RESUMEN
Juvenile myelomonocytic leukemia (JMML) is a myeloproliferative disorder of childhood caused by mutations in the Ras pathway. Outcomes in JMML vary markedly from spontaneous resolution to rapid relapse after hematopoietic stem cell transplantation. Here, we hypothesized that DNA methylation patterns would help predict disease outcome and therefore performed genome-wide DNA methylation profiling in a cohort of 39 patients. Unsupervised hierarchical clustering identifies three clusters of patients. Importantly, these clusters differ significantly in terms of 4-year event-free survival, with the lowest methylation cluster having the highest rates of survival. These findings were validated in an independent cohort of 40 patients. Notably, all but one of 14 patients experiencing spontaneous resolution cluster together and closer to 22 healthy controls than to other JMML cases. Thus, we show that DNA methylation patterns in JMML are predictive of outcome and can identify the patients most likely to experience spontaneous resolution.
Asunto(s)
Metilación de ADN , Genoma Humano/genética , Leucemia Mielomonocítica Juvenil/genética , Regresión Neoplásica Espontánea/genética , Antineoplásicos/uso terapéutico , Biopsia , Niño , Preescolar , Supervivencia sin Enfermedad , Femenino , Trasplante de Células Madre Hematopoyéticas , Humanos , Lactante , Estimación de Kaplan-Meier , Leucemia Mielomonocítica Juvenil/sangre , Leucemia Mielomonocítica Juvenil/mortalidad , Leucemia Mielomonocítica Juvenil/terapia , Masculino , Monocitos , Mutación , Pronóstico , Estudios ProspectivosRESUMEN
Juvenile myelomonocytic leukemia (JMML) is a myeloproliferative neoplasm (MPN) of childhood with a poor prognosis. Mutations in NF1, NRAS, KRAS, PTPN11 or CBL occur in 85% of patients, yet there are currently no risk stratification algorithms capable of predicting which patients will be refractory to conventional treatment and could therefore be candidates for experimental therapies. In addition, few molecular pathways aside from the RAS-MAPK pathway have been identified that could serve as the basis for such novel therapeutic strategies. We therefore sought to genomically characterize serial samples from patients at diagnosis through relapse and transformation to acute myeloid leukemia to expand knowledge of the mutational spectrum in JMML. We identified recurrent mutations in genes involved in signal transduction, splicing, Polycomb repressive complex 2 (PRC2) and transcription. Notably, the number of somatic alterations present at diagnosis appears to be the major determinant of outcome.
Asunto(s)
Predisposición Genética a la Enfermedad/genética , Estudio de Asociación del Genoma Completo/métodos , Leucemia Mielomonocítica Juvenil/genética , Mutación , Transducción de Señal/genética , Enfermedad Aguda , Niño , Preescolar , Variaciones en el Número de Copia de ADN , Progresión de la Enfermedad , Supervivencia sin Enfermedad , Femenino , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Humanos , Lactante , Leucemia Mieloide/diagnóstico , Leucemia Mieloide/genética , Leucemia Mielomonocítica Juvenil/diagnóstico , Masculino , PronósticoRESUMEN
Juvenile myelomonocytic leukemia (JMML) is an aggressive myeloproliferative neoplasm of childhood associated with a poor prognosis. Recently, massively parallel sequencing has identified recurrent mutations in the SKI domain of SETBP1 in a variety of myeloid disorders. These lesions were detected in nearly 10% of patients with JMML and have been characterized as secondary events. We hypothesized that rare subclones with SETBP1 mutations are present at diagnosis in a large portion of patients who relapse, but are below the limits of detection for conventional deep sequencing platforms. Using droplet digital polymerase chain reaction, we identified SETBP1 mutations in 17/56 (30%) of patients who were treated in the Children's Oncology Group sponsored clinical trial, AAML0122. Five-year event-free survival in patients with SETBP1 mutations was 18% ± 9% compared with 51% ± 8% for those without mutations (P = .006).
