Machine learning predicts stem cell transplant response in severe scleroderma.
Machine learning predicts stem cell transplant response in severe scleroderma.
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DOI:
10.1136/annrheumdis-2020-217033
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发表时间:
2020-12
影响因子:
27.4
通讯作者:
Whitfield ML
中科院分区:
文献类型:
--
作者:
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
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. 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). 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. 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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影响因子:
3
作者:
Langfelder P;Horvath S
通讯作者:
Horvath S
影响因子:
6.5
作者:
Pendergrass, Sarah A.;Lemaire, Raphael;Francis, Ian P.;Mahoney, J. Matthew;Lafyatis, Robert;Whitfield, Michael L.
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DOI:
10.1016/j.jid.2018.01.006
发表时间:
2018-06
期刊:
The Journal of investigative dermatology
影响因子:
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Whitfield ML
DOI:
10.1002/art.40358
发表时间:
2018-03
期刊:
Arthritis & rheumatology (Hoboken, N.J.)
影响因子:
--
作者:
Gordon JK;Martyanov V;Franks JM;Bernstein EJ;Szymonifka J;Magro C;Wildman HF;Wood TA;Whitfield ML;Spiera RF
通讯作者:
Spiera RF
DOI:
10.1073/pnas.091062498
发表时间:
2001-04-24
影响因子:
11.1
作者:
Tusher, VG;Tibshirani, R;Chu, G
通讯作者:
Chu, G