Refining diffuse large B-cell lymphoma subgroups using integrated analysis of molecular profiles

• Published in: EBioMedicine 2019-10, Vol.48, p.58-69
• Main Authors: Dubois, Sydney, Tesson, Bruno, Mareschal, Sylvain, Viailly, Pierre-Julien, Bohers, Elodie, Ruminy, Philippe, Etancelin, Pascaline, Peyrouze, Pauline, Copie-Bergman, Christiane, Fabiani, Bettina, Petrella, Tony, Jais, Jean-Philippe, Haioun, Corinne, Salles, Gilles, Molina, Thierry Jo, Leroy, Karen, Tilly, Hervé, Jardin, Fabrice
• Format: Article
• Language: English
• Subjects: Biochemistry, Molecular Biology; Biomarkers, Tumor; Cancer; Chromosome Mapping; Computational Biology - methods; Diffuse large B-cell lymphoma; DNA Copy Number Variations; Female; Gene Expression Profiling - methods; Gene signatures, prognosis; Genetic Association Studies; Genetic Predisposition to Disease; Genomics; Hematology; Human health and pathology; Humans; Immunohistochemistry; Independent component analysis; Life Sciences; Lymphoma, Large B-Cell, Diffuse - diagnosis; Lymphoma, Large B-Cell, Diffuse - genetics; Male; Molecular Sequence Annotation; Research paper; Transcriptome; Transcriptomic variability
• ISSN: 2352-3964; 2352-3964
• DOI: 10.1016/j.ebiom.2019.09.034

Gene expression profiling (GEP), next-generation sequencing (NGS) and copy number variation (CNV) analysis have led to an increasingly detailed characterization of the genomic profiles of DLBCL. The aim of this study was to perform a fully integrated analysis of mutational, genomic, and expression profiles to refine DLBCL subtypes. A comparison of our model with two recently published integrative DLBCL classifiers was carried out, in order to best reflect the current state of genomic subtypes. 223 patients with de novo DLBCL from the prospective, multicenter and randomized LNH-03B LYSA clinical trials were included. GEP data was obtained using Affymetrix GeneChip arrays, mutational profiles were established by Lymphopanel NGS targeting 34 key genes, CNV analysis was obtained by array CGH, and FISH and IHC were performed. Unsupervised independent component analysis (ICA) was applied to GEP data and integrated analysis of multi-level molecular data associated with each component (gene signature) was performed. ICA identified 38 components reflecting transcriptomic variability across our DLBCL cohort. Many of the components were closely related to well-known DLBCL features such as cell-of-origin, stromal and MYC signatures. A component linked to gain of 19q13 locus, among other genomic alterations, was significantly correlated with poor OS and PFS. Through this integrated analysis, a high degree of heterogeneity was highlighted among previously described DLBCL subtypes. The results of this integrated analysis enable a global and multi-level view of DLBCL, as well as improve our understanding of DLBCL subgroups.