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

Autor:	Mosquera Orgueira A; University Hospital of Santiago de Compostela (SERGAS), Department of Hematology, Santiago de Compostela, Spain.; Health Research Institute of Santiago de Compostela, Grupo de Investigación en Síndromes Linfoproliferativos, Santiago de Compostela, Spain.; Departamento de Medicina, University of Santiago de Compostela, Santiago de Compostela, Spain., Peleteiro Raíndo A; University Hospital of Santiago de Compostela (SERGAS), Department of Hematology, Santiago de Compostela, Spain.; Health Research Institute of Santiago de Compostela, Grupo de Investigación en Síndromes Linfoproliferativos, Santiago de Compostela, Spain.; Departamento de Medicina, University of Santiago de Compostela, Santiago de Compostela, Spain., Cid López M; University Hospital of Santiago de Compostela (SERGAS), Department of Hematology, Santiago de Compostela, Spain.; Health Research Institute of Santiago de Compostela, Grupo de Investigación en Síndromes Linfoproliferativos, Santiago de Compostela, Spain.; Departamento de Medicina, University of Santiago de Compostela, Santiago de Compostela, Spain., Díaz Arias JÁ; University Hospital of Santiago de Compostela (SERGAS), Department of Hematology, Santiago de Compostela, Spain.; Health Research Institute of Santiago de Compostela, Grupo de Investigación en Síndromes Linfoproliferativos, Santiago de Compostela, Spain., González Pérez MS; University Hospital of Santiago de Compostela (SERGAS), Department of Hematology, Santiago de Compostela, Spain.; Health Research Institute of Santiago de Compostela, Grupo de Investigación en Síndromes Linfoproliferativos, Santiago de Compostela, Spain., Antelo Rodríguez B; University Hospital of Santiago de Compostela (SERGAS), Department of Hematology, Santiago de Compostela, Spain.; Health Research Institute of Santiago de Compostela, Grupo de Investigación en Síndromes Linfoproliferativos, Santiago de Compostela, Spain.; Departamento de Medicina, University of Santiago de Compostela, Santiago de Compostela, Spain., Alonso Vence N; University Hospital of Santiago de Compostela (SERGAS), Department of Hematology, Santiago de Compostela, Spain.; Health Research Institute of Santiago de Compostela, Grupo de Investigación en Síndromes Linfoproliferativos, Santiago de Compostela, Spain.; Departamento de Medicina, University of Santiago de Compostela, Santiago de Compostela, Spain., Bao Pérez L; University Hospital of Santiago de Compostela (SERGAS), Department of Hematology, Santiago de Compostela, Spain.; Health Research Institute of Santiago de Compostela, Grupo de Investigación en Síndromes Linfoproliferativos, Santiago de Compostela, Spain.; Departamento de Medicina, University of Santiago de Compostela, Santiago de Compostela, Spain., Ferreiro Ferro R; University Hospital of Santiago de Compostela (SERGAS), Department of Hematology, Santiago de Compostela, Spain.; Health Research Institute of Santiago de Compostela, Grupo de Investigación en Síndromes Linfoproliferativos, Santiago de Compostela, Spain., Albors Ferreiro M; University Hospital of Santiago de Compostela (SERGAS), Department of Hematology, Santiago de Compostela, Spain.; Health Research Institute of Santiago de Compostela, Grupo de Investigación en Síndromes Linfoproliferativos, Santiago de Compostela, Spain., Abuín Blanco A; University Hospital of Santiago de Compostela (SERGAS), Department of Hematology, Santiago de Compostela, Spain.; Health Research Institute of Santiago de Compostela, Grupo de Investigación en Síndromes Linfoproliferativos, Santiago de Compostela, Spain., Fontanes Trabazo E; University Hospital of Santiago de Compostela (SERGAS), Department of Hematology, Santiago de Compostela, Spain.; Health Research Institute of Santiago de Compostela, Grupo de Investigación en Síndromes Linfoproliferativos, Santiago de Compostela, Spain., Cerchione C; Hematology Unit, Istituto Tumori della Romagna IRST IRCCS, Meldola, Italy., Martinnelli G; Hematology Unit, Istituto Tumori della Romagna IRST IRCCS, Meldola, Italy., Montesinos Fernández P; Hospital Universitari i Politècnic La Fe, Department of Hematology, Valencia, Spain., Mateo Pérez Encinas M; University Hospital of Santiago de Compostela (SERGAS), Department of Hematology, Santiago de Compostela, Spain.; Health Research Institute of Santiago de Compostela, Grupo de Investigación en Síndromes Linfoproliferativos, Santiago de Compostela, Spain.; Departamento de Medicina, University of Santiago de Compostela, Santiago de Compostela, Spain., Luis Bello López J; University Hospital of Santiago de Compostela (SERGAS), Department of Hematology, Santiago de Compostela, Spain.; Health Research Institute of Santiago de Compostela, Grupo de Investigación en Síndromes Linfoproliferativos, Santiago de Compostela, Spain.; Departamento de Medicina, University of Santiago de Compostela, Santiago de Compostela, Spain.
Jazyk:	angličtina
Zdroj:	Frontiers in oncology [Front Oncol] 2021 Mar 29; Vol. 11, pp. 657191. Date of Electronic Publication: 2021 Mar 29 (Print Publication: 2021).
DOI:	10.3389/fonc.2021.657191
Abstrakt:	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. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2021 Mosquera Orgueira, Peleteiro Raíndo, Cid López, Díaz Arias, González Pérez, Antelo Rodríguez, Alonso Vence, Bao Pérez, Ferreiro Ferro, Albors Ferreiro, Abuín Blanco, Fontanes Trabazo, Cerchione, Martinnelli, Montesinos Fernández, Mateo Pérez Encinas and Luis Bello López.)
Databáze:	MEDLINE
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