Table_1_Evaluation of the Stellae-123 prognostic gene expression signature in acute myeloid leukemia.xlsx

Risk stratification in acute myeloid leukemia (AML) has been extensively improved thanks to the incorporation of recurrent cytogenomic alterations into risk stratification guidelines. However, mortality rates among fit patients assigned to low or intermediate risk groups are still high. Therefore, significant room exists for the improvement of AML prognostication. In a previous work, we presented the Stellae-123 gene expression signature, which achieved a high accuracy in the prognostication of adult patients with AML. Stellae-123 was particularly accurate to restratify patients bearing high-risk mutations, such as ASXL1, RUNX1 and TP53. The intention of the present work was to evaluate the prognostic performance of Stellae-123 in external cohorts using RNAseq technology. For this, we evaluated the signature in 3 different AML cohorts (2 adult and 1 pediatric). Our results indicate that the prognostic performance of the Stellae-123 signature is reproducible in the 3 cohorts of patients. Additionally, we evidenced that the signature was superior to the European LeukemiaNet 2017 and the pediatric clinical risk scores in the prediction of survival at most of the evaluated time points. Furthermore, integration with age substantially enhanced the accuracy of the model. In conclusion, Stellae-123 is a reproducible machine learning algorithm based on a gene expression signature with promising utility in the field of AML.

急性髓系白血病（AML）的风险分层已因反复染色体基因组变异纳入风险分层指南而得到显著提升。然而，即便对于被分配至低或中风险组的健康状况良好的患者，死亡率仍然较高。因此，在AML预后改善方面仍有极大的提升空间。在先前的研究中，我们提出了Stellae-123基因表达特征，该特征在预测成年AML患者预后方面实现了高精度。Stellae-123在重新分层携带高风险突变的患者（如ASXL1、RUNX1和TP53）方面尤其准确。本研究旨在评估Stellae-123在外部队列中利用RNA测序技术的预后性能。为此，我们在3个不同的AML队列（2个成人队列和1个儿童队列）中评估了该特征。我们的结果表明，Stellae-123特征的预后性能在3个患者队列中均具有可重复性。此外，我们还证实，该特征在大多数评估的时间点上，其预测生存的能力优于欧洲白血病网络2017版和儿童临床风险评分。进一步地，与年龄因素的整合显著提高了模型的准确性。综上所述，Stellae-123是一种基于基因表达特征的、具有可重复性的机器学习算法，在AML领域具有广阔的应用前景。