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Gene-expression profiling of 574 cases of diffuse large B-cell lymphoma revealed four new subtypes based on the co-occurrence of mutation patterns. The subtypes had prognostic influence beyond the usual clinical prognostic factors and may aid in directing targeted therapy.

Lisocabtagene maraleucel (liso-cel) is an autologous, CD19-directed, chimeric antigen receptor (CAR) T-cell product. We aimed to assess the activity and safety of liso-cel in patients with relapsed or refractory large B-cell lymphomas. We did a seamless design study at 14 cancer centres in the USA. We enrolled adult patients (aged ≥18 years) with relapsed or refractory large B-cell lymphomas. Eligible histological subgroups included diffuse large B-cell lymphoma, high-grade B-cell lymphoma with rearrangements of MYC and either BCL2, BCL6, or both (double-hit or triple-hit lymphoma), diffuse large B-cell lymphoma transformed from any indolent lymphoma, primary mediastinal B-cell lymphoma, and follicular lymphoma grade 3B. Patients were assigned to one of three target dose levels of liso-cel as they were sequentially tested in the trial (50 × 106 CAR+ T cells [one or two doses], 100 × 106 CAR+ T cells, and 150 × 106 CAR+ T cells), which were administered as a sequential infusion of two components (CD8+ and CD4+ CAR+ T cells) at equal target doses. Primary endpoints were adverse events, dose-limiting toxicities, and the objective response rate (assessed per Lugano criteria); endpoints were assessed by an independent review committee in the efficacy-evaluable set (comprising all patients who had confirmed PET-positive disease and received at least one dose of liso-cel). This trial is registered with ClinicalTrials.gov, NCT02631044. Between Jan 11, 2016, and July 5, 2019, 344 patients underwent leukapheresis for manufacture of CAR+ T cells (liso-cel), of whom 269 patients received at least one dose of liso-cel. Patients had received a median of three (range 1–8) previous lines of systemic treatment, with 260 (97%) patients having had at least two lines. 112 (42%) patients were aged 65 years or older, 181 (67%) had chemotherapy-refractory disease, and seven (3%) had secondary CNS involvement. Median follow-up for overall survival for all 344 patients who had leukapheresis was 18·8 months (95% CI 15·0–19·3). Overall safety and activity of liso-cel did not differ by dose level. The recommended target dose was 100 × 106 CAR+ T cells (50 × 106 CD8+ and 50 × 106 CD4+ CAR+ T cells). Of 256 patients included in the efficacy-evaluable set, an objective response was achieved by 186 (73%, 95% CI 66·8–78·0) patients and a complete response by 136 (53%, 46·8–59·4). The most common grade 3 or worse adverse events were neutropenia in 161 (60%) patients, anaemia in 101 (37%), and thrombocytopenia in 72 (27%). Cytokine release syndrome and neurological events occurred in 113 (42%) and 80 (30%) patients, respectively; grade 3 or worse cytokine release syndrome and neurological events occurred in six (2%) and 27 (10%) patients, respectively. Nine (6%) patients had a dose-limiting toxicity, including one patient who died from diffuse alveolar damage following a dose of 50 × 106 CAR+ T cells. Use of liso-cel resulted in a high objective response rate, with a low incidence of grade 3 or worse cytokine release syndrome and neurological events in patients with relapsed or refractory large B-cell lymphomas, including those with diverse histological subtypes and high-risk features. Liso-cel is under further evaluation at first relapse in large B-cell lymphomas and as a treatment for other relapsed or refractory B-cell malignancies. Juno Therapeutics, a Bristol-Myers Squibb Company.

Background Diffuse large B-cell lymphoma (DLBCL) is an aggressive and complex disease characterized by wide clinical, phenotypic and molecular heterogeneities. The expression pattern and clinical implication of long non-coding RNAs (lncRNAs) between germinal center B-cell-like (GCB) and activated B-cell-like (ABC) subtypes in DLBCL remain unclear. This study aims to determine whether lncRNA can serve as predictive biomarkers for subtype classification and prognosis in DLBCL. Methods Genome-wide comparative analysis of lncRNA expression profiles were performed in a large number of DLBCL patients from Gene Expression Omnibus (GEO), including GSE31312 cohort ( N  = 426), GSE10846 ( N  = 350) cohort and GSE4475 cohort ( N  = 129). Novel lncRNA biomarkers associated with clinically molecular subtype and prognosis were identified in the discovery cohort using differential expression analyses and weighted voting algorithm. The predictive value of the lncRNA signature was then assessed in two independent cohorts. The functional implication of lncRNA signature was also analyzed by integrative analysis of lncRNA and mRNA. Results Seventeen of the 156 differentially expressed lncRNAs between GCB and ABC subtypes were identified as candidate biomarkers and integrated into form a lncRNA-based signature (termed SubSigLnc-17) which was able to discriminate between GCB and ABC subtypes with AUC of 0.974, specificity of 89.6% and sensitivity of 92.5%. Furthermore, subgroups of patients characterized by the SubSigLnc-17 demonstrated significantly different clinical outcome. The reproducible predictive power of SubSigLnc-17 in subtype classification and prognosis was successfully validated in the internal validation cohort and another two independent patient cohorts. Integrative analysis of lncRNA-mRNA suggested that these candidate lncRNA biomarkers were mainly related to immune-associated processes, such as T cell activation, leukocyte activation, lymphocyte activation and Chemokine signaling pathway. Conclusions Our study uncovered differentiated lncRNA expression pattern between GCB and ABC DLBCL and identified a 17-lncRNA signature for subtype classification and prognosis prediction. With further prospective validation, our study will improve the understanding of underlying molecular heterogeneities in DLBCL and provide candidate lncRNA biomarkers in DLBCL classification and prognosis.

In this article, we provide a review of large B-cell lymphomas (LBCLs), comparing the recently published fifth edition of the WHO classification and the International Consensus Classification (ICC) on hematolymphoid tumors. We focus on updates in the classification of LBCL, an heterogeneous group of malignancies with varying clinical behaviors and different pathological and molecular features, providing a comparison between the two classifications. Besides the well-recognized diagnostic role of clinical, morphological and immunohistochemical data, both classifications recognize the ever-growing impact of molecular data in the diagnostic work-up of some entities. The main aim is to offer a guide for clinicians and pathologists on how the new classifications can be applied to LBCL diagnosis in routine practice. In the first part of the paper, we review the following categories: LBLs transformed from indolent B-cell lymphomas, diffuse large B-cell lymphoma, not otherwise specified (DLBCL, NOS), double-hit/triple-hit lymphomas (DH/TH), high-grade large B-cell lymphoma, not otherwise specified (HGBCL, NOS), LBCL with IRF4 rearrangement, Burkitt lymphoma (BL) and HGBCL/LBCL with 11q aberration, focusing on the differences between the two classifications. In the second part of the paper, we provide a practical diagnostic algorithm when facing LBCLs in routine daily practice.

This review summarizes recent evidence concerning molecular changes in the commonest type of lymphoma, diffuse large-B-cell lymphoma. Detailed studies have revealed three subtypes, each with distinctive clinical and molecular features. The molecular dissection of these tumors is uncovering new possibilities for treatment, some of which are already in clinical trials. This review summarizes recent evidence concerning molecular changes in the commonest type of lymphoma, diffuse large-B-cell lymphoma. Detailed studies have revealed three subtypes, each with distinctive clinical and molecular features. Genomewide molecular profiling has revealed new subtypes of lymphoma that originate from lymphocytes that differ in developmental stage and that use distinct oncogenic programs, yet are indistinguishable under the microscope. In this review, we discuss recent progress in the molecular genetics of aggressive lymphomas and focus on the most common form of this disease, diffuse large-B-cell lymphoma, which accounts for 30 to 40% of newly diagnosed lymphomas. B-Cell Development and Lymphomagenesis Non-Hodgkin's B-cell lymphomas co-opt the regulatory biologic features of normal B cells for their own malignant purpose. This means that the function of such neoplasms depends considerably on the . . .

The classification of B cell lymphomas—mainly based on light microscopy evaluation by a pathologist—requires many years of training. Since the B cell receptor (BCR) of the lymphoma clonotype and the microenvironmental immune architecture are important features discriminating different lymphoma subsets, we asked whether BCR repertoire next-generation sequencing (NGS) of lymphoma-infiltrated tissues in conjunction with machine learning algorithms could have diagnostic utility in the subclassification of these cancers. We trained a random forest and a linear classifier via logistic regression based on patterns of clonal distribution, VDJ gene usage and physico-chemical properties of the top-n most frequently represented clonotypes in the BCR repertoires of 620 paradigmatic lymphoma samples—nodular lymphocyte predominant B cell lymphoma (NLPBL), diffuse large B cell lymphoma (DLBCL) and chronic lymphocytic leukemia (CLL)—alongside with 291 control samples. With regard to DLBCL and CLL, the models demonstrated optimal performance when utilizing only the most prevalent clonotype for classification, while in NLPBL—that has a dominant background of non-malignant bystander cells—a broader array of clonotypes enhanced model accuracy. Surprisingly, the straightforward logistic regression model performed best in this seemingly complex classification problem, suggesting linear separability in our chosen dimensions. It achieved a weighted F1-score of 0.84 on a test cohort including 125 samples from all three lymphoma entities and 58 samples from healthy individuals. Together, we provide proof-of-concept that at least the 3 studied lymphoma entities can be differentiated from each other using BCR repertoire NGS on lymphoma-infiltrated tissues by a trained machine learning model.

MYD88 signalling in cancer RNA interference screening and high-throughput RNA resequencing have been used to reveal oncogenic mutations in the signalling adapter MYD88 in human lymphomas. One amino acid substitution, L265P, was found in 29% of biopsies from patients with the activated B-cell-like subtype of diffuse large B-cell lymphoma. The same mutation was observed with lower frequency in mucosa-associated lymphoid tissue lymphomas. MYD88 mediates signalling by Toll-like receptors, and the mutations, most of which affect the same amino acid, were shown to activate the pathway and promote cancer cell survival. This study finds frequent mutations in MYD88 in the activated B-cell-like subtype of diffuse large B-cell lymphoma and, with lower frequency, in mucosa-associated lymphoid tissue lymphomas. MYD88 mediates signalling by Toll-like receptors, and the mutations, most of which affect the same amino acid, are shown to activate the pathway and promote cancer cell survival. The activated B-cell-like (ABC) subtype of diffuse large B-cell lymphoma (DLBCL) remains the least curable form of this malignancy despite recent advances in therapy 1 . Constitutive nuclear factor (NF)-κB and JAK kinase signalling promotes malignant cell survival in these lymphomas, but the genetic basis for this signalling is incompletely understood. Here we describe the dependence of ABC DLBCLs on MYD88, an adaptor protein that mediates toll and interleukin (IL)-1 receptor signalling 2 , 3 , and the discovery of highly recurrent oncogenic mutations affecting MYD88 in ABC DLBCL tumours. RNA interference screening revealed that MYD88 and the associated kinases IRAK1 and IRAK4 are essential for ABC DLBCL survival. High-throughput RNA resequencing uncovered MYD88 mutations in ABC DLBCL lines. Notably, 29% of ABC DLBCL tumours harboured the same amino acid substitution, L265P, in the MYD88 Toll/IL-1 receptor (TIR) domain at an evolutionarily invariant residue in its hydrophobic core. This mutation was rare or absent in other DLBCL subtypes and Burkitt’s lymphoma, but was observed in 9% of mucosa-associated lymphoid tissue lymphomas. At a lower frequency, additional mutations were observed in the MYD88 TIR domain, occurring in both the ABC and germinal centre B-cell-like (GCB) DLBCL subtypes. Survival of ABC DLBCL cells bearing the L265P mutation was sustained by the mutant but not the wild-type MYD88 isoform, demonstrating that L265P is a gain-of-function driver mutation. The L265P mutant promoted cell survival by spontaneously assembling a protein complex containing IRAK1 and IRAK4, leading to IRAK4 kinase activity, IRAK1 phosphorylation, NF-κB signalling, JAK kinase activation of STAT3, and secretion of IL-6, IL-10 and interferon-β. Hence, the MYD88 signalling pathway is integral to the pathogenesis of ABC DLBCL, supporting the development of inhibitors of IRAK4 kinase and other components of this pathway for the treatment of tumours bearing oncogenic MYD88 mutations.

Gene expression profiling (GEP) has stratified diffuse large B-cell lymphoma (DLBCL) into molecular subgroups that correspond to different stages of lymphocyte development–namely germinal center B-cell like and activated B-cell like. This classification has prognostic significance, but GEP is expensive and not readily applicable into daily practice, which has lead to immunohistochemical algorithms proposed as a surrogate for GEP analysis. We assembled tissue microarrays from 475 de novo DLBCL patients who were treated with rituximab-CHOP chemotherapy. All cases were successfully profiled by GEP on formalin-fixed, paraffin-embedded tissue samples. Sections were stained with antibodies reactive with CD10, GCET1, FOXP1, MUM1 and BCL6 and cases were classified following a rationale of sequential steps of differentiation of B cells. Cutoffs for each marker were obtained using receiver-operating characteristic curves, obviating the need for any arbitrary method. An algorithm based on the expression of CD10, FOXP1 and BCL6 was developed that had a simpler structure than other recently proposed algorithms and 92.6% concordance with GEP. In multivariate analysis, both the International Prognostic Index and our proposed algorithm were significant independent predictors of progression-free and overall survival. In conclusion, this algorithm effectively predicts prognosis of DLBCL patients matching GEP subgroups in the era of rituximab therapy.

Gene-expression profiling has been used to define 3 molecular subtypes of diffuse large B-cell lymphoma (DLBCL), termed germinal center B-cell-like (GCB) DLBCL, activated B-cell-like (ABC) DLBCL, and primary mediastinal B-cell lymphoma (PMBL). To investigate whether these DLBCL subtypes arise by distinct pathogenetic mechanisms, we analyzed 203 DLBCL biopsy samples by high-resolution, genome-wide copy number analysis coupled with gene-expression profiling. Of 272 recurrent chromosomal aberrations that were associated with gene-expression alterations, 30 were used differentially by the DLBCL subtypes (P < 0.006). An amplicon on chromosome 19 was detected in 26% of ABC DLBCLs but in only 3% of GCB DLBCLs and PMBLs. A highly up-regulated gene in this amplicon was SPIB, which encodes an ETS family transcription factor. Knockdown of SPIB by RNA interference was toxic to ABC DLBCL cell lines but not to GCB DLBCL, PMBL, or myeloma cell lines, strongly implicating SPIB as an oncogene involved in the pathogenesis of ABC DLBCL. Deletion of the INK4a/ARF tumor suppressor locus and trisomy 3 also occurred almost exclusively in ABC DLBCLs and was associated with inferior outcome within this subtype. FOXP1 emerged as a potential oncogene in ABC DLBCL that was up-regulated by trisomy 3 and by more focal high-level amplifications. In GCB DLBCL, amplification of the oncogenic mir-17-92 microRNA cluster and deletion of the tumor suppressor PTEN were recurrent, but these events did not occur in ABC DLBCL. Together, these data provide genetic evidence that the DLBCL subtypes are distinct diseases that use different oncogenic pathways.

In contrast to the commonly indolent clinical behavior of nodular lymphocyte predominant Hodgkin lymphoma (NLPHL), T cell/histiocyte rich large B cell lymphoma (THRLBCL) is frequently diagnosed in advanced clinical stages and has a poor prognosis. Besides the different clinical presentations of these lymphoma entities, there are variants of NLPHL with considerable histopathologic overlap compared to THRLBCL. Especially THRLBCL-like NLPHL, a diffuse form of NLPHL, often presents a histopathologic pattern similar to THRLBCL, suggesting a close relationship between both lymphoma entities. To corroborate this hypothesis, we performed gene expression profiling of microdissected tumor cells of NLPHL, THRLBCL-like NLPHL and THRLBCL. In unsupervised analyses, the lymphomas did not cluster according to their entity. Moreover, even in supervised analyses, very few consistently differentially expressed transcripts were found, and for these genes the extent of differential expression was only moderate. Hence, there are no clear and consistent differences in the gene expression of the tumor cells of NLPHL, THRLBCL-like NLPHL and THRLBCL. Based on the gene expression studies, we identified BAT3/BAG6, HIGD1A, and FAT10/UBD as immunohistochemical markers expressed in the tumor cells of all three lymphomas. Characterization of the tumor microenvironment for infiltrating T cells and histiocytes revealed significant differences in the cellular composition between typical NLPHL and THRLBCL cases. However, THRLBCL-like NLPHL presented a histopathologic pattern more related to THRLBCL than NLPHL. In conclusion, NLPHL and THRLBCL may represent a spectrum of the same disease. The different clinical behavior of these lymphomas may be strongly influenced by differences in the lymphoma microenvironment, possibly related to the immune status of the patient at the timepoint of diagnosis.