Transcriptomic Approaches to Diagnosis of Inborn Errors of Immunity

Abstract

Molecular diagnosis of inborn errors of immunity (IEI) leads to many benefits including more accurate prognosis, access to molecularly targeted therapies, and better informed genetic counseling for relatives and offspring. However, in practice, diagnostic testing is often limited to gene panels and clinical exome sequencing or rarely genome sequencing. As a result, treating physicians remain unable to identify a specific cause in the majority of suspected IEI patients using currently established technologies, even when a genetic cause is suspected based on similarly affected family members. Even once a diagnosis is made, selection of appropriate treatments provides additional challenges as response to immunomodulatory therapy is heterogeneous. Better strategies are needed to identify which groups of patients are most likely to benefit from different therapies. Determining the optimal diagnostic and therapeutic strategy requires a personalized approach bringing together expertise from multiple clinical specialties and basic scientists to apply new discoveries to patient care and improve outcomes for these complex patients. We hypothesize that adding transcriptomic profiling to existing genomic diagnostic approaches could help close these gaps, facilitating identification of the molecular diagnosis in a larger fraction of patients and monitoring the gene expression response to treatment. To test this, we plan to (1) deploy RNA sequencing of peripheral blood paired with DNA sequencing in a cohort of IEI patients with known (N = 10) IEIs representing a spectrum of disorders as classified by IUIS (e.g. X-linked SCID due to ILR2g, deficiency of adenosine deaminase 2, CD40L deficiency) to validate the pipeline for detecting known disorders, (2) apply the pipeline to a pilot cohort of participants with unknown disorders (N = 10) to assess diagnostic efficacy, and (3) perform longitudinal RNA sequencing in IEI patients undergoing treatment to understand predictors of treatment response. To date, we have obtained whole-blood total RNA from 5 participants (3 known, 2 unknown and pretreatment). In parallel, we have adapted the detection of RNA outliers pipeline (DROP) for local use to identify novel monogenic causes of IEI through detection of aberrant expression, aberrant splicing, and mono-allelic expression, and have tested the pipeline using publicly available, well-characterized datasets.