Integration and validation of host transcript signatures, including a novel 3-transcript tuberculosis signature, to enable one-step multiclass diagnosis of childhood febrile disease.

BACKGROUND: Whole blood host transcript signatures show great potential for diagnosis of infectious and inflammatory illness, with most published signatures performing binary classification tasks. Barriers to clinical implementation include validation studies, and development of strategies that enable simultaneous, multiclass diagnosis of febrile illness based on gene expression. METHODS: We validated five distinct diagnostic signatures for paediatric infectious diseases in parallel using a single NanoString nCounter® experiment. We included a novel 3-transcript signature for childhood tuberculosis, and four published signatures which differentiate bacterial infection, viral infection, or Kawasaki disease from other febrile illnesses. Signature performance was assessed using receiver operating characteristic curve statistics. We also explored conceptual frameworks for multiclass diagnostic signatures, including additional transcripts found to be significantly differentially expressed in previous studies. Relaxed, regularised logistic regression models were used to derive two novel multiclass signatures: a mixed One-vs-All model (MOVA), running multiple binomial models in parallel, and a full-multiclass model. In-sample performance of these models was compared using radar-plots and confusion matrix statistics. RESULTS: Samples from 91 children were included in the study: 23 bacterial infections (DB), 20 viral infections (DV), 14 Kawasaki disease (KD), 18 tuberculosis disease (TB), and 16 healthy controls. The five signatures tested demonstrated cross-platform performance similar to their primary discovery-validation cohorts. The signatures could differentiate: KD from other diseases with area under ROC curve (AUC) of 0.897 [95% confidence interval: 0.822-0.972]; DB from DV with AUC of 0.825 [0.691-0.959] (signature-1) and 0.867 [0.753-0.982] (signature-2); TB from other diseases with AUC of 0.882 [0.787-0.977] (novel signature); TB from healthy children with AUC of 0.910 [0.808-1.000]. Application of signatures outside of their designed context reduced performance. In-sample error rates for the multiclass models were 13.3% for the MOVA model and 0.0% for the full-multiclass model. The MOVA model misclassified DB cases most frequently (18.7%) and TB cases least (2.7%). CONCLUSIONS: Our study demonstrates the feasibility of NanoString technology for cross-platform validation of multiple transcriptomic signatures in parallel. This external cohort validated performance of all five signatures, including a novel sparse TB signature. Two exploratory multi-class models showed high potential accuracy across four distinct diagnostic groups.

A computational framework to improve cross-platform implementation of transcriptomics signatures.

The emergence of next-generation sequencing technologies and computational advances have expanded our understanding of gene expression regulation (i.e., the transcriptome). This has also led to an increased interest in using transcriptomic biomarkers to improve disease diagnosis and stratification, to assess prognosis and predict the response to treatment. Significant progress in identifying transcriptomic signatures for various clinical needs has been made, with large discovery studies accounting for challenges such as patient variability, unwanted batch effects, and data complexities; however, obstacles related to the technical aspects of cross-platform implementation still hinder the successful integration of transcriptomic technologies into standard diagnostic workflows. In this article, we discuss the challenges associated with integrating transcriptomic signatures derived using high-throughput technologies (such as RNA-sequencing) into clinical diagnostic tools using nucleic acid amplification (NAA) techniques. The novelty of the proposed approach lies in our aim to embed constraints related to cross-platform implementation in the process of signature discovery. These constraints could include technical limitations of amplification platform and chemistry, the maximal number of targets imposed by the chosen multiplexing strategy, and the genomic context of identified RNA biomarkers. Finally, we propose to build a computational framework that would integrate these constraints in combination with existing statistical and machine learning models used for signature identification. We envision that this could accelerate the integration of RNA signatures discovered by high-throughput technologies into NAA-based approaches suitable for clinical applications.

Immunomodulatory Therapy for MIS-C.

CONTEXT: Studies comparing initial therapy for multisystem inflammatory syndrome in children (MIS-C) provided conflicting results. OBJECTIVE: To compare outcomes in MIS-C patients treated with intravenous immunoglobulin (IVIG), glucocorticoids, or the combination thereof. DATA SOURCES: Medline, Embase, CENTRAL and WOS, from January 2020 to February 2022. STUDY SELECTION: Randomized or observational comparative studies including MIS-C patients <21 years. DATA EXTRACTION: Two reviewers independently selected studies and obtained individual participant data. The main outcome was cardiovascular dysfunction (CD), defined as left ventricular ejection fraction < 55% or vasopressor requirement ≥ day 2 of initial therapy, analyzed with a propensity score-matched analysis. RESULTS: Of 2635 studies identified, 3 nonrandomized cohorts were included. The meta-analysis included 958 children. IVIG plus glucocorticoids group as compared with IVIG alone had improved CD (odds ratio [OR] 0.62 [0.42-0.91]). Glucocorticoids alone group as compared with IVIG alone did not have improved CD (OR 0.57 [0.31-1.05]). Glucocorticoids alone group as compared with IVIG plus glucocorticoids did not have improved CD (OR 0.67 [0.24-1.86]). Secondary analyses found better outcomes associated with IVIG plus glucocorticoids compared with glucocorticoids alone (fever ≥ day 2, need for secondary therapies) and better outcomes associated with glucocorticoids alone compared with IVIG alone (left ventricular ejection fraction < 55% ≥ day 2). LIMITATIONS: Nonrandomized nature of included studies. CONCLUSIONS: In a meta-analysis of MIS-C patients, IVIG plus glucocorticoids was associated with improved CD compared with IVIG alone. Glucocorticoids alone was not associated with improved CD compared with IVIG alone or IVIG plus glucocorticoids.

Diagnosis of Multisystem Inflammatory Syndrome in Children by a Whole-Blood Transcriptional Signature.

BACKGROUND: To identify a diagnostic blood transcriptomic signature that distinguishes multisystem inflammatory syndrome in children (MIS-C) from Kawasaki disease (KD), bacterial infections, and viral infections. METHODS: Children presenting with MIS-C to participating hospitals in the United Kingdom and the European Union between April 2020 and April 2021 were prospectively recruited. Whole-blood RNA Sequencing was performed, contrasting the transcriptomes of children with MIS-C (n = 38) to those from children with KD (n = 136), definite bacterial (DB; n = 188) and viral infections (DV; n = 138). Genes significantly differentially expressed (SDE) between MIS-C and comparator groups were identified. Feature selection was used to identify genes that optimally distinguish MIS-C from other diseases, which were subsequently translated into RT-qPCR assays and evaluated in an independent validation set comprising MIS-C (n = 37), KD (n = 19), DB (n = 56), DV (n = 43), and COVID-19 (n = 39). RESULTS: In the discovery set, 5696 genes were SDE between MIS-C and combined comparator disease groups. Five genes were identified as potential MIS-C diagnostic biomarkers (HSPBAP1, VPS37C, TGFB1, MX2, and TRBV11-2), achieving an AUC of 96.8% (95% CI: 94.6%-98.9%) in the discovery set, and were translated into RT-qPCR assays. The RT-qPCR 5-gene signature achieved an AUC of 93.2% (95% CI: 88.3%-97.7%) in the independent validation set when distinguishing MIS-C from KD, DB, and DV. CONCLUSIONS: MIS-C can be distinguished from KD, DB, and DV groups using a 5-gene blood RNA expression signature. The small number of genes in the signature and good performance in both discovery and validation sets should enable the development of a diagnostic test for MIS-C.

Immunoglobulin, glucocorticoid, or combination therapy for multisystem inflammatory syndrome in children: a propensity-weighted cohort study.

Background: Multisystem inflammatory syndrome in children (MIS-C), a hyperinflammatory condition associated with SARS-CoV-2 infection, has emerged as a serious illness in children worldwide. Immunoglobulin or glucocorticoids, or both, are currently recommended treatments. Methods: The Best Available Treatment Study evaluated immunomodulatory treatments for MIS-C in an international observational cohort. Analysis of the first 614 patients was previously reported. In this propensity-weighted cohort study, clinical and outcome data from children with suspected or proven MIS-C were collected onto a web-based Research Electronic Data Capture database. After excluding neonates and incomplete or duplicate records, inverse probability weighting was used to compare primary treatments with intravenous immunoglobulin, intravenous immunoglobulin plus glucocorticoids, or glucocorticoids alone, using intravenous immunoglobulin as the reference treatment. Primary outcomes were a composite of inotropic or ventilator support from the second day after treatment initiation, or death, and time to improvement on an ordinal clinical severity scale. Secondary outcomes included treatment escalation, clinical deterioration, fever, and coronary artery aneurysm occurrence and resolution. This study is registered with the ISRCTN registry, ISRCTN69546370. Findings: We enrolled 2101 children (aged 0 months to 19 years) with clinically diagnosed MIS-C from 39 countries between June 14, 2020, and April 25, 2022, and, following exclusions, 2009 patients were included for analysis (median age 8·0 years [IQR 4·2-11·4], 1191 [59·3%] male and 818 [40·7%] female, and 825 [41·1%] White). 680 (33·8%) patients received primary treatment with intravenous immunoglobulin, 698 (34·7%) with intravenous immunoglobulin plus glucocorticoids, 487 (24·2%) with glucocorticoids alone; 59 (2·9%) patients received other combinations, including biologicals, and 85 (4·2%) patients received no immunomodulators. There were no significant differences between treatments for primary outcomes for the 1586 patients with complete baseline and outcome data that were considered for primary analysis. Adjusted odds ratios for ventilation, inotropic support, or death were 1·09 (95% CI 0·75-1·58; corrected p value=1·00) for intravenous immunoglobulin plus glucocorticoids and 0·93 (0·58-1·47; corrected p value=1·00) for glucocorticoids alone, versus intravenous immunoglobulin alone. Adjusted average hazard ratios for time to improvement were 1·04 (95% CI 0·91-1·20; corrected p value=1·00) for intravenous immunoglobulin plus glucocorticoids, and 0·84 (0·70-1·00; corrected p value=0·22) for glucocorticoids alone, versus intravenous immunoglobulin alone. Treatment escalation was less frequent for intravenous immunoglobulin plus glucocorticoids (OR 0·15 [95% CI 0·11-0·20]; p<0·0001) and glucocorticoids alone (0·68 [0·50-0·93]; p=0·014) versus intravenous immunoglobulin alone. Persistent fever (from day 2 onward) was less common with intravenous immunoglobulin plus glucocorticoids compared with either intravenous immunoglobulin alone (OR 0·50 [95% CI 0·38-0·67]; p<0·0001) or glucocorticoids alone (0·63 [0·45-0·88]; p=0·0058). Coronary artery aneurysm occurrence and resolution did not differ significantly between treatment groups. Interpretation: Recovery rates, including occurrence and resolution of coronary artery aneurysms, were similar for primary treatment with intravenous immunoglobulin when compared to glucocorticoids or intravenous immunoglobulin plus glucocorticoids. Initial treatment with glucocorticoids appears to be a safe alternative to immunoglobulin or combined therapy, and might be advantageous in view of the cost and limited availability of intravenous immunoglobulin in many countries. Funding: Imperial College London, the European Union's Horizon 2020, Wellcome Trust, the Medical Research Foundation, UK National Institute for Health and Care Research, and National Institutes of Health.

Characterisation of the blood RNA host response underpinning severity in COVID-19 patients.

Infection with SARS-CoV-2 has highly variable clinical manifestations, ranging from asymptomatic infection through to life-threatening disease. Host whole blood transcriptomics can offer unique insights into the biological processes underpinning infection and disease, as well as severity. We performed whole blood RNA Sequencing of individuals with varying degrees of COVID-19 severity. We used differential expression analysis and pathway enrichment analysis to explore how the blood transcriptome differs between individuals with mild, moderate, and severe COVID-19, performing pairwise comparisons between groups. Increasing COVID-19 severity was characterised by an abundance of inflammatory immune response genes and pathways, including many related to neutrophils and macrophages, in addition to an upregulation of immunoglobulin genes. In this study, for the first time, we show how immunomodulatory treatments commonly administered to COVID-19 patients greatly alter the transcriptome. Our insights into COVID-19 severity reveal the role of immune dysregulation in the progression to severe disease and highlight the need for further research exploring the interplay between SARS-CoV-2 and the inflammatory immune response.

Transcriptomics for child and adolescent tuberculosis.

Tuberculosis (TB) in humans is caused by Mycobacterium tuberculosis (Mtb). It is estimated that 70 million children (<15 years) are currently infected with Mtb, with 1.2 million each year progressing to disease. Of these, a quarter die. The risk of progression from Mtb infection to disease and from disease to death is dependent on multiple pathogen and host factors. Age is a central component in all these transitions. The natural history of TB in children and adolescents is different to adults, leading to unique challenges in the development of diagnostics, therapeutics, and vaccines. The quantification of RNA transcripts in specific cells or in the peripheral blood, using high-throughput methods, such as microarray analysis or RNA-Sequencing, can shed light into the host immune response to Mtb during infection and disease, as well as understanding treatment response, disease severity, and vaccination, in a global hypothesis-free manner. Additionally, gene expression profiling can be used for biomarker discovery, to diagnose disease, predict future disease progression and to monitor response to treatment. Here, we review the role of transcriptomics in children and adolescents, focused mainly on work done in blood, to understand disease biology, and to discriminate disease states to assist clinical decision-making. In recent years, studies with a specific pediatric and adolescent focus have identified blood gene expression markers with diagnostic or prognostic potential that meet or exceed the current sensitivity and specificity targets for diagnostic tools. Diagnostic and prognostic gene expression signatures identified through high-throughput methods are currently being translated into diagnostic tests.

Treatment of Multisystem Inflammatory Syndrome in Children.

BACKGROUND: Evidence is urgently needed to support treatment decisions for children with multisystem inflammatory syndrome (MIS-C) associated with severe acute respiratory syndrome coronavirus 2. METHODS: We performed an international observational cohort study of clinical and outcome data regarding suspected MIS-C that had been uploaded by physicians onto a Web-based database. We used inverse-probability weighting and generalized linear models to evaluate intravenous immune globulin (IVIG) as a reference, as compared with IVIG plus glucocorticoids and glucocorticoids alone. There were two primary outcomes: the first was a composite of inotropic support or mechanical ventilation by day 2 or later or death; the second was a reduction in disease severity on an ordinal scale by day 2. Secondary outcomes included treatment escalation and the time until a reduction in organ failure and inflammation. RESULTS: Data were available regarding the course of treatment for 614 children from 32 countries from June 2020 through February 2021; 490 met the World Health Organization criteria for MIS-C. Of the 614 children with suspected MIS-C, 246 received primary treatment with IVIG alone, 208 with IVIG plus glucocorticoids, and 99 with glucocorticoids alone; 22 children received other treatment combinations, including biologic agents, and 39 received no immunomodulatory therapy. Receipt of inotropic or ventilatory support or death occurred in 56 patients who received IVIG plus glucocorticoids (adjusted odds ratio for the comparison with IVIG alone, 0.77; 95% confidence interval [CI], 0.33 to 1.82) and in 17 patients who received glucocorticoids alone (adjusted odds ratio, 0.54; 95% CI, 0.22 to 1.33). The adjusted odds ratios for a reduction in disease severity were similar in the two groups, as compared with IVIG alone (0.90 for IVIG plus glucocorticoids and 0.93 for glucocorticoids alone). The time until a reduction in disease severity was similar in the three groups. CONCLUSIONS: We found no evidence that recovery from MIS-C differed after primary treatment with IVIG alone, IVIG plus glucocorticoids, or glucocorticoids alone, although significant differences may emerge as more data accrue. (Funded by the European Union's Horizon 2020 Program and others; BATS ISRCTN number, ISRCTN69546370.).