Parallel transcriptomic risk model personalizes stem cell transplant decisions in pediatric AML
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Parallel transcriptomic risk model personalizes stem cell transplant decisions in pediatric AML

10/08/2026 Compuscript Ltd
Pediatric acute myeloid leukemia (AML) is a severe hematological malignancy where allogeneic hematopoietic stem cell transplantation (allo-HSCT) serves as a critical, life-saving intervention. However, selecting the appropriate candidates for this intensive procedure remains a clinical challenge. Current clinical decision-making often relies heavily on minimal residual disease (MRD) testing, which can inadvertently introduce platform-specific biases and subjective clinical assessments.

To address this urgent need for objective evaluation, a new study published in Genes & Diseases by researchers from Chongqing Medical University, Sun Yat-Sen University, and Foshan University investigated a highly advanced transcriptomic approach. The researchers successfully developed HSCT-64, a novel parallel-risk framework designed to optimize precision transplantation for pediatric patients.

By exclusively utilizing comprehensive RNA-sequencing (RNA-seq) data, the research team constructed a powerful machine-learning model to evaluate individual patient transcriptomes. The robustness of this framework was rigorously tested across clinical datasets, featuring a large discovery cohort of 1,647 pediatric AML cases alongside a dedicated validation cohort of 223 patients from an independent Chinese cohort. The extensive bioinformatic data demonstrated that the HSCT-64 framework successfully and accurately identifies which pediatric patients will genuinely benefit from HSCT directly at the time of initial diagnosis.

Mechanistically, because HSCT-64 relies solely on RNA-seq-based gene expression profiles for prognosis, it overcomes the inherent biases introduced by traditional MRD testing platforms. This sophisticated approach minimizes human subjectivity in clinical assessments, providing a highly standardized and objective metric for evaluating disease severity and transplant suitability. By precisely stratifying patient risk and potential HSCT benefit, the model ensures that vulnerable patients receive critical stem cell transplants promptly, improving overall survival probabilities while shielding others from unnecessary transplant-related toxicities.

While these extensive data robustly highlight the critical advantage of utilizing a transcriptomic machine-learning framework to boost prognostic accuracy, continuous clinical integrations will further refine its global application.

In conclusion, implementing the HSCT-64 framework offers an advanced new strategy to refine precision clinical decision-making in pediatric oncology. This significant finding directly positions RNA-seq-based parallel-risk frameworks as highly compelling diagnostic tools, uniquely primed to deliver personalized and highly effective hematopoietic stem cell transplantation strategies for children battling acute myeloid leukemia.

Reference

Title of Original Paper: A parallel-risk framework accurately predicts hematopoietic stem cell transplantation outcomes and identifies benefiting patients in pediatric AML
Journal: Genes & Diseases
Genes & Diseases is a journal for molecular and translational medicine. The journal primarily focuses on publishing investigations on the molecular bases and experimental therapeutics of human diseases. Publication formats include full length research article, review article, short communication, correspondence, perspectives, commentary, views on news, and research watch.
DOI: https://doi.org/10.1016/j.gendis.2025.102003

Funding Information:

The National Natural Science Foundation of China (No. 81911530169)
Joint Project of Chongqing Health Commission and Science and Technology Bureau (China) (No. 2025ZDXM005, No. 2026MSXM022, No. 2026QNXM029)
CQMU Program for Youth Innovation in Future Medicine (China) (No. W0202)
The Science and Technology Research Program of Chongqing Municipal Education Commission (China) (No. KJZD K202300408)
The Innovation Support Program for Chongqing Overseas Returnees (cx2025115)
Chongqing Science and Technology Bureau (Grant No.CSTB2025NSCQ-GPX0396)
The First-Class Discipline Development Program in Clinical Medicine of Children's Hospital of Chongqing Medical University (China) (No. CHCMU-2025-YLXK-008)

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Genes & Diseases publishes rigorously peer-reviewed and high quality original articles and authoritative reviews that focus on the molecular bases of human diseases. Emphasis is placed on hypothesis-driven, mechanistic studies relevant to pathogenesis and/or experimental therapeutics of human diseases. The journal has worldwide authorship, and a broad scope in basic and translational biomedical research of molecular biology, molecular genetics, and cell biology, including but not limited to cell proliferation and apoptosis, signal transduction, stem cell biology, developmental biology, gene regulation and epigenetics, cancer biology, immunity and infection, neuroscience, disease-specific animal models, gene and cell-based therapies, and regenerative medicine.
Scopus Cite Score: 10.4 | Impact Factor: 14.6

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More information: https://www.keaipublishing.com/en/journals/genes-and-diseases/
Editorial Board: https://www.keaipublishing.com/en/journals/genes-and-diseases/editorial-board/
All issues and articles in press are available online in ScienceDirect (https://www.sciencedirect.com/journal/genes-and-diseases).
Submissions to Genes & Diseases may be made using Editorial Manager (https://www.editorialmanager.com/gendis/default.aspx).
Print ISSN: 2352-4820
eISSN: 2352-3042
CN: 50-1221/R
Contact Us: editor@genesndiseases.cn
X (formerly twitter): @GenesNDiseases (https://x.com/GenesNDiseases)

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https://tinyurl.com/3un9j6sz
Attached files
  • A combination of four publicly available pediatric acute myeloid leukemia (pAML) cohorts was used as the discovery set to develop the HSCT-64 framework, which includes two parallel models: aHSCT-64 for allo-HSCT cases and nHSCT-64 for non-HSCT cases. Two independent pAML cohorts were employed for external validation. By comparing risk rankings generated by aHSCT-64 and nHSCT-64 in the independent cohort (n = 233), we identified the HSCT-benefiting subgroup (blue dots) as patients with a decreased risk rank from nHSCT-64 to aHSCT-64 and the HSCT-nonbenefiting subgroup (red dots) as those with an increased risk rank.
  • (A) Comparison of risk ranks generated by aHSCT-64 and nHSCT-64 models in the independent cohort. Patients with a decreased risk rank under aHSCT-64 (blue) were predicted to benefit from HSCT, while those with an increased rank (red) were predicted as non-benefiting. (B) Among the predicted HSCT-benefiting subgroup, transplanted patients showed significantly better overall survival (OS) than non-transplanted patients. (C) In the HSCT-nonbenefiting subgroup, transplantation did not lead to a significant difference in OS. (D, E) Kaplan–Meier survival analysis comparing transplanted and non-transplanted patients in the high-risk subgroup defined by aHSCT-64 (D) and nHSCT-64 (E). (F, G) Kaplan–Meier survival analysis comparing transplanted and non-transplanted patients in the low-risk subgroup defined by aHSCT-64 (F) and nHSCT-64 (G). (H, I) Distributions of p-values (H) and HRs (I) among 1000-replicate bootstrap predicting cases as HSCT-benefiting and HSCT-nonbenefiting illustrated that the parallel-risk HSCT framework had more predictive stratification. (J, K) Risk distributions of genetic (gene fusion) subtypes assessed by aHSCT-64 (J) and nHSCT-64 (K), with subtypes ordered according to median risk. Differences in survival curves were evaluated using the log-rank test. HR: hazard ratio.
  • (A) Model coefficients for aHSCT-64 and nHSCT-64. The 64 core prognostic genes were classified into four categories based on their associations with risk: for example, the expression levels of 21 genes (red) were positively associated with increased mortality risk in both models; the remaining three categories follow similar interpretations. (B, C) Allo-HSCT (B) and non-HSCT (C) cases from the discovery cohorts were randomly split into training (70%) and testing (30%) sets. The aHSCT-64 and nHSCT-64 models were trained on the training sets, and their predictive performance was evaluated on the test sets using the concordance index (C-index) with respect to overall survival (OS). (D, E) As in (B, C), but evaluated using the time-dependent area under the ROC curve (tAUC) for predicting death/alive within 1–6 years respectively. (F, G) Kaplan–Meier survival analysis of high-risk and low-risk subgroups defined by aHSCT-64-rank (F) and nHSCT-64-rank (G) in the respective test sets. Significant survival differences were observed (log-rank test). HR: hazard ratio.
10/08/2026 Compuscript Ltd
Regions: Europe, Ireland, United Kingdom, Asia, China, Extraterrestrial, Sun
Keywords: Science, Life Sciences

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