Gene expression profiling in follicular lymphoma to assess clinical aggressiveness and to guide the choice of treatment.
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Publication year
2005Source
Blood, 105, 1, (2005), pp. 301-7ISSN
Publication type
Article / Letter to editor
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Organization
Pathology
Neurology
Journal title
Blood
Volume
vol. 105
Issue
iss. 1
Page start
p. 301
Page end
p. 7
Subject
DCN 2: Functional Neurogenomics; NCMLS 6: Genetics and epigenetic pathways of disease; ONCOL 1: Hereditary cancer and cancer-related syndromes; ONCOL 2: Age-related aspects of cancer; ONCOL 3: Translational research; UMCN 1.2: Molecular diagnosis, prognosis and monitoringAbstract
Follicular lymphoma (FL) is a disease characterized by a long clinical course marked by frequent relapses that vary in clinical aggressiveness over time. Therefore, the main dilemma at each relapse is the choice for the most effective treatment for optimal disease control and failure-free survival while at the same time avoiding overtreatment and harmful side effects. The selection for more aggressive treatment is currently based on histologic grading and clinical criteria; however, in up to 30% of all cases these methods prove to be insufficient. Using supervised classification on a training set of paired samples from patients who experienced either an indolent or aggressive disease course, a gene expression profile of 81 genes was established that could, with an accuracy of 100%, distinguish low-grade from high-grade disease. This profile accurately classified 93% of the FL samples in an independent validation set. Most important, in a third series of FL cases where histologic grading was ambiguous, precluding meaningful morphologic guidance, the 81-gene profile shows a classification accuracy of 94%. The FL stratification profile is a more reliable marker of clinical behavior than the currently used histologic grading and clinical criteria and may provide an important alternative to guide the choice of therapy in patients with FL both at presentation and at relapse.
This item appears in the following Collection(s)
- Academic publications [246515]
- Electronic publications [134102]
- Faculty of Medical Sciences [93308]
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