Risk stratification for indolent lymphomas

• Autor(a) principal: Hallack Neto,Abrahão Elias
• Data de Publicação: 2010
• Outros Autores: Costa,Renata Oliveira, Atalla,Angelo, Dulley,Frederico Luiz, Chamone,Dalton Alencar Fischer, Pereira,Juliana
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista brasileira de hematologia e hemoterapia (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-84842010000500015
• topic: Lymphoma, non-Hodgkin; Prognosis; Treatment outcome; Biologic markers; Lymphoma, B-cell; Lymphoma, follicular/pathology

Indolent B-cell lymphomas account for approximately 40% of all non-Hodgkin lymphomas (NHLs). Advances in technology have contributed to improvements in the diagnosis and classification of indolent non-Hodgkin lymphomas. Follicular Lymphomas are the most common although the frequency varies significantly throughout the world. The description of the Follicular Lymphoma International Prognostic Index (FLIPI) was an important step in identifying patient subgroups, but its use in the clinical practice has not been established yet. The use of a larger number of paraffin active monoclonal antibodies for immunohistochemistry, molecular cytogenetic studies including standard cytogenetics, multi-color fluorescence in-situ hybridization (FISH), polymerase chain reaction and locus-specific fluorescence insitu hybridization as well as developments in high-resolution techniquesincluding microarray gene expression profiling allow more accurate diagnosis andprecise definition of biomarkers of value in risk stratification. The identification ofdiseasespecific gene lists resulting from expression profiling provides a number ofpotential protein targets that can be validated using immunohistochemistry. Analysesof gene expression profiles or constitutive gene variations may also provide additional insight for prognostication in the near future. A comprehensive understanding of the biology of these distinct lymphoid tumors will allow us to identify novel diseaserelated genes and should facilitate the development of improved diagnosis, outcome prediction, and personalized approaches to treatment.