Novel tools for primary immunodeficiency diagnosis: making a case for deep profiling

Evolving Approach to Clinical Cytometry for Immunodeficiencies and Other Immune Disorders

Primary immunodeficiencies were initially identified on the basis of recurrent, severe or unusual infections. Subsequently, it was noted that these diseases can also manifest with autoimmunity, autoinflammation, allergy, lymphoproliferation and malignancy, hence a conceptual change and their renaming as inborn errors of immunity. Ongoing advances in flow cytometry provide the opportunity to expand or modify the utility and scope of existing laboratory tests in this field to mirror this conceptual change. Here we have used the B cell subset, variably known as CD21low B cells, age-associated B cells and T-bet+ B cells, as an example to demonstrate this possibility.

Leveraging Systems Immunology to Optimize Diagnosis and Treatment of Inborn Errors of Immunity

Inborn errors of immunity (IEI) are monogenic disorders that can cause diverse symptoms, including recurrent infections, autoimmunity and malignancy. While many factors have contributed, the increased availability of next-generation sequencing has been central in the remarkable increase in identification of novel monogenic IEI over the past years. Throughout this phase of disease discovery, it has also become evident that a given gene variant does not always yield a consistent phenotype, while variants in seemingly disparate genes can lead to similar clinical presentations. Thus, it is increasingly clear that the clinical phenotype of an IEI patient is not defined by genetics alone, but is also impacted by a myriad of factors. Accordingly, we need methods to amplify our current diagnostic algorithms to better understand mechanisms underlying the variability in our patients and to optimize treatment. In this review, we will explore how systems immunology can contribute to optimizing both diagnosis and treatment of IEI patients by focusing on identifying and quantifying key dysregulated pathways. To improve mechanistic understanding in IEI we must deeply evaluate our rare IEI patients using multimodal strategies, allowing both the quantification of altered immune cell subsets and their functional evaluation. By studying representative controls and patients, we can identify causative pathways underlying immune cell dysfunction and move towards functional diagnosis. Attaining this deeper understanding of IEI will require a stepwise strategy. First, we need to broadly apply these methods to IEI patients to identify patterns of dysfunction. Next, using multimodal data analysis, we can identify key dysregulated pathways. Then, we must develop a core group of simple, effective functional tests that target those pathways to increase efficiency of initial diagnostic investigations, provide evidence for therapeutic selection and contribute to the mechanistic evaluation of genetic results. This core group of simple, effective functional tests, targeting key pathways, can then be equitably provided to our rare patients. Systems biology is thus poised to reframe IEI diagnosis and therapy, fostering research today that will provide streamlined diagnosis and treatment choices for our rare and complex patients in the future, as well as providing a better understanding of basic immunology.

Screening for primary immunodeficiency diseases by next-generation sequencing in early life

Objective

We aimed to use next-generation sequencing (NGS) for the early diagnosis of primary immunodeficiency diseases (PIDs) and define its effects on medical management for an infant cohort in early life.

Methods

A single-centre study was conducted from November 2015 to April 2018. Infants less than 3 months old with infections or abnormal white blood cell counts were enrolled in the study. Gene variants were analysed by NGS, and once a mutation was found in a PID-associated gene, the immune functions associated with this mutation were detected. The diagnosis rate of PIDs in the cohort was the main outcome. The patients received corresponding management and follow-up treatments.

Results

Among 2392 patients who were genetically tested with NGS, 51 infants were diagnosed with PIDs. Seven types of PIDs were detected, and the most common (25/51, 49%) were combined immunodeficiencies with associated or syndromic features. Thirty-five patients (68.6%) were cured or had improved outcomes after being diagnosed with PID. The NGS cost was US$280 per case.

Conclusions

This study not only highlighted the potential of NGS to rapidly deliver molecular diagnoses of PIDs but also indicated that the prevalence of PIDs is underestimated. With broader use, this approach has the potential to alter clinical strategies.

Flow cytometry: Surface markers and beyond

Flow cytometry is a routinely available laboratory method to study cells in suspension from a variety of human sources. Application of this technology as a clinical laboratory method has evolved from the identification of cell-surface proteins to characterizing intracellular proteins and providing multiple different techniques to assess specific features of adaptive and innate immune function. This expanded menu of flow cytometric testing approaches has increased the utility of this platform in characterizing and diagnosing disorders of immune function.

Diagnostic Tools for Inborn Errors of Human Immunity (Primary Immunodeficiencies and Immune Dysregulatory Diseases)

PURPOSE OF REVIEW

The purpose of this review is to provide an overview of diagnostic testing in primary immunodeficiency and immune dysregulatory disorders (PIDDs), particularly focusing on flow cytometry and genetic techniques, utilizing specific examples of PIDDs.

RECENT FINDINGS

Flow cytometry remains a vital tool in the diagnosis and monitoring of immunological diseases. Its utility ranges from cellular analysis and specific protein quantitation to functional assays and signaling pathway analysis. Mass cytometry combines flow cytometry and mass spectrometry to dramatically increase the throughput of multivariate single-cell analysis. Next-generation sequencing in combination with other molecular techniques and processing algorithms has become more widely available and identified the diverse and heterogeneous genetic underpinnings of these disorders. As the spectrum of disease is further clarified by increasing immunological, genetic, and epigenetic knowledge, the careful application of these diagnostic tools and bioinformatics will assist not only in our understanding of these complex disorders, but also enable the implementation of personalized therapeutic approaches for disease management.

Clinical efficacy of a next-generation sequencing gene panel for primary immunodeficiency diagnostics

Primary immunodeficiencies (PIDs) are rare monogenic inborn errors of immunity that result in impairment of functions of the human immune system. PIDs have a broad phenotype with increased morbidity and mortality, and treatment choices are often complex. With increased accessibility of next-generation sequencing (NGS), the rate of discovery of genetic causes for PID has increased exponentially. Identification of an underlying monogenic diagnosis provides important clinical benefits for patients with the potential to alter treatments, facilitate genetic counselling, and pre-implantation diagnostics. We investigated a NGS PID panel of 242 genes within clinical care across a range of PID phenotypes. We also evaluated Phenomizer to predict causal genes from human phenotype ontology (HPO) terms. Twenty-seven participants were recruited, and a total of 15 reportable variants were identified in 48% (13/27) of the participants. The panel results had implications for treatment in 37% (10/27) of participants. Phenomizer identified the genes harbouring variants from HPO terms in 33% (9/27) of participants. This study shows the clinical efficacy that genetic testing has in the care of PID. However, it also highlights some of the disadvantages of gene panels in the rapidly moving field of PID genomics and current challenges in HPO term assignment for PID.