title: Clinical application of targeted and genome-wide technologies: can we predict treatment responses in chronic lymphocytic leukemia?
abstract: Chronic lymphocytic leukemia (CLL) is low-grade lymphoma of mature B cells and it is considered to be the most common type of hematological malignancy in the western world. CLL is characterized by a chronically relapsing course and clinical and biological heterogeneity. Many patients do not require any treatment for years. Although important progress has been made in the treatment of CLL, none of the conventional treatment options are curative. Recurrent chromosomal abnormalities have been identified and are associated with prognosis and pathogenesis of the disease. More recently, unbiased genome-wide technologies have identified multiple additional recurrent aberrations. The precise predictive value of these has not been established, but it is likely that the genetic heterogeneity observed at least partly reflects the clinical variability. The present article reviews our current knowledge of predictive markers in CLL using whole-genome technologies.
authors.affiliation: Oxford National Institute for Health Research Biomedical Research Centre, University of Oxford, Oxford, UK ; King Abdulaziz University, Faculty of Applied Medical Sciences, Jeddah, Saudi Arabia.
authors.forename: Reem
authors.initials: R
authors.lastname: Alsolami
authors.affiliation: Oxford National Institute for Health Research Biomedical Research Centre, University of Oxford, Oxford, UK ; Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.
authors.forename: Samantha Jl
authors.initials: SJ
authors.lastname: Knight
authors.affiliation: Oxford National Institute for Health Research Biomedical Research Centre, University of Oxford, Oxford, UK.
authors.forename: Anna
authors.initials: A
authors.lastname: Schuh
keywords: SNP microarray; chronic lymphocytic leukemia; genome-wide technologies; massively parallel sequencing; next-generation technology; predictive biomarker; prognostic biomarker