Twenty years later: has cell of origin testing in diffuse large B cell lymphoma run its course?

Classifying Germinal Center Derived Lymphomas-Navigate a Complex Transcriptional Landscape

Classification of lymphoid neoplasms is based mainly on histologic, immunologic, and (rarer) genetic features. It has been supplemented by gene expression profiling (GEP) in the last decade. Despite the considerable success, particularly in associating lymphoma subtypes with specific transcriptional programs and classifier signatures of up- or downregulated genes, competing molecular classifiers were often proposed in the literature by different groups for the same classification tasks to distinguish, e.g., BL versus DLBCL or different DLBCL subtypes. Moreover, rarer sub-entities such as MYC and BCL2 "double hit lymphomas" (DHL), IRF4-rearranged large cell lymphoma (IRF4-LCL), and Burkitt-like lymphomas with 11q aberration pattern (mnBLL-11q) attracted interest while their relatedness regarding the major classes is still unclear in many respects. We explored the transcriptional landscape of 873 lymphomas referring to a wide spectrum of subtypes by applying self-organizing maps (SOM) machine learning. The landscape reveals a continuum of transcriptional states activated in the different subtypes without clear-cut borderlines between them and preventing their unambiguous classification. These states show striking parallels with single cell gene expression of the active germinal center (GC), which is characterized by the cyclic progression of B-cells. The expression patterns along the GC trajectory are discriminative for distinguishing different lymphoma subtypes. We show that the rare subtypes take intermediate positions between BL, DLBCL, and FL as considered by the 5th edition of the WHO classification of haemato-lymphoid tumors in 2022. Classifier gene signatures extracted from these states as modules of coregulated genes are competitive with literature classifiers. They provide functional-defined classifiers with the option of consenting redundant classifiers from the literature. We discuss alternative classification schemes of different granularity and functional impact as possible avenues toward personalization and improved diagnostics of GC-derived lymphomas.

Significance of Single-cell Level Dual Expression of BCL2 and MYC Determined With Multiplex Immunohistochemistry in Diffuse Large B-Cell Lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a fatal heterogenous neoplasm. Recent clinical trials have failed partly due to nebulous criteria for defining high-risk patients. Patients with double-expresser lymphoma (DEL) have a poor prognosis and are resistant to conventional treatment. However, many diagnostic and clinical controversies still surround DEL partly due to the arbitrariness of criteria for the diagnosis of DEL. In this study, we suggest a refined method for diagnosing DEL by evaluating the concurrent expression of BCL2 and MYC at the single-cell level (dual-protein-expressing lymphoma [DUEL]). For the proof of concept, a multiplex immunofluorescence assay for CD20, BCL2, and MYC was performed and quantitatively analyzed using spectral image analysis in patients. The analysis results and clinical applicability were verified by using dual-color immunohistochemistry performed on 353 independent multicenter patients who had been uniformly treated with standard therapy. DUEL showed significantly worse overall survival (OS) and event-free survival (EFS) (P=0.00011 and 0.00035, respectively). DUEL status remained an independent adverse prognostic variable with respect to the International Prognostic Index risk and the cell of origin. Moreover, the advantage of determining DUEL status by dual-color immunohistochemistry was shown by more robust classification and more homogeneous high-risk subgroup patient identification in both training (n=271) (OS: P<0.0001; EFS: P<0.0001) and validation sets (n=82) (OS: P=0.0087; EFS: P<0.0001). This concept of DUEL is more consistent with carcinogenesis and has greater practical utility, hence it may provide a better basis for both basic and clinical research for the development of new therapeutics.