ABSTRACT
Introduction: Anakinra treatment is approved for the treatment of COVID-19 pneumonia in hospitalized adults in need of oxygen and at risk for progression into severe respiratory failure (SRF) defined as circulating concentrations of the biomarker suPAR (soluble urokinase plasminogen activator receptor) >= 6 ng/mL by the EMA and has been authorized for emergency use by FDA under an EUA [1]. This is based on the results of the randomized SAVE-MORE trial where suPAR >= 6 ng/ mL was used to select patients at risk for SRF [2]. The suPAR test is not commercially available in the USA and an alternative method of patient selection was needed. Method(s): In collaboration with the US FDA, an alternative method to select patients most likely to have suPAR >= 6 ng/mL based on commonly measured patient characteristics was developed. Patients with at least 3 of the following criteria are considered likely to have suPAR >= 6 ng/ ml: age >= 75 years, severe pneumonia by WHO criteria, current/previous smoking status, Sequential Organ Failure Assessment score >= 3, neutrophil-to-lymphocyte ratio >= 7, hemoglobin <= 10.5 g/dl, history of ischemic stroke, blood urea >= 50 mg/dl and/or history of renal disease. Result(s): The positive predictive value of this new score was 95.4% in SAVE-MORE population. However, a lower sensitivity meant a small proportion of patients with suPAR >= 6 ng/ml will not be identified by the new score. The adjusted hazard ratio for survival at 60 days for patients meeting this score and who receive anakinra is 0.45 (Fig. 1). Conclusion(s): The developed score predicts accurately patients with suPAR levels >= 6 ng/mL and may be used as an alternative to guide anakinra treatment in patients with COVID-19 pneumonia. Based on these subgroup results, patients in SAVE-MORE who met the new score appeared to show beneficial efficacy results with treatment of anakinra consistent with the overall studied population.
ABSTRACT
Background: COVID-19 is a pandemic caused by the SARS-CoV-2 virus that shares and differs in clinical characteristics of known viral infections. Methods: We obtained RNAseq profiles of 62 prospectively enrolled COVID-19 patients and 24 healthy controls (HC). We collected 23 independent studies profiling 1,855 blood samples from patients covering six viruses (influenza, RSV, HRV, Ebola, Dengue and SARS-CoV-1). We studied host whole-blood transcriptomic responses in COVID-19 compared to non-COVID-19 viral infections to understand similarities and differences in host response. Gene signature threshold was absolute effect size ≥1, FDR ≤ 0.05%. Results: Differential gene expression of COVID-19 vs HC are highly correlated with non-COVID-19 vs HC (r=0.74, p< 0.001). We discovered two gene signatures: COVID-19 vs HC (2002 genes) (COVIDsig) and non-COVID-19 vs HC (635 genes) (nonCOVIDsig). Pathway analysis of over-expressed signature genes in COVIDsig or nonCOVIDsig identified similar pathways including neutrophil activation, innate immune response, immune response to viral infection and cytokine production. Conversely, for under-expressed genes, pathways indicated repression of lymphocyte differentiation and activation (Fig1). Intersecting the two gene signatures found two genes significantly oppositely regulated (ACO1, ATL3). We derived a third gene signature using COCONUT to compare COVID-19 to non-COVID-19 viral infections (416 genes) (Fig2). Pathway analysis did not result in significant enrichment, suggesting identification of novel biology (Fig1). Statistical deconvolution of bulk transcriptomic data found M1 macrophages, plasmacytoid dendritic cells, CD14+ monocytes, CD4+ T cells and total B cells changed in the same direction across COVID-19 and non-COVID-19 infections. Cell types that increased in COVID-19 relative to non-COVID-19 were CD56bright NK cells, M2 macrophages and total NK cells. Those that decreased in non- COVID-19 relative to COVID-19 were CD56dim NK cells & memory B cells and eosinophils (Fig3). Conclusion: The concordant and discordant responses mapped here provide a window to explore the pathophysiology of COVID-19 vs other viral infections and show clear differences in signaling pathways and cellularity as part of the host response to SARS-CoV-2.