A novel lymphoma-associated macrophage interaction signature (LAMIS) provides robust risk prognostication in diffuse large B-cell lymphoma clinical trial cohorts of the DSHNHL

An overview and a roadmap for artificial intelligence in hematology and oncology

BACKGROUND

Artificial intelligence (AI) is influencing our society on many levels and has broad implications for the future practice of hematology and oncology. However, for many medical professionals and researchers, it often remains unclear what AI can and cannot do, and what are promising areas for a sensible application of AI in hematology and oncology. Finally, the limits and perils of using AI in oncology are not obvious to many healthcare professionals.

METHODS

In this article, we provide an expert-based consensus statement by the joint Working Group on "Artificial Intelligence in Hematology and Oncology" by the German Society of Hematology and Oncology (DGHO), the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), and the Special Interest Group Digital Health of the German Informatics Society (GI). We provide a conceptual framework for AI in hematology and oncology.

RESULTS

First, we propose a technological definition, which we deliberately set in a narrow frame to mainly include the technical developments of the last ten years. Second, we present a taxonomy of clinically relevant AI systems, structured according to the type of clinical data they are used to analyze. Third, we show an overview of potential applications, including clinical, research, and educational environments with a focus on hematology and oncology.

CONCLUSION

Thus, this article provides a point of reference for hematologists and oncologists, and at the same time sets forth a framework for the further development and clinical deployment of AI in hematology and oncology in the future.

The prognostic value of tumor-associated macrophages detected by immunostaining in diffuse large B cell lymphoma: A meta-analysis

Background

The prognostic implication of tumor-associated macrophages (TAMs) in the microenvironment of diffuse large B cell lymphoma (DLBCL) remains controversial.

Methods

A systematic and comprehensive search of relevant studies was performed in PubMed, Embase and Web of Science databases. The quality of the included studies was estimated using Newcastle-Ottawa Scale (NOS).

Results

Twenty-three studies containing a total of 2992 DLBCL patients were involved in this study. They were all high-quality studies scoring ≥ 6 points. High density of M2 TAMs in tumor microenvironment significantly associated with both advanced disease stage (OR= 1.937, 95% CI: 1.256-2.988, P = 0.003) and unfavorable overall survival (OS) (HR = 1.750, 95% CI: 1.188-2.579, P = 0.005) but not associated with poor progression free survival (PFS) (HR = 1.672, 95% CI: 0.864-3.237, P = 0.127) and international prognostic index (IPI) (OR= 1.705, 95% CI: 0.843-3.449, P = 0.138) in DLBCL patients. No significant correlation was observed between the density of CD68+ TAMs and disease stage (OR= 1.433, 95% CI: 0.656-3.130, P = 0.366), IPI (OR= 1.391, 95% CI: 0.573-3.379, P = 0.466), OS (HR=0.929, 95% CI: 0.607-1.422, P = 0.734) or PFS (HR= 0.756, 95% CI: 0.415-1.379, P = 0.362) in DLBCL patients.

Conclusion

This meta-analysis demonstrated that high density of M2 TAMs in the tumor microenvironment was a robust predictor of adverse outcome for DLBCL patients.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO, identifier CRD42022343045.

Genetic Profiling of Diffuse Large B-Cell Lymphoma: A Comparison Between Double-Expressor Lymphoma and Non-Double-Expressor Lymphoma

INTRODUCTION

Data are limited regarding the genetic profiling of diffuse large B-cell lymphoma (DLBCL) with double expression of MYC and BCL2 proteins without underlying rearrangements (double-expressor lymphoma [DEL]). This study aimed to describe the genetic profiling and determine the prognostic significance in patients with DEL and in those with non-DEL.

METHODS

Capture-based targeted sequencing was performed on 244 patients with de novo DLBCL, not otherwise specified. Immunohistochemistry staining was performed for evaluating the MYC and BCL2 expression.

RESULTS

Among 244 patients, 46 patients had DEL, and 198 had non-DEL. KMT2D, CD58, EP300, PRDM1, TNFAIP3 and BCL2 gain or amplification (BCL2^GA/AMP) were significantly more frequently altered in the DEL group. Alterations in the BCR/TLR (p = 0.021), B-cell development and differentiation (p = 0.004), and NF-κB (p = 0.034) pathways occurred more frequently in patients with DEL. Thirty-seven DEL patients and 132 non-DEL patients were included for survival analyses. DEL was not significantly associated with progression-free survival (PFS) (p = 0.60) and overall survival (OS) (p = 0.49). In DEL patients, after adjusting for the International Prognostic Index, BCL2 alteration (HR 2.516, 95% CI 1.027-6.161; p = 0.044) remained an independent predictor of inferior PFS. BCL2^GA/AMP also predicted poor PFS, but with marginal statistical significance (HR 2.489, 95% CI 0.995-6.224; p = 0.051).

CONCLUSION

There was difference in profiling of altered genes and signaling pathways between the DEL group and the non-DEL group. The presence of DEL alone should not be considered as an adverse prognostic indicator, and BCL2 alteration could define a subset of patients with poor prognosis within DEL.

Lymphoma Microenvironment in DLBCL and PTCL-NOS: the key to uncovering heterogeneity and the potential for stratification

Diffuse large B-cell lymphoma (DLBCL) and peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS) are the most common subtypes of mature B cell neoplasm and T/NK cell lymphoma, respectively. They share a commonality in that they are, by definition, highly heterogeneous populations. Recent studies are revealing more about the heterogeneity of these diseases, and at the same time, there is an active debate on how to stratify these heterogeneous diseases and make them useful in clinical practice. The various immune cells and non-cellular components surrounding lymphoma cells, i.e., the lymphoma microenvironment, have been the subject of intense research since the late 2000s, and much knowledge has been accumulated over the past decade. As a result, it has become clear that the lymphoma microenvironment, despite its paucity in tissues, significantly impacts the lymphoma pathogenesis and clinical behavior, such as its prognosis and response to therapy. In this article, we review the role of the lymphoma microenvironment in DLBCL and PTCL-NOS, with particular attention given to its impact on the prognosis and stratification.

Biological and Clinical Implications of Gene-Expression Profiling in Diffuse Large B-Cell Lymphoma: A Proposal for a Targeted BLYM-777 Consortium Panel as Part of a Multilayered Analytical Approach

Gene-expression profiling (GEP) is used to study the molecular biology of lymphomas. Here, advancing insights from GEP studies in diffuse large B-cell lymphoma (DLBCL) lymphomagenesis are discussed. GEP studies elucidated subtypes based on cell-of-origin principles and profoundly changed the biological understanding of DLBCL with clinical relevance. Studies integrating GEP and next-generation DNA sequencing defined different molecular subtypes of DLBCL entities originating at specific anatomical localizations. With the emergence of high-throughput technologies, the tumor microenvironment (TME) has been recognized as a critical component in DLBCL pathogenesis. TME studies have characterized so-called "lymphoma microenvironments" and "ecotypes". Despite gained insights, unexplained chemo-refractoriness in DLBCL remains. To further elucidate the complex biology of DLBCL, we propose a novel targeted GEP consortium panel, called BLYM-777. This knowledge-based biology-driven panel includes probes for 777 genes, covering many aspects regarding B-cell lymphomagenesis (f.e., MYC signature, TME, immune surveillance and resistance to CAR T-cell therapy). Regarding lymphomagenesis, upcoming DLBCL studies need to incorporate genomic and transcriptomic approaches with proteomic methods and correlate these multi-omics data with patient characteristics of well-defined and homogeneous cohorts. This multilayered methodology potentially enhances diagnostic classification of DLBCL subtypes, prognostication, and the development of novel targeted therapeutic strategies.

Reproducibility of Gene Expression Signatures in Diffuse Large B-Cell Lymphoma

Multiple gene expression profiles have been identified in diffuse large B-cell lymphoma (DLBCL). Besides the cell of origin (COO) classifier, no signatures have been reproduced in independent studies or evaluated for capturing distinct aspects of DLBCL biology. We reproduced 4 signatures in 175 samples of the HOVON-84 trial on a panel of 117 genes using the NanoString platform. The four gene signatures capture the COO, MYC activity, B-cell receptor signaling, oxidative phosphorylation, and immune response. Performance of our classification algorithms were confirmed in the original datasets. We were able to validate three of the four GEP signatures. The COO algorithm resulted in 94 (54%) germinal center B-cell (GCB) type, 58 (33%) activated B-cell (ABC) type, and 23 (13%) unclassified cases. The MYC-classifier revealed 77 cases with a high MYC-activity score (44%) and this MYC-high signature was observed more frequently in ABC as compared to GCB DLBCL (68% vs. 32%, p < 0.00001). The host response (HR) signature of the consensus clustering was present in 55 (31%) patients, while the B-cell receptor signaling, and oxidative phosphorylation clusters could not be reproduced. The overlap of COO, consensus cluster and MYC activity score differentiated six gene expression clusters: GCB/MYC-high (12%), GCB/HR (16%), GCB/non-HR (27%), COO-Unclassified (13%), ABC/MYC-high (25%), and ABC/MYC-low (7%). In conclusion, the three validated signatures identify distinct subgroups based on different aspects of DLBCL biology, emphasizing that each classifier captures distinct molecular profiles.

Genetic Subtyping and Phenotypic Characterization of the Immune Microenvironment and MYC/BCL2 Double Expression Reveal Heterogeneity in Diffuse Large B-cell Lymphoma

PURPOSE

Diffuse large B-cell lymphoma (DLBCL) is molecularly and clinically heterogeneous, and can be subtyped according to genetic alterations, cell-of-origin, or microenvironmental signatures using high-throughput genomic data at the DNA or RNA level. Although high-throughput proteomic profiling has not been available for DLBCL subtyping, MYC/BCL2 protein double expression (DE) is an established prognostic biomarker in DLBCL. The purpose of this study is to reveal the relative prognostic roles of DLBCL genetic, phenotypic, and microenvironmental biomarkers.

EXPERIMENTAL DESIGN

We performed targeted next-generation sequencing; IHC for MYC, BCL2, and FN1; and fluorescent multiplex IHC for microenvironmental markers in a large cohort of DLBCL. We performed correlative and prognostic analyses within and across DLBCL genetic subtypes and MYC/BCL2 double expressors.

RESULTS

We found that MYC/BCL2 double-high-expression (DhE) had significant adverse prognostic impact within the EZB genetic subtype and LymphGen-unclassified DLBCL cases but not within MCD and ST2 genetic subtypes. Conversely, KMT2D mutations significantly stratified DhE but not non-DhE DLBCL. T-cell infiltration showed favorable prognostic effects within BN2, MCD, and DhE but unfavorable effects within ST2 and LymphGen-unclassified cases. FN1 and PD-1-high expression had significant adverse prognostic effects within multiple DLBCL genetic/phenotypic subgroups. The prognostic effects of DhE and immune biomarkers within DLBCL genetic subtypes were independent although DhE and high Ki-67 were significantly associated with lower T-cell infiltration in LymphGen-unclassified cases.

CONCLUSIONS

Together, these results demonstrated independent and additive prognostic effects of phenotypic MYC/BCL2 and microenvironment biomarkers and genetic subtyping in DLBCL prognostication, important for improving DLBCL classification and identifying prognostic determinants and therapeutic targets.

Identification and Validation of a Prognostic Prediction Model in Diffuse Large B-Cell Lymphoma

BACKGROUND

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous group with varied pathophysiological, genetic, and clinical features, accounting for approximately one-third of all lymphoma cases worldwide. Notwithstanding that unprecedented scientific progress has been achieved over the years, the survival of DLBCL patients remains low, emphasizing the need to develop novel prognostic biomarkers for early risk stratification and treatment optimization.

METHOD

In this study, we screened genes related to the overall survival (OS) of DLBCL patients in datasets GSE117556, GSE10846, and GSE31312 using univariate Cox analysis. Survival-related genes among the three datasets were screened according to the criteria: hazard ratio (HR) >1 or <1 and p-value <0.01. Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate Cox regression analysis were used to optimize and establish the final gene risk prediction model. The TCGA-NCICCR datasets and our clinical cohort were used to validate the performance of the prediction model. CIBERSORT and ssGSEA algorithms were used to estimate immune scores in the high- and low-risk groups.

RESULTS

We constructed an eight-gene prognostic signature that could reliably predict the clinical outcome in training, testing, and validation cohorts. Our prognostic signature also performed distinguished areas under the ROC curve in each dataset, respectively. After stratification based on clinical characteristics such as cell-of-origin (COO), age, eastern cooperative oncology group (ECOG) performance status, international prognostic index (IPI), stage, and MYC/BCL2 expression, the difference in OS between the high- and low-risk groups was statistically significant. Next, univariate and multivariate analyses revealed that the risk score model had a significant prediction value. Finally, a nomogram was established to visualize the prediction model. Of note, we found that the low-risk group was enriched with immune cells.

CONCLUSION

In summary, we identified an eight-gene prognostic prediction model that can effectively predict survival outcomes of patients with DLBCL and built a nomogram to visualize the perdition model. We also explored immune alterations between high- and low-risk groups.

OUP accepted manuscript

MOTIVATION

Estimating the effects of interventions on patient outcome is one of the key aspects of personalized medicine. Their inference is often challenged by the fact that the training data comprises only the outcome for the administered treatment, and not for alternative treatments (the so-called counterfactual outcomes). Several methods were suggested for this scenario based on observational data, i.e. data where the intervention was not applied randomly, for both continuous and binary outcome variables. However, patient outcome is often recorded in terms of time-to-event data, comprising right-censored event times if an event does not occur within the observation period. Albeit their enormous importance, time-to-event data are rarely used for treatment optimization. We suggest an approach named BITES (Balanced Individual Treatment Effect for Survival data), which combines a treatment-specific semi-parametric Cox loss with a treatment-balanced deep neural network; i.e. we regularize differences between treated and non-treated patients using Integral Probability Metrics (IPM).

RESULTS

We show in simulation studies that this approach outperforms the state of the art. Furthermore, we demonstrate in an application to a cohort of breast cancer patients that hormone treatment can be optimized based on six routine parameters. We successfully validated this finding in an independent cohort.

AVAILABILITY AND IMPLEMENTATION

We provide BITES as an easy-to-use python implementation including scheduled hyper-parameter optimization (https://github.com/sschrod/BITES). The data underlying this article are available in the CRAN repository at https://rdrr.io/cran/survival/man/gbsg.html and https://rdrr.io/cran/survival/man/rotterdam.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Impact of Qi-Invigorating Traditional Chinese Medicines on Diffuse Large B Cell Lymphoma Based on Network Pharmacology and Experimental Validation

Background: It has been verified that deficiency of Qi, a fundamental substance supporting daily activities according to the Traditional Chinese Medicine theory, is an important symptom of cancer. Qi-invigorating herbs can inhibit cancer development through promoting apoptosis and improving cancer microenvironment. In this study, we explored the potential mechanisms of Qi-invigorating herbs in diffuse large B cell lymphoma (DLBCL) through network pharmacology and in vitro experiment. Methods: Active ingredients of Qi-invigorating herbs were predicted from the Traditional Chinese Medicine Systems Pharmacology Database. Potential targets were obtained via the SwissTargetPrediction and STITCH databases. Target genes of DLBCL were obtained through the PubMed, the gene-disease associations and the Malacards databases. Overlapping genes between DLBCL and each Qi-invigorating herb were collected. Hub genes were subsequently screened via Cytoscape. The Gene Ontology and pathway enrichment analyses were performed using the DAVID database. Molecular docking was performed among active ingredients and hub genes. Hub genes linked with survival and tumor microenvironment were analyzed through the GEPIA 2.0 and TIMER 2.0 databases, respectively. Additionally, in vitro experiment was performed to verify the roles of common hub genes. Results: Through data mining, 14, 4, 22, 22, 35, 2, 36 genes were filtered as targets of Ginseng Radix et Rhizoma, Panacis Quinquefolii Radix, Codonopsis Radix, Pseudostellariae Radix, Astragali Radix, Dioscoreae Rhizoma, Glycyrrhizae Radix et Rhizoma for DLBCL treatment, respectively. Then besides Panacis Quinquefolii Radix and Dioscoreae Rhizoma, 1,14, 10, 14,13 hub genes were selected, respectively. Molecular docking studies indicated that active ingredients could stably bind to the pockets of hub proteins. CASP3, CDK1, AKT1 and MAPK3 were predicted as common hub genes. However, through experimental verification, only CASP3 was considered as the common target of Qi-invigorating herbs on DLBCL apoptosis. Furthermore, the TIMER2.0 database showed that Qi-invigorating herbs might act on DLBCL microenvironment through their target genes. Tumor-associated neutrophils may be main target cells of DLBCL treated by Qi-invigorating herbs. Conclusion: Our results support the effects of Qi-invigorating herbs on DLBCL. Hub genes and immune infiltrating cells provided the molecular basis for each Qi-invigorating herb acting on DLBCL.

A Germinal Center-Associated Microenvironmental Signature Reflects Malignant Phenotype and Outcome of DLBCL

The DLBCL microenvironment signature scoring system was established using nCounter-based profiling of GC-related microenvironmental genes.

DMS scores stratified DLBCL patients with different prognosis independently of existing prognostic models.

Diffuse large B-cell lymphoma (DLBCL) is the most common B-cell malignancy, with varying prognosis after the gold standard rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). Several prognostic models have been established by focusing primarily on characteristics of lymphoma cells themselves, including cell-of-origin (COO), genomic alterations, and gene/protein expressions. However, the prognostic impact of the lymphoma microenvironment and its association with characteristics of lymphoma cells are not fully understood. Using the nCounter-based gene expression profiling of untreated DLBCL tissues, we assess the clinical impact of lymphoma microenvironment on the clinical outcomes and pathophysiological, molecular signatures in DLBCL. The presence of normal germinal center (GC)-microenvironmental cells, including follicular T cells, macrophage/dendritic cells, and stromal cells in lymphoma tissue indicates a positive therapeutic response. Our prognostic model, based on quantitation of transcripts from distinct GC-microenvironmental cell markers, clearly identified patients with graded prognosis independently of existing prognostic models. We observed increased incidences of genomic alterations and aberrant gene expression associated with poor prognosis in DLBCL tissues lacking GC-microenvironmental cells relative to those containing these cells. These data suggest that the loss of GC-associated microenvironmental signature dictates clinical outcomes of DLBCL patients reflecting the accumulation of “unfavorable” molecular signatures.

SPARC-positive macrophages are the superior prognostic factor in the microenvironment of diffuse large B-cell lymphoma and independent of MYC rearrangement and double-/triple-hit status

BACKGROUND

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease with respect to outcome. Features of the tumor microenvironment (TME) are associated with prognosis when assessed by gene expression profiling. However, it is uncertain whether assessment of the microenvironment can add prognostic information to the most relevant and clinically well-established molecular subgroups when analyzed by immunohistochemistry (IHC).

PATIENTS AND METHODS

We carried out a histopathologic analysis of biomarkers related to TME in a very large cohort (n = 455) of DLBCL treated in prospective trials and correlated with clinicopathologic and molecular data, including chromosomal rearrangements and gene expression profiles for cell-of-origin and TME.

RESULTS

The content of PD1+, FoxP3+ and CD8+, as well as vessel density, was not associated with outcome. However, we found a low content of CD68+ macrophages to be associated with inferior progression-free survival (PFS) and overall survival (OS; P = 0.023 and 0.040, respectively) at both univariable and multivariable analyses, adjusted for the factors of the International Prognostic Index (IPI), MYC break and BCL2/MYC and BCL6/MYC double-hit status. The subgroup of PDL1+ macrophages was not associated with survival. Instead, secreted protein acidic and cysteine rich (SPARC)-positive macrophages were identified as the subtype of macrophages most associated with survival. SPARC-positive macrophages and stromal cells directly correlated with favorable PFS and OS (both, P[log rank] <0.001, P[trend] < 0.001). The association of SPARC with prognosis was independent of the factors of the IPI, MYC double-/triple-hit status, Bcl2/c-myc double expression, cell-of-origin subtype and a recently published gene expression signature [lymphoma-associated macrophage interaction signature (LAMIS)].

CONCLUSIONS

SPARC expression in the TME detected by a single IHC staining with fair-to-good interobserver reproducibility is a powerful prognostic parameter. Thus SPARC expression is a strong candidate for risk assessment in DLBCL in daily practice.

Testicular Diffuse Large B-Cell Lymphoma-Clinical, Molecular, and Immunological Features

Simple Summary

Testicular diffuse large B-cell lymphoma (T-DLBCL) is a rare and aggressive lymphoma entity that mainly affects elderly men. It has a high relapse rate with especially the relapses of the central nervous system associating with dismal outcome. T-DLBCL has a unique biology with distinct genetic characteristics and clinical presentation, and the increasing knowledge on the tumor microenvironment of T-DLBCL highlights the significance of the host immunity and immune escape in this rare lymphoma, presenting in an immune-privileged site of the testis. This review provides an update on the latest progress made in T-DLBCL research and summarizes the clinical perspectives in T-DLBCL.

Abstract

Primary testicular lymphoma is a rare lymphoma entity, yet it is the most common testicular malignancy among elderly men. The majority of the cases represent non-germinal center B-cell-like (non-GCB) diffuse large B-cell lymphoma (DLBCL) with aggressive clinical behavior and a relatively high relapse rate. Due to the rareness of the disease, no randomized clinical trials have been conducted and the currently recognized standard of care is based on retrospective analyses and few phase II trials. During recent years, the tumor microenvironment (TME) and tumor-related immunity have been the focus of many tumor biology studies, and the emergence of targeted therapies and checkpoint inhibitors has significantly modulated the field of cancer therapies. Testicular DLBCL (T-DLBCL) is presented in an immune-privileged site of the testis, and the roles of NF-κB pathway signaling, 9p24.1 aberrations, and tumor-infiltrating immune cells, especially immune checkpoint expressing lymphocytes and macrophages, seem to be unique compared to other lymphoma entities. Preliminary data on the use of immune checkpoint inhibitors in the treatment of T-DLBCL are promising and more studies are ongoing.

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.

The "Burkitt-like" immunophenotype and genotype is rarely encountered in diffuse large B cell lymphoma and high-grade B cell lymphoma, NOS

Burkitt lymphoma (BL) is a B cell lymphoma composed of monomorphic medium-sized blastic cells with basophilic cytoplasm and a high proliferation index. BL has a characteristic immunophenotype of CD10 and BCL6 positive and BCL2 negative and harbours MYC gene rearrangements (MYCR) in >90% of the cases. Owing to its highly aggressive nature, intensified chemotherapy regimens are usually administered, requiring an exact diagnosis. Since the diagnosis usually warrants an integration of morphologic, immunophenotypic and genetic findings and because there is a morphologic overlap with the new WHO category of high-grade B cell lymphoma, not otherwise specified (HGBL, NOS) and some cases of diffuse large B cell lymphoma (DLBCL), we wanted to test the distinctiveness of the CD10+, BCL6+, BCL2- and MYCR positive immunopheno-genotype in a large cohort of >1000 DLBCL and HGBL. Only 9/982 DLBCL classified by an expert panel of haematopathologists (0.9%) displayed a single MYCR and were CD10+, BCL6+ and BCL2-. In a similar fashion, only one out of 32 HGBL, NOS (3%) displayed the "Burkitt-like" genetic/immunophenotypic constitution. The samples of non-BL showing the BL-typic immunopheno-genotype, interestingly, harboured higher copy number variations (CNV) by OncoScan analysis (mean 7.3 CNVs/sample; range: 2-13 vs. 2.4; range 0-6) and were also distinct from pleomorphic BL cases regarding their mutational spectrum by NGS analysis. This implies that the characteristic immunophenotype of BL, in concert with a single MYCR, is uncommon in these aggressive lymphomas, and that this constellation favours BL.

Identification of a miRNA based model to detect prognostic subgroups in patients with aggressive B-cell lymphoma

In order to differentiate prognostic subgroups of patients with aggressive B-cell lymphoma, we analyzed the expression of 800 miRNAs with the NanoString nCounter human miRNA assay on a cohort of 228 FFPE samples of patients enrolled in the RICOVER-60 and MegaCHOEP trials. We identified significant miRNA signatures for overall survival (OS) and progression-free survival (PFS) by LASSO-penalized linear Cox-regression. High expression levels of miR-130a-3p and miR-423-5p indicate a better prognosis, whereas high levels of miR-374b-5p, miR-590-5p, miR-186-5p, and miR-106b-5p increase patients' risk levels for OS. Regarding PFS high expression of miR-365a-5p in addition to the other two miRNAs improves the prognosis and high levels of miR374a-5p, miR-106b-5p, and miR-590-5p, connects with increased risk and poor prognosis. We identified miRNA signatures to subdivide patients into two different risk groups. These prognostic models may be used in risk stratification in future clinical trials and help making personalized therapy decisions.

The Tumor Microenvironment of DLBCL in the Computational Era

Among classical exemplifications of tumor microenvironment (TME) in lymphoma pathogenesis, the “effacement model” resembled by diffuse large B cell lymphoma (DLBCL) implies strong cell autonomous survival and paucity of non-malignant elements. Nonetheless, the magnitude of TME exploration is increasing as novel technologies allow the high-resolution discrimination of cellular and extra-cellular determinants at the functional, more than morphological, level. Results from genomic-scale studies and recent clinical trials revitalized the interest in this field, prompting the use of new tools to dissect DLBCL composition and reveal novel prognostic association. Here we revisited major controversies related to TME in DLBCL, focusing on the use of bioinformatics to mine transcriptomic data and provide new insights to be translated into the clinical setting.