Signatures of GVHD and relapse after posttransplant cyclophosphamide revealed by immune profiling and machine learning.

ABSTRACT: The key immunologic signatures associated with clinical outcomes after posttransplant cyclophosphamide (PTCy)-based HLA-haploidentical (haplo) and HLA-matched bone marrow transplantation (BMT) are largely unknown. To address this gap in knowledge, we used machine learning to decipher clinically relevant signatures from immunophenotypic, proteomic, and clinical data and then examined transcriptome changes in the lymphocyte subsets that predicted major posttransplant outcomes. Kinetics of immune subset reconstitution after day 28 were similar for 70 patients undergoing haplo and 75 patients undergoing HLA-matched BMT. Machine learning based on 35 candidate factors (10 clinical, 18 cellular, and 7 proteomic) revealed that combined elevations in effector CD4+ conventional T cells (Tconv) and C

SUBMITTER: McCurdy SR 

PROVIDER: S-EPMC8796655 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature