Subset of Harder-to-Treat Systemic Sclerosis Patients May Respond to Stem Cell Transplantation

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CNewswise — HICAGO – Hematopoietic stem cell transplantation showed benefit in a subset of patients with systemic sclerosis who tend not to improve on immunosuppressive therapies like mycophenolate mofetil, according to new research findings presented this week at the 2018 ACR/ARHP Annual Meeting (Abstract #1876).

Scleroderma, also called systemic sclerosis, is an autoimmune disease that causes the skin to become thick and hard, a buildup of scar tissue, and damage to internal organs such as the heart and blood vessels, lungs, stomach and kidneys. Scleroderma is relatively rare, affecting about 75,000 to 100,000 people in the U.S., mostly women between the ages of 30 and 50.

In a recent trial, Scleroderma: Cyclophosphamide or Transplantation (SCOT), researchers explored the clinical benefit of myeloablative CD34+ selected autologous hematopoietic stem cell transplantation compared to cyclophosphamide infusions in patients with diffuse systemic sclerosis. In a new study, researchers at the Geisel School of Medicine at Dartmouth College in New Hampshire and other institutions used a machine-learning approach to analyze gene expression data from the peripheral blood of 67 participants in the SCOT trial. It looked for molecular changes and gene expression subsets that may be associated with better treatment response.

“Scleroderma is a very heterogeneous disease and consequently, how individual patients respond to therapy is quite different,” said Michael Whitfield, MD, Professor of Biomedical Data Science, Geisel School of Medicine, and the study’s co-author. “The goal of our study was to identify the scleroderma patients most likely, and least likely, to benefit from stem-cell transplant. To do this, we used a personalized medicine approach that groups each patient by the activity of their genes in blood samples using genome-wide measurements. We then used a machine learning algorithm that we developed to divide patients into what we have named, ‘intrinsic’ gene expression subsets, which tells us about the molecular state of a scleroderma patient’s disease.”

They found that scleroderma patients who received a transplant in the previous trial showed substantially larger changes in gene expression compared to those treated with cyclophosphamide. Transplant patients showed a decrease in the expression of genes associated with cell proliferation and immune response, and an increase in the expression of genes associated with translation compared to pre-treatment.

In both treatment arms, they assigned participants to intrinsic subsets based on gene expression before treatment, using a 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 develop our machine learning approach into a diagnostic tool that physicians can use to identify the patients most likely to benefit, and which patients might benefit from less aggressive therapies. If we can identify the patients most likely to benefit, then it should help improve outcomes,” said Dr. Whitfield.

Event-free survival did not differ between the two treatment arms for participants assigned to the normal-like subset, and this was consistent with a lack of active immune response in these patients. However, in the fibroproliferative subset, patients that 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,” said Jennifer Franks, lead author and a doctoral student at Dartmouth.

“The fibroproliferative group is composed of patients that we have found tend not to improve on immunosuppressive therapies,” Dr. Whitfield added. “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, which we hope will make a major impact on the quality of life of these patients.”

This research was supported by the National Institutes of Health.


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Abstract #: 1876

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

Jennifer Franks1, Viktor Martyanov1, Tammara A. Wood1, Leslie Crofford2, Lynette Keyes-Elstein3, Daniel E. Furst4, Ellen Goldmuntz5, Maureen D. Mayes6, Peter McSweeney7, Richard Nash7, Keith Sullivan8 and Michael L. Whitfield1, 1Geisel School of Medicine at Dartmouth, Hanover, NH, 2Vanderbilt University Medical Center, Nashville, TN, 3Rho, Inc, Chapel Hill, NC, 4University of California Los Angeles, Los Angeles, CA, 5NIAID, NIH, Bethesda, MD, 6University of Texas McGovern Medical School, Houston, TX, 7Colorado Blood Cancer Institute, Denver, CO, 8Duke University Medical Center, Durham, NC

Background/Purpose: The SCOT (Scleroderma: Cyclophosphamide or Transplantation) trial (Sullivan K. et al, 2018) demonstrated the clinical benefit of hematopoietic stem cell transplant (HSCT) compared to cyclophosphamide (CYC) in dcSSc. We analyzed gene expression data from peripheral blood mononuclear cells (PBMCs) of SCOT participants to identify molecular changes and intrinsic gene expression subsets associated with treatment response.

Methods: PBMC gene expression data were generated from 67 SCOT participants at baseline (36 CYC, 31 HSCT) and at 48/54 months (12 CYC, 14 HSCT). Significant differentially expressed genes (DEGs; False Discovery Rate <5%) were identified between baseline and 48/54 month samples using Significance Analysis of Microarrays and were annotated with Gene Ontology functional terms via g:Profiler. Participants who completed treatment protocol (Per-Protocol Population) were assigned to intrinsic gene expression subsets at baseline using a machine learning classifier based on elastic net multinomial regression previously trained and tested on five independent SSc cohorts. The relationship between intrinsic subsets and event-free survival (EFS) was analyzed at 54 months.

Results: The participants included in the gene expression analyses were representative of the full cohort in the SCOT trial; there were no significant differences in sex, age, race, or baseline phenotypic measures. In the subset of participants with 48/54 month samples, we observed considerably more DEGs in HSCT arm (4788 genes) than in CYC arm (21 genes). Participants in HSCT showed a decrease in the expression of genes associated with cell proliferation and immune response and an increase in expression of genes associated with translation compared to baseline (Fig. 1A). Participants were assigned to intrinsic gene expression subsets at baseline (Fig. 1B) in both treatment arms. EFS did not differ between treatment arms for the participants assigned to the normal-like subset (p=0.94, Fig. 1C), consistent with the lack of active immune response in these participants. Participants assigned to the inflammatory subset trended towards improved chance of EFS in HSCT arm (p=0.1, Fig. 2D). Participants assigned to the fibroproliferative subset who received HSCT experienced significant improvement in EFS compared to fibroproliferative participants who received CYC (p=0.0091, Fig. 1E).

Conclusion: The HSCT arm of the SCOT trial showed substantially larger changes in gene expression compared to CYC arm. Participants assigned to the normal-like subset did not show benefit from HSCT treatment over CYC. Importantly, participants assigned to the fibroproliferative subset, who tend not to improve on immunosuppressive therapy (e.g. MMF or abatacept), were the most likely to benefit from HSCT.

Disclosures: J. Franks, None V. Martyanov, None T. A. Wood, None L. Crofford, None L. Keyes-Elstein, None D. E. Furst, None E. Goldmuntz, None M. D. Mayes, None P. McSweeney, None R. Nash, None K. Sullivan, None M. L. Whitfield, None


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