Molecular subtyping of diffuse large B-cell lymphoma: update on biology, diagnosis and emerging platforms for practising pathologists

Biomarkers and novel therapeutic approaches for diffuse large B-cell lymphoma in the era of precision medicine

Despite the great efforts for better treatment options for diffuse large B-cell lymphoma (DLBCL) (most common form of non-Hodgkin lymphoma, NHL) to treat and prevent relapse, it continues to be a challenge. Here, we present an overview of DLBCL and address the diagnostic assays and molecular techniques used in its diagnosis, role of biomarkers in detection, treatment of early and advanced stage DLBCL, and novel drug regimens. We discuss the significant biomarkers that have emerged as essential tools for stratifying patients according to risk factors and for providing insights into the use of more targeted and individualized therapeutics. We discuss techniques such as gene expression studies, including next-generation sequencing, which have enabled a more understanding of the complex pathogenesis of DLBCL and have helped determine molecular targets for novel therapeutic agents. We examine current treatment approaches, outline the findings of completed clinical trials, and provide updates for ongoing clinical trials. We highlight clinical trials relevant to the significant fraction of DLBCL patients who present with complex cases marked by high relapse rates. Supported by an increased understanding of targetable pathways in DLBCL, clinical trials involving specialized combination therapies are bringing us within reach the promise of an effective cure to DLBCL using precision medicine. Optimization of therapy remains a crucial objective, with the end goal being a balance between high survival rates through targeted and personalized treatment while reducing adverse effects in DLBCL patients of all subsets.

Objective quantification of BCL2 protein by multiplex immunofluorescence in routine biopsy samples of diffuse large B-cell lymphoma demonstrates associations with survival and BCL2 gene alterations

Up-regulation of BCL2 in cases of diffuse large B-cell lymphoma (DLBCL) can confer treatment resistance. Quantitative immunofluorescence (QIF) histology allows objective quantification of protein-based biomarkers. We investigated the utility of QIF for evaluating BCL2 as a biomarker in DLBCL by quantifying BCL2 selectively in CD20-expressing lymphoma cells in biopsy samples from 116 cases of DLBCL in two cohorts one of which consisted of relapsed/refractory cases from a clinical trial. BCL2 protein by QIF correlated with BCL2 mRNA abundance and was associated with both translocation and copy number gain of the BCL2 gene. Elevated BCL2 protein expression by QIF, but not immunohistochemistry or mRNA quantification, was associated with inferior overall and relapse-free survival in the relapsed/refractory cohort. QIF is an effective means of quantifying BCL2 protein objectively in routine cancer biopsy specimens and shows promise for identifying relapsed/refractory DLBCL patients at risk of inferior outcomes after salvage therapy.

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.

A novel approach for medical research on lymphomas: A study validation of claims-based algorithms to identify incident cases

The use of claims database to study lymphomas in real-life conditions is a crucial issue in the future. In this way, it is essential to develop validated algorithms for the identification of lymphomas in these databases. The aim of this study was to assess the validity of diagnosis codes in the French health insurance database to identify incident cases of lymphomas according to results of a regional cancer registry, as the gold standard.Between 2010 and 2013, incident lymphomas were identified in hospital data through 2 algorithms of selection. The results of the identification process and characteristics of incident lymphomas cases were compared with data from the Tarn Cancer Registry. Each algorithm's performance was assessed by estimating sensitivity, predictive positive value, specificity (SPE), and negative predictive value.During the period, the registry recorded 476 incident cases of lymphomas, of which 52 were Hodgkin lymphomas and 424 non-Hodgkin lymphomas. For corresponding area and period, algorithm 1 provides a number of incident cases close to the Registry, whereas algorithm 2 overestimated the number of incident cases by approximately 30%. Both algorithms were highly specific (SPE = 99.9%) but moderately sensitive. The comparative analysis illustrates that similar distribution and characteristics are observed in both sources.Given these findings, the use of claims database can be consider as a pertinent and powerful tool to conduct medico-economic or pharmacoepidemiological studies in lymphomas.

DNMT1 is predictive of survival and associated with Ki-67 expression in R-CHOP-treated diffuse large B-cell lymphomas

DNMT1 is a target of approved anti-cancer drugs including decitabine. However, the prognostic value of DNMT1 protein expression in R-CHOP-treated diffuse large B-cell lymphomas (DLBCLs) remains unexplored. Here we showed that DNMT1 was expressed in the majority of DLBCL cases (n = 209/230, 90.9%) with higher expression in germinal centre B-cell-like (GCB)-DLBCL subtype. Low and negative DNMT1 expression (20% cut-off, n = 33/230, 14.3%) was predictive of worse overall survival (OS; p < 0.001) and progression-free survival (PFS; p < 0.001). Nonetheless, of the 209 DNMT1 positive patients, 33% and 42% did not achieve 5-year OS and PFS, respectively, indicating that DNMT1 positive patients showed considerably heterogeneous outcomes. Moreover, DNMT1 was frequently expressed in mitotic cells and significantly correlated with Ki-67 or BCL6 expression (r = 0.60 or 0.44, respectively; p < 0.001). We demonstrate that DNMT1 is predictive of DLBCL patients' survival, and suggest that DNMT1 could be a DLBCL therapeutic target due to its significant association with Ki-67.

Marginal zone lymphoma-derived interfollicular diffuse large B-cell lymphoma harboring 20q12 chromosomal deletion and missense mutation of BIRC3 gene: a case report

Background

Diffuse large B-cell lymphoma (DLBCL) typically leads to effacement of the nodal architecture by an infiltrate of malignant cells. Rarely (<1%), DLBCL can present with an interfollicular pattern (DLBCL-IF) preserving the lymphoid follicles. It has been postulated that DLBCL-IF is derived from marginal zone B cells and may represent a large-cell transformation of marginal zone lymphoma (MZL), however no direct evidence has been provided to date. Here we describe a rare case of a diagnostically challenging DLBCL-IF involving a lymph node in a patient with a prior history of lymphadenopathy for several years and MZL involving skin.

Case presentation

A 53-year old man presented to our Dermatology Clinic due to a 1-year history of generalized itching, fatigue of 2–3 month’s duration, nausea and mid back rash that was biopsied. PET (positron emission tomography)/CT (computed tomography) was performed and revealed inguinal, pelvic, retroperitoneal, axillary, and cervical lymphadenopathy. The patient was referred to surgery for excisional biopsy of a right inguinal lymph node.

Diagnostic H&E stained slides and ancillary studies were reviewed for the lymph node and skin specimens. B-cell clonality by PCR and sequencing studies were performed on both specimens.

We demonstrate that this patient’s MZL and DLBCL-IF are clonally related, strongly suggesting that transformation of MZL to DLBCL had occurred. Furthermore, we identified a novel deletion of the long arm of chromosome 20 (del(20q12)) and a missense mutation in BIRC3 (Baculoviral IAP repeat-containing protein 3) in this patient’s DLBCL that are absent from his MZL, suggesting that these genetic alterations contributed to the large cell transformation.

Conclusions

To our knowledge, this is the first report providing molecular evidence for a previously suspected link between MZL and DLBCL-IF. In addition, we describe for the first time del(20q12) and a missense mutation in BIRC3 in DLBCL. Our findings also raise awareness of DLBCL-IF and discuss the diagnostic pitfalls of this rare entity.