Machine learning predicts stem cell transplant response in severe scleroderma.

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Franks, JM; Martyanov, V; Wang, Y; Wood, TA; Pinckney, A; Crofford, LJ; Keyes-Elstein, L; Furst, DE; Goldmuntz, E; Mayes, MD; McSweeney, P; Nash, RA; Sullivan, KM; Whitfield, ML

Published Date

  • December 2020

Published In

Volume / Issue

  • 79 / 12

Start / End Page

  • 1608 - 1615

PubMed ID

  • 32933919

Pubmed Central ID

  • PMC8582621

Electronic International Standard Serial Number (EISSN)

  • 1468-2060

Digital Object Identifier (DOI)

  • 10.1136/annrheumdis-2020-217033

Language

  • eng

Conference Location

  • England