Systemic Sclerosis: Fibroproliferative Gene Expression Subset Most Likely to Benefit From HSCT

Systemic sclerosis: fibroproliferative gene expression subset most likely to benefit from HSCT Study findings suggest that a diagnostic test for intrinsic gene expression stratification could help clinical decision-making in the future


The Scleroderma: Cyclophosphamide or Transplantation (SCOT) trial demonstrated that myeloablative autologous hematopoietic stem cell transplantation (HSCT) is superior to monthly cyclophosphamide treatment in individuals with systemic sclerosis (SSc). A new study has analyzed gene expression data from SCOT participants to determine whether intrinsic gene expression subsets can predict response to either cyclophosphamide or HSCT.

Michael L. Whitfield, PhD, lead author of the study and from the Geisel School of Medicine at Dartmouth in Hanover, NH, presented these findings at the 2018 American College of Rheumatology and Association of Rheumatology Healthcare Professionals Annual Meeting held October 19 to 24 in Chicago, IL. Keith M. Sullivan, MD, from the Duke Cancer Institute and Duke School of Medicine in Durham, NC, participated in the study.

“The goal is to ask, ‘Can we really do precision medicine in diseases like systemic sclerosis the way we are doing in cancer already?’” Whitfield told attendees. “Gene expression profiling is one way to do that.”

Whitfield provided a brief background on gene expression in SSc tissues. There are four commonly identified intrinsic gene expression subsets: (1) fibroproliferative, seen in diffuse SSc, (2) inflammatory, observed in both diffuse SSc or limited SSc, (3) limited, found in cutaneous SSc only, and (4) normal-like, in which individuals have clinically active SSc, but a molecular signature similar to normal controls. These subsets have been identified in multiple cohorts of patients with SSc and in multiple end-target tissues, Whitfield said. Those in the fibroproliferative subset are more likely to have interstitial lung disease.

Several small trials of agents that modulate the inflammatory response in SSc, such as mycophenolate mofetil and abatacept, and where molecular gene expression data is available, have found that clinical improvement occurs primarily in the inflammatory subset. “We rarely see the fibroproliferative patients improve on these immunomodulatory therapies,” Whitfield commented.

To identify molecular changes and intrinsic gene expression in participants in the SCOT trial, Whitfield and colleagues analyzed gene expression data from samples of peripheral blood mononuclear cells from the per-protocol population, defined as participants who had received HSCT (N = 30) or at least 9 monthly doses of cyclophosphamide (N = 33). There were no significant differences in baseline characteristics of participants enrolled in the gene expression cohort. Researchers analyzed a total of 229 RNA samples from 63 patients, at baseline and periodically over 48/54 months.

Differential gene analysis revealed a significantly larger number of differentially expressed genes (DEGs) in the HSCT group than in the cyclophosphamide arm (P = .0079). Specifically, there were essentially zero DEGs in the cyclophosphamide group, whereas the number of DEGs in the HSCT group rose to approximately 5000 at month 48/54.

Researchers then developed and validated an SSc subset classifier using a machine learning model, allowing for the gene expression subset stratification of event-free survival of SCOT participants. Individuals with a fibroproliferative gene expression subset who received HSCT had significantly improved event-free survival times than those with a fibroproliferative subset who received cyclophosphamide (P = .0091), unlike previous studies where the fibroproliferative subset tended not to improve on immunosuppressive therapy. Although there was a trend toward improved event-free survival among the inflammatory subset that received HSCT, this did not reach statistical significance (P = .1). Participants in the normal-like subset did not benefit from HSCT (P = .94).

The results of this study suggest that intrinsic gene expression stratification could be developed into a diagnostic test to identify patients who are most likely to benefit from HSCT and to assist patients, caregivers, and providers in treatment decision-making.

Source: Franks J, Martyanov V, Wood TA, et al. Machine learning classification of peripheral blood gene expression identifies a subset of patients with systemic sclerosis most likely to show clinical improvement in response to hematopoietic stem cell transplant. Presented at: 2018 ACR/ARHP Annual Meeting; October 19-24; Chicago, IL. Abstract 1876.