Advanced deep learning enables prediction of allogeneic stem cell mobilization success

作者：Asif Adil, Jingyu Xiang, Nicola Piccirillo, Hillary G. Harris, Simona Sica, John F. DiPersio, Stephanie N. Hurwitz · 发表于：Bone Marrow Transplantation · 年份：2026 · DOI：10.1038/s41409-026-02811-6 · 被引用次数：1 · 研究领域：Single-cell and spatial transcriptomics、Cancer Cells and Metastasis、Pluripotent Stem Cells Research

Efficient mobilization of donor hematopoietic stem and progenitor cells (HSPCs) to peripheral blood (PB) is essential to the success of HSPC transplantation [ 1 ]. Although granulocyte colony-stimulating factor (G-CSF) is widely used to induce mobilization of HSPCs, healthy donors exhibit marked inter-individual variability in CD34⁺ cell yield [ 2 , 3 , 4 ]. Across institutions, variability may be in part due to differences in mobilization regimens, dosing, and apheresis procedures. Inadequate mobilization can compromise cell collection, and has consequences, including delayed engraftment, graft failure, and relapse. Donors may also face complications of prolonged G-CSF treatment and multiple apheresis sessions [ 5 ]. However, there remains no reliable method to identify these individuals prior to apheresis collection [ 6 ].