Sample collection for systems evaluation of patients with unknown or incompletely characterized immune defects

National Institute Of Allergy And Infectious Diseases

Investigators

Abstract

Disease classifications, guidelines, and practice parameters exist for both common and previously discovered primary immune disorders, but it can be challenging to identify appropriate treatments for patients with such disorders that are novel or incompletely characterized. Patients with these diagnoses often experience a wide array of treatments and misdiagnoses, sometimes referred to as a diagnostic odyssey, which can result in a lack of tailored approaches to their clinical management. Delays in accurate diagnosis can lead to postponed administration of treatment and increased morbidity and mortality. Technological developments over the past decades have expanded our ability to characterize patients with immunological disorders of unclear genetic etiology. One example is improved sensitivity in measuring circulating cytokine concentrations and cell subset frequencies. While both have been useful to characterize numerous immunological diseases, specific markers can be shared by multiple diseases, making them insufficiently specific for disease diagnosis. Within the past two decades, evaluation of gene expression in patient immune cells has become increasingly useful for characterizing immunological disease. Gene expression data obtained from microarrays and, more recently, RNA sequencing (RNA-seq), have been used clinically since the late 1990s to evaluate gene expression differences between healthy and diseased individuals, first in cancer and later in immunology. A more complete picture of the immune system is needed to better characterize patients whose immunological diseases remain poorly understood despite the above diagnostic approaches. Many of our diagnostic tools, such as characterization of cell subset frequencies, look at only one parameter in the immune system, which is typically insufficient to capture the systems complexity. Although we have substantial information at the level of individual molecules and processes of the immune system that operate in isolation or as a part of smaller functional units, a significant gap in knowledge exists regarding how the different parts of the immune system work together as a whole. Over the past decade, significant advances have been made in the volume and type of scientific data that can now be generated in both cell lines and living organisms, even to the point of characterizing proteins and RNA transcripts of single cells. When combined, these types of data provide a much more comprehensive picture of disease states than was previously possible. The challenge for the future will be to integrate these data types and clinical data together for medically actionable endpoints. By using new modeling techniques to combine multiple types of data, we can build a more complete picture of the immune system, in both healthy and sick individuals. This is particularly relevant for understanding and managing individuals affected by immunological diseases for which there are no clear diagnoses or treatments. Better characterizing the immune systems of patients with immunodeficiencies will provide a starting place for future studies of specific clinically actionable targets. The goal of this study is to evaluate patients with incompletely characterized immunological conditions to facilitate hypothesis generation regarding disease mechanisms and etiologies. By applying novel technologies to more deeply phenotype patients at the molecular and cellular levels, we aim to identify clinically significant disease mechanisms and potentially actionable therapeutic targets in patients. We hypothesize that unknown or incompletely characterized immunological defects can be more completely understood, to include underlying molecular mechanisms and potential therapies, through detailed molecular and cellular phenotyping combined with systems biology analysis approaches. This study was IRB approved in May 2020, but recruitment of subjects has not yet started due to the COVID-19 pandemic.