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]).

Identification of genetic outliers due to sub-structure and cryptic relationships.

MOTIVATION: In order to minimize the effects of genetic confounding on the analysis of high-throughput genetic association studies, e.g. (whole-genome) sequencing (WGS) studies, genome-wide association studies (GWAS), etc., we propose a general framework to assess and to test formally for genetic heterogeneity among study subjects. As the approach fully utilizes the recent ancestor information captured by rare variants, it is especially powerful in WGS studies. Even for relatively moderate sample sizes, the proposed testing framework is able to identify study subjects that are genetically too similar, e.g. cryptic relationships, or that are genetically too different, e.g. population substructure. The approach is computationally fast, enabling the application to whole-genome sequencing data, and straightforward to implement. RESULTS: Simulation studies illustrate the overall performance of our approach. In an application to the 1000 Genomes Project, we outline an analysis/cleaning pipeline that utilizes our approach to formally assess whether study subjects are related and whether population substructure is present. In the analysis of the 1000 Genomes Project data, our approach revealed subjects that are most likely related, but had previously passed standard qc-filters. AVAILABILITY AND IMPLEMENTATION: An implementation of our method, Similarity Test for Estimating Genetic Outliers (STEGO), is available in the R package stego from Github at https://github.com/dschlauch/stego . CONTACT: dschlauch@fas.harvard.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Estimating gene regulatory networks with pandaR.

CONTACT: johnq@jimmy.harvard.edu or dschlauch@fas.harvard.edu. AVAILABILITY AND IMPLEMENTATION: PandaR is provided as a Bioconductor R Package and is available at bioconductor.org/packages/pandaR.

Treatment patterns, resource utilization and clinical outcomes in patients with higher risk myelodysplastic syndromes (MDS) in United States community practices.

Management of higher-risk myelodysplastic syndromes (HR-MDS) is challenging in the real world. We studied 200 patients with HR-MDS within a large US community hospital network. We describe the clinical presentation, patient-related factors, prognostic characteristics, treatment patterns, clinical outcomes and resource utilization. Patients with HR-MDS, treated in our community setting, were elderly (median age 76 years) with a high comorbidity burden. First-line therapy was hypomethylating agent (HMA) monotherapy (20%), lenalidomide (2%), and venetoclax (2%), while the rest were treated with supportive care. Sixty-one percent of the 200, were subsequently hospitalized within 6 months of initial diagnosis. Overall survival was 11.8 months. Curative transplantation was infrequent, HMA-based therapy was underutilized, responses were not durable, most patients became transfusion-dependent or transformed to AML, and resource utilization was substantial and was highly correlated with total in-hospital days. There is a clear unmet need for tolerable treatments that can produce durable remissions in this population.

Extracorporeal Membrane Oxygenation for Acute Respiratory Failure Due to COVID-19: A Multicenter Matched Cohort Study.

Mechanical ventilation for respiratory failure due to COVID-19 is associated with significant morbidity and mortality. Veno-venous extracorporeal membrane oxygenation (ECMO) is an attractive management option. This study sought to determine the effect of ECMO on hospital mortality and discharge condition in this population. We conducted a retrospective multicenter study to emulate a pragmatic targeted trial comparing ECMO to mechanical ventilation without ECMO for severe COVID-19. Data were gathered from a large hospital network database in the US. Adults admitted with COVID-19 were included if they were managed with ECMO or mechanical ventilation for severe hypoxemia and excluded if they had significant comorbidities or lacked functional independence on admission. The groups underwent coarsened exact matching on multiple clinical variables. The primary outcome was adjusted in-hospital mortality; secondary outcomes included ventilator days, intensive care days, and discharge destination. A total of 278 ECMO patients were matched to 2,054 comparison patients. Adjusted in-hospital mortality was significantly less in the ECMO group (38.8% vs. 60.1%, p < 0.001). Extracorporeal membrane oxygenation was associated with higher rates of liberation from mechanical ventilation, intensive care discharge, and favorable discharge destination. These findings support the use of ECMO for well-selected patients with severe acute respiratory failure due to COVID-19.

The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks.

Inference and analysis of gene regulatory networks (GRNs) require software that integrates multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is a collection of open-source methods to infer GRNs, conduct differential network analyses, estimate community structure, and explore the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages and between methods to allow better integration of these tools into analytical pipelines. We demonstrate the utility using multi-omic data from the Cancer Cell Line Encyclopedia. We will continue to expand the netZoo to incorporate additional methods.

RNA-Seq analysis in MeV.

SUMMARY: RNA-Seq is an exciting methodology that leverages the power of high-throughput sequencing to measure RNA transcript counts at an unprecedented accuracy. However, the data generated from this process are extremely large and biologist-friendly tools with which to analyze it are sorely lacking. MultiExperiment Viewer (MeV) is a Java-based desktop application that allows advanced analysis of gene expression data through an intuitive graphical user interface. Here, we report a significant enhancement to MeV that allows analysis of RNA-Seq data with these familiar, powerful tools. We also report the addition to MeV of several RNA-Seq-specific functions, addressing the differences in analysis requirements between this data type and traditional gene expression data. These tools include automatic conversion functions from raw count data to processed RPKM or FPKM values and differential expression detection and functional annotation enrichment detection based on published methods.

Tumor-Specific and Tumor-Agnostic Molecular Signatures Associated With Response to Immune Checkpoint Inhibitors.

PURPOSE: Next-generation sequencing (NGS) testing is being incorporated into routine standard of care for patients with cancer. Immune checkpoint inhibitors (CPIs) are approved for use in both tumor-specific and tumor-agnostic indications. We sought to determine tumor type-specific or tumor-agnostic correlations between mutations detected by NGS and response to CPIs. MATERIALS AND METHODS: A retrospective analysis of 26,004 patient records with NGS data available was conducted. Time to treatment failure and overall survival analyses were performed. Hazard ratios and associated statistics were computed in the R programming language. The study was considered exempt from internal review board review and data were considered nonhuman subjects. RESULTS: Response to CPIs varied between tumor types with melanoma and lung cancer performing relatively better on CPIs than other tumor types. Within tumor types, response to CPIs was stratified by mutations in specific genes. Tumor-agnostic markers including high tumor mutation burden and microsatellite instability-high were also associated with longer time to treatment failure on CPIs. Importantly, within the high tumor mutation burden and microsatellite instability-high groups, mutations in individual genes correlate with response to CPIs. CONCLUSION: The results from commercial NGS panels may be used to stratify patients for response to CPIs. In tumors where CPIs show relatively low efficacy, there may be distinct patient populations-based on gene mutation status-that are predicted to have better response to CPIs. Likewise, there may be distinct patient populations who do relatively worse on CPIs within tumor types known to respond well to CPIs.

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.

Estimating drivers of cell state transitions using gene regulatory network models.

BACKGROUND: Specific cellular states are often associated with distinct gene expression patterns. These states are plastic, changing during development, or in the transition from health to disease. One relatively simple extension of this concept is to recognize that we can classify different cell-types by their active gene regulatory networks and that, consequently, transitions between cellular states can be modeled by changes in these underlying regulatory networks. RESULTS: Here we describe MONSTER, MOdeling Network State Transitions from Expression and Regulatory data, a regression-based method for inferring transcription factor drivers of cell state conditions at the gene regulatory network level. As a demonstration, we apply MONSTER to four different studies of chronic obstructive pulmonary disease to identify transcription factors that alter the network structure as the cell state progresses toward the disease-state. CONCLUSIONS: We demonstrate that MONSTER can find strong regulatory signals that persist across studies and tissues of the same disease and that are not detectable using conventional analysis methods based on differential expression. An R package implementing MONSTER is available at github.com/QuackenbushLab/MONSTER.