Dennis D. Weisenburger

Bio: Dennis D. Weisenburger is an academic researcher from University of Nebraska Medical Center. The author has contributed to research in topics: Lymphoma & Diffuse large B-cell lymphoma. The author has an hindex of 95, co-authored 344 publications receiving 49630 citations. Previous affiliations of Dennis D. Weisenburger include National Institutes of Health & City of Hope National Medical Center.

Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling

Abstract: 12 Pathology and Microbiology, and 13 Diffuse large B-cell lymphoma (DLBCL), the most common subtype of non-Hodgkin's lymphoma, is clinically heterogeneous: 40% of patients respond well to current therapy and have prolonged survival, whereas the remainder succumb to the disease. We proposed that this variability in natural history reflects unrecognized molecular heterogeneity in the tumours. Using DNA microarrays, we have conducted a systematic characterization of gene expression in B-cell malignancies. Here we show that there is diversity in gene expression among the tumours of DLBCL patients, apparently reflecting the variation in tumour proliferation rate, host response and differentiation state of the tumour. We identified two molecularly distinct forms of DLBCL which had gene expression patterns indicative of different stages of B-cell differentiation. One type expressed genes characteristic of germinal centre B cells ('germinal centre B-like DLBCL'); the second type expressed genes normally induced during in vitro activation of peripheral blood B cells ('activated B-like DLBCL'). Patients with germinal centre B-like DLBCL had a significantly better overall survival than those with activated B-like DLBCL. The molecular classification of tumours on the basis of gene expression can thus identify previously undetected and clinically significant subtypes of cancer.

The Use of Molecular Profiling to Predict Survival after Chemotherapy for Diffuse Large-B-Cell Lymphoma

Abstract: Background The survival of patients with diffuse large-B-cell lymphoma after chemotherapy is influenced by molecular features of the tumors. We used the gene-expression profiles of these lymphomas to develop a molecular predictor of survival. Methods Biopsy samples of diffuse large-B-cell lymphoma from 240 patients were examined for gene expression with the use of DNA microarrays and analyzed for genomic abnormalities. Subgroups with distinctive gene-expression profiles were defined on the basis of hierarchical clustering. A molecular predictor of risk was constructed with the use of genes with expression patterns that were associated with survival in a preliminary group of 160 patients and was then tested in a validation group of 80 patients. The accuracy of this predictor was compared with that of the international prognostic index. Results Three gene-expression subgroups — germinal-center B-cell–like, activated B-cell–like, and type 3 diffuse large-B-cell lymphoma — were identified. Two common oncogeni...

Stromal gene signatures in large-B-cell lymphomas

Abstract: Background The addition of rituximab to combination chemotherapy with cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP), or R-CHOP, has significantly improved the survival of patients with diffuse large-B-cell lymphoma. Whether gene-expression signatures correlate with survival after treatment of diffuse large-B-cell lymphoma is unclear. Methods We profiled gene expression in pretreatment biopsy specimens from 181 patients with diffuse large-B-cell lymphoma who received CHOP and 233 patients with this disease who received R-CHOP. A multivariate gene-expression–based survival-predictor model derived from a training group was tested in a validation group. Results A multivariate model created from three gene-expression signatures — termed “germinal-center B-cell,” “stromal-1,” and “stromal-2” — predicted survival both in patients who received CHOP and patients who received R-CHOP. The prognostically favorable stromal-1 signature reflected extracellular-matrix deposition and histiocytic infilt...

Chronic active B-cell-receptor signalling in diffuse large B-cell lymphoma

Abstract: A role for B-cell-receptor (BCR) signalling in lymphomagenesis has been inferred by studying immunoglobulin genes in human lymphomas and by engineering mouse models, but genetic and functional evidence for its oncogenic role in human lymphomas is needed. Here we describe a form of 'chronic active' BCR signalling that is required for cell survival in the activated B-cell-like (ABC) subtype of diffuse large B-cell lymphoma (DLBCL). The signalling adaptor CARD11 is required for constitutive NF-kappaB pathway activity and survival in ABC DLBCL. Roughly 10% of ABC DLBCLs have mutant CARD11 isoforms that activate NF-kappaB, but the mechanism that engages wild-type CARD11 in other ABC DLBCLs was unknown. An RNA interference genetic screen revealed that a BCR signalling component, Bruton's tyrosine kinase, is essential for the survival of ABC DLBCLs with wild-type CARD11. In addition, knockdown of proximal BCR subunits (IgM, Ig-kappa, CD79A and CD79B) killed ABC DLBCLs with wild-type CARD11 but not other lymphomas. The BCRs in these ABC DLBCLs formed prominent clusters in the plasma membrane with low diffusion, similarly to BCRs in antigen-stimulated normal B cells. Somatic mutations affecting the immunoreceptor tyrosine-based activation motif (ITAM) signalling modules of CD79B and CD79A were detected frequently in ABC DLBCL biopsy samples but rarely in other DLBCLs and never in Burkitt's lymphoma or mucosa-associated lymphoid tissue lymphoma. In 18% of ABC DLBCLs, one functionally critical residue of CD79B, the first ITAM tyrosine, was mutated. These mutations increased surface BCR expression and attenuated Lyn kinase, a feedback inhibitor of BCR signalling. These findings establish chronic active BCR signalling as a new pathogenetic mechanism in ABC DLBCL, suggesting several therapeutic strategies.

Molecular portraits of human breast tumours

Abstract: Human breast tumours are diverse in their natural history and in their responsiveness to treatments. Variation in transcriptional programs accounts for much of the biological diversity of human cells and tumours. In each cell, signal transduction and regulatory systems transduce information from the cell's identity to its environmental status, thereby controlling the level of expression of every gene in the genome. Here we have characterized variation in gene expression patterns in a set of 65 surgical specimens of human breast tumours from 42 different individuals, using complementary DNA microarrays representing 8,102 human genes. These patterns provided a distinctive molecular portrait of each tumour. Twenty of the tumours were sampled twice, before and after a 16-week course of doxorubicin chemotherapy, and two tumours were paired with a lymph node metastasis from the same patient. Gene expression patterns in two tumour samples from the same individual were almost always more similar to each other than either was to any other sample. Sets of co-expressed genes were identified for which variation in messenger RNA levels could be related to specific features of physiological variation. The tumours could be classified into subtypes distinguished by pervasive differences in their gene expression patterns.

WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues

Abstract: WHO CLASSIFICATION OF TUMOURS OF HAEMATOPOIETIC AND LYMPHOID TISSUES , WHO CLASSIFICATION OF TUMOURS OF HAEMATOPOIETIC AND LYMPHOID TISSUES , کتابخانه مرکزی دانشگاه علوم پزشکی تهران

Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications

Abstract: The purpose of this study was to classify breast carcinomas based on variations in gene expression patterns derived from cDNA microarrays and to correlate tumor characteristics to clinical outcome. A total of 85 cDNA microarray experiments representing 78 cancers, three fibroadenomas, and four normal breast tissues were analyzed by hierarchical clustering. As reported previously, the cancers could be classified into a basal epithelial-like group, an ERBB2-overexpressing group and a normal breast-like group based on variations in gene expression. A novel finding was that the previously characterized luminal epithelial/estrogen receptor-positive group could be divided into at least two subgroups, each with a distinctive expression profile. These subtypes proved to be reasonably robust by clustering using two different gene sets: first, a set of 456 cDNA clones previously selected to reflect intrinsic properties of the tumors and, second, a gene set that highly correlated with patient outcome. Survival analyses on a subcohort of patients with locally advanced breast cancer uniformly treated in a prospective study showed significantly different outcomes for the patients belonging to the various groups, including a poor prognosis for the basal-like subtype and a significant difference in outcome for the two estrogen receptor-positive groups.

The blockade of immune checkpoints in cancer immunotherapy

Abstract: Immune checkpoints refer to the plethora of inhibitory pathways that are crucial to maintaining self-tolerance. Tumour cells induce immune checkpoints to evade immunosurveillance. This Review discusses the progress in targeting immune checkpoints, the considerations for combinatorial therapy and the potential for additional immune-checkpoint targets.

MicroRNA expression profiles classify human cancers

Abstract: Recent work has revealed the existence of a class of small non-coding RNA species, known as microRNAs (miRNAs), which have critical functions across various biological processes. Here we use a new, bead-based flow cytometric miRNA expression profiling method to present a systematic expression analysis of 217 mammalian miRNAs from 334 samples, including multiple human cancers. The miRNA profiles are surprisingly informative, reflecting the developmental lineage and differentiation state of the tumours. We observe a general downregulation of miRNAs in tumours compared with normal tissues. Furthermore, we were able to successfully classify poorly differentiated tumours using miRNA expression profiles, whereas messenger RNA profiles were highly inaccurate when applied to the same samples. These findings highlight the potential of miRNA profiling in cancer diagnosis.