BACKGROUND: The differentiation of biologically and clinically different malignant lymphoma diseases or subtypes is crucial because it leads to better prognostication and therapeutic decision-making. Attempts have been made at subtype classification for diagnosing lymphomas on the basis of gene-expression profiling. Although array-based comparative genomic hybridization (array CGH) has identified a characteristic genomic alteration pattern for each disease entity, it has not been clear whether each patient with certain genomic alterations can be classified by array CGH data. DESIGN AND METHODS: Data on copy number gains and losses for 46 diffuse large B-cell lymphomas and 29 mantle cell lymphomas were used. The gene expressions of the diffuse large B-cell lymphomas cases were profiled and hierarchical clustering revealed that 28 of them were of the activated B-cell type and 18 were of the germinal center-B-cell type. Using these data, we developed a computer algorithm to classify lymphoma diseases or subtypes on the basis of copy number gains and losses. RESULTS: The method correctly classified 88% of the diffuse large B-cell lymphomas and mantle cell lymphomas, and 83% of the activated B-cell and germinal center-B-cell subtypes. These results demonstrate that copy number gains and losses detected by array CGH can be used for classifying lymphomas into biologically and clinically distinct diseases or subtypes. CONCLUSIONS: Our computer algorithm based on array CGH data successfully classified diffuse large B-cell lymphomas and mantle cell lymphomas and activated B-cell and germinal center-B-cell subtypes with high accuracy. An important finding is that the regions automatically identified by the computer algorithm were located in the critical regions that are likely to be involved in the development of lymphoma.
BACKGROUND: The differentiation of biologically and clinically different malignant lymphoma diseases or subtypes is crucial because it leads to better prognostication and therapeutic decision-making. Attempts have been made at subtype classification for diagnosing lymphomas on the basis of gene-expression profiling. Although array-based comparative genomic hybridization (array CGH) has identified a characteristic genomic alteration pattern for each disease entity, it has not been clear whether each patient with certain genomic alterations can be classified by array CGH data. DESIGN AND METHODS: Data on copy number gains and losses for 46 diffuse large B-cell lymphomas and 29 mantle cell lymphomas were used. The gene expressions of the diffuse large B-cell lymphomas cases were profiled and hierarchical clustering revealed that 28 of them were of the activated B-cell type and 18 were of the germinal center-B-cell type. Using these data, we developed a computer algorithm to classify lymphoma diseases or subtypes on the basis of copy number gains and losses. RESULTS: The method correctly classified 88% of the diffuse large B-cell lymphomas and mantle cell lymphomas, and 83% of the activated B-cell and germinal center-B-cell subtypes. These results demonstrate that copy number gains and losses detected by array CGH can be used for classifying lymphomas into biologically and clinically distinct diseases or subtypes. CONCLUSIONS: Our computer algorithm based on array CGH data successfully classified diffuse large B-cell lymphomas and mantle cell lymphomas and activated B-cell and germinal center-B-cell subtypes with high accuracy. An important finding is that the regions automatically identified by the computer algorithm were located in the critical regions that are likely to be involved in the development of lymphoma.
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Authors: S Ramaswamy; P Tamayo; R Rifkin; S Mukherjee; C H Yeang; M Angelo; C Ladd; M Reich; E Latulippe; J P Mesirov; T Poggio; W Gerald; M Loda; E S Lander; T R Golub Journal: Proc Natl Acad Sci U S A Date: 2001-12-11 Impact factor: 11.205
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Authors: Andreas Rosenwald; George Wright; Wing C Chan; Joseph M Connors; Elias Campo; Richard I Fisher; Randy D Gascoyne; H Konrad Muller-Hermelink; Erlend B Smeland; Jena M Giltnane; Elaine M Hurt; Hong Zhao; Lauren Averett; Liming Yang; Wyndham H Wilson; Elaine S Jaffe; Richard Simon; Richard D Klausner; John Powell; Patricia L Duffey; Dan L Longo; Timothy C Greiner; Dennis D Weisenburger; Warren G Sanger; Bhavana J Dave; James C Lynch; Julie Vose; James O Armitage; Emilio Montserrat; Armando López-Guillermo; Thomas M Grogan; Thomas P Miller; Michel LeBlanc; German Ott; Stein Kvaloy; Jan Delabie; Harald Holte; Peter Krajci; Trond Stokke; Louis M Staudt Journal: N Engl J Med Date: 2002-06-20 Impact factor: 91.245
Authors: Judith Dierlamm; Eva M Murga Penas; Stefan Bentink; Swen Wessendorf; Hilmar Berger; Michael Hummel; Wolfram Klapper; Dido Lenze; Andreas Rosenwald; Eugenia Haralambieva; German Ott; Sergio B Cogliatti; Peter Möller; Carsten Schwaenen; Harald Stein; Markus Löffler; Rainer Spang; Lorenz Trümper; Reiner Siebert Journal: Haematologica Date: 2008-03-26 Impact factor: 9.941
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Authors: Minna Taskinen; Riku Louhimo; Satu Koivula; Ping Chen; Ville Rantanen; Harald Holte; Jan Delabie; Marja-Liisa Karjalainen-Lindsberg; Magnus Björkholm; Øystein Fluge; Lars Møller Pedersen; Karin Fjordén; Mats Jerkeman; Mikael Eriksson; Sampsa Hautaniemi; Sirpa Leppä Journal: PLoS One Date: 2014-03-13 Impact factor: 3.240