Machine learning predicts stem cell transplant response in severe scleroderma
Open Access
- 15 September 2020
- journal article
- research article
- Published by BMJ in Annals Of The Rheumatic Diseases
- Vol. 79 (12), 1608-1615
- https://doi.org/10.1136/annrheumdis-2020-217033
Abstract
Objective The Scleroderma: Cyclophosphamide or Transplantation (SCOT) trial demonstrated clinical benefit of haematopoietic stem cell transplant (HSCT) compared with cyclophosphamide (CYC). We mapped PBC (peripheral blood cell) samples from the SCOT clinical trial to scleroderma intrinsic subsets and tested the hypothesis that they predict long-term response to HSCT. Methods We analysed gene expression from PBCs of SCOT participants to identify differential treatment response. PBC gene expression data were generated from 63 SCOT participants at baseline and follow-up timepoints. Participants who completed treatment protocol were stratified by intrinsic gene expression subsets at baseline, evaluated for event-free survival (EFS) and analysed for differentially expressed genes (DEGs). Results Participants from the fibroproliferative subset on HSCT experienced significant improvement in EFS compared with fibroproliferative participants on CYC (p=0.0091). In contrast, EFS did not significantly differ between CYC and HSCT arms for the participants from the normal-like subset (p=0.77) or the inflammatory subset (p=0.1). At each timepoint, we observed considerably more DEGs in HSCT arm compared with CYC arm with HSCT arm showing significant changes in immune response pathways. Conclusions Participants from the fibroproliferative subset showed the most significant long-term benefit from HSCT compared with CYC. This study suggests that intrinsic subset stratification of patients may be used to identify patients with SSc who receive significant benefit from HSCT.Keywords
Funding Information
- National Institutes of Health (5T32LM012204-03)
- Dr. Ralph and Marian Falk Medical Research Trust
- National Institute of Allergy and Infectious Diseases (1UM2AI117870, HHSN272200900057C, HHSN272201100025C, N01-AI05419, N01-AI25481)
- Scleroderma Research Foundation
- Burroughs Wellcome Fund (PUP Big Data in the Life Sciences Training Program)
This publication has 29 references indexed in Scilit:
- Costs of autologous and allogeneic hematopoietic cell transplantation in the United States: a study using a large National Private Claims DatabaseBone Marrow Transplantation, 2012
- Intrinsic Gene Expression Subsets of Diffuse Cutaneous Systemic Sclerosis Are Stable in Serial Skin BiopsiesJournal of Investigative Dermatology, 2012
- WGCNA: an R package for weighted correlation network analysisBMC Bioinformatics, 2008
- Molecular Subsets in the Gene Expression Signatures of Scleroderma SkinPLOS ONE, 2008
- g:Profiler—a web-based toolset for functional profiling of gene lists from large-scale experimentsNucleic Acids Research, 2007
- Changes in causes of death in systemic sclerosis, 1972-2002Annals Of The Rheumatic Diseases, 2007
- GenePattern 2.0Nature Genetics, 2006
- Open source clustering softwareBioinformatics, 2004
- Phase I/II trial of autologous stem cell transplantation in systemic sclerosis: procedure related mortality and impact on skin diseaseAnnals Of The Rheumatic Diseases, 2001
- Significance analysis of microarrays applied to the ionizing radiation responseProceedings of the National Academy of Sciences of the United States of America, 2001