CLN - Feature
Twenty years ago, the diagnosis of a rare immunological disease often meant years of uncertainty for patients and their families. Since then, the advent of next-generation sequencing (NGS) technology has revolutionized the identification and treatment of rare genetic disorders, particularly inborn errors of immunity (IEI).
IEI are a heterogeneous group of disorders characterized by immune dysfunction. Formerly known as primary immunodeficiency disorders (PID), they were initially associated with an increased susceptibility to infection. However, the application of NGS revealed that they actually span a diverse clinical spectrum, including autoimmunity, autoinflammation, atopy, and malignancy. This led experts to adopt the term IEI. The most recent International Union of Immunological Societies (IUIS) classification, a key diagnostic reference guide for these disorders, lists nearly 600 gene defects associated with IEI (1,2).
At the same time, NGS also revealed that the underlying genetic etiology is far more complex than previously believed. As more patients undergo sequencing, the classical model of genetic diseases — in which mutations to a single gene result in an inborn disorder — is being challenged. In fact, the growing recognition that autoantibodies or somatic gene variants developed after birth cause some forms of IEI has prompted some in the field to advocate for returning to the use of the term “PID” (3,4).
Further complicating matters, the same genetic variant can cause a variable degree of clinical manifestation, or even the absence of symptoms, sometimes among patients within the same family. This occurs due to two distinct but related phenomena: incomplete penetrance and variable expressivity. Together, they suggest that the clinical picture for IEI not only depends on the variant, but also on additional regulatory mechanisms. Moreover, a significant proportion of patients who have symptoms consistent with IEI remain without a molecular diagnosis despite undergoing comprehensive testing.
These diagnostic challenges may result from both the limitations of the current sequencing platforms and the inherent biological complexity of IEI. Addressing them requires clinical laboratory professionals and clinicians to work together, moving beyond a narrow genetic lens and refining their diagnostic practices accordingly.
This article reviews some of the increasingly recognized genetic mechanisms that complicate the diagnosis of IEI and discusses their implications for laboratory professionals.
Traditionally, IEIs were considered monogenic disorders. They were thought to be linked to a single gene that followed standard Mendelian patterns of inheritance, including X-linked, autosomal dominant, or autosomal recessive inheritance.
However, recent studies reveal that far more complex genetic mechanisms are at play. In some cases, clinical disease development requires pathogenic variants in two genes (digenic inheritance) or three or more genes (oligogenic inheritance), challenging the “one gene, one disease” paradigm. This has been well described in some IEIs, such as hemophagocytic lymphohistiocytosis and certain subsets of combined variable immunodeficiency.
These mechanisms affect laboratory and clinical diagnosis in multiple ways. First, they necessitate the development of bioinformatics pipelines capable of finding multiple gene variants, because existing pipelines are largely optimized for single-variant analysis. Second, they underscore the need to perform validation studies to determine the functional consequence of each variant.
Third, they illustrate that the combined effect of multiple variants may lead to an atypical clinical presentation. Thus, standard treatment protocols may not be applicable, and clinicians may need to personalize therapeutic decisions. This complexity highlights the importance of multi-disciplinary collaboration between laboratorians, immunologists, and other clinicians for accurate diagnosis and improved patient care.
IEIs have long been considered to result from germline variants, or gene changes that get incorporated into the DNA of every cell of a person’s offspring. However, recently there has been growing recognition that this may not always be the case, because IEI sometimes shows somatic mosaicism.
Somatic mosaicism results from a postzygotic mutation that leads to the presence of a pathogenic variant in only a subset of a person’s cells. The timing of the post-zygotic mutation determines the variant allele frequency (VAF) and tissue distribution of the variant. Somatic variants with high VAFs can be detected relatively easily, compared with clinically relevant somatic variants with low VAFs (below 10–15%), because standard sequencing pipelines filter out low VAFs as background noise.
Tissue restriction of these variants further affects their detection. For instance, a variant may be missed if the only bodily substance analyzed is a patient’s blood. Finding the variant may require the testing of additional tissues such as hair, nail, buccal swab, or fibroblast samples.
Experts recognized the role of somatic mosaicism in IEI in the 2000s, and the concept gained renewed interest in recent years. This has been largely driven by the application of deep sequencing (200–500x) and amplicon-based sequencing methods with high sensitivity and specificity for detecting variants with low VAFs (5). Although most somatic variants have been identified in genes previously linked to IEI, the discovery of diseases primarily driven by somatic mosaicism is the most exciting development in the field. For example, the discovery of somatic mosaicism in the gene UBA1 explains the cause of a previously uncharacterized adult-onset autoinflammatory disease (6).
Unfortunately, the application of high-depth sequencing technology and the integration of multitissue testing remains limited in routine diagnostics because of the cost and logistical challenges. However, it is worth considering a targeted approach that involves deep sequencing in IEI patients, particularly those with sporadic symptoms that remain genetically uncharacterized. Along with functional validation studies, these tools might uncover the underlying cause of IEI in patients who would otherwise remain undiagnosed.
Identifying a pathogenic variant might not always equate to finding disease or predicting clinical severity in affected individuals. Multiple factors may significantly influence the penetrance and expressivity of a genetic variant, including epigenetic silencing, modifier genes, and environmental triggers such as microbiome composition or infection. These dynamic changes work at the level of gene regulation or involve environmental interactions invisible to DNA sequencing.
The process of skewed X-inactivation in female carriers of X-linked disorders offers a classic example of epigenetic silencing that influences disease presentation. These disorders include X-linked chronic granulomatous disease, Wiskott-Aldrich syndrome, or X-linked agammaglobulinemia. The mechanism for X-inactivation is random, with around 50% of cells expressing a wild type (healthy) copy of the gene and the other 50% expressing a defective copy. Skewed inactivation of the X-chromosome that leads to a preferential inactivation of the wild type gene can also lead to clinical symptoms even among female carriers. The severity of symptoms varies depending on the degree of skewing, with some showing minor symptoms and others demonstrating a full-blown manifestation of the disease.
Autosomal random monoallelic expression is a more recently recognized cause that contributes to phenotypic variability, or variability in how IEI manifests in different patients (7). In this process, experts believe that cell type–specific bias in expression due to random inactivation of an allele influences the phenotype.
An interesting example is the study of a JAK1 variant that showed varying patterns of expression in multiple members of a family with different clinical presentations. Although the affected carrier expressed both the healthy and defective JAK1 allele, the unaffected carrier expressed only the healthy JAK1 allele, suggesting that both the genotype (gene makeup) and transcriptotype (pattern of RNA transcription) are required to understand the molecular basis of a few IEIs.
These findings emphasize the need to integrate genomic data with assays such as X-inactivation analysis and RNA sequencing studies to aid diagnosis, particularly in patients with an uncharacterized or atypical presentation.
Diagnosing an IEI is not a straightforward process. It requires a strategic approach that integrates data from multiple teams. Although genetic sequencing is essential, it represents only one piece of the clinical puzzle. Determining the pathogenicity of the variant is equally crucial.
Further, the diagnostic yield of genetic testing depends on both the testing methodology and the patient cohort. For example, whole exome sequencing or whole genome sequencing typically yield a molecular diagnosis in approximately 40% of cases (8), with higher yields observed in early-onset or familial presentations and lower yields in adult sporadic cohorts.
That leaves a significant percentage of patients who have either variants of uncertain significance or who remain genetically uncharacterized despite a strong clinical suspicion of IEI. Addressing this diagnostic gap requires labs to embrace the genetic complexities outlined in this article.
Increasingly, this may involve deploying additional tools such as high-depth sequencing, RNA studies, single-cell sequencing, protein studies, machine learning algorithms, and artificial intelligence (9). Additionally, functional characterization, including flow cytometry, cytokine studies, and animal models, remain critical for validating uncertain findings and translating the genetic information into clinically actionable insights (10). Diagnosing IEI should involve a team-based approach, since these disorders sit at the intersection of immunology, genomics, and functional biology.
Just as white light passing through a prism reveals a spectrum of colors, the study of IEI reflects a spectrum of biological and clinical diversity. And as an inverted prism can blend those colors back into clear white light, a collaborative analysis of clinical, functional, and genetic data can yield precise diagnosis.
Clinical laboratorians are tasked with recombining these scattered pieces of data — which often requires recognizing the “gray” areas in immunogenetics, including outliers and unexpected patterns. This demands questioning our standard genetic pipelines and, in some instances, adopting a completely different strategy tailored to the patient. Only through an integrated approach can the diagnostic ambiguity of IEI be addressed to provide meaningful answers to patients.
Jahnavi Aluri, PhD, is the assistant director of diagnostic immunology in the department of pathology at Nationwide Children’s Hospital, in Columbus, Ohio. +Email: [email protected]
Read the full May-June issue of CLN.