Go to The Journal of Clinical Investigation
  • About
  • Editors
  • Consulting Editors
  • For authors
  • Publication ethics
  • Publication alerts by email
  • Transfers
  • Advertising
  • Job board
  • Contact
  • Physician-Scientist Development
  • Current issue
  • Past issues
  • By specialty
    • COVID-19
    • Cardiology
    • Immunology
    • Metabolism
    • Nephrology
    • Oncology
    • Pulmonology
    • All ...
  • Videos
  • Collections
    • In-Press Preview
    • Resource and Technical Advances
    • Clinical Research and Public Health
    • Research Letters
    • Editorials
    • Perspectives
    • Physician-Scientist Development
    • Reviews
    • Top read articles

  • Current issue
  • Past issues
  • Specialties
  • In-Press Preview
  • Resource and Technical Advances
  • Clinical Research and Public Health
  • Research Letters
  • Editorials
  • Perspectives
  • Physician-Scientist Development
  • Reviews
  • Top read articles
  • About
  • Editors
  • Consulting Editors
  • For authors
  • Publication ethics
  • Publication alerts by email
  • Transfers
  • Advertising
  • Job board
  • Contact

Submit a comment

Machine-learning classification identifies patients with early systemic sclerosis as abatacept responders via CD28 pathway modulation
Bhaven K. Mehta, Monica E. Espinoza, Jennifer M. Franks, Yiwei Yuan, Yue Wang, Tammara Wood, Johann E. Gudjonsson, Cathie Spino, David A. Fox, Dinesh Khanna, Michael L. Whitfield
Bhaven K. Mehta, Monica E. Espinoza, Jennifer M. Franks, Yiwei Yuan, Yue Wang, Tammara Wood, Johann E. Gudjonsson, Cathie Spino, David A. Fox, Dinesh Khanna, Michael L. Whitfield
View: Text | PDF
Research Article Clinical trials

Machine-learning classification identifies patients with early systemic sclerosis as abatacept responders via CD28 pathway modulation

  • Text
  • PDF
Abstract

Here, the efficacy of abatacept in patients with early diffuse systemic sclerosis (dcSSc) was analyzed to test the hypothesis that patients in the inflammatory intrinsic subset would show the most significant clinical improvement. Eighty-four participants with dcSSc were randomized to receive abatacept or placebo for 12 months. RNA-Seq was performed on 233 skin paired biopsies at baseline and at 3 and 6 months. Improvement was defined as a 5-point or more than 20% change in modified Rodnan skin score (mRSS) between baseline and 12 months. Samples were assigned to intrinsic gene expression subsets (inflammatory, fibroproliferative, or normal-like subsets). In the abatacept arm, change in mRSS was most pronounced for the inflammatory and normal-like subsets relative to the placebo subset. Gene expression for participants on placebo remained in the original molecular subset, whereas inflammatory participants treated with abatacept had gene expression that moved toward the normal-like subset. The Costimulation of the CD28 Family Reactome Pathway decreased in patients who improved on abatacept and was specific to the inflammatory subset. Patients in the inflammatory subset had elevation of the Costimulation of the CD28 Family pathway at baseline relative to that of participants in the fibroproliferative and normal-like subsets. There was a correlation between improved ΔmRSS and baseline expression of the Costimulation of the CD28 Family pathway. This study provides an example of precision medicine in systemic sclerosis clinical trials.

Authors

Bhaven K. Mehta, Monica E. Espinoza, Jennifer M. Franks, Yiwei Yuan, Yue Wang, Tammara Wood, Johann E. Gudjonsson, Cathie Spino, David A. Fox, Dinesh Khanna, Michael L. Whitfield

×

Guidelines

The Editorial Board will only consider comments that are deemed relevant and of interest to readers. The Journal will not post data that have not been subjected to peer review; or a comment that is essentially a reiteration of another comment.

  • Comments appear on the Journal’s website and are linked from the original article’s web page.
  • Authors are notified by email if their comments are posted.
  • The Journal reserves the right to edit comments for length and clarity.
  • No appeals will be considered.
  • Comments are not indexed in PubMed.

Specific requirements

  • Maximum length, 400 words
  • Entered as plain text or HTML
  • Author’s name and email address, to be posted with the comment
  • Declaration of all potential conflicts of interest (even if these are not ultimately posted); see the Journal’s conflict-of-interest policy
  • Comments may not include figures
This field is required
This field is required
This field is required
This field is required
This field is required
This field is required

Copyright © 2025 American Society for Clinical Investigation
ISSN 2379-3708

Sign up for email alerts