BACKGROUND: Until recently, few molecular aberrations were recognized in acute lymphoblastic leukemia of T-cell origin; novel lesions have recently been identified and a certain degree of overlap between acute myeloid leukemia and T-cell acute lymphoblastic leukemia has been suggested. To identify novel T-cell acute lymphoblastic leukemia entities, gene expression profiling was performed and clinico-biological features were studied. DESIGN AND METHODS: Sixty-nine untreated adults with T-cell acute lymphoblastic leukemia were evaluated by oligonucleotide arrays: unsupervised and supervised analyses were performed. The up-regulation of myeloid genes and miR-223 expression were validated by quantitative polymerase chain reaction analysis. RESULTS: Using unsupervised clustering, we identified five subgroups. Of these, one branch included seven patients whose gene expression profile resembled that of acute myeloid leukemia. These cases were characterized by over-expression of a large set of myeloid-related genes for surface antigens, transcription factors and granule proteins. Real-time quantitative polymerase chain reaction analysis confirmed over-expression of MPO, CEBPA, CEBPB, GRN and IL8. We, therefore, evaluated the expression levels of miR-223, involved in myeloid differentiation: these cases had significantly higher levels of miR-223 than had the other cases of T-cell acute lymphoblastic leukemia, with values comparable to those observed in acute myeloid leukemia. Finally, these patients appear to have an unfavorable clinical course. CONCLUSIONS: Using gene profiling we identified a subset of adult T-cell acute lymphoblastic leukemia, accounting for 10% of the cases analyzed, which displays myeloid features. These cases were not recognized by standard approaches, underlining the importance of gene profiling in identifying novel acute leukemia subsets. The recognition of this subgroup may have clinical, prognostic and therapeutic implications.
BACKGROUND: Until recently, few molecular aberrations were recognized in acute lymphoblastic leukemia of T-cell origin; novel lesions have recently been identified and a certain degree of overlap between acute myeloid leukemia and T-cell acute lymphoblastic leukemia has been suggested. To identify novel T-cell acute lymphoblastic leukemia entities, gene expression profiling was performed and clinico-biological features were studied. DESIGN AND METHODS: Sixty-nine untreated adults with T-cell acute lymphoblastic leukemia were evaluated by oligonucleotide arrays: unsupervised and supervised analyses were performed. The up-regulation of myeloid genes and miR-223 expression were validated by quantitative polymerase chain reaction analysis. RESULTS: Using unsupervised clustering, we identified five subgroups. Of these, one branch included seven patients whose gene expression profile resembled that of acute myeloid leukemia. These cases were characterized by over-expression of a large set of myeloid-related genes for surface antigens, transcription factors and granule proteins. Real-time quantitative polymerase chain reaction analysis confirmed over-expression of MPO, CEBPA, CEBPB, GRN and IL8. We, therefore, evaluated the expression levels of miR-223, involved in myeloid differentiation: these cases had significantly higher levels of miR-223 than had the other cases of T-cell acute lymphoblastic leukemia, with values comparable to those observed in acute myeloid leukemia. Finally, these patients appear to have an unfavorable clinical course. CONCLUSIONS: Using gene profiling we identified a subset of adult T-cell acute lymphoblastic leukemia, accounting for 10% of the cases analyzed, which displays myeloid features. These cases were not recognized by standard approaches, underlining the importance of gene profiling in identifying novel acute leukemia subsets. The recognition of this subgroup may have clinical, prognostic and therapeutic implications.
Authors: Kim De Keersmaecker; Carlos Graux; Maria D Odero; Nicole Mentens; Riet Somers; Johan Maertens; Iwona Wlodarska; Peter Vandenberghe; Anne Hagemeijer; Peter Marynen; Jan Cools Journal: Blood Date: 2005-02-15 Impact factor: 22.113
Authors: M R Ricciardi; T McQueen; D Chism; M Milella; E Estey; E Kaldjian; J Sebolt-Leopold; M Konopleva; M Andreeff Journal: Leukemia Date: 2005-09 Impact factor: 11.528
Authors: W A Dik; W Brahim; C Braun; V Asnafi; N Dastugue; O A Bernard; J J M van Dongen; A W Langerak; E A Macintyre; E Delabesse Journal: Leukemia Date: 2005-11 Impact factor: 11.528
Authors: Paolo Gorello; Roberta La Starza; Danika Di Giacomo; Monica Messina; Maria Cristina Puzzolo; Barbara Crescenzi; Alessandra Santoro; Sabina Chiaretti; Cristina Mecucci Journal: Haematologica Date: 2010-09-17 Impact factor: 9.941
Authors: Marc R Mansour; Takaomi Sanda; Lee N Lawton; Xiaoyu Li; Taras Kreslavsky; Carl D Novina; Marjorie Brand; Alejandro Gutierrez; Michelle A Kelliher; Catriona H M Jamieson; Harald von Boehmer; Richard A Young; A Thomas Look Journal: J Exp Med Date: 2013-07-15 Impact factor: 14.307
Authors: M Neumann; S Heesch; N Gökbuget; S Schwartz; C Schlee; O Benlasfer; N Farhadi-Sartangi; J Thibaut; T Burmeister; D Hoelzer; W-K Hofmann; E Thiel; C D Baldus Journal: Blood Cancer J Date: 2012-01-27 Impact factor: 11.037
Authors: Hamilton L Gimenes-Teixeira; Antonio R Lucena-Araujo; Guilherme A Dos Santos; Dalila L Zanette; Priscila S Scheucher; Luciana C Oliveira; Leandro F Dalmazzo; Wilson A Silva-Júnior; Roberto P Falcão; Eduardo M Rego Journal: Exp Hematol Oncol Date: 2013-04-08