Stratification of hospitalized COVID-19 patients into clinical severity progression groups by immuno-phenotyping and machine learning

Quantitative or qualitative differences in immunity may drive clinical severity in COVID-19. Although longitudinal studies to record the course of immunological changes are ample, they do not necessarily predict clinical progression at the time of hospital admission. Here we show, by a machine learning approach using serum pro-inflammatory, anti-inflammatory and anti-viral cytokine and anti-SARS-CoV-2 antibody measurements as input data, that COVID-19 patients cluster into three distinct immune phenotype groups. These immune-types, determined by unsupervised hierarchical clustering that is agnostic to severity, predict clinical course. The identified immune-types do not associate with disease duration at hospital admittance, but rather reflect variations in the nature and kinetics of individual patient's immune response. Thus, our work provides an immune-type based scheme to stratify COVID-19 patients at hospital admittance into high and low risk clinical categories with distinct cytokine and antibody profiles that may guide personalized therapy. Developing predictive methods to identify patients with high risk of severe COVID-19 disease is of crucial importance. Authors show here that by measuring anti-SARS-CoV-2 antibody and cytokine levels at the time of hospital admission and integrating the data by unsupervised hierarchical clustering/machine learning, it is possible to predict unfavourable outcome

Image_1_Ibrutinib directly reduces CD8+T cell exhaustion independent of BTK.tif

IntroductionCytotoxic CD8+ T cell (CTL) exhaustion is a dysfunctional state of T cells triggered by persistent antigen stimulation, with the characteristics of increased inhibitory receptors, impaired cytokine production and a distinct transcriptional profile. Evidence from immune checkpoint blockade therapy supports that reversing T cell exhaustion is a promising strategy in cancer treatment. Ibrutinib, is a potent inhibitor of BTK, which has been approved for the treatment of chronic lymphocytic leukemia. Previous studies have reported improved function of T cells in ibrutinib long-term treated patients but the mechanism remains unclear. We investigated whether ibrutinib directly acts on CD8+ T cells and reinvigorates exhausted CTLs. MethodsWe used an established in vitro CTL exhaustion system to examine whether ibrutinib can directly ameliorate T cell exhaustion. Changes in inhibitory receptors, transcription factors, cytokine production and killing capacity of ibrutinib-treated exhausted CTLs were detected by flow cytometry. RNA-seq was performed to study transcriptional changes in these cells. Btk deficient mice were used to confirm that the effect of ibrutinib was independent of BTK expression.ResultsWe found that ibrutinib reduced exhaustion-related features of CTLs in an in vitro CTL exhaustion system. These changes included decreased inhibitory receptor expression, enhanced cytokine production, and downregulation of the transcription factor TOX with upregulation of TCF1. RNA-seq further confirmed that ibrutinib directly reduced the exhaustion-related transcriptional profile of these cells. Importantly, using btk deficient mice we showed the effect of ibrutinib was independent of BTK expression, and therefore mediated by one of its other targets. DiscussionOur study demonstrates that ibrutinib directly ameliorates CTL exhaustion, and provides evidence for its synergistic use with cancer immunotherapy.</p