBB-2 and 8% LBBB-3 at week 8; 75% LBBB-3 and 25% LBBB-4 at week 16) (P=0.023). CONCLUSIONS: The strain-based LBBB classification reflects a pathophysiological continuum of LBBB-induced remodeling over time and is associated with the extent of reverse remodeling in observational cardiac resynchronization therapy-eligible patients.

Dendritic Cells or Macrophages? The Microenvironment of Human Clear Cell Renal Cell Carcinoma Imprints a Mosaic Myeloid Subtype Associated with Patient Survival.

Since their initial description by Elie Metchnikoff, phagocytes have sparked interest in a variety of biologic disciplines. These important cells perform central functions in tissue repair and immune activation as well as tolerance. Myeloid cells can be immunoinhibitory, particularly in the tumor microenvironment, where their presence is generally associated with poor patient prognosis. These cells are highly adaptable and plastic, and can be modulated to perform desired functions such as antitumor activity, if key programming molecules can be identified. Human clear cell renal cell carcinoma (ccRCC) is considered immunogenic; yet checkpoint blockades that target T cell dysfunction have shown limited clinical efficacy, suggesting additional layers of immunoinhibition. We previously described "enriched-in-renal cell carcinoma" (erc) DCs that were often found in tight contact with dysfunctional T cells. Using transcriptional profiling and flow cytometry, we describe here that ercDCs represent a mosaic cell type within the macrophage continuum co-expressing M1 and M2 markers. The polarization state reflects tissue-specific signals that are characteristic of RCC and renal tissue homeostasis. ErcDCs are tissue-resident with increasing prevalence related to tumor grade. Accordingly, a high ercDC score predicted poor patient survival. Within the profile, therapeutic targets (VSIG4, NRP1, GPNMB) were identified with promise to improve immunotherapy.

Untargeted stable isotope-resolved metabolomics to assess the effect of PI3Kß inhibition on metabolic pathway activities in a PTEN null breast cancer cell line.

The combination of high-resolution LC-MS untargeted metabolomics with stable isotope-resolved tracing is a promising approach for the global exploration of metabolic pathway activities. In our established workflow we combine targeted isotopologue feature extraction with the non-targeted X13CMS routine. Metabolites, detected by X13CMS as differentially labeled between two biological conditions are subsequently integrated into the original targeted library. This strategy enables monitoring of changes in known pathways as well as the discovery of hitherto unknown metabolic alterations. Here, we demonstrate this workflow in a PTEN (phosphatase and tensin homolog) null breast cancer cell line (MDA-MB-468) exploring metabolic pathway activities in the absence and presence of the selective PI3Kß inhibitor AZD8186. Cells were fed with [U-13C] glucose and treated for 1, 3, 6, and 24 h with 0.5 µM AZD8186 or vehicle, extracted by an optimized sample preparation protocol and analyzed by LC-QTOF-MS. Untargeted differential tracing of labels revealed 286 isotope-enriched features that were significantly altered between control and treatment conditions, of which 19 features could be attributed to known compounds from targeted pathways. Other 11 features were unambiguously identified based on data-dependent MS/MS spectra and reference substances. Notably, only a minority of the significantly altered features (11 and 16, respectively) were identified when preprocessing of the same data set (treatment vs. control in 24 h unlabeled samples) was performed with tools commonly used for label-free (i.e. w/o isotopic tracer) non-targeted metabolomics experiments (Profinder´s batch recursive feature extraction and XCMS). The structurally identified metabolites were integrated into the existing targeted isotopologue feature extraction workflow to enable natural abundance correction, evaluation of assay performance and assessment of drug-induced changes in pathway activities. Label incorporation was highly reproducible for the majority of isotopologues in technical replicates with a RSD below 10%. Furthermore, inter-day repeatability of a second label experiment showed strong correlation (Pearson R 2 > 0.99) between tracer incorporation on different days. Finally, we could identify prominent pathway activity alterations upon PI3Kß inhibition. Besides pathways in central metabolism, known to be changed our workflow revealed additional pathways, like pyrimidine metabolism or hexosamine pathway. All pathways identified represent key metabolic processes associated with cancer metabolism and therapy.

Modulation of Amyloid-ß and Tau in Alzheimer's Disease Plasma Neuronal-Derived Extracellular Vesicles by Cerebrolysin® and Donepezil.

BACKGROUND: Plasma neuronal-derived extracellular vesicles (NDEV) contain proteins of pathological, diagnostic, and therapeutic relevance. OBJECTIVE: We investigated the associations of six plasma NDEV markers with Alzheimer's disease (AD) severity, cognition and functioning, and changes in these biomarkers after Cerebrolysin®, donepezil, and a combination therapy in AD. METHODS: Plasma NDEV levels of Aß42, total tau, P-T181-tau, P-S393-tau, neurogranin, and REST were determined in: 1) 116 mild to advanced AD patients and in 20 control subjects; 2) 110 AD patients treated with Cerebrolysin®, donepezil, or combination therapy in a randomized clinical trial (RCT). Samples for NDEV determinations were obtained at baseline in the NDEV study and at baseline and study endpoint in the RCT. Cognition and functioning were assessed at the same time points. RESULTS: NDEV levels of Aß42, total tau, P-T181-tau, and P-S393-tau were higher and those of neurogranin and REST were lower in mild-to-moderate AD than in controls (p < 0.05 to p < 0.001). NDEV total tau, neurogranin, and REST increased with AD severity (p < 0.05 to p < 0.001). NDEV Aß42 and P-T181-tau correlated negatively with serum BDNF (p < 0.05), and total-tau levels were associated to plasma TNF-α (p < 0.01) and cognitive impairment (p < 0.05). Combination therapy reduced NDEV Aß42 with respect to monotherapies (p < 0.05); and NDEV total tau, P-T181-tau, and P-S396-tau were decreased in Cerebrolysin-treated patients compared to those on donepezil monotherapy (p < 0.05). CONCLUSION: The present results demonstrate the utility of NDEV determinations of pathologic and synaptic proteins as effective AD biomarkers, as markers of AD severity, and as potential tools for monitoring the effects of anti-AD drugs.

A novel molecular signature identifies mixed subtypes in renal cell carcinoma with poor prognosis and independent response to immunotherapy.

BACKGROUND: Renal cell carcinoma (RCC) is a heterogeneous disease comprising histologically defined subtypes. For therapy selection, precise subtype identification and individualized prognosis are mandatory, but currently limited. Our aim was to refine subtyping and outcome prediction across main subtypes, assuming that a tumor is composed of molecular features present in distinct pathological subtypes. METHODS: Individual RCC samples were modeled as linear combination of the main subtypes (clear cell (ccRCC), papillary (pRCC), chromophobe (chRCC)) using computational gene expression deconvolution. The new molecular subtyping was compared with histological classification of RCC using the Cancer Genome Atlas (TCGA) cohort (n = 864; ccRCC: 512; pRCC: 287; chRCC: 65) as well as 92 independent histopathologically well-characterized RCC. Predicted continuous subtypes were correlated to cancer-specific survival (CSS) in the TCGA cohort and validated in 242 independent RCC. Association with treatment-related progression-free survival (PFS) was studied in the JAVELIN Renal 101 (n = 726) and IMmotion151 trials (n = 823). CSS and PFS were analyzed using the Kaplan-Meier and Cox regression analysis. RESULTS: One hundred seventy-four signature genes enabled reference-free molecular classification of individual RCC. We unambiguously assign tumors to either ccRCC, pRCC, or chRCC and uncover molecularly heterogeneous tumors (e.g., with ccRCC and pRCC features), which are at risk of worse outcome. Assigned proportions of molecular subtype-features significantly correlated with CSS (ccRCC (P = 4.1E - 10), pRCC (P = 6.5E - 10), chRCC (P = 8.6E - 06)) in TCGA. Translation into a numerical RCC-R(isk) score enabled prognosis in TCGA (P = 9.5E - 11). Survival modeling based on the RCC-R score compared to pathological categories was significantly improved (P = 3.6E - 11). The RCC-R score was validated in univariate (P = 3.2E - 05; HR = 3.02, 95% CI: 1.8-5.08) and multivariate analyses including clinicopathological factors (P = 0.018; HR = 2.14, 95% CI: 1.14-4.04). Heterogeneous PD-L1-positive RCC determined by molecular subtyping showed increased PFS with checkpoint inhibition versus sunitinib in the JAVELIN Renal 101 (P = 3.3E - 04; HR = 0.52, 95% CI: 0.36 - 0.75) and IMmotion151 trials (P = 0.047; HR = 0.69, 95% CI: 0.48 - 1). The prediction of PFS significantly benefits from classification into heterogeneous and unambiguous subtypes in both cohorts (P = 0.013 and P = 0.032). CONCLUSION: Switching from categorical to continuous subtype classification across most frequent RCC subtypes enables outcome prediction and fosters personalized treatment strategies.

Hepatic Expression of the Na+-Taurocholate Cotransporting Polypeptide Is Independent from Genetic Variation.

The hepatic Na+-taurocholate cotransporting polypeptide NTCP/SLC10A1 is important for the uptake of bile salts and selected drugs. Its inhibition results in increased systemic bile salt concentrations. NTCP is also the entry receptor for the hepatitis B/D virus. We investigated interindividual hepatic SLC10A1/NTCP expression using various omics technologies. SLC10A1/NTCP mRNA expression/protein abundance was quantified in well-characterized 143 human livers by real-time PCR and LC-MS/MS-based targeted proteomics. Genome-wide SNP arrays and SLC10A1 next-generation sequencing were used for genomic analyses. SLC10A1 DNA methylation was assessed through MALDI-TOF MS. Transcriptomics and untargeted metabolomics (UHPLC-Q-TOF-MS) were correlated to identify NTCP-related metabolic pathways. SLC10A1 mRNA and NTCP protein levels varied 44-fold and 10.4-fold, respectively. Non-genetic factors (e.g., smoking, alcohol consumption) influenced significantly NTCP expression. Genetic variants in SLC10A1 or other genes do not explain expression variability which was validated in livers (n = 50) from The Cancer Genome Atlas. The identified two missense SLC10A1 variants did not impair transport function in transfectants. Specific CpG sites in SLC10A1 as well as single metabolic alterations and pathways (e.g., peroxisomal and bile acid synthesis) were significantly associated with expression. Inter-individual variability of NTCP expression is multifactorial with the contribution of clinical factors, DNA methylation, transcriptional regulation as well as hepatic metabolism, but not genetic variation.

Homophilic Interaction Between Transmembrane-JAM-A and Soluble JAM-A Regulates Thrombo-Inflammation: Implications for Coronary Artery Disease.

Genetic predisposition through F11R-single-nucleotide variation (SNV) influences circulatory soluble junctional adhesion molecule-A (sJAM-A) levels in coronary artery disease (CAD) patients. Homozygous carriers of the minor alleles (F11R-SNVs rs2774276, rs790056) show enhanced levels of thrombo-inflammatory sJAM-A. Both F11R-SNVs and sJAM-A are associated with worse prognosis for recurrent myocardial infarction in CAD patients. Platelet surface-associated JAM-A correlate with platelet activation markers in CAD patients. Activated platelets shed transmembrane-JAM-A, generating proinflammatory sJAM-A and JAM-A-bearing microparticles. Platelet transmembrane-JAM-A and sJAM-A as homophilic interaction partners exaggerate thrombotic and thrombo-inflammatory platelet monocyte interactions. Therapeutic strategies interfering with this homophilic interface may regulate thrombotic and thrombo-inflammatory platelet response in cardiovascular pathologies where circulatory sJAM-A levels are elevated.

Nicotinamide-N-methyltransferase is a promising metabolic drug target for primary and metastatic clear cell renal cell carcinoma.

BACKGROUND: The metabolic enzyme nicotinamide-N-methyltransferase (NNMT) is highly expressed in various cancer entities, suggesting tumour-promoting functions. We systematically investigated NNMT expression and its metabolic interactions in clear cell renal cell carcinoma (ccRCC), a prominent RCC subtype with metabolic alterations, to elucidate its role as a drug target. METHODS: NNMT expression was assessed in primary ccRCC (n = 134), non-tumour tissue and ccRCC-derived metastases (n = 145) by microarray analysis and/or immunohistochemistry. Findings were validated in The Cancer Genome Atlas (kidney renal clear cell carcinoma [KIRC], n = 452) and by single-cell analysis. Expression was correlated with clinicopathological data and survival. Metabolic alterations in NNMT-depleted cells were assessed by nontargeted/targeted metabolomics and extracellular flux analysis. The NNMT inhibitor (NNMTi) alone and in combination with the inhibitor 2-deoxy-D-glucose for glycolysis and BPTES (bis-2-(5-phenylacetamido-1,3,4-thiadiazol-2-yl)ethyl-sulfide) for glutamine metabolism was investigated in RCC cell lines (786-O, A498) and in two 2D ccRCC-derived primary cultures and three 3D ccRCC air-liquid interface models. RESULTS: NNMT protein was overexpressed in primary ccRCC (p = 1.32 × 10-16 ) and ccRCC-derived metastases (p = 3.92 × 10-20 ), irrespective of metastatic location, versus non-tumour tissue. Single-cell data showed predominant NNMT expression in ccRCC and not in the tumour microenvironment. High NNMT expression in primary ccRCC correlated with worse survival in independent cohorts (primary RCC-hazard ratio [HR] = 4.3, 95% confidence interval [CI]: 1.5-12.4; KIRC-HR = 3.3, 95% CI: 2.0-5.4). NNMT depletion leads to intracellular glutamine accumulation, with negative effects on mitochondrial function and cell survival, while not affecting glycolysis or glutathione metabolism. At the gene level, NNMT-depleted cells upregulate glycolysis, oxidative phosphorylation and apoptosis pathways. NNMTi alone or in combination with 2-deoxy-D-glucose and BPTES resulted in inhibition of cell viability in ccRCC cell lines and primary tumour and metastasis-derived models. In two out of three patient-derived ccRCC air-liquid interface models, NNMTi treatment induced cytotoxicity. CONCLUSIONS: Since efficient glutamine utilisation, which is essential for ccRCC tumours, depends on NNMT, small-molecule NNMT inhibitors provide a novel therapeutic strategy for ccRCC and act as sensitizers for combination therapies.

Toward a digital decision- and workflow-support system for initiation and control of long-term non-invasive ventilation in stable hypercapnic COPD patients.

Introduction: Due to an increasing demand for the initiation and control of non-invasive ventilation (NIV), digital algorithms are suggested to support therapeutic decisions and workflows in an ambulatory setting. The DIGIVENT project established and implemented such algorithms for patients with chronic hypercapnic respiratory failure due to chronic obstructive pulmonary disease (COPD) by a predefined process. Methods: Based on long-term clinical experience and guideline recommendations as provided by the German Respiratory Society, detailed graphical descriptions of how to perform NIV in stable COPD patients were created. Subsequently, these clinical workflows were implemented in the Business Process Model and Notation (BPMN) as one tool to formalize these workflows serving as input for an executable digital implementation. Results: We succeeded in creating an executable digital implementation that reflects clinical decision-making and workflows in digital algorithms. Furthermore, we built a user-friendly graphical interface that allows easy interaction with the DIGIVENT support algorithms. Conclusion: The DIGIVENT project established digital treatment algorithms and implemented a decision- and workflow-support system for NIV whose validation in a clinical cohort is planned.