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Machine learning predicts stem cell transplant response in severe scleroderma.

Publication ,  Journal Article
Franks, JM; Martyanov, V; Wang, Y; Wood, TA; Pinckney, A; Crofford, LJ; Keyes-Elstein, L; Furst, DE; Goldmuntz, E; Mayes, MD; McSweeney, P ...
Published in: Ann Rheum Dis
December 2020

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.

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Published In

Ann Rheum Dis

DOI

EISSN

1468-2060

Publication Date

December 2020

Volume

79

Issue

12

Start / End Page

1608 / 1615

Location

England

Related Subject Headings

  • Treatment Outcome
  • Transcriptome
  • Scleroderma, Diffuse
  • Middle Aged
  • Male
  • Machine Learning
  • Immunosuppressive Agents
  • Humans
  • Hematopoietic Stem Cell Transplantation
  • Gene Expression Profiling
 

Citation

APA
Chicago
ICMJE
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Franks, J. M., Martyanov, V., Wang, Y., Wood, T. A., Pinckney, A., Crofford, L. J., … Whitfield, M. L. (2020). Machine learning predicts stem cell transplant response in severe scleroderma. Ann Rheum Dis, 79(12), 1608–1615. https://doi.org/10.1136/annrheumdis-2020-217033
Franks, Jennifer M., Viktor Martyanov, Yue Wang, Tammara A. Wood, Ashley Pinckney, Leslie J. Crofford, Lynette Keyes-Elstein, et al. “Machine learning predicts stem cell transplant response in severe scleroderma.Ann Rheum Dis 79, no. 12 (December 2020): 1608–15. https://doi.org/10.1136/annrheumdis-2020-217033.
Franks JM, Martyanov V, Wang Y, Wood TA, Pinckney A, Crofford LJ, et al. Machine learning predicts stem cell transplant response in severe scleroderma. Ann Rheum Dis. 2020 Dec;79(12):1608–15.
Franks, Jennifer M., et al. “Machine learning predicts stem cell transplant response in severe scleroderma.Ann Rheum Dis, vol. 79, no. 12, Dec. 2020, pp. 1608–15. Pubmed, doi:10.1136/annrheumdis-2020-217033.
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. Machine learning predicts stem cell transplant response in severe scleroderma. Ann Rheum Dis. 2020 Dec;79(12):1608–1615.

Published In

Ann Rheum Dis

DOI

EISSN

1468-2060

Publication Date

December 2020

Volume

79

Issue

12

Start / End Page

1608 / 1615

Location

England

Related Subject Headings

  • Treatment Outcome
  • Transcriptome
  • Scleroderma, Diffuse
  • Middle Aged
  • Male
  • Machine Learning
  • Immunosuppressive Agents
  • Humans
  • Hematopoietic Stem Cell Transplantation
  • Gene Expression Profiling