Frontiers in Oncology (Mar 2021)

Personalized Survival Prediction of Patients With Acute Myeloblastic Leukemia Using Gene Expression Profiling

  • Adrián Mosquera Orgueira,
  • Adrián Mosquera Orgueira,
  • Adrián Mosquera Orgueira,
  • Andrés Peleteiro Raíndo,
  • Andrés Peleteiro Raíndo,
  • Andrés Peleteiro Raíndo,
  • Miguel Cid López,
  • Miguel Cid López,
  • Miguel Cid López,
  • José Ángel Díaz Arias,
  • José Ángel Díaz Arias,
  • Marta Sonia González Pérez,
  • Marta Sonia González Pérez,
  • Beatriz Antelo Rodríguez,
  • Beatriz Antelo Rodríguez,
  • Beatriz Antelo Rodríguez,
  • Natalia Alonso Vence,
  • Natalia Alonso Vence,
  • Natalia Alonso Vence,
  • Laura Bao Pérez,
  • Laura Bao Pérez,
  • Laura Bao Pérez,
  • Roi Ferreiro Ferro,
  • Roi Ferreiro Ferro,
  • Manuel Albors Ferreiro,
  • Manuel Albors Ferreiro,
  • Aitor Abuín Blanco,
  • Aitor Abuín Blanco,
  • Emilia Fontanes Trabazo,
  • Emilia Fontanes Trabazo,
  • Claudio Cerchione,
  • Giovanni Martinnelli,
  • Pau Montesinos Fernández,
  • Manuel Mateo Pérez Encinas,
  • Manuel Mateo Pérez Encinas,
  • Manuel Mateo Pérez Encinas,
  • José Luis Bello López,
  • José Luis Bello López,
  • José Luis Bello López

DOI
https://doi.org/10.3389/fonc.2021.657191
Journal volume & issue
Vol. 11

Abstract

Read online

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.

Keywords