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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., (© 2020 by The American Society of Hematology.)

A major clinical challenge of diffuse large B-cell lymphoma (DLBCL) is that up to 40% of patients have refractory disease or relapse after initial response to therapy as a result of drug-specific molecular resistance. The purpose of the present study was to investigate microRNA (miRNA) involvement in vincristine resistance in DLBCL, which was pursued by functional in vitro analysis in DLBCL cell lines and by outcome analysis of patients with DLBCL treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). Differential miRNA expression analysis identified miR-155 as highly expressed in vincristine-sensitive DLBCL cell lines compared with resistant ones. Ectopic upregulation of miR-155 sensitized germinal-center B-cell-like (GCB)-DLBCL cell lines to vincristine, and consistently, reduction and knockout of miR-155 induced vincristine resistance, documenting that miR-155 functionally induces vincristine sensitivity. Target gene analysis identified miR-155 as inversely correlated with Wee1, supporting Wee1 as a target of miR-155 in DLBCL. Chemical inhibition of Wee1 sensitized GCB cells to vincristine, suggesting that miR-155 controls vincristine response through Wee1. Outcome analysis in clinical cohorts of DLBCL revealed that high miR-155 expression level was significantly associated with superior survival for R-CHOP-treated patients of the GCB subclass, independent of international prognostic index, challenging the commonly accepted perception of miR-155 as an oncomiR. However, miR-155 did not provide prognostic information when analyzing the entire DLBCL cohort or activated B-cell-like classified patients. In conclusion, we experimentally confirmed a direct link between high miR-155 expression and vincristine sensitivity in DLBCL and documented an improved clinical outcome of GCB-classified patients with high miR-155 expression level., (© 2019 by The American Society of Hematology.)

Despite the recent progress in treatment of multiple myeloma (MM), it is still an incurable malignant disease, and we are therefore in need of new risk stratification tools that can help us to understand the disease and optimize therapy. Here we propose a new subtyping of myeloma plasma cells (PCs) from diagnostic samples, assigned by normal B-cell subset associated g ene signatures (BAGS). For this purpose, we combined fluorescence-activated cell sorting and gene expression profiles from normal bone marrow (BM) Pre-BI, Pre-BII, immature, naïve, memory, and PC subsets to generate BAGS for assignment of normal BM subtypes in diagnostic samples. The impact of the subtypes was analyzed in 8 available data sets from 1772 patients' myeloma PC samples. The resulting tumor assignments in available clinical data sets exhibited similar BAGS subtype frequencies in 4 cohorts from de novo MM patients across 1296 individual cases. The BAGS subtypes were significantly associated with progression-free and overall survival in a meta-analysis of 916 patients from 3 prospective clinical trials. The major impact was observed within the Pre-BII and memory subtypes, which had a significantly inferior prognosis compared with other subtypes. A multiple Cox proportional hazard analysis documented that BAGS subtypes added significant, independent prognostic information to the translocations and cyclin D classification. BAGS subtype analysis of patient cases identified transcriptional differences, including a number of differentially spliced genes. We identified subtype differences in myeloma at diagnosis, with prognostic impact and predictive potential, supporting an acquired B-cell trait and phenotypic plasticity as a pathogenetic hallmark of MM., (© 2018 by The American Society of Hematology.)

Gene expression profiling (GEP) by microarrays of diffuse large B-cell lymphoma (DLBCL) has enabled the categorization of DLBCL into activated B-cell-like and germinal center B-cell-like subclasses. However, as this does not fully embrace the great diversity of B-cell subtypes, we recently developed a gene expression assay for B-cell-associated gene signature (BAGS) classification. To facilitate quick and easy-to-use BAGS profiling, we developed in this study the NanoString-based BAGS2Clinic assay. Microarray data from 4 different cohorts (n = 970) were used to select genes and train the assay. The locked assay was validated in an independent cohort of 88 sample biopsies. The assay showed good correspondence with the original BAGS classifier, with an overall accuracy of 84% (95% confidence interval, 72% to 93%) and a subtype-specific accuracy ranging between 80% and 99%. BAGS classification has the potential to provide valuable insight into tumor biology as well as differences in resistance to immuno- and chemotherapy that can lead to novel treatment strategies for DLBCL patients. BAGS2Clinic can facilitate this and the implementation of BAGS classification as a routine clinical tool to improve prognosis and treatment guidance for DLBCL patients., (© 2018 by The American Society of Hematology.)

Stringent complete remission (sCR) of acute myeloid leukemia is defined as normal hematopoiesis after therapy. Less sCR, including non-sCR, was introduced as insufficient blood platelet, neutrophil, or erythrocyte recovery. These latter characteristics were defined retrospectively as postremission transfusion dependency and were suggested to be of prognostic value. In the present report, we evaluated the prognostic impact of achieving sCR and non-sCR in the Danish National Acute Leukaemia Registry, including 769 patients registered with classical CR (ie, <5% blasts in the postinduction bone marrow analysis). Individual patients were classified as having sCR (n = 360; 46.8%) or non-sCR (n = 409; 53.2%) based on data from our national laboratory and transfusion databases. Survival analysis revealed that patients achieving sCR had superior overall survival (hazard ratio [HR], 1.34; 95% confidence interval [CI], 1.10-1.64) as well as relapse-free survival (HR, 1.25; 95% CI, 1.03-1.51) compared with those with non-sCR after adjusting for covariates. Cox regression analysis regarding the impact of the stringent criteria for blood cell recovery identified these as significant and independent variables. In conclusion, this real-life register study supports the international criteria for response evaluation on prognosis and, most importantly, documents each of the 3 lineage recovery criteria as contributing independently., (© 2018 by The American Society of Hematology.)