Rationale, Objective and Aims: Systemic lupus erythematosus (SLE) is a complex, highly variable autoimmune disease that predominantly strikes young people, ages 15-40 years, causing inflammation and decreasing quality of life and shortening lifespan. SLE’s onset and early signs and symptoms are often vague or could be seen in other conditions, increasing the risk of a delayed diagnosis. About half of patients wait several years for diagnosis and many receive an incorrect first diagnosis. Delays in diagnosis lead to poor outcomes, yet even rheumatology clinicians often do not have the ability to accurately spot developing SLE. Being able to accurately identify the development of SLE and its subtypes among the many patients who are seen in clinic for evaluation of suspected disease would be a huge breakthrough, allowing earlier treatment to prevent its complications. Additionally, we are only beginning to understand the many immune pathways involved in the development of SLE, and we have only a handful of effective therapies, most of which do not always work well or have high rates of side effects. We clearly need better and smarter targeted therapies to treat those with both early and existing SLE in all its forms.

We have identified features associated with progression to SLE among patients presenting for initial evaluation of suspected SLE. In preliminary studies, we also used a cutting-edge technique called high-dimensional immunophenotyping and found four specific lymphocyte cell populations that were very increased in the blood of patients with SLE compared to healthy people. These are called T peripheral helper cells, T follicular helper cells, age-associated B cells, and plasmablasts. Separately, we also have recently used new machine learning techniques to identify independent groups of people with SLE based on their ongoing clinical signs and symptoms, and their blood proteins called antibodies.

Addressing the FY21 Lupus Research Program Focus Areas. Our research, entitled “Dissecting Lymphocyte Dynamics to Identify Early Lupus” specifically addresses two Focus Areas of the Lupus Impact Award Program Announcement: “Understanding how lupus disease heterogeneity impacts risk of disease and disease presentation” and “Understanding the biological mechanisms of lupus disease.”

Our novel studies will examine the specific kinds of T and B lymphocyte cells in the blood that we have recently found to be increased in patients with SLE, including those with newly diagnosed SLE, and whether they can be used to identify people developing SLE. We will use new machine learning techniques to dissect how they are related to SLE clinical signs and symptoms, as well as to the production of blood proteins seen in SLE, autoantibodies, and cytokines. We will develop a new “Lupus Lymphocyte Activation Score” that we hypothesize will be related to progression toward SLE over one year. We will dig deep into the function and replication of these T and B cells and how their own identity may change or remain the same over one year. The results will tell us whether new drugs for SLE should aim to destroy just one or more specific clones of lymphocytes that has gone haywire, or aim to reduce all lymphocyte production, as new clones are constantly turning up. This is a central question in the biology of SLE and identification of new targets for therapy that we simply do not yet know.

Research methods and applicability of results to people with SLE. To perform these innovative studies, we will recruit participants from the large Brigham and Women’s Hospital (BWH) SLE Clinic who are being seen for evaluation of whether they may be developing SLE by expert rheumatologists. The BWH is a high-volume clinical center with over 200 new visits a year for the question of suspected SLE. We will follow a group of 40 of these patients, who we have found to be highly motivated to participate in such studies, as they too want to know whether they are developing disease, over one year. We will collect extensive data, including their SLE signs, symptoms, and treatments, blood protein markers, and characterize T and B cell types and their clonality based on their receptors. Our expert research team involves scientists in rheumatology, immunology, translational epidemiology, computer science/machine learning, and biostatistics. Our studies will take three years to perform, and we will then pursue them to prove and validate our new methods for identifying early SLE and dissecting the variability of this disease. Our research results will have a great impact on both clinical care and basic science research to find new SLE therapies. For people suspected of having SLE, these new approaches have high potential to allow earlier and accurate diagnosis of impending disease and type of disease, as well as to let those who do not have high-risk features know they can stop worrying. For those who are developing or have SLE, deep understanding of what is occurring at the cellular level as the disease progresses will allow for new and targeted strategies to prevent and treat this devastating disease.