The Future of Blood Testing Is the Immunome

Abstract

It is increasingly clear that an extraordinarily diverse range of clinically important conditions-including infections, vaccinations, autoimmune diseases, transplants, transfusion reactions, aging, and cancers-leave telltale signatures in the millions of V(D)J-rearranged antibody and T cell receptor [TR per the Human Genome Organization (HUGO) nomenclature but more commonly known as TCR] genes collectively expressed by a person's B cells (antibodies) and T cells. We refer to these as the immunome. Because of its diversity and complexity, the immunome provides singular opportunities for advancing personalized medicine by serving as the substrate for a highly multiplexed, near-universal blood test. Here we discuss some of these opportunities, the current state of immunome-based diagnostics, and highlight some of the challenges involved. We conclude with a call to clinicians, researchers, and others to join efforts with the Adaptive Immune Receptor Repertoire Community (AIRR-C) to realize the diagnostic potential of the immunome.

Keywords: T-cell receptor repertoire; adaptive immune receptor repertoire (AIRR); analyses; antibody repertoire; clinical laboratory testing; diagnostic test; immunome; immunomics.

Figure 1

Immunome-based diagnostic testing. (A) Testing begins with a standard clinical blood draw. The recombined immunoglobulin (B cell receptor) and TR (T cell receptor) rearranged genes are sequenced, leading to a list of the hundreds of thousands of different sequences present in the sample: i.e., the patient’s immunome. (B) To develop a test for a specific condition, immunomes are sequenced from a set of cases positive for the condition and an appropriately matched set of controls. Robust statistical and mathematical techniques are used to identify patterns in the form of specific sequences, motifs (e.g., the IGH CDR3 shown in red), and clusters, as well as changes in overall sequence diversity, that are characteristic of the cases but not the controls. Based on these and other sequence features, and with the help of computational techniques, a classifier is developed that reliably separates the two groups. Using this classifier, a patient of unknown status (large gray circle) can be diagnosed by sequencing that patient’s immunome and looking for presence or absence of the pattern. (C) By applying classifiers for many different conditions to the sequence from a single blood draw, many different conditions can be diagnosed simultaneously, yielding a highly multiplexed diagnostic assay. (D) As more individuals are tested for a specific condition, the classifier for that condition will be refined—in AI terms, it “learns”—allowing individuals who were previously unclassifiable to be diagnosed and potentially allowing stratification of patients who might benefit from different treatments or who might have a different prognosis or risk of disease development.