Mostrar el registro sencillo del ítem

dc.contributor.authorMOSQUERA ORGUEIRA, ADRIAN 
dc.contributor.authorAntelo Rodríguez, Beatriz
dc.contributor.authorAlonso Vence, Natalia 
dc.contributor.authorBendaña López, Mª Ángeles
dc.contributor.authorDíaz Arias, José 
dc.contributor.authorDíaz Varela, Nicolás 
dc.contributor.authorGonzález Pérez, Marta Sonia 
dc.contributor.authorPérez Encinas, Manuel Mateo 
dc.contributor.authorBello López, José Luis 
dc.date.accessioned2021-11-30T11:12:13Z
dc.date.available2021-11-30T11:12:13Z
dc.date.issued2019
dc.identifier.issn2234-943X
dc.identifier.otherhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6384245/pdf/fonc-09-00079.pdfes
dc.identifier.otherhttps://www.ncbi.nlm.nih.gov/pubmed/30828568es
dc.identifier.urihttp://hdl.handle.net/20.500.11940/15778
dc.description.abstractChronic 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.isoenges
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleTime to treatment prediction in chronic lymphocytic leukemia based on new transcriptional patternsen
dc.typeArtigoes
dc.authorsophosOrgueira, A. M.
dc.authorsophosRodríguez, B. A.
dc.authorsophosVence, N. A.
dc.authorsophosLópez, Á B.
dc.authorsophosArias, J. A. D.
dc.authorsophosVarela, N. D.
dc.authorsophosPérez, M. S. G.
dc.authorsophosEncinas, M. M. P.
dc.authorsophosLópez, J. L. B.
dc.identifier.doi10.3389/fonc.2019.00079
dc.identifier.pmid30828568
dc.identifier.sophos31795
dc.issue.numberFEBes
dc.journal.titleFRONTIERS IN ONCOLOGYes
dc.organizationServizo Galego de Saúde::Estrutura de Xestión Integrada (EOXI)::EOXI de Santiago de Compostela - Complexo Hospitalario Universitario de Santiago de Compostela::Hematoloxía clínicaes
dc.organizationServizo Galego de Saúde::Estrutura de Xestión Integrada (EOXI)::Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS)es
dc.rights.accessRightsopenAccesses
dc.subject.keywordCHUSes
dc.subject.keywordIDISes
dc.typefidesArtículo Originales
dc.typesophosArtículo Originales
dc.volume.number9es


Ficheros en el ítem

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem

Atribución 4.0 Internacional
Excepto si se señala otra cosa, la licencia del ítem se describe como Atribución 4.0 Internacional