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Machine-learning classification identifies patients with early systemic sclerosis as abatacept responders via CD28 pathway modulation
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 abatac...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society for Clinical Investigation
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9869963/ https://www.ncbi.nlm.nih.gov/pubmed/36355434 http://dx.doi.org/10.1172/jci.insight.155282 |
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author | Mehta, Bhaven K. Espinoza, Monica E. Franks, Jennifer M. Yuan, Yiwei Wang, Yue Wood, Tammara Gudjonsson, Johann E. Spino, Cathie Fox, David A. Khanna, Dinesh Whitfield, Michael L. |
author_facet | Mehta, Bhaven K. Espinoza, Monica E. Franks, Jennifer M. Yuan, Yiwei Wang, Yue Wood, Tammara Gudjonsson, Johann E. Spino, Cathie Fox, David A. Khanna, Dinesh Whitfield, Michael L. |
author_sort | Mehta, Bhaven K. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9869963 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Society for Clinical Investigation |
record_format | MEDLINE/PubMed |
spelling | pubmed-98699632023-02-06 Machine-learning classification identifies patients with early systemic sclerosis as abatacept responders via CD28 pathway modulation Mehta, Bhaven K. Espinoza, Monica E. Franks, Jennifer M. Yuan, Yiwei Wang, Yue Wood, Tammara Gudjonsson, Johann E. Spino, Cathie Fox, David A. Khanna, Dinesh Whitfield, Michael L. JCI Insight Research Article 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. American Society for Clinical Investigation 2022-12-22 /pmc/articles/PMC9869963/ /pubmed/36355434 http://dx.doi.org/10.1172/jci.insight.155282 Text en © 2022 Mehta et al. https://creativecommons.org/licenses/by/4.0/This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Article Mehta, Bhaven K. Espinoza, Monica E. Franks, Jennifer M. Yuan, Yiwei Wang, Yue Wood, Tammara Gudjonsson, Johann E. Spino, Cathie Fox, David A. Khanna, Dinesh Whitfield, Michael L. Machine-learning classification identifies patients with early systemic sclerosis as abatacept responders via CD28 pathway modulation |
title | Machine-learning classification identifies patients with early systemic sclerosis as abatacept responders via CD28 pathway modulation |
title_full | Machine-learning classification identifies patients with early systemic sclerosis as abatacept responders via CD28 pathway modulation |
title_fullStr | Machine-learning classification identifies patients with early systemic sclerosis as abatacept responders via CD28 pathway modulation |
title_full_unstemmed | Machine-learning classification identifies patients with early systemic sclerosis as abatacept responders via CD28 pathway modulation |
title_short | Machine-learning classification identifies patients with early systemic sclerosis as abatacept responders via CD28 pathway modulation |
title_sort | machine-learning classification identifies patients with early systemic sclerosis as abatacept responders via cd28 pathway modulation |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9869963/ https://www.ncbi.nlm.nih.gov/pubmed/36355434 http://dx.doi.org/10.1172/jci.insight.155282 |
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