AUTHOR=Mosquera Orgueira Adrián , Antelo Rodríguez Beatriz , Alonso Vence Natalia , Bendaña López Ángeles , Díaz Arias José Ángel , Díaz Varela Nicolás , González Pérez Marta Sonia , Pérez Encinas Manuel Mateo , Bello López José Luis TITLE=Time to Treatment Prediction in Chronic Lymphocytic Leukemia Based on New Transcriptional Patterns JOURNAL=Frontiers in Oncology VOLUME=9 YEAR=2019 URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2019.00079 DOI=10.3389/fonc.2019.00079 ISSN=2234-943X ABSTRACT=

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.