Portal de investigación
Evaluation of the Stellae-123 prognostic gene expression signature in acute myeloid leukemia
Fecha de publicación:
Fecha Ahead of Print:
Autores de IIS La Fe
Participantes ajenos a IIS La Fe
- Mosquera Orgueira, Adrian
- Peleteiro Raindo, Andres
- Diaz Arias, Jose angel
- Antelo Rodriguez, Beatriz
- Lopez Rinon, Monica
- Cerchione, Claudio
- de la Fuente Burguera, Adolfo
- Gonzalez Perez, Marta Sonia
- Martinelli, Giovanni
- Perez Encinas, Manuel Mateo
Grupos
Abstract
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.
Copyright © 2022 Mosquera Orgueira, Peleteiro Raíndo, Díaz Arias, Antelo Rodríguez, López Riñón, Cerchione, de la Fuente Burguera, González Pérez, Martinelli, Montesinos Fernández and Pérez Encinas.
Datos de la publicación
- ISSN/ISSNe:
- 2234-943X, 2234-943X
- Tipo:
- Article
- Páginas:
- 968340-968340
- PubMed:
- 36059646
- Factor de Impacto:
- 1,291 SCImago ℠
- Cuartil:
- Q1 SCImago ℠
Frontiers in Oncology FRONTIERS MEDIA SA
Citas Recibidas en Web of Science: 3
Documentos
- No hay documentos
Filiaciones
Keywords
- leukemia; transcriptome; machine learning; survival; risk; prediction
Campos de Estudio
Cita
Mosquera A,Peleteiro A,Diaz JA,Antelo B,Lopez M,Cerchione C,de la Fuente A,Gonzalez MS,Martinelli G,MONTESINOS P,Perez MM. Evaluation of the Stellae-123 prognostic gene expression signature in acute myeloid leukemia. Front. Oncol. 2022. 12. p. 968340-968340. IF:4,700. (2).