Incorporation of Digital Gene Expression Profiling for Cell-of-Origin Determination (Lymph2Cx Testing) into the Routine Work-Up of Diffuse Large B-Cell Lymphoma

Diffuse large B cell lymphoma (DLBCL): epidemiology, pathophysiology, risk stratification, advancement in diagnostic approaches and prospects: narrative review

Diffuse large B-cell lymphoma (DLBCL) is an aggressive subtype of non-Hodgkin's lymphoma that arises from the germinal center. It represents a heterogeneous disease characterized by different pathological, clinical, and molecular entities. Gene expression profiling based on the alleged cell of origin differentiates transcriptional subtypes such as germinal center and activated B cell-like. DLBCL accounts for around 40% of all non-Hodgkin's lymphomas worldwide. Its incidence generally increases with age. The international prognostic index remains the most important tool for disease stratification.The diagnosis of DLBCL is best made through an excisional biopsy of a suspicious lymph node. Nowadays, advanced techniques are employed to accurately diagnose and determine the clinical outcomes of patients. Immunohistochemistry, next-generation sequencing, and array-based comparative hybridization facilitate the global identification of diverse and numerous genetic alterations. However, further validation should be necessary to apply advanced techniques in clinical practice. In this review, we summarize the current literature and discuss the pathophysiology, epidemiology, and diagnostic advancements of DLBCL.

Bifunctional Inhibitor Reveals NEK2 as a Therapeutic Target and Regulator of Oncogenic Pathways in Lymphoma

Expression of the serine/threonine kinase never in mitosis gene A (NIMA)-related kinase 2 (NEK2) is essential for entry into mitosis via its role in facilitating centrosome separation. Its overactivity can lead to tumorigenesis and drug resistance through the activation of several oncogenic pathways, including AKT. Although the cancer-enabling activities of NEK2 are documented in many malignancies, including correlations with poor survival in myeloma, breast, and non-small cell lung cancer, little is known about the role of NEK2 in lymphoma. Here, in tumors from patients with diffuse large B-cell lymphoma (DLBCL), the most common, aggressive non-Hodgkin lymphoma, we found a high abundance of NEK2 mRNA and protein associated with an inferior overall survival. Using our recently developed NEK2 inhibitor, NBI-961, we discovered that DLBCL cell lines and patient-derived cells exhibit a dependency on NEK2 for their viability. This compromised cell fitness was directly attributable to efficient NEK2 inhibition and proteasomal degradation by NBI-961. In a subset of particularly sensitive DLBCL cells, NBI-961 induced G2/mitosis arrest and apoptosis. In contrast, an existing indirect NEK2 inhibitor, INH154, did not prevent NEK2 autophosphorylation, induce NEK2 proteasomal degradation, or affect cell viability. Global proteomics and phospho-proteomics revealed that NEK2 orchestrates cell-cycle and apoptotic pathways through regulation of both known and new signaling molecules. We show the loss of NEK2-sensitized DLBCL to the chemotherapy agents, doxorubicin and vincristine, and effectively suppressed tumor growth in mice. These studies establish the oncogenic activity of NEK2 in DLBCL and set the foundation for development of anti-NEK2 therapeutic strategies in this frequently refractory and relapse-prone cancer.

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.

Exploring new prognostic biomarkers in Mantle Cell Lymphoma: a comparison of the circSCORE and the MCL35 score

Mantle cell lymphoma (MCL) is a biologically and clinically heterogeneous disease, emphasizing the need for prognostic biomarkers. In this study we aimed at comparing the prognostic value of two RNA-based risk scores, circSCORE and MCL35, in 149 patients from the MCL2 (ISRCTN87866680) and MCL3 (NCT00514475) patient cohorts. Both risk scores provided significant stratification of high versus low risk for progression free survival (PFS) and overall survival (OS). The circSCORE retained significant prognostic value in adjusted multivariable Cox regressions for PFS, but not for OS. Furthermore, circSCORE added significant prognostic value to MIPI in the pooled cohort (MCL2 and MCL3) for PFS and OS, and for PFS in MCL3 alone, outperforming Ki67 and MCL35. We suggest a new, combined MIPI-circSCORE with improved prognostic value, and with potential for future clinical implementation, if validated in a larger, independent cohort.

Primary pulmonary intravascular large B‑cell lymphoma misdiagnosed as pneumonia: Four case reports and a literature review

Primary pulmonary intravascular large B-cell lymphoma (IVLBCL) is a rare, malignant extranodal lymphoma. It is difficult to diagnose clinically as it requires a combination of clinical and computed tomography (CT) evaluations, as well as laboratory and pathological examinations. In the present study, 4 cases of primary pulmonary IVLBCL were reviewed. The patients' ages ranged from 60 to 69 years old. Of the 4 patients, 3 developed progressive dyspnea on exertion and intermittent fever. Other symptoms included coughing, chest tightness and weight loss. Laboratory data indicated that all patients had anemia, thrombocytopenia, hypoxemia, a markedly high serum lactate dehydrogenase level, elevated erythrocyte sedimentation rate and increased C-reactive protein. CT demonstrated increased attenuation in bilateral lung parenchyma, especially in the upper lobes, with multiple ground-glass opacities associated with small nodules in these patients. Initially, all 4 patients were misdiagnosed with pneumonia. However, none of them responded to anti-inflammatory treatments. The pathologies of all patients were confirmed using lung biopsy. Only 1 patient received regular combination chemotherapy. Based on the observations of the present study, a standard regimen for lymphoma treatment may result in a notable clinical response.

Transformation of FL into DLBCL with a PMBL gene expression signature

We investigated the clinicopathologic features of 5 follicular lymphomas (FLs) that transformed (tFL) morphologically to diffuse large B-cell lymphomas (DLBCLs) and had a primary mediastinal large B-cell lymphoma (PMBL)-like gene expression profile (tFL-PMBLsig-pos). None of the tFL-PMBLsig-pos cases arose in the mediastinum, all cases tested had a germinal center B-cell phenotype, 20% were CD30+, 60% CD23+, 80% MAL+, 20% CD200+, and 0% CD273/PDL2+. Whole-exome sequencing detected alterations in genes associated with both FL/DLBCL (CREBBP, KMT2C, KMT2D, ARID1A, HIST1 members, and TNFRSF14) and PMBL (JAK-STAT pathway genes, B2M, and CD58). Copy number (CN) analysis detected gains/amplification of REL and STAT6 in 60%, gains of SOCS1 in 40%, and gains of chromosome 16, including IL4R, in 40% of the cases. CN gains/amplification of BCL6 and MYC and loss of TNFRSF14 and TNFAIP3 were identified in 20% of the cases. Three of 5 cases lacked a BCL2 rearrangement. Despite having some features that are less common in DLBCL (MAL and CD23 expression and JAK-STAT activation), these tFL-PMBLsig-pos cases lack the most characteristic CN alteration seen in PMBL (9p24.1 gain/amplification). This cohort expands the biologic heterogeneity of tFL, illustrating a subset with gene expression and some genetic features reminiscent of PMBL, with potential treatment implications that include the use of novel targeted therapies.

Sequencing therapy in relapsed DLBCL

Diffuse large B-cell lymphoma (DLBCL) is the most common lymphoid malignancy worldwide, comprising approximately 30% of all lymphomas. Currently, 50% to 60% of patients diagnosed with DLBCL are alive at 5 years and cured with modern therapy, but about 10% to 15% of patients are refractory to first-line therapy, and an additional 20% to 30% relapse following a complete response. Patients who have relapses beyond 2 years may experience more favorable outcomes and have forms of DLBCL that can be distinguished biologically. Patients who experience early relapse or who have primary refractory disease (less than a complete response or relapse within 3 to 6 months of initial therapy) have worse outcomes. For decades, the standard of care treatment strategy for fit patients with relapsed DLBCL has been salvage therapy with non-cross-resistant combination chemoimmunotherapy regimens followed by high-dose chemotherapy and autologous stem cell transplantation (ASCT) as stem cell rescue for patients with chemosensitive disease. Recent data suggest that certain patients may benefit from chimeric antigen receptor T-cell therapy (CAR T) in the second-line setting. Additional novel therapies exist for patients who are ineligible, who are unable to access these therapies, or who fail ASCT and/or CAR T. Despite the advent of new therapies for DLBCL and improved outcomes, DLBCL remains a life-threatening illness. Thus, it is essential for clinicians to engage in serious illness conversations with their patients. Goals-of-care communication can be improved through skills-based training and has been demonstrated to have an impact on patient experiences.

Artificial Intelligence Predicted Overall Survival and Classified Mature B-Cell Neoplasms Based on Immuno-Oncology and Immune Checkpoint Panels

Artificial intelligence (AI) can identify actionable oncology biomarkers. This research integrates our previous analyses of non-Hodgkin lymphoma. We used gene expression and immunohistochemical data, focusing on the immune checkpoint, and added a new analysis of macrophages, including 3D rendering. The AI comprised machine learning (C5, Bayesian network, C&R, CHAID, discriminant analysis, KNN, logistic regression, LSVM, Quest, random forest, random trees, SVM, tree-AS, and XGBoost linear and tree) and artificial neural networks (multilayer perceptron and radial basis function). The series included chronic lymphocytic leukemia, mantle cell lymphoma, follicular lymphoma, Burkitt, diffuse large B-cell lymphoma, marginal zone lymphoma, and multiple myeloma, as well as acute myeloid leukemia and pan-cancer series. AI classified lymphoma subtypes and predicted overall survival accurately. Oncogenes and tumor suppressor genes were highlighted (MYC, BCL2, and TP53), along with immune microenvironment markers of tumor-associated macrophages (M2-like TAMs), T-cells and regulatory T lymphocytes (Tregs) (CD68, CD163, MARCO, CSF1R, CSF1, PD-L1/CD274, SIRPA, CD85A/LILRB3, CD47, IL10, TNFRSF14/HVEM, TNFAIP8, IKAROS, STAT3, NFKB, MAPK, PD-1/PDCD1, BTLA, and FOXP3), apoptosis (BCL2, CASP3, CASP8, PARP, and pathway-related MDM2, E2F1, CDK6, MYB, and LMO2), and metabolism (ENO3, GGA3). In conclusion, AI with immuno-oncology markers is a powerful predictive tool. Additionally, a review of recent literature was made.

Molecular subtyping of CD5+ diffuse large B-cell lymphoma based on DNA-targeted sequencing and Lymph2Cx

Background

CD5-positive diffuse large B-cell lymphoma (CD5+ DLBCL) showed poor prognosis in the rituximab era, with limited research on its genetic characteristics and cell of origin (COO). We aimed to demonstrate the molecular characteristics of CD5+ DLBCL and to discover potential prognostic factors.

Methods

We included 24 cases of CD5+ DLBCL and 23 CD5-negative (CD5-) counterparts and collected their clinicopathological features. Targeted DNA sequencing of 475 lymphoma-related genes was performed, and all cases were assigned to distinct genetic subtypes using the LymphGen tool. The COO was determined by the Lymph2Cx assay. The Kaplan–Meier method and Cox proportional hazards model were applied to identify the possible prognostic factors.

Results

Compared with their CD5- counterparts, patients with CD5+ DLBCL tended to have a worse prognosis and a higher incidence of MYD88L265P and CD79B double mutation (MCD) subtype (54.17%, P = 0.005) and activated B cell-like (ABC) subtype (62.5%, P = 00017), as determined by next-generation sequencing and Lymph2Cx, respectively. Moreover, PIM1, MYD88, and KMT2D mutations were detected more frequently in CD5+ DLBCL cases (P < 0.05). According to multivariate analysis, MYC/BCL2 double expression and ABC subtype were correlated with unfavorable overall survival (OS). High mRNA expression of SERPINA9 and MME showed a significant correlation with a better OS, and high expression of MME showed a significant correlation with better progression-free survival in CD5+ DLBCL.

Conclusion

The genetic profile of CD5+ DLBCL is characterized by PIM1, MYD88, and KMT2D mutations, with a higher incidence of MCD and ABC subtypes. MYC/BCL2 double expression, ABC subtype, and mRNA expression of SERPINA9 and MME are independently predictive of the prognosis of CD5+ DLBCL.

Transcriptional profiles define drug refractory disease in myeloma

Identifying biomarkers associated with disease progression and drug resistance are important for personalized care. We investigated the expression of 121 curated genes, related to immunomodulatory drugs (IMiDs) and proteasome inhibitors (PIs) responsiveness. We analyzed 28 human multiple myeloma (MM) cell lines with known drug sensitivities and 130 primary MM patient samples collected at different disease stages, including newly diagnosed (ND), on therapy (OT), and relapsed and refractory (RR, collected within 12 months before the patients' death) timepoints. Our findings led to the identification of a subset of genes linked to clinical drug resistance, poor survival, and disease progression following combination treatment containing IMIDs and/or PIs. Finally, we built a seven-gene model (MM-IMiD and PI sensitivity-7 genes [IP-7]) using digital gene expression profiling data that significantly separates ND patients from IMiD- and PI-refractory RR patients. Using this model, we retrospectively analyzed RNA sequcencing (RNAseq) data from the Mulltiple Myeloma Research Foundation (MMRF) CoMMpass (n = 578) and Mayo Clinic MM patient registry (n = 487) to divide patients into probabilities of responder and nonresponder, which subsequently correlated with overall survival, disease stage, and number of prior treatments. Our findings suggest that this model may be useful in predicting acquired resistance to treatments containing IMiDs and/or PIs.

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.

Clinical laboratory validation of the MCL35 assay for molecular risk stratification of mantle cell lymphoma

Mantle cell lymphoma (MCL) is a clinically heterogeneous B cell malignancy for which a variety of prognostic factors have been proposed. Previously, a digital gene expression profiling "proliferation signature" capable of risk stratifying MCL was identified and subsequently developed into a multi-analyte prognostic assay, known as the "MCL35" assay. In this study, we sought to explore the performance characteristics of the MCL35 assay in a clinical laboratory and compare results with the Ki67 proliferation marker. The results describe the clinical validation of the MCL35 assay for molecular risk stratification of MCL including accuracy, sensitivity, specificity, use in acid-decalcified bone marrow core biopsies, fixatives, lower limit of RNA input, quality metrics, and other laboratory parameters. The resulting data indicate that this is a robust technique with outstanding reproducibility. Overall, the data support the concept of molecular signatures, as assessed with digital gene expression profiling, for improved standardization and reproducibility for proliferation assessment in MCL.

Remaining challenges in predicting patient outcomes for diffuse large B-cell lymphoma

Introduction: Diffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma and is an aggressive malignancy with heterogeneous outcomes. Diverse methods for DLBCL outcomes assessment ranging from clinical to genomic have been developed with variable predictive and prognostic success.Areas covered: The authors provide an overview of the various methods currently used to estimate prognosis in DLBCL patients. Models incorporating cell of origin, genomic features, sociodemographic factors, treatment effectiveness measures, and machine learning are described.Expert opinion: The clinical and genetic heterogeneity of DLBCL presents distinct challenges in predicting response to therapy and overall prognosis. Successful integration of predictive and prognostic tools in clinical trials and in a standard clinical workflow for DLBCL will likely require a combination of methods incorporating clinical, sociodemographic, and molecular factors with the aid of machine learning and high-dimensional data analysis.