Systemic Sclerosis Lung Disease Trajectory Modeling Project

Scott Zeger, PhD
Ami Shah, MD PhD
Antony Rosen, MD
Livia Casciola-Rosen, PhD
Laura Hummers, MD, MPH
Fredrick Wigley, MD

Johns Hopkins University School of Medicine

Project Overview

Although scleroderma and other rheumatic diseases are quite heterogeneous in their presentation and disease course, recent studies underscore that these diseases can be effectively divided into much more homogeneous subgroups when relevant filters are applied. For example, the association of cancer and scleroderma appears highly variable in different studies. When the population is filtered by the types of autoantibodies each patient has, and by whether cancer and scleroderma appear clustered together in time, there is a striking clarification of the cancer-scleroderma association. This analysis reveals two clear subgroups within scleroderma – patients with anti-RNA polymerase III antibodies or patients lacking the three most frequent scleroderma autoantibodies (called CTP-negative). In these two groups, cancer and scleroderma are clustered in time, and carry a higher overall risk of cancer. Within the CTP-negative group, we were able to apply new measurement approaches to define novel antibodies associated with cancer. Such approaches are very valuable for their definition of diagnostic and prognostic tools, but particularly for their identification of novel pathways relevant to disease pathogenesis.

Many filters can potentially be applied to segregate disease into more homogeneous subgroups. The more closely related to biology, the more useful such filters will be. One of the most powerful indicators of disease biology is its trajectory over time, with different subgroups developing distinct complications at different rates. This integrated representation of forward and reverse pathways has great power to define subgroups, measurements of their distinct states, and their underlying biological pathways. This project will combine the biostatistical expertise of Dr. Zeger and colleagues around defining disease trajectories, with the clinical expertise in scleroderma (Drs. Hummers, Shah and Wigley) in defining disease subgroups as well as the immunological expertise of Drs. Rosen, Casciola-Rosen and colleagues to identify clinically relevant, biologically-driven subgroups in scleroderma.

From the Johns Hopkins (JH) clinical database, we are working to identify a group of scleroderma patients with well-defined skin disease and antibody status, and characterize each patient by their trajectory of lung function, heart and skin function (FVC, DLCO, RVSP, MRSS). We will externally validate our resulting trajectory measure by predicting key clinical events, including death. Then, among these patients, we will identify an appropriate group of cases (patients with a steep trajectories), plus a group of relevant control cases (patients lacking steep trajectories). We will use a novel antibody discovery approach to define whether novel autoantibody specificities are associated with either the cases or controls. In subsequent studies, we will also address whether genetic polymorphisms known to be associated with scleroderma susceptibility are enriched in the different trajectory subgroups.

Research Update

Phase 1 has been completed – the patient groups to be studied have been identified and the relevant clinical parameters have been meticulously collected.  We have begun mathematical modeling and statistical analysis of these datasets. If our novel autoantibody measure (“signature”) predicts whether the person is more likely to have a steep or shallow trajectory, clinicians will have an early indicator that can guide their therapeutic strategy by balancing treatment benefits and risks. It may also point toward a mechanism that can be targeted to produce novel therapies. We are also exploring adding numerous additional measures to differentiate between clinically distinct trajectories.

We are grateful to the SRF for funding this project, and are excited to share new data with our colleagues at the next SRF Workshop.

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