Mosquera Orgueira Adrián, Peleteiro Raíndo Andrés, Díaz Arias José Ángel, Antelo Rodríguez Beatriz, López Riñón Mónica, Cerchione Claudio, de la Fuente Burguera Adolfo, González Pérez Marta Sonia, Martinelli Giovanni, Montesinos Fernández Pau, Pérez Encinas Manuel Mateo
Department of Hematology, University Hospital of Santiago de Compostela, Santiago de Compostela, Spain.
Department of Hematology, Tomelloso Hospital, Ciudad Real, Spain.
Front Oncol. 2022 Aug 17;12:968340. doi: 10.3389/fonc.2022.968340. eCollection 2022.
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 and . 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.
由于将复发性细胞基因组改变纳入风险分层指南，急性髓系白血病（AML）的风险分层有了显著改善。然而，被归类为低风险或中风险组的适合患者的死亡率仍然很高。因此，AML预后的改善仍有很大空间。在之前的一项工作中，我们提出了Stellae - 123基因表达特征，其在成年AML患者的预后评估中具有很高的准确性。Stellae - 123对于重新分层携带高危突变的患者（如 和 ）特别准确。本研究的目的是使用RNA测序技术评估Stellae - 123在外部队列中的预后性能。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特征在这3个患者队列中的预后性能具有可重复性。此外，我们证明在大多数评估时间点，该特征在预测生存率方面优于欧洲白血病网2017版和儿童临床风险评分。此外，将年龄纳入模型显著提高了其准确性。总之，Stellae - 123是一种基于基因表达特征的可重复机器学习算法，在AML领域具有广阔的应用前景。
