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Time to treatment prediction in chronic lymphocytic leukemia based on new transcriptional patterns
dc.contributor.author | MOSQUERA ORGUEIRA, ADRIAN | |
dc.contributor.author | Antelo Rodríguez, Beatriz | |
dc.contributor.author | Alonso Vence, Natalia | |
dc.contributor.author | Bendaña López, Mª Ángeles | |
dc.contributor.author | Díaz Arias, José | |
dc.contributor.author | Díaz Varela, Nicolás | |
dc.contributor.author | González Pérez, Marta Sonia | |
dc.contributor.author | Pérez Encinas, Manuel Mateo | |
dc.contributor.author | Bello López, José Luis | |
dc.date.accessioned | 2021-11-30T11:12:13Z | |
dc.date.available | 2021-11-30T11:12:13Z | |
dc.date.issued | 2019 | |
dc.identifier.issn | 2234-943X | |
dc.identifier.other | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6384245/pdf/fonc-09-00079.pdf | es |
dc.identifier.other | https://www.ncbi.nlm.nih.gov/pubmed/30828568 | es |
dc.identifier.uri | http://hdl.handle.net/20.500.11940/15778 | |
dc.description.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. | en |
dc.language.iso | eng | es |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.title | Time to treatment prediction in chronic lymphocytic leukemia based on new transcriptional patterns | en |
dc.type | Artigo | es |
dc.authorsophos | Orgueira, A. M. | |
dc.authorsophos | Rodríguez, B. A. | |
dc.authorsophos | Vence, N. A. | |
dc.authorsophos | López, Á B. | |
dc.authorsophos | Arias, J. A. D. | |
dc.authorsophos | Varela, N. D. | |
dc.authorsophos | Pérez, M. S. G. | |
dc.authorsophos | Encinas, M. M. P. | |
dc.authorsophos | López, J. L. B. | |
dc.identifier.doi | 10.3389/fonc.2019.00079 | |
dc.identifier.pmid | 30828568 | |
dc.identifier.sophos | 31795 | |
dc.issue.number | FEB | es |
dc.journal.title | FRONTIERS IN ONCOLOGY | es |
dc.organization | Servizo Galego de Saúde::Estrutura de Xestión Integrada (EOXI)::EOXI de Santiago de Compostela - Complexo Hospitalario Universitario de Santiago de Compostela::Hematoloxía clínica | es |
dc.organization | Servizo Galego de Saúde::Estrutura de Xestión Integrada (EOXI)::Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS) | es |
dc.rights.accessRights | openAccess | es |
dc.subject.keyword | CHUS | es |
dc.subject.keyword | IDIS | es |
dc.typefides | Artículo Original | es |
dc.typesophos | Artículo Original | es |
dc.volume.number | 9 | es |