Reliable subtype classification of diffuse large B-cell lymphoma samples from GELA LNH2003 trials using the Lymph2Cx gene expression assay

Evolving molecular classification of aggressive B-cell lymphoma

This review aims to provide an overview of the latest developments in the classification and molecular understanding of aggressive B-cell lymphomas, specifically focusing on diffuse large B-cell lymphoma (DLBCL) and high-grade B-cell lymphoma (HGBL). Advances in molecular techniques have led to novel ways to classify these lymphomas based on clinical, histological, transcriptional, and genetic properties. While these methods have predominantly focused on the malignant compartment, recent studies emphasize the value of profiling the tumour microenvironment for a more comprehensive disease classification. Additionally, the integration of liquid biopsies represents a promising advancement, offering less invasive and dynamic insights into tumour characteristics and treatment response. Although molecular profiles are not yet routinely used to guide therapy, emerging data highlight their potential to predict responses to novel treatments. It is our belief that integrating molecular profiling and liquid biopsies into clinical practice and research now will pave the way for more personalized and effective therapies in the future.

2024 Update: Advances in the risk stratification and management of large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease with varying clinical outcomes. Our understanding of its molecular makeup continues to improve risk stratification, and artificial-intelligence and ctDNA-based analyses have the potential to enhance risk assessment and disease monitoring. R-CHOP and Pola-R-CHP are used in the frontline setting; chimeric antigen receptor therapy (CART) is now the new standard-of-care for most with primary refractory disease; both CART and autologous stem cell transplantation are utilized in the relapsed and refractory setting. In this review, we summarize the classification and management of DLBCL with an emphasis on recent advances in the field.

Routine application of the Lymph2Cx assay for the subclassification of aggressive B-cell lymphoma: report of a prospective real-world series

The subclassification of diffuse large B-cell lymphoma (DLBCL) into germinal center B-cell-like (GCB) and activated B-cell-like (ABC) subtypes has become mandatory in the 2017 update of the WHO classification of lymphoid neoplasms and will continue to be used in the WHO 5^th edition. The RNA-based Lymph2Cx assay has been validated as a reliable surrogate of high-throughput gene expression profiling assays for distinguishing between GCB and ABC DLBCL and provides reliable results from formalin-fixed, paraffin-embedded (FFPE) material. This test has been previously used in clinical trials, but experience from real-world routine application is rare. We routinely applied the Lymph2Cx assay to day-to-day diagnostics on a series of 147 aggressive B-cell lymphoma cases and correlated our results with the immunohistochemical subclassification using the Hans algorithm and fluorescence in situ hybridization findings using break-apart probes for MYC, BCL2, and BCL6. The routine use of the Lymph2Cx assay had a high technical success rate (94.6%) with a low rate of failure due to poor material and/or RNA quality. The Lymph2Cx assay was discordant with the Hans algorithm in 18% (23 of 128 cases). Discordant cases were mainly classified as GCB by the Hans algorithm and as ABC by Lymph2Cx (n = 11, 8.6%). Only 5 cases (3.9%) were classified as non-GCB by the Hans algorithm and as GCB by Lymph2Cx. Additionally, 5.5% of cases (n = 7) were left unclassified by Lymph2Cx, whereas they were defined as GCB (n = 4) or non-GCB (n = 3) by the Hans algorithm. Our data support the routine applicability of the Lymph2Cx assay.

Comparison of the Lymph2Cx Assay and Hans Algorithm in Determining the Cell-of-Origin of Diffuse Large B-Cell Lymphomas, Not Otherwise Specified

In the era of precision medicine, accurate and reproducible assignment of cell-of-origin (COO) in diffuse large B-cell lymphoma patients has become important. The Lymph2Cx assay is accurately determining COO by analyzing RNA expression of 20 selected genes while the Hans algorithm based on immunohistochemistry is the most popular method for routine daily diagnosis. However, there are discrepancies between the 2 methods, which need to be evaluated for better correlation. We prospectively analyzed 156 cases of diffuse large B-cell lymphoma, not otherwise specified to analyze the characteristics of discrepancy groups of COO determined by Lymph2Cx and Hans algorithm. We investigated the pattern and cause of discrepancy of COO assigned by the 2 methods. Hans algorithm classified 50 cases (32%) as germinal-center B-cell-like (GCB) type and 106 cases (68%) as non-GCB type. Lymph2Cx assay assigned 43 cases (28%) as GCB type, 94 cases (60%) as activated B-cell-like type, and 19 cases (12%) as intermediate/unclassified type. The agreement rate was 86% after exclusion of unclassified type. With regard to the clinicopathologic factors related with discrepancy between Hans algorithm and Lymph2Cx assay, endoscopic biopsy of the gastrointestinal tract (4/11, 36%) showed higher discrepancy rate (P=0.052). Immunophenotypically, CD10/BCL6/MUM1 GCB type and CD10/BCL6//MUM1 (=30%, low level expression) non-GCB type exhibited a significantly higher discrepancy rate (6/13, 46%; 4/13, 31%) (P=0.0001). Activated B-cell-like subgroup via Lymph2Cx assay predicted poor progression-free survival (mean survival duration 28.6 mo, P=0.049) compared with the GCB and unclassified type. Hans algorithm revealed no significant difference in progression-free survival and overall survival (P=0.122 and 0.121). These results suggest that when assigning COO via Hans algorithm, CD10/BCL6/MUM1 GCB type and CD10/BCL6/MUM1 (=30%, low level) non-GCB type require careful interpretation, especially if the MUM1 staining is weak and heterogeneous in the biopsied specimen.

Prognostic molecular biomarkers in diffuse large B-cell lymphoma in the rituximab era and their therapeutic implications

Diffuse large B-cell lymphoma (DLBCL) represents a group of tumors characterized by substantial heterogeneity in terms of their pathological and biological features, a causal factor of their varied clinical outcome. This variation has persisted despite the implementation of rituximab in treatment regimens over the last 20 years. In this context, prognostic biomarkers are of great importance in order to identify high-risk patients that might benefit from treatment intensification or the introduction of novel therapeutic agents. Herein, we review current knowledge on specific immunohistochemical or genetic biomarkers that might be useful in clinical practice. Gene-expression profiling is a tool of special consideration in this effort, as it has enriched our understanding of DLBCL biology and has allowed for the classification of DLBCL by cell-of-origin as well as by more elaborate molecular signatures based on distinct gene-expression profiles. These subgroups might outperform individual biomarkers in terms of prognostication; however, their use in clinical practice is still limited. Moreover, the underappreciated role of the tumor microenvironment in DLBCL prognosis is discussed in terms of prognostic gene-expression signatures, as well as in terms of individual biomarkers of prognostic significance. Finally, the efficacy of novel therapeutic agents for the treatment of DLBCL patients are discussed and an evidence-based therapeutic approach by specific genetic subgroup is suggested.

Predictive and Prognostic Molecular Factors in Diffuse Large B-Cell Lymphomas

Diffuse large B-cell lymphoma (DLBCL) is the commonest form of lymphoid malignancy, with a prevalence of about 40% worldwide. Its classification encompasses a common form, also termed as “not otherwise specified” (NOS), and a series of variants, which are rare and at least in part related to viral agents. Over the last two decades, DLBCL-NOS, which accounts for more than 80% of the neoplasms included in the DLBCL chapter, has been the object of an increasing number of molecular studies which have led to the identification of prognostic/predictive factors that are increasingly entering daily practice. In this review, the main achievements obtained by gene expression profiling (with respect to both neoplastic cells and the microenvironment) and next-generation sequencing will be discussed and compared. Only the amalgamation of molecular attributes will lead to the achievement of the long-term goal of using tailored therapies and possibly chemotherapy-free protocols capable of curing most (if not all) patients with minimal or no toxic effects.

Molecular Genetics of Relapsed Diffuse Large B-Cell Lymphoma: Insight into Mechanisms of Therapy Resistance

The majority of patients with diffuse large B-cell lymphoma (DLBCL) can be treated successfully with a combination of chemotherapy and the monoclonal anti-CD20 antibody rituximab. Nonetheless, approximately one-third of the patients with DLBCL still experience relapse or refractory (R/R) disease after first-line immunochemotherapy. Whole-exome sequencing on large cohorts of primary DLBCL has revealed the mutational landscape of DLBCL, which has provided a framework to define novel prognostic subtypes in DLBCL. Several studies have investigated the genetic alterations specifically associated with R/R DLBCL, thereby uncovering molecular pathways linked to therapy resistance. Here, we summarize the current state of knowledge regarding the genetic alterations that are enriched in R/R DLBCL, and the corresponding pathways affected by these gene mutations. Furthermore, we elaborate on their potential role in mediating therapy resistance, also in connection with findings in other B-cell malignancies, and discuss alternative treatment options. Hence, this review provides a comprehensive overview on the gene lesions and molecular mechanisms underlying R/R DLBCL, which are considered valuable parameters to guide treatment.

A refined cell-of-origin classifier with targeted NGS and artificial intelligence shows robust predictive value in DLBCL

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous entity of B-cell lymphoma. Cell-of-origin (COO) classification of DLBCL is required in routine practice by the World Health Organization classification for biological and therapeutic insights. Genetic subtypes uncovered recently are based on distinct genetic alterations in DLBCL, which are different from the COO subtypes defined by gene expression signatures of normal B cells retained in DLBCL. We hypothesize that classifiers incorporating both genome-wide gene-expression and pathogenetic variables can improve the therapeutic significance of DLBCL classification. To develop such refined classifiers, we performed targeted RNA sequencing (RNA-Seq) with a commercially available next-generation sequencing (NGS) platform in a large cohort of 418 DLBCLs. Genetic and transcriptional data obtained by RNA-Seq in a single run were explored by state-of-the-art artificial intelligence (AI) to develop a NGS-COO classifier for COO assignment and NGS survival models for clinical outcome prediction. The NGS-COO model built through applying AI in the training set was robust, showing high concordance with COO classification by either Affymetrix GeneChip microarray or the NanoString Lymph2Cx assay in 2 validation sets. Although the NGS-COO model was not trained for clinical outcome, the activated B-cell-like compared with the germinal-center B-cell-like subtype had significantly poorer survival. The NGS survival models stratified 30% high-risk patients in the validation set with poor survival as in the training set. These results demonstrate that targeted RNA-Seq coupled with AI deep learning techniques provides reproducible, efficient, and affordable assays for clinical application. The clinical grade assays and NGS models integrating both genetic and transcriptional factors developed in this study may eventually support precision medicine in DLBCL.

Molecular Complexity of Diffuse Large B-Cell Lymphoma: Can It Be a Roadmap for Precision Medicine?

Diffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma; it features extreme molecular heterogeneity regardless of the classical cell-of-origin (COO) classification. Despite this, the standard therapeutic approach is still immunochemotherapy (rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone-R-CHOP), which allows a 60% overall survival (OS) rate, but up to 40% of patients experience relapse or refractory (R/R) disease. With the purpose of searching for new clinical parameters and biomarkers helping to make a better DLBCL patient characterization and stratification, in the last years a series of large discovery genomic and transcriptomic studies has been conducted, generating a wealth of information that needs to be put in order. We reviewed these researches, trying ultimately to understand if there are bases offering a roadmap toward personalized and precision medicine also for DLBCL.

Discriminant analysis and machine learning approach for evaluating and improving the performance of immunohistochemical algorithms for COO classification of DLBCL

Background

Diffuse large B-cell lymphoma (DLBCL) is classified into germinal center-like (GCB) and non-germinal center-like (non-GCB) cell-of-origin groups, entities driven by different oncogenic pathways with different clinical outcomes. DLBCL classification by immunohistochemistry (IHC)-based decision tree algorithms is a simpler reported technique than gene expression profiling (GEP). There is a significant discrepancy between IHC-decision tree algorithms when they are compared to GEP.

Methods

To address these inconsistencies, we applied the machine learning approach considering the same combinations of antibodies as in IHC-decision tree algorithms. Immunohistochemistry data from a public DLBCL database was used to perform comparisons among IHC-decision tree algorithms, and the machine learning structures based on Bayesian, Bayesian simple, Naïve Bayesian, artificial neural networks, and support vector machine to show the best diagnostic model. We implemented the linear discriminant analysis over the complete database, detecting a higher influence of BCL6 antibody for GCB classification and MUM1 for non-GCB classification.

Results

The classifier with the highest metrics was the four antibody-based Perfecto–Villela (PV) algorithm with 0.94 accuracy, 0.93 specificity, and 0.95 sensitivity, with a perfect agreement with GEP (κ = 0.88, P < 0.001). After training, a sample of 49 Mexican-mestizo DLBCL patient data was classified by COO for the first time in a testing trial.

Conclusions

Harnessing all the available immunohistochemical data without reliance on the order of examination or cut-off value, we conclude that our PV machine learning algorithm outperforms Hans and other IHC-decision tree algorithms currently in use and represents an affordable and time-saving alternative for DLBCL cell-of-origin identification.

Electronic supplementary material

The online version of this article (10.1186/s12967-019-1951-y) contains supplementary material, which is available to authorized users.

Dissecting diffuse large B-cell lymphomas of the "not otherwise specified" type: the impact of molecular techniques

The updated edition of the Classification of Tumours of Haematopoietic and Lymphoid Tissues, published in September 2017 by the World Health Organization (WHO), presents many important changes to the document published in 2008. Most of these novelties are linked to the exceptional development of biomolecular techniques during the last 10 years. To illustrate how much new technologies have contributed to the better classification of single entities, as well as the discovery of new ones, would go beyond the objectives of this work. For this reason, we will take diffuse large B-cell lymphoma as an example of the cognitive improvement produced by high-yield technologies (such as the gene expression profile, the study of copy number variation, and the definition of the mutational spectrum). The acquisition of this knowledge not only has a speculative value but also represents the elements for effective application in daily practice. On the one hand, it would allow the development of personalised therapy programs, and on the other it would promote the transition from the bench of the researcher's laboratory to the patient's bedside.

Dissecting aggressive B-cell lymphoma through genomic analysis - What is clinically relevant?

The aggressive B-cell lymphomas are a diverse collection of cancers grouped together based on clinical behavior and derivation from B lymphocytes. Genomic analyses on these tumours are now translating into improved classification systems and identification of underpinning targetable biology. Simple karyotyping revealed key translocations involving MYC, BCL2, and BCL6 that have impacted lymphoma classification in the World Health Organization classification scheme. Subsequently, gene expression profiling identified molecular subgroups within the most common lymphoma, diffuse large B-cell lymphoma (DLBCL): activated B-cell-like and germinal centre B-cell-like. Finally, next generation sequencing has revealed a modest number of frequently mutated genes and a long list of infrequent mutations. The mutational landscapes involve diverse genes associated with dysregulated signalling, epigenetic modification, blockade of cellular differentiation, and immune evasion. These mutational "signatures" are enriched in the different aggressive lymphoma subtypes impacting phenotypes and identifying therapeutic targets. Challenges to implementing genomic assays into clinical practice remain.