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BackgroundCOVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. ObjectiveThe aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. MethodsWe used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19–positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. ResultsUpon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. ConclusionsWe externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.

BACKGROUND: The real-world impact of bivalent vaccines for wild type (WA.1) and Omicron variant (BA.5) is largely unknown in immunocompromised patients with Multiple Myeloma (MM). We characterize the humoral and cellular immune responses in patients with MM before and after receiving the bivalent booster, including neutralizing assays to identify patterns associated with continuing vulnerability to current variants (XBB1.16, EG5) in the current post-pandemic era. METHODS: We studied the humoral and cellular immune responses before and after bivalent booster immunization in 48 MM patients. Spike binding IgG antibody levels were measured by SARS-CoV-2 spike binding ELISA and neutralization capacity was assessed by a SARS-CoV-2 multi-cycle microneutralization assays to assess inhibition of live virus. We measured spike specific T-cell function using the QuantiFERON SARS-CoV-2 (Qiagen) assay as well as flow-cytometry based T-cell. In a subset of 38 patients, high-dimensional flow cytometry was performed to identify immune cell subsets associated with lack of humoral antibodies. FINDINGS: We find that bivalent vaccination provides significant boost in protection to the omicron variant in our MM patients, in a treatment specific manner. MM patients remain vulnerable to newer variants with mutations in the spike portion. Anti-CD38 and anti-BCMA therapies affect the immune machinery needed to produce antibodies. INTERPRETATION: Our study highlights varying immune responses observed in MM patients after receiving bivalent COVID-19 vaccination. Specifically, a subgroup of MM patients undergoing anti-CD38 and anti-BCMA therapy experience impairment in immune cells such DCs, B cells, NK cells and TFH cells, leading to an inability to generate adequate humoral and cellular responses to vaccination. FUNDING: National Cancer Institute (National Institutes of Health), National Institute of Allergy and Infectious Diseases (National Institutes of Health), NCI Serological Sciences Network for COVID-19 (SeroNet) and The Icahn School of Medicine at Mount Sinai.

Prostate cancer (PCa), the second leading cause of death in American men, includes distinct genetic subtypes with distinct therapeutic vulnerabilities. The DACH1 gene encodes a winged helix/Forkhead DNA-binding protein that competes for binding to FOXM1 sites. Herein, DACH1 gene deletion within the 13q21.31-q21.33 region occurs in up to 18% of human PCa and was associated with increased AR activity and poor prognosis. In prostate OncoMice, prostate-specific deletion of the Dach1 gene enhanced prostatic intraepithelial neoplasia (PIN), and was associated with increased TGFβ activity and DNA damage. Reduced Dach1 increased DNA damage in response to genotoxic stresses. DACH1 was recruited to sites of DNA damage, augmenting recruitment of Ku70/Ku80. Reduced Dach1 expression was associated with increased homology directed repair and resistance to PARP inhibitors and TGFβ kinase inhibitors. Reduced Dach1 expression may define a subclass of PCa that warrants specific therapies.

In October 2020, the National Cancer Institute (NCI) Serological Sciences Network (SeroNet) was established to study the immune response to COVID-19, and "to develop, validate, improve, and implement serological testing and associated technologies" (https://www.cancer.gov/research/key-initiatives/covid-19/coronavirus-research-initiatives/serological-sciences-network). SeroNet is comprised of 25 participating research institutions partnering with the Frederick National Laboratory for Cancer Research (FNLCR) and the SeroNet Coordinating Center. Since its inception, SeroNet has supported collaborative development and sharing of COVID-19 serological assay procedures and has set forth plans for assay harmonization. To facilitate collaboration and procedure sharing, a detailed survey was sent to collate comprehensive assay details and performance metrics on COVID-19 serological assays within SeroNet. In addition, FNLCR established a protocol to calibrate SeroNet serological assays to reference standards, such as the U.S. severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) serology standard reference material and first WHO international standard (IS) for anti-SARS-CoV-2 immunoglobulin (20/136), to facilitate harmonization of assay reporting units and cross-comparison of study data. SeroNet institutions reported development of a total of 27 enzyme-linked immunosorbent assay (ELISA) methods, 13 multiplex assays, and 9 neutralization assays and use of 12 different commercial serological methods. FNLCR developed a standardized protocol for SeroNet institutions to calibrate these diverse serological assays to reference standards. In conclusion, SeroNet institutions have established a diverse array of COVID-19 serological assays to study the immune response to SARS-CoV-2 and vaccines. Calibration of SeroNet serological assays to harmonize results reporting will facilitate future pooled data analyses and study cross-comparisons. IMPORTANCE SeroNet institutions have developed or implemented 61 diverse COVID-19 serological assays and are collaboratively working to harmonize these assays using reference materials to establish standardized reporting units. This will facilitate clinical interpretation of serology results and cross-comparison of research data.

To develop a map of cell-cell communication mediated by extracellular RNA (exRNA), the NIH Extracellular RNA Communication Consortium created the exRNA Atlas resource (https://exrna-atlas.org). The Atlas version 4P1 hosts 5,309 exRNA-seq and exRNA qPCR profiles from 19 studies and a suite of analysis and visualization tools. To analyze variation between profiles, we apply computational deconvolution. The analysis leads to a model with six exRNA cargo types (CT1, CT2, CT3A, CT3B, CT3C, CT4), each detectable in multiple biofluids (serum, plasma, CSF, saliva, urine). Five of the cargo types associate with known vesicular and non-vesicular (lipoprotein and ribonucleoprotein) exRNA carriers. To validate utility of this model, we re-analyze an exercise response study by deconvolution to identify physiologically relevant response pathways that were not detected previously. To enable wide application of this model, as part of the exRNA Atlas resource, we provide tools for deconvolution and analysis of user-provided case-control studies.

Tumor cure with conventional fractionated radiotherapy is 65%, dependent on tumor cell-autonomous gradual buildup of DNA double-strand break (DSB) misrepair. Here we report that single-dose radiotherapy (SDRT), a disruptive technique that ablates more than 90% of human cancers, operates a distinct dual-target mechanism, linking acid sphingomyelinase-mediated (ASMase-mediated) microvascular perfusion defects to DNA unrepair in tumor cells to confer tumor cell lethality. ASMase-mediated microcirculatory vasoconstriction after SDRT conferred an ischemic stress response within parenchymal tumor cells, with ROS triggering the evolutionarily conserved SUMO stress response, specifically depleting chromatin-associated free SUMO3. Whereas SUMO3, but not SUMO2, was indispensable for homology-directed repair (HDR) of DSBs, HDR loss of function after SDRT yielded DSB unrepair, chromosomal aberrations, and tumor clonogen demise. Vasoconstriction blockade with the endothelin-1 inhibitor BQ-123, or ROS scavenging after SDRT using peroxiredoxin-6 overexpression or the SOD mimetic tempol, prevented chromatin SUMO3 depletion, HDR loss of function, and SDRT tumor ablation. We also provide evidence of mouse-to-human translation of this biology in a randomized clinical trial, showing that 24 Gy SDRT, but not 3×9 Gy fractionation, coupled early tumor ischemia/reperfusion to human cancer ablation. The SDRT biology provides opportunities for mechanism-based selective tumor radiosensitization via accessing of SDRT/ASMase signaling, as current studies indicate that this pathway is tractable to pharmacologic intervention.

From eukaryotes to prokaryotes, all cells secrete extracellular vesicles (EVs) as part of their regular homeostasis, intercellular communication, and cargo disposal. Accumulating evidence suggests that small EVs carry functional small RNAs, potentially serving as extracellular messengers and liquid‐biopsy markers. Yet, the complete transcriptomic landscape of EV‐associated small RNAs during disease progression is poorly delineated due to critical limitations including the protocols used for sequencing, suboptimal alignment of short reads (20–50 nt), and uncharacterized genome annotations—often denoted as the 'dark matter' of the genome. In this study, we investigate the EV‐associated small unannotated RNAs that arise from endogenous genes and are part of the genomic 'dark matter', which may play a key emerging role in regulating gene expression and translational mechanisms. To address this, we created a distinct small RNAseq dataset from human prostate cancer & benign tissues, and EVs derived from blood (pre‐ & post‐prostatectomy), urine, and human prostate carcinoma epithelial cell line. We then developed an unsupervised data‐based bioinformatic pipeline that recognizes biologically relevant transcriptional signals irrespective of their genomic annotation. Using this approach, we discovered distinct EV‐RNA expression patterns emerging from the un‐annotated genomic regions (UGRs) of the transcriptomes associated with tissue‐specific phenotypes. We have named these novel EV‐associated small RNAs as 'EV‐UGRsʼ or "EV‐dark matter". Here, we demonstrate that EV‐UGR gene expressions are downregulated by ∼100 fold (FDR < 0.05) in the circulating serum EVs from aggressive prostate cancer subjects. Remarkably, these EV‐UGRs expression signatures were regained (upregulated) after radical prostatectomy in the same follow‐up patients. Finally, we developed a stem‐loop RT‐qPCR assay that validated prostate cancer‐specific EV‐UGRs for selective fluid‐based diagnostics. Overall, using an unsupervised data driven approach, we investigate the 'dark matter' of EV‐transcriptome and demonstrate that EV‐UGRs carry tissue‐specific Information that significantly alters pre‐ and post‐prostatectomy in the prostate cancer patients. Although further validation in randomized clinical trials is required, this new class of EV‐RNAs hold promise in liquid‐biopsy by avoiding highly invasive biopsy procedures in prostate cancer. [ABSTRACT FROM AUTHOR]

Recurrent broad-scale heterozygous deletions are frequently observed in human cancer. Here we tested the hypothesis that compound haploinsufficiency of neighboring genes at chromosome 8p promotes tumorigenesis. By targeting the mouse orthologs of human DOK2 and DUSP4 genes, which were co-deleted in approximately half of human lung adenocarcinomas, we found that compound-heterozygous deletion of Dok2 and Dusp4 in mice resulted in lung tumorigenesis with short latency and high incidence, and that their co-deletion synergistically activated MAPK signaling and promoted cell proliferation. Conversely, restoration of DOK2 and DUSP4 in lung cancer cells suppressed MAPK activation and cell proliferation. Importantly, in contrast to downregulation of DOK2 or DUSP4 alone, concomitant downregulation of DOK2 and DUSP4 was associated with poor survival in human lung adenocarcinoma. Therefore, our findings lend in vivo experimental support to the notion that compound haploinsufficiency, due to broad-scale chromosome deletions, constitutes a driving force in tumorigenesis., IntroductionHuman cancers are characterized by recurrent somatic copy number alterations (SCNAs), which include both focal and broad arm-level amplifications and deletions (1). How these alterations contribute to tumorigenesis remains a [...]

Importance: SARS-CoV-2 infection can result in ongoing, relapsing, or new symptoms or organ dysfunction after the acute phase of infection, termed Post-Acute Sequelae of SARS-CoV-2 (PASC), or long COVID. The characteristics, prevalence, trajectory and mechanisms of PASC are poorly understood. The objectives of the Researching COVID to Enhance Recovery (RECOVER) tissue pathology study (RECOVER-Pathology) are to: (1) characterize prevalence and types of organ injury/disease and pathology occurring with PASC; (2) characterize the association of pathologic findings with clinical and other characteristics; (3) define the pathophysiology and mechanisms of PASC, and possible mediation via viral persistence; and (4) establish a post-mortem tissue biobank and post-mortem brain imaging biorepository. Methods: RECOVER-Pathology is a cross-sectional study of decedents dying at least 15 days following initial SARS-CoV-2 infection. Eligible decedents must meet WHO criteria for suspected, probable, or confirmed infection and must be aged 18 years or more at the time of death. Enrollment occurs at 7 sites in four U.S. states and Washington, DC. Comprehensive autopsies are conducted according to a standardized protocol within 24 hours of death; tissue samples are sent to the PASC Biorepository for later analyses. Data on clinical history are collected from the medical records and/or next of kin. The primary study outcomes include an array of pathologic features organized by organ system. Causal inference methods will be employed to investigate associations between risk factors and pathologic outcomes. Discussion: RECOVER-Pathology is the largest autopsy study addressing PASC among US adults. Results of this study are intended to elucidate mechanisms of organ injury and disease and enhance our understanding of the pathophysiology of PASC. [ABSTRACT FROM AUTHOR]