RESUMEN
The clinical course of patients with chronic lymphocytic leukemia (CLL) is heterogeneous. Several prognostic factors have been identified that can stratify patients into groups that differ in their relative tendency for disease progression and/or survival. Here, we pursued a subnetwork-based analysis of gene expression profiles to discriminate between groups of patients with disparate risks for CLL progression. From an initial cohort of 130 patients, we identified 38 prognostic subnetworks that could predict the relative risk for disease progression requiring therapy from the time of sample collection, more accurately than established markers. The prognostic power of these subnetworks then was validated on 2 other cohorts of patients. We noted reduced divergence in gene expression between leukemia cells of CLL patients classified at diagnosis with aggressive versus indolent disease over time. The predictive subnetworks vary in levels of expression over time but exhibit increased similarity at later time points before therapy, suggesting that degenerate pathways apparently converge into common pathways that are associated with disease progression. As such, these results have implications for understanding cancer evolution and for the development of novel treatment strategies for patients with CLL.
Asunto(s)
Biomarcadores de Tumor/genética , Perfilación de la Expresión Génica , Leucemia Linfocítica Crónica de Células B/genética , Leucemia Linfocítica Crónica de Células B/patología , Transducción de Señal , Biomarcadores de Tumor/metabolismo , Progresión de la Enfermedad , Redes Reguladoras de Genes , Humanos , Leucemia Linfocítica Crónica de Células B/mortalidad , Análisis de Secuencia por Matrices de Oligonucleótidos , Pronóstico , ARN Mensajero/genética , Reacción en Cadena en Tiempo Real de la Polimerasa , Reacción en Cadena de la Polimerasa de Transcriptasa Inversa , Tasa de Supervivencia , Células Tumorales CultivadasRESUMEN
For most sample types, the automation of RNA and DNA sample preparation workflows enables high throughput next-generation sequencing (NGS) library preparation. Greater adoption of small RNA (sRNA) sequencing has been hindered by high sample input requirements and inherent ligation side products formed during library preparation. These side products, known as adapter dimer, are very similar in size to the tagged library. Most sRNA library preparation strategies thus employ a gel purification step to isolate tagged library from adapter dimer contaminants. At very low sample inputs, adapter dimer side products dominate the reaction and limit the sensitivity of this technique. Here we address the need for improved specificity of sRNA library preparation workflows with a novel library preparation approach that uses modified adapters to suppress adapter dimer formation. This workflow allows for lower sample inputs and elimination of the gel purification step, which in turn allows for an automatable sRNA library preparation protocol.