AUTHOR=Mosquera Orgueira Adrián , Peleteiro Raíndo Andrés , Cid López Miguel , Díaz Arias José Ángel , González Pérez Marta Sonia , Antelo Rodríguez Beatriz , Alonso Vence Natalia , Bao Pérez Laura , Ferreiro Ferro Roi , Albors Ferreiro Manuel , Abuín Blanco Aitor , Fontanes Trabazo Emilia , Cerchione Claudio , Martinnelli Giovanni , Montesinos Fernández Pau , Mateo Pérez Encinas Manuel , Luis Bello López José TITLE=Personalized Survival Prediction of Patients With Acute Myeloblastic Leukemia Using Gene Expression Profiling JOURNAL=Frontiers in Oncology VOLUME=11 YEAR=2021 URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.657191 DOI=10.3389/fonc.2021.657191 ISSN=2234-943X ABSTRACT=

Acute Myeloid Leukemia (AML) is a heterogeneous neoplasm characterized by cytogenetic and molecular alterations that drive patient prognosis. Currently established risk stratification guidelines show a moderate predictive accuracy, and newer tools that integrate multiple molecular variables have proven to provide better results. In this report, we aimed to create a new machine learning model of AML survival using gene expression data. We used gene expression data from two publicly available cohorts in order to create and validate a random forest predictor of survival, which we named ST-123. The most important variables in the model were age and the expression of KDM5B and LAPTM4B, two genes previously associated with the biology and prognostication of myeloid neoplasms. This classifier achieved high concordance indexes in the training and validation sets (0.7228 and 0.6988, respectively), and predictions were particularly accurate in patients at the highest risk of death. Additionally, ST-123 provided significant prognostic improvements in patients with high-risk mutations. Our results indicate that survival of patients with AML can be predicted to a great extent by applying machine learning tools to transcriptomic data, and that such predictions are particularly precise among patients with high-risk mutations.