PURPOSE: To unravel the regulatory network underlying nucleophosmin-anaplastic lymphoma kinase (NPM-ALK) -mediated lymphomagenesis of anaplastic large-cell lymphoma (ALCL) and to discover diagnostic genomic classifiers for the recognition of patients with ALK-positive and ALK-negative ALCL among T-cell non-Hodgkin's lymphoma (T-NHL). PATIENTS AND METHODS: The transcriptome of NPM-ALK-positive ALCL cell lines was characterized by silencing the expression of ALK or STAT3, a major effector of ALK oncogenic activity. Gene expression profiling (GEP) was performed in a series of systemic primary T-NHL (n = 70), including a set of ALK-positive and ALK-negative ALCL (n = 36). Genomic classifiers for ALK-positive and ALK-negative ALCL were generated by prediction analyses and validated by quantitative reverse-transcriptase polymerase chain reaction and/or immunohistochemistry. RESULTS: In ALCL cell lines, two thirds of ALK-regulated genes were concordantly dependent on STAT3 expression. GEP of systemic primary T-NHL significantly clustered ALK-positive ALCL samples in a separate subgroup, underscoring the relevance of in vitro ALK/STAT3 signatures. A set of genomic classifiers for ALK-positive ALCL and for ALCL were identified by prediction analyses. These gene clusters were instrumental for the distinction of ALK-negative ALCL from peripheral T-cell lymphomas not otherwise specified (PTCLs-NOS) and angioimmunoblastic lymphomas. CONCLUSION: We proved that experimentally controlled GEP in ALCL cell lines represents a powerful tool to identify meaningful signaling networks for the recognition of systemic primary T-NHL. The identification of a molecular signature specific for ALCL suggests that these T-NHLs may represent a unique entity discernible from other PTCLs, and that a restricted number of genes can be instrumental for clinical stratification and, possibly, therapy of T-NHL.
PURPOSE: To unravel the regulatory network underlying nucleophosmin-anaplastic lymphoma kinase (NPM-ALK) -mediated lymphomagenesis of anaplastic large-cell lymphoma (ALCL) and to discover diagnostic genomic classifiers for the recognition of patients with ALK-positive and ALK-negative ALCL among T-cell non-Hodgkin's lymphoma (T-NHL). PATIENTS AND METHODS: The transcriptome of NPM-ALK-positive ALCL cell lines was characterized by silencing the expression of ALK or STAT3, a major effector of ALK oncogenic activity. Gene expression profiling (GEP) was performed in a series of systemic primary T-NHL (n = 70), including a set of ALK-positive and ALK-negative ALCL (n = 36). Genomic classifiers for ALK-positive and ALK-negative ALCL were generated by prediction analyses and validated by quantitative reverse-transcriptase polymerase chain reaction and/or immunohistochemistry. RESULTS: In ALCL cell lines, two thirds of ALK-regulated genes were concordantly dependent on STAT3 expression. GEP of systemic primary T-NHL significantly clustered ALK-positive ALCL samples in a separate subgroup, underscoring the relevance of in vitro ALK/STAT3 signatures. A set of genomic classifiers for ALK-positive ALCL and for ALCL were identified by prediction analyses. These gene clusters were instrumental for the distinction of ALK-negative ALCL from peripheral T-cell lymphomas not otherwise specified (PTCLs-NOS) and angioimmunoblastic lymphomas. CONCLUSION: We proved that experimentally controlled GEP in ALCL cell lines represents a powerful tool to identify meaningful signaling networks for the recognition of systemic primary T-NHL. The identification of a molecular signature specific for ALCL suggests that these T-NHLs may represent a unique entity discernible from other PTCLs, and that a restricted number of genes can be instrumental for clinical stratification and, possibly, therapy of T-NHL.
Authors: Ramona Crescenzo; Francesco Abate; Elena Lasorsa; Fabrizio Tabbo'; Marcello Gaudiano; Nicoletta Chiesa; Filomena Di Giacomo; Elisa Spaccarotella; Luigi Barbarossa; Elisabetta Ercole; Maria Todaro; Michela Boi; Andrea Acquaviva; Elisa Ficarra; Domenico Novero; Andrea Rinaldi; Thomas Tousseyn; Andreas Rosenwald; Lukas Kenner; Lorenzo Cerroni; Alexander Tzankov; Maurilio Ponzoni; Marco Paulli; Dennis Weisenburger; Wing C Chan; Javeed Iqbal; Miguel A Piris; Alberto Zamo'; Carmela Ciardullo; Davide Rossi; Gianluca Gaidano; Stefano Pileri; Enrico Tiacci; Brunangelo Falini; Leonard D Shultz; Laurence Mevellec; Jorge E Vialard; Roberto Piva; Francesco Bertoni; Raul Rabadan; Giorgio Inghirami Journal: Cancer Cell Date: 2015-04-13 Impact factor: 31.743
Authors: Rebecca A Luchtel; Surendra Dasari; Naoki Oishi; Martin Bjerregård Pedersen; Guangzhen Hu; Karen L Rech; Rhett P Ketterling; Jagmohan Sidhu; Xueju Wang; Ryohei Katoh; Ahmet Dogan; N Sertac Kip; Julie M Cunningham; Zhifu Sun; Saurabh Baheti; Julie C Porcher; Jonathan W Said; Liuyan Jiang; Stephen Jacques Hamilton-Dutoit; Michael Boe Møller; Peter Nørgaard; N Nora Bennani; Wee-Joo Chng; Gaofeng Huang; Brian K Link; Fabio Facchetti; James R Cerhan; Francesco d'Amore; Stephen M Ansell; Andrew L Feldman Journal: Blood Date: 2018-08-09 Impact factor: 22.113
Authors: Bettina Bisig; Aurélien de Reyniès; Christophe Bonnet; Pierre Sujobert; David S Rickman; Teresa Marafioti; Georges Delsol; Laurence Lamant; Philippe Gaulard; Laurence de Leval Journal: Haematologica Date: 2013-05-28 Impact factor: 9.941