ArticleAnnals of the rheumatic diseases2020
Machine learning predicts stem cell transplant response in severe scleroderma.
Article in Annals of the rheumatic diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- A genomic meta-analysis of clinical variables and their association with intrinsic molecular subsets in systemic sclerosis.Rheumatology (Oxford, England) · 2022Pooled it
- Clinical and Molecular Findings After Autologous Stem Cell Transplantation or Cyclophosphamide for Scleroderma: Handling Missing Longitudinal Data.Arthritis care & research · 2023Trial
- Harnessing artificial intelligence to advance insights in systemic sclerosis skin and lung disease.Current opinion in rheumatology · 2025Review
- An international perspective on the future of systemic sclerosis research.Nature reviews. Rheumatology · 2025Review
- Can machine learning assist in systemic sclerosis diagnosis and management? A scoping review.Journal of scleroderma and related disorders · 2024Article
- Artificial intelligence for predicting treatment responses in autoimmune rheumatic diseases: advancements, challenges, and future perspectives.Frontiers in immunology · 2024Review
- Deep learning-enabled quantification of simultaneous PET/MRI for cell transplantation monitoring.iScience · 2023Article
- Current Concepts on the Pathogenesis of Systemic Sclerosis.Clinical reviews in allergy & immunology · 2023Review
- Characterising the autoantibody repertoire in systemic sclerosis following myeloablative haematopoietic stem cell transplantation.Annals of the rheumatic diseases · 2023Article
- Three Distinct Transcriptional Profiles of Monocytes Associate with Disease Activity in Scleroderma Patients.Arthritis & rheumatology (Hoboken, N.J.) · 2023Article
- Myeloablative autologous haematopoietic stem cell transplantation resets the B cell repertoire to a more naïve state in patients with systemic sclerosis.Annals of the rheumatic diseases · 2023Article
- Review
- Precision medicine: the precision gap in rheumatic disease.Nature reviews. Rheumatology · 2022Review
- The Use and Utility of Machine Learning in Achieving Precision Medicine in Systemic Sclerosis: A Narrative Review.Journal of personalized medicine · 2022Review
- Insights Into Systemic Sclerosis from Gene Expression Profiling.Current treatment options in rheumatology · 2021Article
- Moving towards a molecular categorization of autoimmune disease.Nature reviews. Rheumatology · 2021Article
- Hematopoietic stem cell transplantation in systemic sclerosis: Yes!! BUT. . .Journal of scleroderma and related disorders · 2021Article
- Genetic Signatures From RNA Sequencing of Pediatric Localized Scleroderma Skin.Frontiers in pediatrics · 2021Article
- Life after Autologous Hematopoietic Stem Cell Transplantation for Systemic Sclerosis.Journal of blood medicine · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 7 institutions in 1 country.
Funding
Abstract
objectiveThe 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.
methodsWe 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).
resultsParticipants 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.
conclusionsParticipants 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.