ABSTRACT
BACKGROUND: COVID-19 is a multi-system disorder with high variability in clinical outcomes among patients who are admitted to hospital. Although some cytokines such as interleukin (IL)-6 are believed to be associated with severity, there are no early biomarkers that can reliably predict patients who are more likely to have adverse outcomes. Thus, it is crucial to discover predictive markers of serious complications. METHODS: In this retrospective cohort study, we analysed samples from 455 participants with COVID-19 who had had a positive SARS-CoV-2 RT-PCR result between April 14, 2020, and Dec 1, 2020 and who had visited one of three Mayo Clinic sites in the USA (Minnesota, Arizona, or Florida) in the same period. These participants were assigned to three subgroups depending on disease severity as defined by the WHO ordinal scale of clinical improvement (outpatient, severe, or critical). Our control cohort comprised of 182 anonymised age-matched and sex-matched plasma samples that were available from the Mayo Clinic Biorepository and banked before the COVID-19 pandemic. We did a deep profiling of circulatory cytokines and other proteins, lipids, and metabolites from both cohorts. Most patient samples were collected before, or around the time of, hospital admission, representing ideal samples for predictive biomarker discovery. We used proximity extension assays to quantify cytokines and circulatory proteins and tandem mass spectrometry to measure lipids and metabolites. Biomarker discovery was done by applying an AutoGluon-tabular classifier to a multiomics dataset, producing a stacked ensemble of cutting-edge machine learning algorithms. Global proteomics and glycoproteomics on a subset of patient samples with matched pre-COVID-19 plasma samples was also done. FINDINGS: We quantified 1463 cytokines and circulatory proteins, along with 902 lipids and 1018 metabolites. By developing a machine-learning-based prediction model, a set of 102 biomarkers, which predicted severe and clinical COVID-19 outcomes better than the traditional set of cytokines, were discovered. These predictive biomarkers included several novel cytokines and other proteins, lipids, and metabolites. For example, altered amounts of C-type lectin domain family 6 member A (CLEC6A), ether phosphatidylethanolamine (P-18:1/18:1), and 2-hydroxydecanoate, as reported here, have not previously been associated with severity in COVID-19. Patient samples with matched pre-COVID-19 plasma samples showed similar trends in muti-omics signatures along with differences in glycoproteomics profile. INTERPRETATION: A multiomic molecular signature in the plasma of patients with COVID-19 before being admitted to hospital can be exploited to predict a more severe course of disease. Machine learning approaches can be applied to highly complex and multidimensional profiling data to reveal novel signatures of clinical use. The absence of validation in an independent cohort remains a major limitation of the study. FUNDING: Eric and Wendy Schmidt.
Subject(s)
COVID-19 , Biomarkers , COVID-19/diagnosis , Cohort Studies , Cytokines , Humans , Lipidomics/methods , Lipids , Metabolomics/methods , Pandemics , Prognosis , Proteomics/methods , Retrospective Studies , SARS-CoV-2Subject(s)
Betacoronavirus , Coronavirus Infections/epidemiology , Myelodysplastic Syndromes/therapy , Myeloproliferative Disorders/therapy , Pandemics , Pneumonia, Viral/epidemiology , Aged , COVID-19 , Cytokines , Humans , Leukemia, Myelomonocytic, Chronic/genetics , Leukemia, Myelomonocytic, Chronic/therapy , Male , Myelodysplastic Syndromes/epidemiology , Myeloproliferative Disorders/epidemiology , SARS-CoV-2ABSTRACT
Three cardinal manifestations of neoplasia, namely inflammation, immune dysfunction, and coagulopathy are also seen in patients with severe SARS-CoV-2 infection, providing a biological rationale for testing selected anticancer drugs for their ability to control the symptoms and/or modify the course of COVID-19.
Subject(s)
Antineoplastic Agents/therapeutic use , Betacoronavirus , Coronavirus Infections/drug therapy , Coronavirus Infections/immunology , Drug Repositioning/methods , Pneumonia, Viral/drug therapy , Pneumonia, Viral/immunology , Adrenal Cortex Hormones/therapeutic use , Anticoagulants/therapeutic use , Antineoplastic Agents/pharmacology , COVID-19 , Coronavirus Infections/physiopathology , Coronavirus Infections/virology , Humans , Immunologic Factors/therapeutic use , Inflammation/drug therapy , Interleukin-6/antagonists & inhibitors , Pandemics , Pneumonia, Viral/physiopathology , Pneumonia, Viral/virology , Programmed Cell Death 1 Receptor/antagonists & inhibitors , Protein Kinase Inhibitors/therapeutic use , SARS-CoV-2 , Treatment Outcome , Vascular Endothelial Growth Factor A/antagonists & inhibitors , COVID-19 Drug TreatmentABSTRACT
The ongoing COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 is a global public health crisis. Multiple observations indicate poorer post-infection outcomes for patients with cancer than for the general population. Herein, we highlight the challenges in caring for patients with acute leukaemias and myeloid neoplasms amid the COVID-19 pandemic. We summarise key changes related to service allocation, clinical and supportive care, clinical trial participation, and ethical considerations regarding the use of lifesaving measures for these patients. We recognise that these recommendations might be more applicable to high-income countries and might not be generalisable because of regional differences in health-care infrastructure, individual circumstances, and a complex and highly fluid health-care environment. Despite these limitations, we aim to provide a general framework for the care of patients with acute leukaemias and myeloid neoplasms during the COVID-19 pandemic on the basis of recommendations from international experts.