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Patients with heart failure (HF) are at high risk for adverse outcomes when they have COVID-19. Reports of COVID-19 vaccine-related cardiac complications may contribute to vaccine hesitancy in patients with HF.To analyze the impact of COVID-19 vaccine status on clinical outcomes in patients with HF, we conducted a retrospective cohort study of the association of COVID-19 vaccination status with hospitalizations, intensive care unit admission and mortality after adjustment for covariates. Inverse probability treatment-weighted models were used to adjust for potential confounding.Of 7094 patients with HF, 645 (9.1%) were partially vaccinated, 2200 (31.0%) were fully vaccinated, 1053 were vaccine-boosted (14.8%), and 3196 remained unvaccinated (45.1%) by January 2022. The mean age was 73.3 ± 14.5 years, and 48% were female. Lower mortality rates were observed in patients who were vaccine-boosted, followed by those who were fully vaccinated; they experienced lower mortality rates (HR 0.33; CI 0.23, 0.48) and 0.36 (CI 0.30, 0.43), respectively, compared to unvaccinated individuals (P0.001) over the mean follow-up time of 276.5 ± 104.9 days, whereas no difference was observed between those who were unvaccinated or only partially vaccinated.COVID-19 vaccination was associated with significant reduction in all-cause hospitalization rates and mortality rates, lending further evidence to support the importance of vaccination implementation in the high-risk population of patients living with HF.

Identifying genomic alterations of cancer proteins has guided the development of targeted therapies, but proteomic analyses are required to validate and reveal new treatment opportunities. Herein, we develop a new algorithm, OPPTI, to discover overexpressed kinase proteins across 10 cancer types using global mass spectrometry proteomics data of 1,071 cases. OPPTI outperforms existing methods by leveraging multiple co-expressed markers to identify targets overexpressed in a subset of tumors. OPPTI-identified overexpression of ERBB2 and EGFR proteins correlates with genomic amplifications, while CDK4/6, PDK1, and MET protein overexpression frequently occur without corresponding DNA- and RNA-level alterations. Analyzing CRISPR screen data, we confirm expression-driven dependencies of multiple currently-druggable and new target kinases whose expressions are validated by immunochemistry. Identified kinases are further associated with up-regulated phosphorylation levels of corresponding signaling pathways. Collectively, our results reveal protein-level aberrations—sometimes not observed by genomics—represent cancer vulnerabilities that may be targeted in precision oncology., Elmas et al. develop an algorithm, OPPTI, to identify overexpressed kinase proteins across 10 cancer types using global mass spectrometry proteomics data from over 1000 cases. They reveal that protein-level aberrations, which are sometimes not observed using genomics, represent cancer vulnerabilities that may be targeted in precision oncology.

Background and objectives AKI treated with dialysis initiation is a common complication of coronavirus disease 2019 (COVID-19) among hospitalized patients. However, dialysis supplies and personnel are often limited. Design, setting, participants, & measurements Using data from adult patients hospitalized with COVID-19 from five hospitals from the Mount Sinai Health System who were admitted between March 10 and December 26, 2020, we developed and validated several models (logistic regression, Least Absolute Shrinkage and Selection Operator (LASSO), random forest, and eXtreme GradientBoosting [XGBoost; with and without imputation]) for predicting treatment with dialysis or death at various time horizons (1, 3, 5, and 7 days) after hospital admission. Patients admitted to the Mount Sinai Hospital were used for internal validation, whereas the other hospitals formed part of the external validation cohort. Features included demographics, comorbidities, and laboratory and vital signs within 12 hours of hospital admission. Results A total of 6093 patients (2442 in training and 3651 in external validation) were included in the final cohort. Of the different modeling approaches used, XGBoost without imputation had the highest area under the receiver operating characteristic (AUROC) curve on internal validation (range of 0.93–0.98) and area under the precision-recall curve (AUPRC; range of 0.78–0.82) for all time points. XGBoost without imputation also had the highest test parameters on external validation (AUROC range of 0.85–0.87, and AUPRC range of 0.27–0.54) across all time windows. XGBoost without imputation outperformed all models with higher precision and recall (mean difference in AUROC of 0.04; mean difference in AUPRC of 0.15). Features of creatinine, BUN, and red cell distribution width were major drivers of the model’s prediction. Conclusions An XGBoost model without imputation for prediction of a composite outcome of either death or dialysis in patients positive for COVID-19 had the best performance, as compared with standard and other machine learning models. Podcast This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2021_07_09_CJN17311120.mp3

Purpose Limited mechanical ventilators (MV) during the Coronavirus disease (COVID-19) pandemic have led to the use of non-invasive ventilation (NIV) in hypoxemic patients, which has not been studied well. We aimed to assess the association of NIV versus MV with mortality and morbidity during respiratory intervention among hypoxemic patients admitted with COVID-19. Methods We performed a retrospective multi-center cohort study across 5 hospitals during March–April 2020. Outcomes included mortality, severe COVID-19-related symptoms, time to discharge, and final oxygen saturation (SpO2) at the conclusion of the respiratory intervention. Multivariable regression of outcomes was conducted in all hypoxemic participants, 4 subgroups, and propensity-matched analysis. Results Of 2381 participants with laboratory-confirmed SARS-CoV-2, 688 were included in the study who were hypoxemic upon initiation of respiratory intervention. During the study period, 299 participants died (43%), 163 were admitted to the ICU (24%), and 121 experienced severe COVID-19-related symptoms (18%). Participants on MV had increased mortality than those on NIV (128/154 [83%] versus 171/534 [32%], OR = 30, 95% CI 16–60) with a mean survival of 6 versus 15 days, respectively. The MV group experienced more severe COVID-19-related symptoms [55/154 (36%) versus 66/534 (12%), OR = 4.3, 95% CI 2.7–6.8], longer time to discharge (mean 17 versus 7.1 days), and lower final SpO2 (92 versus 94%). Across all subgroups and propensity-matched analysis, MV was associated with a greater OR of death than NIV. Conclusions NIV was associated with lower respiratory intervention mortality and morbidity than MV. However, findings may be liable to unmeasured confounding and further study from randomized controlled trials is needed to definitively determine the role of NIV in hypoxemic patients with COVID-19. Supplementary Information The online version contains supplementary material available at 10.1007/s15010-021-01633-6.

Background/Aims: Acute kidney injury (AKI) in critically ill patients is common, and continuous renal replacement therapy (CRRT) is a preferred mode of renal replacement therapy (RRT) in hemodynamically unstable patients. Prediction of clinical outcomes in patients on CRRT is challenging. We utilized several approaches to predict RRT-free survival (RRTFS) in critically ill patients with AKI requiring CRRT. Methods: We used the Medical Information Mart for Intensive Care (MIMIC-III) database to identify patients ≥18 years old with AKI on CRRT, after excluding patients who had ESRD on chronic dialysis, and kidney transplantation. We defined RRTFS as patients who were discharged alive and did not require RRT ≥7 days prior to hospital discharge. We utilized all available biomedical data up to CRRT initiation. We evaluated 7 approaches, including logistic regression (LR), random forest (RF), support vector machine (SVM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), multilayer perceptron (MLP), and MLP with long short-term memory (MLP + LSTM). We evaluated model performance by using area under the receiver operating characteristic (AUROC) curves. Results: Out of 684 patients with AKI on CRRT, 205 (30%) patients had RRTFS. The median age of patients was 63 years and their median Simplified Acute Physiology Score (SAPS) II was 67 (interquartile range 52–84). The MLP + LSTM showed the highest AUROC (95% CI) of 0.70 (0.67–0.73), followed by MLP 0.59 (0.54–0.64), LR 0.57 (0.52–0.62), SVM 0.51 (0.46–0.56), AdaBoost 0.51 (0.46–0.55), RF 0.44 (0.39–0.48), and XGBoost 0.43 (CI 0.38–0.47). Conclusions: A MLP + LSTM model outperformed other approaches for predicting RRTFS. Performance could be further improved by incorporating other data types.

Coronavirus disease 2019 (COVID-19) due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had devastating effects worldwide. Patients with kidney failure on dialysis may have a higher risk of worse outcomes. Reports from China found that these patients with SARS-CoV-2 had fewer

Background and objectives Sepsis-associated AKI is a heterogeneous clinical entity. We aimed to agnostically identify sepsis-associated AKI subphenotypes using deep learning on routinely collected data in electronic health records. Design, setting, participants, & measurements We used the Medical Information Mart for Intensive Care III database, which consists of electronic health record data from intensive care units in a tertiary care hospital in the United States. We included patients ≥18 years with sepsis who developed AKI within 48 hours of intensive care unit admission. We then used deep learning to utilize all available vital signs, laboratory measurements, and comorbidities to identify subphenotypes. Outcomes were mortality 28 days after AKI and dialysis requirement. Results We identified 4001 patients with sepsis-associated AKI. We utilized 2546 combined features for K-means clustering, identifying three subphenotypes. Subphenotype 1 had 1443 patients, and subphenotype 2 had 1898 patients, whereas subphenotype 3 had 660 patients. Subphenotype 1 had the lowest proportion of liver disease and lowest Simplified Acute Physiology Score II scores compared with subphenotypes 2 and 3. The proportions of patients with CKD were similar between subphenotypes 1 and 3 (15%) but highest in subphenotype 2 (21%). Subphenotype 1 had lower median bilirubin levels, aspartate aminotransferase, and alanine aminotransferase compared with subphenotypes 2 and 3. Patients in subphenotype 1 also had lower median lactate, lactate dehydrogenase, and white blood cell count than patients in subphenotypes 2 and 3. Subphenotype 1 also had lower creatinine and BUN than subphenotypes 2 and 3. Dialysis requirement was lowest in subphenotype 1 (4% versus 7% [subphenotype 2] versus 26% [subphenotype 3]). The mortality 28 days after AKI was lowest in subphenotype 1 (23% versus 35% [subphenotype 2] versus 49% [subphenotype 3]). After adjustment, the adjusted odds ratio for mortality for subphenotype 3, with subphenotype 1 as a reference, was 1.9 (95% confidence interval, 1.5 to 2.4). Conclusions Utilizing routinely collected laboratory variables, vital signs, and comorbidities, we were able to identify three distinct subphenotypes of sepsis-associated AKI with differing outcomes.

Background Despite advances in cardiovascular disease and risk factor management, mortality from ischemic heart failure (HF) in patients with coronary artery disease (CAD) remains high. Given the partial role of genetics in HF and lack of reliable risk stratification tools, we developed and validated a polygenic risk score for HF in patients with CAD, which we term HF‐PRS. Methods and Results Using summary statistics from a recent genome‐wide association study for HF, we developed candidate PRSs in the Mount Sinai Bio Me CAD patient cohort (N=6274) by using the pruning and thresholding method and LDPred. We validated the best score in the Penn Medicine BioBank (N=7250) and performed a subgroup analysis in a high‐risk cohort who had undergone coronary catheterization. We observed a significant association between HF‐PRS score and ischemic HF even after adjusting for evidence of obstructive CAD in patients of European ancestry in both Bio Me (odds ratio [OR], 1.14 per SD; 95% CI, 1.05–1.24; P =0.003) and Penn Medicine BioBank (OR, 1.07 per SD; 95% CI, 1.01–1.13; P =0.016). In European patients with CAD in Penn Medicine BioBank who had undergone coronary catheterization, individuals in the top 10th percentile of PRS had a 2‐fold increased odds of ischemic HF (OR, 2.0; 95% CI, 1.1–3.7; P =0.02) compared with the bottom 10th percentile. Conclusions A PRS for HF enables risk stratification in patients with CAD. Future prospective studies aimed at demonstrating clinical utility are warranted for adoption in the patient setting.

Aims Clinical scoring systems for pulmonary embolism (PE) screening have low specificity and contribute to computed tomography pulmonary angiogram (CTPA) overuse. We assessed whether deep learning models using an existing and routinely collected data modality, electrocardiogram (ECG) waveforms, can increase specificity for PE detection. Methods and results We create a retrospective cohort of 21 183 patients at moderate- to high suspicion of PE and associate 23 793 CTPAs (10.0% PE-positive) with 320 746 ECGs and encounter-level clinical data (demographics, comorbidities, vital signs, and labs). We develop three machine learning models to predict PE likelihood: an ECG model using only ECG waveform data, an EHR model using tabular clinical data, and a Fusion model integrating clinical data and an embedded representation of the ECG waveform. We find that a Fusion model [area under the receiver-operating characteristic curve (AUROC) 0.81 ± 0.01] outperforms both the ECG model (AUROC 0.59 ± 0.01) and EHR model (AUROC 0.65 ± 0.01). On a sample of 100 patients from the test set, the Fusion model also achieves greater specificity (0.18) and performance (AUROC 0.84 ± 0.01) than four commonly evaluated clinical scores: Wells’ Criteria, Revised Geneva Score, Pulmonary Embolism Rule-Out Criteria, and 4-Level Pulmonary Embolism Clinical Probability Score (AUROC 0.50–0.58, specificity 0.00–0.05). The model is superior to these scores on feature sensitivity analyses (AUROC 0.66–0.84) and achieves comparable performance across sex (AUROC 0.81) and racial/ethnic (AUROC 0.77–0.84) subgroups. Conclusion Synergistic deep learning of ECG waveforms with traditional clinical variables can increase the specificity of PE detection in patients at least at moderate suspicion for PE.

Federated learning is a technique for training predictive models without sharing patient-level data, thus maintaining data security while allowing inter-institutional collaboration. We used federated learning to predict acute kidney injury within three and seven days of admission, using demographics, comorbidities, vital signs, and laboratory values, in 4029 adults hospitalized with COVID-19 at five sociodemographically diverse New York City hospitals, between March-October 2020. Prediction performance of federated models was generally higher than single-hospital models and was comparable to pooled-data models. In the first use-case in kidney disease, federated learning improved prediction of a common complication of COVID-19, while preserving data privacy.

Acute Kidney Injury (AKI) is among the most common complications of Coronavirus Disease 2019 (COVID-19). Throughout 2020 pandemic, the clinical approach to COVID-19 has progressively improved, but it is unknown how these changes have affected AKI incidence and severity. In this retrospective analysis, we report the trend over time of COVID-19 associated AKI and need of renal replacement therapy in a large health system in New York City, the first COVID-19 epicenter in United States.

Background Multiple genes with genetic variants or expression changes associated with Alzheimer’s disease (AD) have been identified. Altering their expression and activity levels serve as potential therapeutic strategies, yet the cellular implications of such alterations remain largely unknown. Methods Cellular dependencies of 105 AD-associated genes, previously identified by genome-wide association studies (GWAS) and gene expression network studies, were evaluated in over 700 cell lines with CRISPR knockout/RNAi knockdown screen data. Expression-driven dependencies were examined within multiple tissue lineages. ResultsMultiple genes showed widespread cell dependencies across tissue lineages, suggesting their inhibition may yield off-target effects. SPI1, MEF2C, GAB2, and ABCC11 were identified as genes of interest since their genetic knockouts specifically affected high-expressing cells of the hematopoietic and lymphoid lineage related to microglia. Other genes exhibiting expression-driven dependencies include ACTG1 in autonomic ganglia and GLS in central nervous system lineages. Conclusions Analyses of genetic screen data identified AD-associated genes whose knockout or knockdown selectively affected cell lines of relevant tissue lineages, prioritizing targets for potential AD treatments.

ObjectiveThe COVID-19 pandemic is a global public health crisis, with over 33 million cases and 999 000 deaths worldwide. Data are needed regarding the clinical course of hospitalised patients, particularly in the USA. We aimed to compare clinical characteristic of patients with COVID-19 who had in-hospital mortality with those who were discharged alive.DesignDemographic, clinical and outcomes data for patients admitted to five Mount Sinai Health System hospitals with confirmed COVID-19 between 27 February and 2 April 2020 were identified through institutional electronic health records. We performed a retrospective comparative analysis of patients who had in-hospital mortality or were discharged alive.SettingAll patients were admitted to the Mount Sinai Health System, a large quaternary care urban hospital system.ParticipantsParticipants over the age of 18 years were included.Primary outcomesWe investigated in-hospital mortality during the study period.ResultsA total of 2199 patients with COVID-19 were hospitalised during the study period. As of 2 April, 1121 (51%) patients remained hospitalised, and 1078 (49%) completed their hospital course. Of the latter, the overall mortality was 29%, and 36% required intensive care. The median age was 65 years overall and 75 years in those who died. Pre-existing conditions were present in 65% of those who died and 46% of those discharged. In those who died, the admission median lymphocyte percentage was 11.7%, D-dimer was 2.4 μg/mL, C reactive protein was 162 mg/L and procalcitonin was 0.44 ng/mL. In those discharged, the admission median lymphocyte percentage was 16.6%, D-dimer was 0.93 μg/mL, C reactive protein was 79 mg/L and procalcitonin was 0.09 ng/mL.ConclusionsIn our cohort of hospitalised patients, requirement of intensive care and mortality were high. Patients who died typically had more pre-existing conditions and greater perturbations in inflammatory markers as compared with those who were discharged.

BACKGROUND Machine learning models require large datasets that may be siloed across different health care institutions. Machine learning studies that focus on COVID-19 have been limited to single-hospital data, which limits model generalizability. OBJECTIVE We aimed to use federated learning, a machine learning technique that avoids locally aggregating raw clinical data across multiple institutions, to predict mortality in hospitalized patients with COVID-19 within 7 days. METHODS Patient data were collected from the electronic health records of 5 hospitals within the Mount Sinai Health System. Logistic regression with L1 regularization/least absolute shrinkage and selection operator (LASSO) and multilayer perceptron (MLP) models were trained by using local data at each site. We developed a pooled model with combined data from all 5 sites, and a federated model that only shared parameters with a central aggregator. RESULTS The LASSOfederated model outperformed the LASSOlocal model at 3 hospitals, and the MLPfederated model performed better than the MLPlocal model at all 5 hospitals, as determined by the area under the receiver operating characteristic curve. The LASSOpooled model outperformed the LASSOfederated model at all hospitals, and the MLPfederated model outperformed the MLPpooled model at 2 hospitals. CONCLUSIONS The federated learning of COVID-19 electronic health record data shows promise in developing robust predictive models without compromising patient privacy.

Background Machine learning models require large datasets that may be siloed across different health care institutions. Machine learning studies that focus on COVID-19 have been limited to single-hospital data, which limits model generalizability. Objective We aimed to use federated learning, a machine learning technique that avoids locally aggregating raw clinical data across multiple institutions, to predict mortality in hospitalized patients with COVID-19 within 7 days. Methods Patient data were collected from the electronic health records of 5 hospitals within the Mount Sinai Health System. Logistic regression with L1 regularization/least absolute shrinkage and selection operator (LASSO) and multilayer perceptron (MLP) models were trained by using local data at each site. We developed a pooled model with combined data from all 5 sites, and a federated model that only shared parameters with a central aggregator. Results The LASSOfederated model outperformed the LASSOlocal model at 3 hospitals, and the MLPfederated model performed better than the MLPlocal model at all 5 hospitals, as determined by the area under the receiver operating characteristic curve. The LASSOpooled model outperformed the LASSOfederated model at all hospitals, and the MLPfederated model outperformed the MLPpooled model at 2 hospitals. Conclusions The federated learning of COVID-19 electronic health record data shows promise in developing robust predictive models without compromising patient privacy.

Multisystem inflammatory syndrome in children (MIS-C) presents with fever, inflammation and pathology of multiple organs in individuals under 21 years of age in the weeks following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Although an autoimmune pathogenesis has been proposed, the genes, pathways and cell types causal to this new disease remain unknown. Here we perform RNA sequencing of blood from patients with MIS-C and controls to find disease-associated genes clustered in a co-expression module annotated to CD56dimCD57+ natural killer (NK) cells and exhausted CD8+ T cells. A similar transcriptome signature is replicated in an independent cohort of Kawasaki disease (KD), the related condition after which MIS-C was initially named. Probing a probabilistic causal network previously constructed from over 1,000 blood transcriptomes both validates the structure of this module and reveals nine key regulators, including TBX21, a central coordinator of exhausted CD8+ T cell differentiation. Together, this unbiased, transcriptome-wide survey implicates downregulation of NK cells and cytotoxic T cell exhaustion in the pathogenesis of MIS-C., Multisystem inflammatory syndrome in children (MIS-C) onsets in COVID-19 patients with manifestations similar to Kawasaki disease (KD). Here the author probe the peripheral blood transcriptome of MIS-C patients to find signatures related to natural killer (NK) cell activation and CD8+ T cell exhaustion that are shared with KD patients.

Machine learning (ML) models require large datasets which may be siloed across different healthcare institutions. Using federated learning, a ML technique that avoids locally aggregating raw clinical data across multiple institutions, we predict mortality within seven days in hospitalized COVID-19 patients. Patient data was collected from Electronic Health Records (EHRs) from five hospitals within the Mount Sinai Health System (MSHS). Logistic Regression with L1 regularization (LASSO) and Multilayer Perceptron (MLP) models were trained using local data at each site, a pooled model with combined data from all five sites, and a federated model that only shared parameters with a central aggregator. Both the federated LASSO and federated MLP models performed better than their local model counterparts at four hospitals. The federated MLP model also outperformed the federated LASSO model at all hospitals. Federated learning shows promise in COVID-19 EHR data to develop robust predictive models without compromising patient privacy.

Urinary tract stones have high heritability indicating a strong genetic component. However, genome-wide association studies (GWAS) have uncovered only a few genome wide significant single nucleotide polymorphisms (SNPs). Polygenic risk scores (PRS) sum cumulative effect of many SNPs and shed light on underlying genetic architecture. Using GWAS summary statistics from 361,141 participants in the United Kingdom Biobank, we generated a PRS and determined association with stone diagnosis in 28,877 participants in the Mount Sinai BioMe Biobank. In BioMe (1,071 cases and 27,806 controls), for every standard deviation increase, we observed a significant increment in adjusted odds ratio of a factor of 1.2 (95% confidence interval 1.13-1.26). In comparison, a risk score comprised of GWAS significant SNPs was not significantly associated with diagnosis. After stratifying individuals into low and high-risk categories on clinical risk factors, there was a significant increment in adjusted odds ratio of 1.3 (1.12-1.6) in the low- and 1.2 (1.1-1.2) in the high-risk group for every standard deviation increment in PRS. In a 14,348-participant validation cohort (Penn Medicine Biobank), every standard deviation increment was associated with a significant adjusted odds ratio of 1.1 (1.03 – 1.2). Thus, a genome-wide PRS is associated with urinary tract stones overall and in the absence of known clinical risk factors and illustrates their complex polygenic architecture.

Significant disparities exist in kidney disease, with blacks facing a higher burden of CKD and kidney failure. In addition to socioeconomic/health system factors, these disparities are linked to two sequence variants (G1 and G2) in the apoL1 ( APOL1 ) gene. The high-risk APOL1 genotype (two copies

Deep Learning (DL) models typically require large-scale, balanced training data to be robust, generalizable, and effective in the context of healthcare. This has been a major issue for developing DL models for the coronavirus-disease 2019 (COVID-19) pandemic where data are highly class imbalanced. Conventional approaches in DL use cross-entropy loss (CEL) which often suffers from poor margin classification. We show that contrastive loss (CL) improves the performance of CEL especially in imbalanced electronic health records (EHR) data for COVID-19 analyses. We use a diverse EHR data set to predict three outcomes: mortality, intubation, and intensive care unit (ICU) transfer in hospitalized COVID-19 patients over multiple time windows. To compare the performance of CEL and CL, models are tested on the full data set and a restricted data set. CL models consistently outperform CEL models with differences ranging from 0.04 to 0.15 for AUPRC and 0.05 to 0.1 for AUROC., Deep learning models applied on EHR data often utilize cross-entropy loss (CEL) as the primary optimization function. But CEL may not be suitable for real-world scenarios with imbalanced data. We develop a learning framework that incorporates both CEL and contrastive loss (CL) to tackle this issue. Our framework achieves better predictive performance and feature interpretability, particularly for imbalanced data.

Background & Aims The protective intestinal mucosal barrier consists of multiple elements including mucus and epithelial layers and immune defense; nonetheless, barrier dysfunction is common in various disorders. The imprinted and developmentally regulated long noncoding RNA H19 is involved in many cell processes and diseases. Here, we investigated the role of H19 in regulating Paneth and goblet cells and autophagy, and its impact on intestinal barrier dysfunction induced by septic stress. Methods Studies were conducted in H19-deficient (H19-/-) mice, mucosal tissues from patients with sepsis, primary enterocytes, and Caco-2 cells. Septic stress was induced by cecal ligation and puncture (CLP), and gut permeability was detected by tracer fluorescein isothiocyanate–dextran assays. The function of Paneth and goblet cells was examined by immunostaining for lysozyme and mucin 2, respectively, and autophagy was examined by microtubule-associated proteins 1A/1B light chain 3 II immunostaining and Western blot analysis. Intestinal organoids were isolated from H19-/- and control littermate mice and treated with lipopolysaccharide (LPS). Results Intestinal mucosal tissues in mice 24 hours after exposure to CLP and in patients with sepsis showed high H19 levels, associated with intestinal barrier dysfunction. Targeted deletion of the H19 gene in mice enhanced the function of Paneth and goblet cells and promoted autophagy in the small intestinal mucosa. Knockout of H19 protected Paneth and goblet cells against septic stress, preserved autophagy activation, and promoted gut barrier function after exposure to CLP. Compared with organoids from control littermate mice, intestinal organoids isolated from H19-/- mice had increased numbers of lysozyme- and mucin 2–positive cells and showed increased tolerance to LPS. Conversely, ectopic overexpression of H19 in cultured intestinal epithelial cells prevented rapamycin-induced autophagy and abolished the rapamycin-induced protection of the epithelial barrier against LPS. Conclusions In investigations of mice, human tissues, primary organoids, and intestinal epithelial cells, we found that increased H19 inhibited the function of Paneth and goblet cells and suppressed autophagy, thus potentially contributing to barrier dysfunction in intestinal pathologies., Graphical abstract

Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the associated Coronavirus Disease 2019 (COVID-19) is a public health emergency. Acute kidney injury (AKI) is a common complication in hospitalized patients with COVID-19 although mechanisms underlying AKI are yet unclear. There may be a direct effect of SARS-CoV-2 virus on the kidney; however, there is currently no data linking SARS-CoV-2 viral load (VL) to AKI. We explored the association of SARS-CoV-2 VL at admission to AKI in a large diverse cohort of hospitalized patients with COVID-19. Methods and findings We included patients hospitalized between March 13th and May 19th, 2020 with SARS-CoV-2 in a large academic healthcare system in New York City (N = 1,049) with available VL at admission quantified by real-time RT-PCR. We extracted clinical and outcome data from our institutional electronic health records (EHRs). AKI was defined by KDIGO guidelines. We fit a Fine-Gray competing risks model (with death as a competing risk) using demographics, comorbidities, admission severity scores, and log10 transformed VL as covariates and generated adjusted hazard ratios (aHR) and 95% Confidence Intervals (CIs). VL was associated with an increased risk of AKI (aHR = 1.04, 95% CI: 1.01–1.08, p = 0.02) with a 4% increased hazard for each log10 VL change. Patients with a viral load in the top 50th percentile had an increased adjusted hazard of 1.27 (95% CI: 1.02–1.58, p = 0.03) for AKI as compared to those in the bottom 50th percentile. Conclusions VL is weakly but significantly associated with in-hospital AKI after adjusting for confounders. This may indicate the role of VL in COVID-19 associated AKI. This data may inform future studies to discover the mechanistic basis of COVID-19 associated AKI.

The disruption of the intestinal epithelial barrier function occurs commonly in various pathologies, but the exact mechanisms responsible are unclear. The H19 long noncoding RNA (lncRNA) regulates the expression of different genes and has been implicated in human genetic disorders and cancer. Here, we report that H19 plays an important role in controlling the intestinal epithelial barrier function by serving as a precursor for microRNA 675 (miR-675). H19 overexpression increased the cellular abundance of miR-675, which in turn destabilized and repressed the translation of mRNAs encoding tight junction protein ZO-1 and adherens junction E-cadherin, resulting in the dysfunction of the epithelial barrier. Increasing the level of the RNA-binding protein HuR in cells overexpressing H19 prevented the stimulation of miR-675 processing from H19, promoted ZO-1 and E-cadherin expression, and restored the epithelial barrier function to a nearly normal level. In contrast, the targeted deletion of HuR in intestinal epithelial cells enhanced miR-675 production in the mucosa and delayed the recovery of the gut barrier function after exposure to mesenteric ischemia/reperfusion. These results indicate that H19 interacts with HuR and regulates the intestinal epithelial barrier function via the H19-encoded miR-675 by altering ZO-1 and E-cadherin expression posttranscriptionally.

Dysregulated oncogenic signaling is a key hallmark of cancer. Protein kinase alterations, including genomic mutations and copy-number amplifications, are known druggable targets in multiple cancer types. Thus, kinase inhibitor drugs act on protein and direct observation of protein/phospho-level overexpression may reveal new treatment opportunities. Herein, we developed and applied a new algorithm, OPPTI, to provide a catalog of overexpressed kinases across 10 cancer types using curated global mass spectrometry proteomic data of 1,071 cases. Aside from overexpressed proteins through genomic mutations, amplifications, or fusions, including ERBB2, AKT2, and EGFR, kinase overexpression also occurred in cancers without genomic-level alterations, such as CDK6 in hepatocellular carcinoma (HCC), FGFR1/2 in ovarian cancer, and PDGFRA in both gastric cancer and lung adenocarcinoma. Integrating cancer-cell dependency data, we further identified 38 new cancer vulnerability targets overexpressed in >10% of the patient cohorts, including TK1 in breast and gastric cancers, NRBP2 in liver and lung cancers, and CKB in ovarian, endometrial, and colorectal cancers. A fraction of overexpressed kinases was validated by immunostainings and correlated with phosphosignaling pathway activities. Finally, we demonstrated that a subset of HCC mouse models that overexpressed PDGFRB -as detected in parallel in human HCC cohorts- could be specifically treated by a receptor tyrosine kinase inhibitor. Overall, our results suggest that proteomic-level alterations not readily observed by genomics represent cancer vulnerabilities that need to be carefully considered as potential targets for personalized treatments. Citation Format: Abdulkadir Elmas, Pedro Molina-Sanchez, Serena Tharakan, Suraj Jaladanki, Tao Liu, Amaia Lujambio, Kuan-lin Huang. Pancancer proteomic investigation identifies overexpressed kinases as novel cancer dependent targets [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-329.

480 Background: Immune related adverse events (IRAEs) with immune checkpoint inhibitor (ICI) therapy are well recognized, but predictors for IRAEs are not well defined. We aim to characterize the type, timing, and clinical risk factors associated with (w/) IRAEs in ICI-treated, advanced urothelial carcinoma (UC) and renal cell carcinoma (RCC) patients (pts). Methods: We retrospectively reviewed charts of pts w/ advanced UC and RCC who received at least 2 ICI doses at our institution from 1/1/10 to 10/31/18. Patient baseline characteristics, treatment course, and clinical outcomes were collected. IRAEs were identified and graded (GR) based on CTCAE (v.4.0). Fisher’s exact test was used to study the differences between pts w/ versus without IRAE. Results: Of the 71 pts identified (UC n = 53; RCC n = 18), 27 pts (38%) developed IRAEs with 42 total events (38% GR1, 60% GR2, and 2% GR≥3) [table]. The majority of pts with dermatitis (70%) also developed a secondary, systemic IRAE(s). Systemic steroid (SS) was required in 17 events. The median time to any IRAE was 17.5 weeks (w, range 1-93). ECOG ≤ 1 predicted IRAE development (p < 0.05). No other characteristics (demographics, co-morbidities, metastatic sites, ICI type, line of therapy, and duration of ICI > 12w) were associated with IRAE. Conclusions: In our study, good function status is associated with the development of IRAE. Time to IRAE ranged from immediately to 93w after initiating ICI. Clinical validation with additional datasets will be needed to confirm these findings. [Table: see text]

482 Background: Despite the widespread use of immune checkpoint inhibitors (ICIs), patterns of disease progression (POD) are poorly characterized. We aim to define these characteristics in patients (pts) with advanced urothelial carcinoma (UC) and renal cell carcinoma (RCC) treated with ICIs. Methods: We retrospectively reviewed charts of pts with advanced UC and RCC who received at least 2 ICI doses at our institution from 12/1/10 – 10/31/18. Demographics, medical history, ICI course, toxicity, and outcomes were recorded. Characteristics at the time of radiographic POD including location of metastases (mets), symptoms (sx), and hospitalization details were collected. Fisher’s exact test was used to study differences in pts with and without hospitalization at POD. Results: Of the 71 pts identified (UC N=53; RCC N=18), 59 pts had POD. At POD, 19 (32.2%) pts had new sites of disease involvement, while the remaining pts (N=40, 67.8%) had progression only at previously known sites of disease. Fourty-six (78.0%) pts had sx at POD: 1 sx (N=19, 32.2%), 2 sx (N=13, 22.0%), 3+ sx (N=14, 23.7%). Pain was the most common sx at POD (N=32, 54.2%), followed by loss of energy (N=18, 30.5%), and loss of appetite (N=14, 23.7%). Twenty-five (42.4%) pts were hospitalized at POD, most commonly for sepsis (N=8, 32%). No clinical factors were identified to predict for pts being hospitalized at POD. Conclusions: In our review of GU cancer patients on ICIs, a large proportion of pts reported clinical sx at POD, pain being the most frequent. Furthermore, a substantial number of pts were hospitalized at POD, most commonly for sepsis. Thus, further studies are warranted to confirm these findings, and potentially identify strategies to optimize patients’ quality-of-life and reduce rates of hospitalizations at the time of POD on ICIs.