OC 05.2 - Metabolomics profile and machine learning prediction of treatment responses in immune thrombocytopenia: a prospective cohort study

Saturday, June 22, 2024

13:15 – 13:30 ICT

Room: 110 A-C

Presenting Author(s)

Yang Li, Master student

Tianjin
Chinese Academy of Medical Sciences and Peking Union Medical College
Tianjin, Tianjin, China (People's Republic)

Co-Author(s)

TS

Ting Sun

Tianjin
Chinese academy of medical science
Tianjin, Tianjin, China (People's Republic)
Jia Chen, MD

tianjin
Chinese academy of medical sciences
Tianjin, Tianjin, China (People's Republic)
Renchi Yang, MD

Head of Thrombosis and Hemostasis Center
Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
Tianjin, Hebei, China (People's Republic)
LZ

Lei Zhang

professor
State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Tianjin 300020, China
Tianjin, Tianjin, China (People's Republic)

Background: The mechanisms underlying immune thrombocytopenia (ITP) and biomarkers predicting the response of drug treatments are elusive.

Aims: We performed a metabolomic profiling of bone marrow biopsy sample collected from ITP patient admission in a National Longitudinal Cohort of Hematological Diseases prospective study. Machine learning algorithms were conducted to discover novel biomarkers to enhance ITP patient treatment responses.

Methods: In this study, we examined metabolomic changes in bone marrow biopsies from 91 ITP patients using LC-MS.

Results: From 91 ITP patients, we quantified a total of 4,494 metabolites, including 1456 metabolites in the positive mode and 3,038 metabolites in the negative mode. Metabolic patterns varied significantly between groups of newly diagnosed and chronic, with a total of 876 differential metabolites involved in 181 unique metabolic pathways. Enrichment factors and P-values revealed the top metabolically enriched pathways to be sphingolipid metabolism, the sphingolipid signaling pathway, ubiquinone and other terpenoid-quinone biosynthesis, thiamine metabolism, tryptophan metabolism, and cofactors biosynthesis, the phospholipase D signaling pathway, and the phosphatidylinositol signaling system.Lipids and their metabolism, including long-chain fatty acids, oxidized lipids, glycerophospholipids and phosphatidylcholine and phosphatidylethanolamine biosynthesis helped differentiate drug treatment responses.

Conclusion(s): In conclusion, this study revealed novel metabolic alterations associated with ITP in bone marrow supernatants and a potential biomarker predicting the response on ITP.