Different immunohistochemical algorithms for the classification of the activated B-cell (ABC) and germinal center B-cell (GCB) subtypes of diffuse large B-cell lymphoma (DLBCL) are applied in different laboratories. In the present study, 127 patients with DLCBL were investigated, all treated with rituximab and cyclophosphamide, hydroxydaunorubicin, oncovin and prednisone (CHOP) or CHOP-like regimens between April 2004 and December 2010. Multi-tumor tissue microarrays were prepared and were tested according to 4 algorithms: Hans; modified Hans; Choi; and modified Choi. For 39 patients, the flow cytometric quantification of CD19 and CD20 antigen expression was performed and the level of expression presented as molecules of equivalent soluble fluorochrome units. The Choi algorithm was demonstrated to be prognostic for OS and classified patients into the GCB subgroup with an HR of 0.91. No difference in the expression of the CD19 antigen between the ABC and GCB groups was observed, but the ABC subtype exhibited a decreased expression of the CD20 antigen compared with the GCB subtype.
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http://dx.doi.org/10.3892/ol.2018.8243 | DOI Listing |
Blood
January 2025
University of Chicago, Chicago, Illinois, United States.
Most diffuse large B-cell lymphoma (DLBCL) patients treated with immunotherapies such as bispecific antibodies (BsAb) or chimeric antigen receptor (CAR) T cells fail to achieve durable treatment responses, underscoring the need for a deeper understanding of mechanisms that regulate the immune environment and response to treatment. Here, an integrative, multi-omic approach was applied to multiple large independent datasets in order to characterize DLBCL immune environments, and to define their association with tumor cell-intrinsic genomic alterations and outcomes to CD19-directed CAR T-cell and CD20 x CD3 BsAb therapies. This approach effectively segregated DLBCLs into four immune quadrants (IQ) defined by cell-of-origin and immune-related gene set expression scores.
View Article and Find Full Text PDFCureus
December 2024
Emergency Medicine, Northwell Health, Manhasset, USA.
Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma (NHL) in adults, constituting a significant portion of global incidence rates. DLBCL can be further classified via genetic expression profiling into molecular subsets consisting of not-otherwise specified (NOS) subset being the most prevalent, germinal center B-cell-like (GCB) subset, and activated B-cell-like (ABC) subset. The ABC subset, marked by abnormal NF-κB signaling, is associated with poorer outcomes.
View Article and Find Full Text PDFPrimary central nervous system lymphoma (PCNSL) is clinically challenging due to its location and small biopsy size, leading to a lack of comprehensive molecular and biologic description. We previously demonstrated that 91% of PCNSL belong to the activated B-cell-like (ABC) molecular subtype of diffuse large B-cell lymphoma (DLBCL). Here we investigated the expression of 739 cancer related genes in HIV (-) patients using NanoString digital gene expression profiling in 25 ABC-PCNSL and 43 ABC-systemic DLBCL, all tumors were EBV (-).
View Article and Find Full Text PDFHaematologica
December 2024
Research Programs Unit, Applied Tumor Genomics, University of Helsinki, Helsinki, Finland; Department of Oncology, University of Helsinki and Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland; iCAN Digital Precision Medicine Flagship, Helsinki.
The tumor microenvironments (TME) of diffuse large B-cell lymphoma (DLBCL) subgroups have remained poorly characterized. Here, we dissected the composition and spatial organization of the TME in germinal center B-cell (GCB), activated B-cell (ABC), and testicular DLBCLs (T-DLBCL) using gene expression profiling and multiplex immunohistochemistry. We found that high proportions of M2-like tumor-associated macrophages (TAMs) and cytotoxic tumor-infiltrating T cells (TILs) were characteristic of ABC DLBCL TME.
View Article and Find Full Text PDFBioinformatics
November 2024
Institute for Statistical Bioinformatics, Faculty of Informatics and Data Science, University of Regensburg, Am Biopark 9, 93053 Regensburg, Germany.
Motivation: Bulk RNA expression data are widely accessible, whereas single-cell data are relatively scarce in comparison. However, single-cell data offer profound insights into the cellular composition of tissues and cell type-specific gene regulation, both of which remain hidden in bulk expression analysis.
Results: Here, we present tissueResolver, an algorithm designed to extract single-cell information from bulk data, enabling us to attribute expression changes to individual cell types.
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