TY - JOUR
T1 - The Tumor Microenvironment of DLBCL in the Computational Era
AU - Opinto, Giuseppina
AU - Vegliante, Maria Carmela
AU - Negri, Antonio
AU - Skrypets, Tetiana
AU - Loseto, Giacomo
AU - Pileri, Stefano Aldo
AU - Guarini, Attilio
AU - Ciavarella, Sabino
N1 - Funding Information:
This work was supported by Italian Ministry of Health (RRC-2018-2020 to SC) and AIRC 5x1000 (grant no. 21198 to SP).
Publisher Copyright:
© Copyright © 2020 Opinto, Vegliante, Negri, Skrypets, Loseto, Pileri, Guarini and Ciavarella.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/3/31
Y1 - 2020/3/31
N2 - Among classical exemplifications of tumor microenvironment (TME) in lymphoma pathogenesis, the “effacement model” resembled by diffuse large B cell lymphoma (DLBCL) implies strong cell autonomous survival and paucity of non-malignant elements. Nonetheless, the magnitude of TME exploration is increasing as novel technologies allow the high-resolution discrimination of cellular and extra-cellular determinants at the functional, more than morphological, level. Results from genomic-scale studies and recent clinical trials revitalized the interest in this field, prompting the use of new tools to dissect DLBCL composition and reveal novel prognostic association. Here we revisited major controversies related to TME in DLBCL, focusing on the use of bioinformatics to mine transcriptomic data and provide new insights to be translated into the clinical setting.
AB - Among classical exemplifications of tumor microenvironment (TME) in lymphoma pathogenesis, the “effacement model” resembled by diffuse large B cell lymphoma (DLBCL) implies strong cell autonomous survival and paucity of non-malignant elements. Nonetheless, the magnitude of TME exploration is increasing as novel technologies allow the high-resolution discrimination of cellular and extra-cellular determinants at the functional, more than morphological, level. Results from genomic-scale studies and recent clinical trials revitalized the interest in this field, prompting the use of new tools to dissect DLBCL composition and reveal novel prognostic association. Here we revisited major controversies related to TME in DLBCL, focusing on the use of bioinformatics to mine transcriptomic data and provide new insights to be translated into the clinical setting.
KW - deconvolution
KW - DLBCL
KW - prognostication
KW - transcriptomics
KW - tumor microenvironment
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U2 - 10.3389/fonc.2020.00351
DO - 10.3389/fonc.2020.00351
M3 - Review article
AN - SCOPUS:85083340460
VL - 10
JO - Frontiers in Oncology
JF - Frontiers in Oncology
SN - 2234-943X
M1 - 351
ER -