Laboratory for Excellence in Systems Biomedicine of Pediatric Oncology, Department of Pediatric Hematology and Oncology, Chongqing Key Laboratory of Pediatrics, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, National Clinical Research Center for Child Health and Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China.
NHC Key Laboratory of Birth Defects and Reproductive Health, Chongqing Population and Family Planning Science and Technology Research Institute, Chongqing, China.
Mol Biomed. 2024 Jan 2;5(1):1. doi: 10.1186/s43556-023-00162-y.
Risk classification in pediatric acute myeloid leukemia (P-AML) is crucial for personalizing treatments. Thus, we aimed to establish a risk-stratification tool for P-AML patients and eventually guide individual treatment. A total of 256 P-AML patients with accredited mRNA-seq data from the TARGET database were divided into training and internal validation datasets. A gene-expression-based prognostic score was constructed for overall survival (OS), by using univariate Cox analysis, LASSO regression analysis, Kaplan-Meier (K-M) survival, and multivariate Cox analysis. A P-AML-5G prognostic score bioinformatically derived from expression levels of 5 genes (ZNF775, RNFT1, CRNDE, COL23A1, and TTC38), clustered P-AML patients in training dataset into high-risk group (above optimal cut-off) with shorter OS, and low-risk group (below optimal cut-off) with longer OS (p < 0.0001). Meanwhile, similar results were obtained in internal validation dataset (p = 0.005), combination dataset (p < 0.001), two treatment sub-groups (p < 0.05), intermediate-risk group defined with the Children's Oncology Group (COG) (p < 0.05) and an external Japanese P-AML dataset (p = 0.005). The model was further validated in the COG study AAML1031(p = 0.001), and based on transcriptomic analysis of 943 pediatric patients and 70 normal bone marrow samples from this dataset, two genes in the model demonstrated significant differential expression between the groups [all log2(foldchange) > 3, p < 0.001]. Independent of other prognostic factors, the P-AML-5G groups presented the highest concordance-index values in training dataset, chemo-therapy only treatment subgroups of the training and internal validation datasets, and whole genome-sequencing subgroup of the combined dataset, outperforming two Children's Oncology Group (COG) risk stratification systems, 2022 European LeukemiaNet (ELN) risk classification tool and two leukemic stem cell expression-based models. The 5-gene prognostic model generated by a single assay can further refine the current COG risk stratification system that relies on numerous tests and may have the potential for the risk judgment and identification of the high-risk pediatric AML patients receiving chemo-therapy only treatment.
儿童急性髓系白血病（P-AML）的风险分类对于治疗方案的个体化至关重要。因此，我们旨在建立一个用于 P-AML 患者的风险分层工具，最终指导个体化治疗。我们从 TARGET 数据库中收集了 256 名 P-AML 患者的经认证的 mRNA-seq 数据，将其分为训练数据集和内部验证数据集。通过单因素 Cox 分析、LASSO 回归分析、Kaplan-Meier（K-M）生存分析和多因素 Cox 分析，构建了基于基因表达的总生存（OS）预后评分。从 5 个基因（ZNF775、RNFT1、CRNDE、COL23A1 和 TTC38）的表达水平中，推导出一个 P-AML-5G 预后评分，将训练数据集中的 P-AML 患者聚类为高危组（高于最佳截断值），OS 更短，低危组（低于最佳截断值），OS 更长（p < 0.0001）。同时，在内部验证数据集（p = 0.005）、组合数据集（p < 0.001）、两种治疗亚组（p < 0.05）、根据儿童肿瘤学组（COG）定义的中危组（p < 0.05）和一个外部日本 P-AML 数据集（p = 0.005）中获得了类似的结果。该模型在 COG 研究 AAML1031 中进一步得到验证（p = 0.001），并基于来自该数据集的 943 名儿科患者和 70 个正常骨髓样本的转录组分析，模型中的两个基因在组间表现出显著的差异表达[所有 log2（foldchange）> 3，p < 0.001]。独立于其他预后因素，P-AML-5G 组在训练数据集中、训练和内部验证数据集的化疗仅治疗亚组以及组合数据集中的全基因组测序亚组中具有最高的一致性指数值，优于两个儿童肿瘤学组（COG）风险分层系统、2022 年欧洲白血病网络（ELN）风险分类工具和两个白血病干细胞表达模型。由单一检测生成的 5 个基因预后模型可以进一步完善当前依赖于多种检测的 COG 风险分层系统，并有潜力用于风险判断和识别仅接受化疗的高危儿科 AML 患者。
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