Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity: a retrospective cohort study.

Autor:	Byeon SK; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA., Madugundu AK; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA; Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka, India; Center for Molecular Medicine, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India; Manipal Academy of Higher Education, Manipal, Karnataka, India., Garapati K; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA; Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka, India; Center for Molecular Medicine, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India; Manipal Academy of Higher Education, Manipal, Karnataka, India., Ramarajan MG; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA; Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka, India; Center for Molecular Medicine, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India; Manipal Academy of Higher Education, Manipal, Karnataka, India., Saraswat M; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA; Manipal Academy of Higher Education, Manipal, Karnataka, India., Kumar-M P; nference Labs, Bangalore, Karnataka, India., Hughes T; nference, Cambridge, MA, USA., Shah R; Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, USA., Patnaik MM; Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA; Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA., Chia N; Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA; Department of Surgery, Mayo Clinic, Rochester, MN, USA., Ashrafzadeh-Kian S; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA., Yao JD; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA., Pritt BS; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA., Cattaneo R; Department of Molecular Medicine, Mayo Clinic, Rochester, MN, USA., Salama ME; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA., Zenka RM; Proteomics Core, Mayo Clinic, Rochester, MN, USA., Kipp BR; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA., Grebe SKG; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA., Singh RJ; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA., Sadighi Akha AA; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA., Algeciras-Schimnich A; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA., Dasari S; Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA., Olson JE; Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA., Walsh JR; Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA., Venkatakrishnan AJ; nference, Cambridge, MA, USA., Jenkinson G; Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA., O'Horo JC; Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA., Badley AD; Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA., Pandey A; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA; Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA. Electronic address: pandey.akhilesh@mayo.edu.
Jazyk:	angličtina
Zdroj:	The Lancet. Digital health [Lancet Digit Health] 2022 Sep; Vol. 4 (9), pp. e632-e645. Date of Electronic Publication: 2022 Jul 11.
DOI:	10.1016/S2589-7500(22)00112-1
Abstrakt:	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. Competing Interests: Declaration of interests PK-M, TH, and AJV are employees of nference. JCO receives grants from nference and personal fees from Elsevier and Bates College, outside the submitted work. ADB is supported by grants from NIAID (AI110173 and AI120698), Amfar (109593), and Mayo Clinic (HH Shieck Khalifa Bib Zayed Al-Nahyan Named Professorship of Infectious Diseases). ADB is a paid consultant for AbbVie, Gilead, Freedom Tunnel, Pinetree Therapeutics, Primmune, Immunome, MarPam, Rion, and Flambeau Diagnostics; is a paid member of the DSMB for Corvus Pharmaceuticals, Equilium, and Excision Biotherapeutics; has received fees for speaking for Reach MD, Peer Voice, and Medscape; owns equity for scientific advisory work in Zentalis Rion and nference; and is founder and President of Splissen Therapeutics. (Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)
Databáze:	MEDLINE
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