09:32pm Sunday 18 November 2018

Stem Cell Transplantation May Aid Hard-to-Treat Scleroderma Patients

Results from a new study conducted by researchers at Dartmouth’s Geisel School of Medicine and a number of collaborating institutions are giving hope to patients who suffer from the autoimmune disease scleroderma or systemic sclerosis.

Their findings, presented this week at the American College of Rheumatology Annual Scientific Meeting in Chicago, showed that hematopoietic stem cell transplantation was beneficial to a subset of patients with systemic sclerosis who were not responsive to immunosuppressive therapies such as abatacept or mycophenolate mofetil.

Systemic sclerosis, an autoimmune disease of the connective tissue that is relatively rare and affects primarily women between the ages of 30 and 50, can cause severe, even life-threatening symptoms. These include fibrosis (hardening) of the skin, vascular dysfunction, and inflammation and scarring of internal organs. While some medications may help control symptoms and prevent complications, there is currently no cure for scleroderma, and to date, no FDA-approved therapies have been developed for the disorder.

“Scleroderma is a very heterogeneous disease, so consequently how individual patients respond to therapy is quite different,” explains Michael Whitfield, PhD, interim chair and professor of biomedical data science, and professor of molecular and systems biology at Geisel. “The goal of our study was to identify the scleroderma patients most likely, and least likely, to benefit from stem cell transplant.”

To accomplish this, the researchers employed a personalized medicine approach that grouped patients by the activity of their genes in blood samples using genome-wide measurements.

“We then developed an algorithm using machine learning that we used to divide patients into ‘intrinsic’ gene expression subsets, which tells us about the molecular state of a scleroderma patient’s disease,” says Whitfield, whose lab uses genomic and computational approaches to identify the molecular basis of autoimmunity and fibrosis with an emphasis on systemic sclerosis, and was the first to identify molecular subsets in an autoimmune disease.

For their study, the investigators analyzed gene expression data from 67 participants of the recent SCOT (Scleroderma: Cyclophosphamide or Transplantation) trial—which demonstrated the clinical benefit of stem cell transplantation compared to infusions of cyclophosphamide (a medication used to suppress the immune system) in patients with systemic sclerosis.

The researchers found that scleroderma patients who received a transplant in the SCOT trial showed dramatically larger changes in gene expression compared to those treated with cyclophosphamide.

They then assigned participants in both treatment arms to intrinsic subsets based on gene expression before treatment, using their machine learning classifier. They wanted to determine if the subsets were predictive of treatment success measured as event-free survival. “Our hope is that we can identify the patients most likely to benefit from certain therapies to improve outcomes,” Whitfield says.

Surprisingly, event-free survival did not differ between the two treatment arms for participants assigned to the normal-like subset, suggesting patients in this subset do not benefit from stem cell transplant. However, in the fibroproliferative subset, patients who underwent a transplant experienced significant improvement in event-free survival compared to fibroproliferative patients who received cyclophosphamide, the study found.

“The results are striking because one group of patients showed little difference in event-free survival between treatment arms, whereas another group, the fibroproliferative subset, showed the most significant improvement in event-free survival in the stem cell transplant arm,” says lead author Jennifer Franks, a quantitative biomedical sciences PhD candidate in the Whitfield Lab at Geisel. “And the fibroproliferative group is composed of patients who we have found tend not to improve on immunosuppressive therapies.”

Whitfield hopes to develop his machine learning approach into a diagnostic tool that physicians can use to identify the patients most likely to benefit from certain treatment regimens, and which patients might benefit from less aggressive therapies, to ultimately improve clinical outcomes.

“Our next steps are to continue to try to understand the molecular differences between these patients, so we can better determine why some improve and some do not, and ultimately identify therapies most likely to benefit each set of patients,” adds Whitfield. “We hope this will have a major impact on the quality of life of these patients.”

 

Geisel School of Medicine

 


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