ArticleEBioMedicine2019
Refining diffuse large B-cell lymphoma subgroups using integrated analysis of molecular profiles.
Article in EBioMedicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed, 41 citations in OpenAlex.
- Risk stratification of patients with diffuse large B-cell lymphoma using plasma NMR-based metabolomics at diagnosis.Blood advances · 2026Trial
- PD-L2 is associated with lineage-related transcriptional programs distinct from PD-L1 in primary mediastinal large B-cell lymphoma.Leukemia · 2026Article
- A cross-platform transcriptomic risk score integrating LymphoMAP microenvironment archetypes and immune evasion-associated programs in diffuse large B-cell lymphoma.Oncology letters · 2026Article
- A Novel CD58 Mutation-Related Signature Predicts Prognosis Risk in Diffuse Large B-Cell Lymphoma Patients.Cancer research and treatment · 2026Article
- NPC2 deficiency as a potential target drives malignancy and chemo-resistance in DLBCL.Biology direct · 2026Article
- Single cell profiling reveals malignant states and immune landscapes in PCNSL and systemic DLBCL.iScience · 2026Article
- Machine learning based on clinical and gene expression data assists in survival prediction and treatment optimization for diffuse large B-Cell lymphoma patients.Annals of hematology · 2026Article
- Neuro-related gene signatures predict prognosis in diffuse large B-Cell lymphoma and uncover TRPV2-mediated tumor microenvironment regulation.Annals of hematology · 2026Article
- Multimodal-based analysis of single-cell ATAC-seq data enables highly accurate delineation of clinically relevant tumor cell subpopulations.Genome medicine · 2026Article
- Integrative Multiomics and Single-Cell Profiling Identify TNFRSF1AComputational and structural biotechnology journal · 2026Article
- Article
- Spatial transcriptomics reveals prognostically LYZCancer immunology, immunotherapy : CII · 2025Article
- Integrating genetic subtypes with PET scan monitoring to predict outcome in diffuse large B-cell lymphoma.Nature communications · 2025Article
- Construction and Validation of T-cell Exhaustion-related Gene Signature for Predicting Prognosis in Diffuse Large B-cell Lymphoma.Current pharmaceutical biotechnology · 2025Article
- Multiomic analysis identifies a high-risk signature that predicts early clinical failure in DLBCL.Blood cancer journal · 2024Article
- A Cluster of Evolutionarily Recent KRAB Zinc Finger Proteins Protects Cancer Cells from Replicative Stress-Induced Inflammation.Cancer research · 2024Article
- Longitudinal expression profiling identifies a poor risk subset of patients with ABC-type diffuse large B-cell lymphoma.Blood advances · 2023Article
- Molecular characterization of Richter syndrome identifies de novo diffuse large B-cell lymphomas with poor prognosis.Nature communications · 2023Article
- Obesity and Risk for Lymphoma: Possible Role of Leptin.International journal of molecular sciences · 2022Review
- Novel eIF4A1 inhibitors with anti-tumor activity in lymphoma.Molecular medicine (Cambridge, Mass.) · 2022Article
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Authors and funding
19 authors at 9 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGene 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.
methods223 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.
findingsICA 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.
interpretationThe results of this integrated analysis enable a global and multi-level view of DLBCL, as well as improve our understanding of DLBCL subgroups.
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