COVID-19 Biomarkers in Research and Associations with Comorbidities 1The Department of Biochemistry & Molecular Medicine, The George Washington University Medical Center, Washington, DC 20037 2The McCormick Genomic and Proteomic Center, The George Washington University, Washington, DC 20037 Signature molecules, including genes, proteins, target panels, and glycans termed as biomarkers, are becoming increasingly significant in the Coronavirus disease 2019 (COVID-19) pandemic. High-throughput molecular characterization technologies have greatly accelerated the development of tests for risk assessment, diagnosis, prognosis, disease monitoring, and therapeutic evaluation. The disease complexity emphasizes the need for biomarker characterization that can help stratify patients based on their variable clinical manifestations and the presence of comorbidities. Powered by an NCI ITCR funded, comprehensive data model built to capture cancer biomarker data (OncoMX), and incorporating crowdsourced data collection and integration techniques, we have efficiently harmonized COVID-19 biomarker data. As of now, we have 146 potential biomarkers for COVID-19. Most biomarkers are associated with the immune system (including complement factors, inflammatory modulators, and pro-inflammatory factors) as well as coagulation factors. These trends suggest a vascular pathobiology of the COVID-19 disease. Utilizing this collated biomarker data, we propose to identify common features and attributes of COVID-19, with cancer and other metabolic syndromes. Preliminary analysis shows biomarkers such as ACE2, IL-6, IL-4, and IL-2 have similar expression profiles in COVID-19 and cancer. Furthermore, COVID-19 biomarkers such as D-dimer, neutrophil to lymphocyte ratio (NLR), C-reactive protein (CRP), and low-density lipoprotein (LDL) shed light on the underlying physiological or pathological association with metabolic syndrome, diabetes, and hyperlipidemia. The importance of this study lies in identifying specific biomarkers that can successfully stratify patients based on distinct clinical presentations and the presence of comorbidities. Exploration of these patterns will benefit researchers and diagnosticians alike. ABSTRACT Gogate N1, Bell A1, Lyman D1, Cauley E1 , Joseph A1, Kahsay R1, and Mazumder R1,2. Project Overview: Steps for collection, organization, standardization and integration of the data elements in the COVID-19 biomarker resource data model. Rubric for Biomarker data type and column content: Harmonization of biomarker-centric knowledge (including terms, definitions, synonyms), enriched with objects imported from related reference ontologies under a unified framework. Research reported is supported in part by National Cancer Institute Grant No. U01CA215010 to RM. We would like to acknowledge all our HIVE lab volunteers for their hard work and contributions towards this resource. All data reported is freely available at https://data.oncomx.org/covid19 under the Creative Commons CC-BY-4.0 license. COVID-19 Biomarker Highlights: Top biomarkers are depicted as increased or decreased levels. The size of the circle is indicative of the number of articles supporting the biomarker. The color of the circle corresponds to the BEST biomarker type (Monitoring – blue, Prognostic – green, Diagnostic – purple, Predictive – pink). Data suggests that hyper-activation of the immune system, coagulopathies and the targeting of specific types of cells are the primary modus operandi of SARS-CoV-2. It also sheds light on the underlying physiological or pathological association with cancer as well as metabolic syndrome, diabetes, and hyperlipidemia. Model of BEST biomarker subtypes. BEST biomarker types modeled on disease progression. Each category of BEST biomarker fulfils a distinct role ”as an indicator of normal biological processes, pathogenic processes, or responses to an exposure or intervention. 1. Gogate N et al. bioRxiv [Preprint]. 2020 Sep 2. FDA-NIH_Biomark