Mosquera Orgueira, AdriánAntelo Rodríguez, BeatrizAlonso Vence, NataliaBendaña López, María ÁngelesDíaz Arias, José ÁngelDíaz Varela, Nicolás AntonioGonzález Pérez, Marta SoniaPérez Encinas, Manuel MateoBello López, José Luis2020-04-082020-04-082019Mosquera 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.2019.00079http://hdl.handle.net/10347/21265Chronic 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.eng© 2019 Mosquera Orgueira, Antelo Rodríguez, Alonso Vence, Bendaña López, Díaz Arias, Díaz Varela, González Pérez, Pérez Encinas and Bello López. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these termshttps://creativecommons.org/licenses/by/4.0/Chronic lymphocytic leukemiaTime to treatment predictionGene expressionRNAseqMachine learningPrognostic factorsIGHVjournal article10.3389/fonc.2019.000792234-943Xopen access