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

Francesco Maura, Luca Agnelli, Daniel Leongamornlert, Niccolò Bolli, Wing C. Chan, Anna Dodero, Cristiana Carniti, Tayla B. Heavican, Alessio Pellegrinelli, Giancarlo Pruneri, Adam Butler, Shriram G. Bhosle, Annalisa Chiappella, Alice Di Rocco, Pier Luigi Zinzani, Francesco Zaja, Roberto Piva, Giorgio Inghirami, Wenyi Wang, Teresa PalomeroJaveed Iqbal, Antonino Neri, Peter J. Campbell, Paolo Corradini

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)628-634
Number of pages7
JournalAmerican Journal of Hematology
Volume94
Issue number6
DOIs
StatePublished - Jun 2019

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Peripheral T-Cell Lymphoma
Lymphoma
Genes
RNA Sequence Analysis
Anaplastic Large-Cell Lymphoma
Molecular Conformation
Dermatoglyphics
Gene Expression Profiling

ASJC Scopus subject areas

  • Hematology

Cite this

Maura, F., Agnelli, L., Leongamornlert, D., Bolli, N., Chan, W. C., Dodero, A., ... Corradini, P. (2019). Integration of transcriptional and mutational data simplifies the stratification of peripheral T-cell lymphoma. American Journal of Hematology, 94(6), 628-634. https://doi.org/10.1002/ajh.25450

Integration of transcriptional and mutational data simplifies the stratification of peripheral T-cell lymphoma. / Maura, Francesco; Agnelli, Luca; Leongamornlert, Daniel; Bolli, Niccolò; Chan, Wing C.; Dodero, Anna; Carniti, Cristiana; Heavican, Tayla B.; Pellegrinelli, Alessio; Pruneri, Giancarlo; Butler, Adam; Bhosle, Shriram G.; Chiappella, Annalisa; Di Rocco, Alice; Zinzani, Pier Luigi; Zaja, Francesco; Piva, Roberto; Inghirami, Giorgio; Wang, Wenyi; Palomero, Teresa; Iqbal, Javeed; Neri, Antonino; Campbell, Peter J.; Corradini, Paolo.

In: American Journal of Hematology, Vol. 94, No. 6, 06.2019, p. 628-634.

Research output: Contribution to journalArticle

Maura, F, Agnelli, L, Leongamornlert, D, Bolli, N, Chan, WC, Dodero, A, Carniti, C, Heavican, TB, Pellegrinelli, A, Pruneri, G, Butler, A, Bhosle, SG, Chiappella, A, Di Rocco, A, Zinzani, PL, Zaja, F, Piva, R, Inghirami, G, Wang, W, Palomero, T, Iqbal, J, Neri, A, Campbell, PJ & Corradini, P 2019, 'Integration of transcriptional and mutational data simplifies the stratification of peripheral T-cell lymphoma', American Journal of Hematology, vol. 94, no. 6, pp. 628-634. https://doi.org/10.1002/ajh.25450
Maura, Francesco ; Agnelli, Luca ; Leongamornlert, Daniel ; Bolli, Niccolò ; Chan, Wing C. ; Dodero, Anna ; Carniti, Cristiana ; Heavican, Tayla B. ; Pellegrinelli, Alessio ; Pruneri, Giancarlo ; Butler, Adam ; Bhosle, Shriram G. ; Chiappella, Annalisa ; Di Rocco, Alice ; Zinzani, Pier Luigi ; Zaja, Francesco ; Piva, Roberto ; Inghirami, Giorgio ; Wang, Wenyi ; Palomero, Teresa ; Iqbal, Javeed ; Neri, Antonino ; Campbell, Peter J. ; Corradini, Paolo. / Integration of transcriptional and mutational data simplifies the stratification of peripheral T-cell lymphoma. In: American Journal of Hematology. 2019 ; Vol. 94, No. 6. pp. 628-634.
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abstract = "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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AU - Maura, Francesco

AU - Agnelli, Luca

AU - Leongamornlert, Daniel

AU - Bolli, Niccolò

AU - Chan, Wing C.

AU - Dodero, Anna

AU - Carniti, Cristiana

AU - Heavican, Tayla B.

AU - Pellegrinelli, Alessio

AU - Pruneri, Giancarlo

AU - Butler, Adam

AU - Bhosle, Shriram G.

AU - Chiappella, Annalisa

AU - Di Rocco, Alice

AU - Zinzani, Pier Luigi

AU - Zaja, Francesco

AU - Piva, Roberto

AU - Inghirami, Giorgio

AU - Wang, Wenyi

AU - Palomero, Teresa

AU - Iqbal, Javeed

AU - Neri, Antonino

AU - Campbell, Peter J.

AU - Corradini, Paolo

PY - 2019/6

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N2 - 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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