Outcomes of COVID-19 in hospitalized solid organ transplant recipients compared to a matched cohort of non-transplant patients at a national healthcare system in the United States.

Data describing outcomes of solid organ transplant (SOT) recipients with coronavirus disease 2019 (COVID-19) are variable, and the association between SOT status and mortality remains unclear. In this study, we compare clinical outcomes of SOT recipients hospitalized with COVID-19 between March 10, and September 1, 2020, to a matched cohort of non-SOT recipients at a national healthcare system in the United States (US). From a population of 43 461 hospitalized COVID-19-positive patients, we created a coarsened exact matched cohort of 4035 patients including 128 SOT recipients and 3907 weighted matched non-SOT controls. Multiple logistic regression was used to evaluate association between SOT status and clinical outcomes. Among the 4035 patients, median age was 60 years, 61.7% were male, 21.9% were Black/African American, and 50.8% identified as Hispanic/Latino ethnicity. Patients with a history of SOT were more likely to die within the study period when compared to matched non-SOT recipients (21.9% and 14.9%, respectively; odds ratio [OR] 1.93; 95% confidence interval [CI]: 1.18-3.15). Moreover, SOT status was associated with increased odds of receiving invasive mechanical ventilation (OR [95% CI]: 2.34 [1.51-3.65]), developing acute kidney injury (OR [95% CI]: 2.41 [1.59-3.65]), and receiving vasopressor support during hospitalization (OR [95% CI]: 2.14 [1.31-3.48]).

Exome sequencing reveals recurrent germ line variants in patients with familial Waldenström macroglobulinemia.

Familial aggregation of Waldenström macroglobulinemia (WM) cases, and the clustering of B-cell lymphoproliferative disorders among first-degree relatives of WM patients, has been reported. Nevertheless, the possible contribution of inherited susceptibility to familial WM remains unrevealed. We performed whole exome sequencing on germ line DNA obtained from 4 family members in which coinheritance for WM was documented in 3 of them, and screened additional independent 246 cases by using gene-specific mutation sequencing. Among the shared germ line variants, LAPTM5(c403t) and HCLS1(g496a) were the most recurrent, being present in 3/3 affected members of the index family, detected in 8% of the unrelated familial cases, and present in 0.5% of the nonfamilial cases and in <0.05 of a control population. LAPTM5 and HCLS1 appeared as relevant WM candidate genes that characterized familial WM individuals and were also functionally relevant to the tumor clone. These findings highlight potentially novel contributors for the genetic predisposition to familial WM and indicate that LAPTM5(c403t) and HCLS1(g496a) may represent predisposition alleles in patients with familial WM.

Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins.

Genotypic differences greatly influence susceptibility and resistance to disease. Understanding genotype-phenotype relationships requires that phenotypes be viewed as manifestations of network properties, rather than simply as the result of individual genomic variations. Genome sequencing efforts have identified numerous germline mutations, and large numbers of somatic genomic alterations, associated with a predisposition to cancer. However, it remains difficult to distinguish background, or 'passenger', cancer mutations from causal, or 'driver', mutations in these data sets. Human viruses intrinsically depend on their host cell during the course of infection and can elicit pathological phenotypes similar to those arising from mutations. Here we test the hypothesis that genomic variations and tumour viruses may cause cancer through related mechanisms, by systematically examining host interactome and transcriptome network perturbations caused by DNA tumour virus proteins. The resulting integrated viral perturbation data reflects rewiring of the host cell networks, and highlights pathways, such as Notch signalling and apoptosis, that go awry in cancer. We show that systematic analyses of host targets of viral proteins can identify cancer genes with a success rate on a par with their identification through functional genomics and large-scale cataloguing of tumour mutations. Together, these complementary approaches increase the specificity of cancer gene identification. Combining systems-level studies of pathogen-encoded gene products with genomic approaches will facilitate the prioritization of cancer-causing driver genes to advance the understanding of the genetic basis of human cancer.

Assessment of microRNA differential expression and detection in multiplexed small RNA sequencing data.

Small RNA sequencing can be used to gain an unprecedented amount of detail into the microRNA transcriptome. The relatively high cost and low throughput of sequencing bases technologies can potentially be offset by the use of multiplexing. However, multiplexing involves a trade-off between increased number of sequenced samples and reduced number of reads per sample (i.e., lower depth of coverage). To assess the effect of different sequencing depths owing to multiplexing on microRNA differential expression and detection, we sequenced the small RNA of lung tissue samples collected in a clinical setting by multiplexing one, three, six, nine, or 12 samples per lane using the Illumina HiSeq 2000. As expected, the numbers of reads obtained per sample decreased as the number of samples in a multiplex increased. Furthermore, after normalization, replicate samples included in distinct multiplexes were highly correlated (R > 0.97). When detecting differential microRNA expression between groups of samples, microRNAs with average expression >1 reads per million (RPM) had reproducible fold change estimates (signal to noise) independent of the degree of multiplexing. The number of microRNAs detected was strongly correlated with the log2 number of reads aligning to microRNA loci (R = 0.96). However, most additional microRNAs detected in samples with greater sequencing depth were in the range of expression which had lower fold change reproducibility. These findings elucidate the trade-off between increasing the number of samples in a multiplex with decreasing sequencing depth and will aid in the design of large-scale clinical studies exploring microRNA expression and its role in disease.

A function for cyclin D1 in DNA repair uncovered by protein interactome analyses in human cancers.

Cyclin D1 is a component of the core cell cycle machinery. Abnormally high levels of cyclin D1 are detected in many human cancer types. To elucidate the molecular functions of cyclin D1 in human cancers, we performed a proteomic screen for cyclin D1 protein partners in several types of human tumours. Analyses of cyclin D1 interactors revealed a network of DNA repair proteins, including RAD51, a recombinase that drives the homologous recombination process. We found that cyclin D1 directly binds RAD51, and that cyclin D1-RAD51 interaction is induced by radiation. Like RAD51, cyclin D1 is recruited to DNA damage sites in a BRCA2-dependent fashion. Reduction of cyclin D1 levels in human cancer cells impaired recruitment of RAD51 to damaged DNA, impeded the homologous recombination-mediated DNA repair, and increased sensitivity of cells to radiation in vitro and in vivo. This effect was seen in cancer cells lacking the retinoblastoma protein, which do not require D-cyclins for proliferation. These findings reveal an unexpected function of a core cell cycle protein in DNA repair and suggest that targeting cyclin D1 may be beneficial also in retinoblastoma-negative cancers which are currently thought to be unaffected by cyclin D1 inhibition.

Relationship of DNA methylation and gene expression in idiopathic pulmonary fibrosis.

RATIONALE: Idiopathic pulmonary fibrosis (IPF) is an untreatable and often fatal lung disease that is increasing in prevalence and is caused by complex interactions between genetic and environmental factors. Epigenetic mechanisms control gene expression and are likely to regulate the IPF transcriptome. OBJECTIVES: To identify methylation marks that modify gene expression in IPF lung. METHODS: We assessed DNA methylation (comprehensive high-throughput arrays for relative methylation arrays [CHARM]) and gene expression (Agilent gene expression arrays) in 94 patients with IPF and 67 control subjects, and performed integrative genomic analyses to define methylation-gene expression relationships in IPF lung. We validated methylation changes by a targeted analysis (Epityper), and performed functional validation of one of the genes identified by our analysis. MEASUREMENTS AND MAIN RESULTS: We identified 2,130 differentially methylated regions (DMRs; <5% false discovery rate), of which 738 are associated with significant changes in gene expression and enriched for expected inverse relationship between methylation and expression (P < 2.2 × 10(-16)). We validated 13/15 DMRs by targeted analysis of methylation. Methylation-expression quantitative trait loci (methyl-eQTL) identified methylation marks that control cis and trans gene expression, with an enrichment for cis relationships (P < 2.2 × 10(-16)). We found five trans methyl-eQTLs where a methylation change at a single DMR is associated with transcriptional changes in a substantial number of genes; four of these DMRs are near transcription factors (castor zinc finger 1 [CASZ1], FOXC1, MXD4, and ZDHHC4). We studied the in vitro effects of change in CASZ1 expression and validated its role in regulation of target genes in the methyl-eQTL. CONCLUSIONS: These results suggest that DNA methylation may be involved in the pathogenesis of IPF.

CXCR4 pathway associated with family history of melanoma.

PURPOSE: Genetic predisposition plays a major role in the etiology of melanoma, but known genetic markers only account for a limited fraction of family-history-associated melanoma cases. Expression microarrays have offered the opportunity to identify further genomic profiles correlated with family history of melanoma. We aimed to distinguish mRNA expression signatures between melanoma cases with and without a family history of melanoma. METHODS: Based on the Nurses' Health Study, family history was defined as having one or more first-degree family members diagnosed with melanoma. Melanoma diagnosis was confirmed by reviewing pathology reports, and tumor blocks were collected by mail from across the USA. Genomic interrogation was accomplished through evaluating expression profiling of formalin-fixed paraffin-embedded tissues from 78 primary cutaneous invasive melanoma cases, on either a 6K or whole-genome (24K) Illumina gene chip. Gene set enrichment analysis was performed for each batch to determine the differentially enriched pathways and key contributing genes. RESULTS: The CXC chemokine receptor 4 (CXCR4) pathway was consistently up-regulated within cases of familial melanoma in both platforms. Leading edge analysis showed four genes from the CXCR4 pathway, including MAPK1, PLCG1, CRK, and PTK2, were among the core members that contributed to the enrichment of this pathway. There was no association between the enrichment of CXCR4 pathway and NRAS, BRAF mutation, or Breslow thickness of the primary melanoma cases. CONCLUSIONS: We found that the CXCR4 pathway might constitute a novel susceptibility pathway associated with family history of melanoma in first-degree relatives.

Predictive networks: a flexible, open source, web application for integration and analysis of human gene networks.

Genomics provided us with an unprecedented quantity of data on the genes that are activated or repressed in a wide range of phenotypes. We have increasingly come to recognize that defining the networks and pathways underlying these phenotypes requires both the integration of multiple data types and the development of advanced computational methods to infer relationships between the genes and to estimate the predictive power of the networks through which they interact. To address these issues we have developed Predictive Networks (PN), a flexible, open-source, web-based application and data services framework that enables the integration, navigation, visualization and analysis of gene interaction networks. The primary goal of PN is to allow biomedical researchers to evaluate experimentally derived gene lists in the context of large-scale gene interaction networks. The PN analytical pipeline involves two key steps. The first is the collection of a comprehensive set of known gene interactions derived from a variety of publicly available sources. The second is to use these 'known' interactions together with gene expression data to infer robust gene networks. The PN web application is accessible from http://predictivenetworks.org. The PN code base is freely available at https://sourceforge.net/projects/predictivenets/.

GeneSigDB: a manually curated database and resource for analysis of gene expression signatures.

GeneSigDB (http://www.genesigdb.org or http://compbio.dfci.harvard.edu/genesigdb/) is a database of gene signatures that have been extracted and manually curated from the published literature. It provides a standardized resource of published prognostic, diagnostic and other gene signatures of cancer and related disease to the community so they can compare the predictive power of gene signatures or use these in gene set enrichment analysis. Since GeneSigDB release 1.0, we have expanded from 575 to 3515 gene signatures, which were collected and transcribed from 1604 published articles largely focused on gene expression in cancer, stem cells, immune cells, development and lung disease. We have made substantial upgrades to the GeneSigDB website to improve accessibility and usability, including adding a tag cloud browse function, facetted navigation and a 'basket' feature to store genes or gene signatures of interest. Users can analyze GeneSigDB gene signatures, or upload their own gene list, to identify gene signatures with significant gene overlap and results can be viewed on a dynamic editable heatmap that can be downloaded as a publication quality image. All data in GeneSigDB can be downloaded in numerous formats including .gmt file format for gene set enrichment analysis or as a R/Bioconductor data file. GeneSigDB is available from http://www.genesigdb.org.

The Stem Cell Discovery Engine: an integrated repository and analysis system for cancer stem cell comparisons.

Mounting evidence suggests that malignant tumors are initiated and maintained by a subpopulation of cancerous cells with biological properties similar to those of normal stem cells. However, descriptions of stem-like gene and pathway signatures in cancers are inconsistent across experimental systems. Driven by a need to improve our understanding of molecular processes that are common and unique across cancer stem cells (CSCs), we have developed the Stem Cell Discovery Engine (SCDE)-an online database of curated CSC experiments coupled to the Galaxy analytical framework. The SCDE allows users to consistently describe, share and compare CSC data at the gene and pathway level. Our initial focus has been on carefully curating tissue and cancer stem cell-related experiments from blood, intestine and brain to create a high quality resource containing 53 public studies and 1098 assays. The experimental information is captured and stored in the multi-omics Investigation/Study/Assay (ISA-Tab) format and can be queried in the data repository. A linked Galaxy framework provides a comprehensive, flexible environment populated with novel tools for gene list comparisons against molecular signatures in GeneSigDB and MSigDB, curated experiments in the SCDE and pathways in WikiPathways. The SCDE is available at http://discovery.hsci.harvard.edu.

Mutational analysis clopidogrel resistance and platelet function in patients scheduled for coronary artery bypass grafting.

Clopidogrel is an oral antiplatelet pro-drug prescribed to 40 million patients worldwide who are at risk for thrombotic events or receiving percutaneous coronary intervention (PCI). However about a fifth of patients treated with clopidogrel do not respond adequately to the drug. From a cohort of 105 patients on whom we had functional data on clopidogrel response, we used ultra-high throughput sequencing to assay mutations in CYP2C19 and ABCB1, the two genes genetically linked to respond. Testing for mutations in CYP2C19, as recommended by the FDA, only correctly predicted if a patient would respond to clopidogrel 52.4% of the time. Similarly, testing of the ABCB1 gene only correctly foretold response in 51 (48.6%) patients. These results are clinically relevant and suggest that until additional genetic factors are discovered that predict response more completely, functional assays are more appropriate for clinical use.

Next-generation sequencing and microarray-based interrogation of microRNAs from formalin-fixed, paraffin-embedded tissue: preliminary assessment of cross-platform concordance.

Next-generation sequencing is increasingly employed in biomedical investigations. Strong concordance between microarray and mRNA-seq levels has been reported in high quality specimens but information is lacking on formalin-fixed, paraffin-embedded (FFPE) tissues, and particularly for microRNA (miRNA) analysis. We conducted a preliminary examination of the concordance between miRNA-seq and cDNA-mediated annealing, selection, extension, and ligation (DASL) miRNA assays. Quantitative agreement between platforms is moderate (Spearman correlation 0.514-0.596) and there is discordance of detection calls on a subset of miRNAs. Quantitative PCR (q-RT-PCR) performed for several discordant miRNAs confirmed the presence of most sequences detected by miRNA-seq but not by DASL but also that miRNA-seq did not detect some sequences, which DASL confidently detected. Our results suggest that miRNA-seq is specific, with few false positive calls, but it may not detect certain abundant miRNAs in FFPE tissue. Further work is necessary to fully address these issues that are pertinent for translational research.

Performance of point-of-care severity scores to predict prognosis in patients admitted through the emergency department with COVID-19.

BACKGROUND: Identifying COVID-19 patients at the highest risk of poor outcomes is critical in emergency department (ED) presentation. Sepsis risk stratification scores can be calculated quickly for COVID-19 patients but have not been evaluated in a large cohort. OBJECTIVE: To determine whether well-known risk scores can predict poor outcomes among hospitalized COVID-19 patients. DESIGNS, SETTINGS, AND PARTICIPANTS: A retrospective cohort study of adults presenting with COVID-19 to 156 Hospital Corporation of America (HCA) Healthcare EDs, March 2, 2020, to February 11, 2021. INTERVENTION: Quick Sequential Organ Failure Assessment (qSOFA), Shock Index, National Early Warning System-2 (NEWS2), and quick COVID-19 Severity Index (qCSI) at presentation. MAIN OUTCOME AND MEASURES: The primary outcome was in-hospital mortality. Secondary outcomes included intensive care unit (ICU) admission, mechanical ventilation, and vasopressors receipt. Patients scored positive with qSOFA ≥ 2, Shock Index > 0.7, NEWS2 ≥ 5, and qCSI ≥ 4. Test characteristics and area under the receiver operating characteristics curves (AUROCs) were calculated. RESULTS: We identified 90,376 patients with community-acquired COVID-19 (mean age 64.3 years, 46.8% female). 17.2% of patients died in-hospital, 28.6% went to the ICU, 13.7% received mechanical ventilation, and 13.6% received vasopressors. There were 3.8% qSOFA-positive, 45.1% Shock Index-positive, 49.8% NEWS2-positive, and 37.6% qCSI-positive at ED-triage. NEWS2 exhibited the highest AUROC for in-hospital mortality (0.593, confidence interval [CI]: 0.588-0.597), ICU admission (0.602, CI: 0.599-0.606), mechanical ventilation (0.614, CI: 0.610-0.619), and vasopressor receipt (0.600, CI: 0.595-0.604). CONCLUSIONS: Sepsis severity scores at presentation have low discriminative power to predict outcomes in COVID-19 patients and are not reliable for clinical use. Severity scores should be developed using features that accurately predict poor outcomes among COVID-19 patients to develop more effective risk-based triage.

A microRNA activity map of human mesenchymal tumors: connections to oncogenic pathways; an integrative transcriptomic study.

BACKGROUND: MicroRNAs (miRNAs) are nucleic acid regulators of many human mRNAs, and are associated with many tumorigenic processes. miRNA expression levels have been used in profiling studies, but some evidence suggests that expression levels do not fully capture miRNA regulatory activity. In this study we integrate multiple gene expression datasets to determine miRNA activity patterns associated with cancer phenotypes and oncogenic pathways in mesenchymal tumors - a very heterogeneous class of malignancies. RESULTS: Using a computational method, we identified differentially activated miRNAs between 77 normal tissue specimens and 135 sarcomas and we validated many of these findings with microarray interrogation of an independent, paraffin-based cohort of 18 tumors. We also showed that miRNA activity is imperfectly correlated with miRNA expression levels. Using next-generation miRNA sequencing we identified potential base sequence alterations which may explain differential activity. We then analyzed miRNA activity changes related to the RAS-pathway and found 21 miRNAs that switch from silenced to activated status in parallel with RAS activation. Importantly, nearly half of these 21 miRNAs were predicted to regulate integral parts of the miRNA processing machinery, and our gene expression analysis revealed significant reductions of these transcripts in RAS-active tumors. These results suggest an association between RAS signaling and miRNA processing in which miRNAs may attenuate their own biogenesis. CONCLUSIONS: Our study represents the first gene expression-based investigation of miRNA regulatory activity in human sarcomas, and our findings indicate that miRNA activity patterns derived from integrated transcriptomic data are reproducible and biologically informative in cancer. We identified an association between RAS signaling and miRNA processing, and demonstrated sequence alterations as plausible causes for differential miRNA activity. Finally, our study highlights the value of systems level integrative miRNA/mRNA assessment with high-throughput genomic data, and the applicability of paraffin-tissue-derived RNA for validation of novel findings.

Ovarian reserve status in young women is associated with altered gene expression in membrana granulosa cells.

Diminished ovarian reserve (DOR) is a challenging diagnosis of infertility, as there are currently no tests to predict who may become affected with this condition, or at what age. We designed the present study to compare the gene expression profile of membrana granulosa cells from young women affected with DOR with those from egg donors of similar age and to determine if distinct genetic patterns could be identified to provide insight into the etiology of DOR. Young women with DOR were identified based on FSH level in conjunction with poor follicular development during an IVF cycle (n = 13). Egg donors with normal ovarian reserve (NOR) comprised the control group (n = 13). Granulosa cells were collected following retrieval, RNA was extracted and microarray analysis was conducted to evaluate genetic differences between the groups. Confirmatory studies were undertaken with quantitative RT-PCR (qRT-PCR). Multiple significant differences in gene expression were observed between the DOR patients and egg donors. Two genes linked with ovarian function, anti-Mullerian hormone (AMH) and luteinizing hormone receptor (LHCGR), were further analyzed with qRT-PCR in all patients. The average expression of AMH was significantly higher in egg donors (adjusted P-value = 0.01), and the average expression of LHCGR was significantly higher in DOR patients (adjusted P-value = 0.005). Expression levels for four additional genes, progesterone receptor membrane component 2 (PGRMC2), prostaglandin E receptor 3 (subtype EP3) (PTGER3), steroidogenic acute regulatory protein (StAR), and StAR-related lipid transfer domain containing 4 (StarD4), were validated in a group consisting of five NOR and five DOR patients. We conclude that gene expression analysis has substantial potential to determine which young women may be affected with DOR. More importantly, our analysis suggests that DOR patients fall into two distinct subgroups based on gene expression profiles, indicating that different mechanisms may be involved during development of this pathology.

COLD-PCR enrichment of rare cancer mutations prior to targeted amplicon resequencing.

BACKGROUND: Despite widespread interest in next-generation sequencing (NGS), the adoption of personalized clinical genomics and mutation profiling of cancer specimens is lagging, in part because of technical limitations. Tumors are genetically heterogeneous and often contain normal/stromal cells, features that lead to low-abundance somatic mutations that generate ambiguous results or reside below NGS detection limits, thus hindering the clinical sensitivity/specificity standards of mutation calling. We applied COLD-PCR (coamplification at lower denaturation temperature PCR), a PCR methodology that selectively enriches variants, to improve the detection of unknown mutations before NGS-based amplicon resequencing. METHODS: We used both COLD-PCR and conventional PCR (for comparison) to amplify serially diluted mutation-containing cell-line DNA diluted into wild-type DNA, as well as DNA from lung adenocarcinoma and colorectal cancer samples. After amplification of TP53 (tumor protein p53), KRAS (v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog), IDH1 [isocitrate dehydrogenase 1 (NADP(+)), soluble], and EGFR (epidermal growth factor receptor) gene regions, PCR products were pooled for library preparation, bar-coded, and sequenced on the Illumina HiSeq 2000. RESULTS: In agreement with recent findings, sequencing errors by conventional targeted-amplicon approaches dictated a mutation-detection limit of approximately 1%-2%. Conversely, COLD-PCR amplicons enriched mutations above the error-related noise, enabling reliable identification of mutation abundances of approximately 0.04%. Sequencing depth was not a large factor in the identification of COLD-PCR-enriched mutations. For the clinical samples, several missense mutations were not called with conventional amplicons, yet they were clearly detectable with COLD-PCR amplicons. Tumor heterogeneity for the TP53 gene was apparent. CONCLUSIONS: As cancer care shifts toward personalized intervention based on each patient's unique genetic abnormalities and tumor genome, we anticipate that COLD-PCR combined with NGS will elucidate the role of mutations in tumor progression, enabling NGS-based analysis of diverse clinical specimens within clinical practice.

GeneSigDB--a curated database of gene expression signatures.

The primary objective of most gene expression studies is the identification of one or more gene signatures; lists of genes whose transcriptional levels are uniquely associated with a specific biological phenotype. Whilst thousands of experimentally derived gene signatures are published, their potential value to the community is limited by their computational inaccessibility. Gene signatures are embedded in published article figures, tables or in supplementary materials, and are frequently presented using non-standard gene or probeset nomenclature. We present GeneSigDB (http://compbio.dfci.harvard.edu/genesigdb) a manually curated database of gene expression signatures. GeneSigDB release 1.0 focuses on cancer and stem cells gene signatures and was constructed from more than 850 publications from which we manually transcribed 575 gene signatures. Most gene signatures (n = 560) were successfully mapped to the genome to extract standardized lists of EnsEMBL gene identifiers. GeneSigDB provides the original gene signature, the standardized gene list and a fully traceable gene mapping history for each gene from the original transcribed data table through to the standardized list of genes. The GeneSigDB web portal is easy to search, allows users to compare their own gene list to those in the database, and download gene signatures in most common gene identifier formats.

DW4TR: A Data Warehouse for Translational Research.

The linkage between the clinical and laboratory research domains is a key issue in translational research. Integration of clinicopathologic data alone is a major task given the number of data elements involved. For a translational research environment, it is critical to make these data usable at the point-of-need. Individual systems have been developed to meet the needs of particular projects though the need for a generalizable system has been recognized. Increased use of Electronic Medical Record data in translational research will demand generalizing the system for integrating clinical data to support the study of a broad range of human diseases. To ultimately satisfy these needs, we have developed a system to support multiple translational research projects. This system, the Data Warehouse for Translational Research (DW4TR), is based on a light-weight, patient-centric modularly-structured clinical data model and a specimen-centric molecular data model. The temporal relationships of the data are also part of the model. The data are accessed through an interface composed of an Aggregated Biomedical-Information Browser (ABB) and an Individual Subject Information Viewer (ISIV) which target general users. The system was developed to support a breast cancer translational research program and has been extended to support a gynecological disease program. Further extensions of the DW4TR are underway. We believe that the DW4TR will play an important role in translational research across multiple disease types.

Convalescent plasma associates with reduced mortality and improved clinical trajectory in patients hospitalized with COVID-19.

BACKGROUNDEvidence supporting convalescent plasma (CP), one of the first investigational treatments for coronavirus disease 2019 (COVID-19), has been inconclusive, leading to conflicting recommendations. The primary objective was to perform a comparative effectiveness study of CP for all-cause, in-hospital mortality in patients with COVID-19.METHODSThe multicenter, electronic health records-based, retrospective study included 44,770 patients hospitalized with COVID-19 in one of 176 HCA Healthcare-affiliated community hospitals. Coarsened exact matching (1:k) was employed, resulting in a sample of 3774 CP and 10,687 comparison patients.RESULTSExamination of mortality using a shared frailty model, controlling for concomitant medications, date of admission, and days from admission to transfusion, demonstrated a significant association of CP with lower mortality risk relative to the comparison group (adjusted hazard ratio [aHR] = 0.71; 95% CI, 0.59-0.86; P < 0.001). Examination of patient risk trajectories, represented by 400 clinico-demographic features from our real-time risk model (RTRM), indicated that patients who received CP recovered more quickly. The stratification of days to transfusion revealed that CP within 3 days after admission, but not within 4 to 7 days, was associated with a significantly lower mortality risk (aHR = 0.53; 95% CI, 0.47-0.60; P < 0.001). CP serology level was inversely associated with mortality when controlling for its interaction with days to transfusion (HR = 0.998; 95% CI, 0.997-0.999; P = 0.013), yet it did not reach univariable significance.CONCLUSIONSThis large, diverse, multicenter cohort study demonstrated that CP, compared with matched controls, is significantly associated with reduced risk of in-hospital mortality. These observations highlight the utility of real-world evidence and suggest the need for further evaluation prior to abandoning CP as a viable therapy for COVID-19.FUNDINGThis research was supported in whole by HCA Healthcare and/or an HCA Healthcare-affiliated entity, including Sarah Cannon and Genospace.

Addressing informatics challenges in Translational Research with workflow technology.

Interest in Translational Research has been growing rapidly in recent years. In this collision of different data, technologies and cultures lie tremendous opportunities for the advancement of science and business for organisations that are able to integrate, analyse and deliver this information effectively to users. Workflow-based integration and analysis systems are becoming recognised as a fast and flexible way to build applications that are tailored to scientific areas, yet are built on a common platform. Workflow systems are allowing organisations to meet the key informatics challenges in Translational Research and improve disease understanding and patient care.