Brigham Rheumatology’s Cutting-edge Research at ACR 2023

Ellen Gravallese, MD, stands beside American College of Rheumatology (ACR) president Douglas White, MD, PhD, after she received the Presidential Gold Medal award at ACR 2023. Photo courtesy of the ACR.

Thousands of rheumatology experts worldwide attended this year’s American College of Rheumatology (ACR) Convergence conference in San Diego, CA. Each year, the ACR conference showcases the latest rheumatology research and offers hundreds of educational sessions on rheumatic diseases.

The Division of Rheumatology, Inflammation and Immunity at Brigham and Women’s Hospital had a strong presence at ACR 2023. They presented over 120 abstracts at the conference and received several awards—including Division Chief Ellen Gravallese, MD, who received the Presidential Gold Medal award. As the highest award that the ACR can bestow, Dr. Gravallese was recognized for her outstanding achievements in rheumatology throughout an entire career.

Other Brigham ACR 2023 awardees include:

  • ACR Distinguished Fellow Award

Gregory McDermott, MD, MPH

  • ACR Emerging Investigator Excellence Awards

Jennifer Hanberg, MD

Alice Horisberger, MD

Pierre-Antoine Juge, MD, PhD

Below we highlight a select number of ACR presentations shared by Brigham faculty at the conference.

CXCL13+ T Cell Differentiation in Systemic Lupus Erythematosus Is Controlled by Opposing Effects of Aryl Hydrocarbon Receptor and Type I Interferon

Deepak Rao, MD, PhD

Systemic lupus erythematosus (SLE) is characterized by abnormal activation of B cell by T cells and production of autoantibodies. Dr. Rao and team previously discovered a population of T cells called T peripheral helper (Tph) cells that is highly expanded in patients with SLE and can drive B cell activation and differentiation.

There is now substantial interest in identifying ways to disrupt the function of Tph cells to treat lupus. In a collaborative effort between Dr. Rao and Jaehyuk Choi, MD, PhD, at Northwestern University, the investigators identified a specific transcription factor, the aryl hydrocarbon receptor (AHR), that can selectively inhibit the development of Tph cells. The investigators also demonstrated that type I interferon, a factor highly produced in SLE, can inhibit the actions of AHR, a finding that may help explain why Tph cells are so highly increased in patients with SLE. The work opens several opportunities for new strategies to inhibit the abnormal T-cell response in SLE.

The Role of β-catenin in Synovial Lining Fibroblast Differentiation

Kevin Wei, MD, PhD

Synovial fibroblasts are the parenchymal cells of the joint lining membrane that secrete lubrication to support normal joint movement. In patients with rheumatoid arthritis (RA), the healthy fibroblasts that line the joint membrane are diminished.

Dr. Wei and team developed a novel 3D tissue organoid system to examine the differentiation of joint fibroblasts. By leveraging single-cell gene expression profiling in patient-derived organoids, they identified a role for beta-catenin signaling in driving lining fibroblast differentiation and compaction, a key feature of healthy joint lining. Defining the molecular signals that maintain a healthy synovial lining compartment is an important next step to better understand regulators of synovial fibroblast identity and could provide novel therapeutic approaches to restore healthy synovial lining in RA patients.

Personalizing Cardiovascular Risk Prediction for Patients With Systemic Lupus Erythematosus

Karen Costenbader, MD, MPH

Cardiovascular disease (CVD) risk is highly elevated in patients with SLE but has been shown to be underestimated by current general population prediction algorithms that do not include SLE-related variables.

Dr. Costenbader and colleagues studied 1,243 patients in the Brigham’s SLE cohort without known CVD status at baseline and followed them from their cohort enrollment for up to 10 years for their first major adverse cardiovascular event (MACE; non-fatal myocardial infarction, non-fatal stroke, and cardiac death). They collected extensive data concerning traditional CVD risk factors, demographic, and SLE-related clinical features. They used machine learning methods to derive a novel SLE-specific CVD risk tool based on the American College of Cardiology/American Heart Association risk score, but also incorporating several SLE-related factors. The new SLECRISK model was slightly more sensitive and accurate than the traditional CVD generic tools for predicting moderate- and high-risk for MACE over 10 years of follow-up, particularly among young women who would be found to be low risk by traditional risk calculators. After validation, SLECRISK may be incorporated into SLE management guidelines to help guide decision-making in the primary prevention of CVD in clinical practice.

Characterizing Spatial Organization of Immune Infiltrates in Rheumatoid Arthritis Synovia Using Spatial Transcriptomic Analysis

Ilya Korsunsky, PhD

The NIH-funded Accelerating Medical Partnerships RA program has deeply characterized 77 cell types and activity states present in the inflamed synovial joint tissue of patients with active RA.

To understand how these diverse cells organize in tissue and communicate with one another, Dr. Korsunsky and Dr. Wei initiated a project to use novel, high-dimensional imaging to visualize these diverse cell states in tissue. By simultaneously visualizing >40 million individual mRNA transcripts from nearly 1,000 genes, the team has been able to pinpoint the location of 55 immune and stromal cell types and states in distinct anatomical zones. Moreover, by employing robust spatial statistics tools, they identified novel patterns of T and B cell organization within large immune aggregates. This high-dimensional characterization of cells within intact tissue will bring new insights into inflamed synovial tissue pathology and provide new hypotheses regarding the complex network of interacting cells that drive chronic inflammation.

Inferring Disease Activity Scores and Low Disease Activity at Registry Visits Based on Structured and Narrative Data from Electronic Health Records

Katherine P. Liao, MD

A large unmet need in RA is knowing how a patient will respond to one of the many RA treatments available. While there are vast amounts of electronic health record (EHR) data on treatments and whether they were effective in controlling RA for a particular patient, studies of treatment response require more precise measurements of disease activity levels, in the form of a score or number that can be compared across time and patients.

To address this need, Dr. Liao and team outlined the results of their approach for extracting information about disease activity from the notes using natural language processing and machine learning to develop algorithms that can infer a disease activity score for an individual in a specific time window. This is the first critical step of the NIH R01 funded project to use the inferred disease activity, allowing the team to compare the efficacy of treatments across populations of patients with RA.

Risk Factors and Mortality of Immune Checkpoint Inhibitor-Induced Flares of Pre-Existing Rheumatoid Arthritis

Jeffrey A. Sparks, MD, MMSc

Immune checkpoint inhibitors (ICI) have revolutionized the treatment of cancer but may invoke flares of pre-existing autoimmune conditions such as RA.

In this study, Dr. Sparks and team identified every patient in the Brigham healthcare system with pre-existing RA who initiated ICI to treat cancer. They found that nearly half experienced an RA flare after ICI. However, most were mild and typically did not cause disruption of the ICI. The team found that seropositivity was associated with RA flare after ICI, even accounting for competing risk of death. However, RA flare was not as associated with mortality. These results reiterate the safety of initiating ICI among patients with pre-existing RA.

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