Key pathways underlying different subsets of systemic sclerosis discovered

Sorting out patients with SSc according to their shared biology, Dartmouth investigators discovered how disease heterogeneity can be defined, allowing for targeted selection of patients for clinical trials. Michael Whitfield, PhD of the Geisel School of Medicine at Dartmouth's Department of Genetics and Norris Cotton Cancer Center, along with Post-doctoral Fellows Michael Johnson, PhD and J. Matthew Mahoney, PhD, led the research, publishing two papers: "Experimentally-Derived Fibroblast Gene Signatures Identify Molecular Pathways Associated with Distinct Subsets of Systemic Sclerosis Patients in Three Independent Cohorts" in PLoS One, and "Systems Level Analysis of Systemic Sclerosis Shows a Network of Immune and Profibrotic Pathways Connected with Genetic Polymorphisms," in PLoS Computational Biology. Clinical collaborators include Dr. Monique Hinchcliff and Dr. John Varga of Northwestern's Feinberg School of Medicine.

"This work uses rare patient samples collected from three different clinical centers and analyzed by modern genomic technologies in my laboratory, and then integrated using a novel bioinformatics strategy that we developed," explained Whitfield.

Systemic sclerosis is a heterogeneous autoimmune disease, which has a complex and variable clinical course. It is difficult to treat due to a lack of effective therapies. Making it more complicated, there are multiple subtypes of the disease that currently can only be identified by measuring a patient's gene expression profile. In prior studies, the Whitfield team used translational genomic and bioinformatic approaches to analyze systemic sclerosis skin biopsies. The result was identification of four "intrinsic" gene expression subsets that can be used to classify the patients based on the genes expressed in their skin. The subsets are: fibroproliferative, inflammatory, limited, and normal-like.

The Whitfield studies asked:

* What are the major pathways underlying each intrinsic subset that may be driving pathogenesis?

* What genes or features are commonly deregulated?

* How are they related to genetic risk factors for the disease?

The purpose of this translational work is to use the biology of a patient's disease to predict which individuals are more likely to respond to specific therapies.

Results include the most comprehensive analysis of signaling pathways in SSc to-date, which may be used to both diagnose and treat patients. Also groundbreaking are the identification of molecular pathways by which patients may progress through the disease, the linking of deregulated genes to a patient's underlying genetic risk factors, and identification of a small set of genes that may be critically important in disease pathogenesis from the thousands differentially regulated in the genome. The team put the genetic risk factors for SSc into the context of deregulated pathways and showed that they largely cluster within the immune system, suggesting inflammation is an initiating event in the disease.

"Our methods and results demonstrate the efficacy of team science," said Whitfield. "This was a truly interdisciplinary effort by physicians, mathematicians, molecular, and computational biologists to understand this disease. It represents a true bench-to-bedside translational research project."

Looking ahead, Whitfield's team is working to identify new drugs that may target the deregulated pathways in SSc and be effective for the treatment of specific SSc subtypes, as well as developing a new diagnostic assay to facilitate the subtyping of patients' disease.