@article{Feng2026, 
author = {Yance Feng and Yali Shen and Ke Huang and Qian Li and Yu Tao and Rongqiu Liu and Liping Zhan and Hua Yang and Yang Xun and Yichao Xu and Wenli Tang and Binjun Xiong and Hui Shi and Liting Cheng and Li Wei and Hua You},
title = {A parallel-risk framework accurately predicts hematopoietic stem cell transplantation outcomes and identifies benefiting patients in pediatric AML},
year = {2026},
journal = {Genes & Diseases},
volume = {13},
number = {5},
keywords = {Hematopoietic stem cell transplantation, Parallel-risk framework, Pediatric acute myeloid leukemia, Prognosis, Transcriptome},
url = {https://www.sciopen.com/article/10.1016/j.gendis.2025.102003},
doi = {10.1016/j.gendis.2025.102003},
abstract = {Pediatric acute myeloid leukemia (pAML) has a poorer prognosis than acute lymphoblastic leukemia, and hematopoietic stem cell transplantation (HSCT) offers curative potential in high-risk or relapsed cases. Current models cannot accurately determine which individual patients will truly benefit from HSCT, leading to overtreatment or undertreatment. We developed HSCT-64, the first parallel transcriptomic risk framework for pediatric AML, conceptually analogous to a causal G-formula approach. It comprises two treatment-specific models, aHSCT-64 for allo-HSCT recipients and nHSCT-64 for non-HSCT patients, derived from a shared 64-gene signature identified from diagnostic RNA-sequencing data, enabling individualized survival prediction under both treatment scenarios at diagnosis. Trained on 1647 cases from four COG/TARGET cohorts and validated in 233 independent patients, HSCT-64 achieved a C-index of 0.791 and AUC of 0.794 for allo-HSCT overall survival, outperforming existing clinical, cytogenetic, and leukemia stem cell-based models. Comparing risk ranks between two models identified an HSCT-benefiting subgroup patients with a predicted risk rank reduction from HSCT who experienced a 5.88-fold mortality reduction post-transplant (Hazard Ratio, HR = 0.17, P = 0.0066), while no survival gain was seen in the nonbenefiting subgroup (HR = 0.94, P = 0.899). HSCT-64 enables precise, diagnosis-time identification of pAML patients most likely to benefit from transplantation, marking a shift from high-risk-based recommendations toward individualized, transcriptome-driven decision-making.}
}