RT Journal Article
T1 Time to Treatment Prediction in Chronic Lymphocytic Leukemia Based on New Transcriptional Patterns
A1 Mosquera Orgueira, Adrián
A1 Antelo Rodríguez, Beatriz
A1 Alonso Vence, Natalia
A1 Bendaña López, María Ángeles
A1 Díaz Arias, José Ángel
A1 Díaz Varela, Nicolás Antonio
A1 González Pérez, Marta Sonia
A1 Pérez Encinas, Manuel Mateo
A1 Bello López, José Luis
K1 Chronic lymphocytic leukemia
K1 Time to treatment prediction
K1 Gene expression
K1 RNAseq
K1 Machine learning
K1 Prognostic factors
K1 IGHV
AB Chronic lymphocytic leukemia (CLL) is the most frequent lymphoproliferative syndrome in western countries. CLL evolution is frequently indolent, and treatment is mostly reserved for those patients with signs or symptoms of disease progression. In this work, we used RNA sequencing data from the International Cancer Genome Consortium CLL cohort to determine new gene expression patterns that correlate with clinical evolution.We determined that a 290-gene expression signature, in addition to immunoglobulin heavy chain variable region (IGHV) mutation status, stratifies patients into four groups with notably different time to first treatment. This finding was confirmed in an independent cohort. Similarly, we present a machine learning algorithm that predicts the need for treatment within the first 5 years following diagnosis using expression data from 2,198 genes. This predictor achieved 90% precision and 89% accuracy when classifying independent CLL cases. Our findings indicate that CLL progression risk largely correlates with particular transcriptomic patterns and paves the way for the identification of high-risk patients who might benefit from prompt therapy following diagnosis.
PB Frontiers Media
YR 2019
FD 2019
LK http://hdl.handle.net/10347/21265
UL http://hdl.handle.net/10347/21265
LA eng
NO Mosquera Orgueira, A., Antelo Rodríguez, B., Alonso Vence, N., Bendaña López, Á, Díaz Arias, J. Á, Díaz Varela, N., . . . Bello López, J. L. (2019). Time to treatment prediction in chronic lymphocytic leukemia based on new transcriptional patterns. Frontiers in Oncology, 9, 79. doi:10.3389/fonc.2019.00079
DS Minerva
RD 24 abr 2026