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Am J Hematol. 2019 Jun;94(6):628-634. doi: 10.1002/ajh.25450. Epub 2019 Mar 19.

Integration of transcriptional and mutational data simplifies the stratification of peripheral T-cell lymphoma.

Author information

Myeloma Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.
Cancer, Ageing and Somatic Mutation Programme, Wellcome Trust Sanger Institute, Hinxton, United Kingdom.
Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy.
Hematology, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
Division of Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
Department of Pathology, City of Hope National Medical Center, Duarte, California.
Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, Nebraska.
Department of Pathology and Laboratory Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
Department of Hematology, Azienda Ospedaliera Città della Salute e della Scienza, Torino, Italy.
Department of Cellular Biotechnology and Hematology, Sapienza University of Rome, Rome, Italy.
Institute of Hematology, University of Bologna, Bologna, Italy.
Clinical Ematologica, DAME, University of Udine, Udine, Italy.
Department of Molecular Biotechnology and Health Sciences, Center for Experimental Research and Medical Studies, University of Torino, Torino, Italy.
Pathology and Laboratory Medicines, Weill Cornell Medical College, New York, New York.
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Institute for Cancer Genetics, Columbia University, New York, New York.


The histological diagnosis of peripheral T-cell lymphoma (PTCL) can represent a challenge, particularly in the case of closely related entities such as angioimmunoblastic T-lymphoma (AITL), PTCL-not otherwise specified (PTCL-NOS), and ALK-negative anaplastic large-cell lymphoma (ALCL). Although gene expression profiling and next generations sequencing have been proven to define specific features recurrently associated with distinct entities, genomic-based stratifications have not yet led to definitive diagnostic criteria and/or entered into the routine clinical practice. Herein, to improve the current molecular classification between AITL and PTCL-NOS, we analyzed the transcriptional profiles from 503 PTCLs stratified according to their molecular configuration and integrated them with genomic data of recurrently mutated genes (RHOA G17V , TET2, IDH2 R172 , and DNMT3A) in 53 cases (39 AITLs and 14 PTCL-NOSs) included in the series. Our analysis unraveled that the mutational status of RHOA G17V , TET2, and DNMT3A poorly correlated, individually, with peculiar transcriptional fingerprints. Conversely, in IDH2 R172 samples a strong transcriptional signature was identified that could act as a surrogate for mutational status. The integrated analysis of clinical, mutational, and molecular data led to a simplified 19-gene signature that retains high accuracy in differentiating the main nodal PTCL entities. The expression levels of those genes were confirmed in an independent cohort profiled by RNA-sequencing.

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