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Objective To evaluate the success of initiation of adjunctive brivaracetam in patients who required a change in antiepileptic drug (AED) regimen and substituted at least one AED with brivaracetam. Methods In this retrospective noninterventional study conducted in specialized epilepsy centers across Germany, patients initiated adjunctive brivaracetam between February 15, 2016, and August 31, 2016, as part of an intended change in AED regimen. The primary effectiveness variable was the proportion of patients who continued on brivaracetam after 3 months, and withdrew at least one AED either before or within 6 months after brivaracetam initiation. Results Five hundred and six patients had at least one brivaracetam dose and were included in the safety set (SS). Four hundred and seventy patients started to reduce the dose of one AED before/after brivaracetam initiation, had at least one concomitant AED at brivaracetam initiation, and were included in the full analysis set (FAS) for effectiveness analyses. At baseline, patients had a median of seven lifetime AEDs and a median of 3.8 seizures/28 days. In the SS, 85.2% of patients withdrew one AED before/after initiation of brivaracetam, most commonly levetiracetam (49.4%). 46.2% of patients substituted another AED with brivaracetam within 24 hours (fast withdrawal). The proportions of patients (FAS) who continued on brivaracetam after 3 and 6 months and withdrew one AED were 75.5% and 46.6%, respectively. After 6 months, 32.1% of patients were 50% responders; 13.0% were seizure‐free. In the SS, 34.6% of patients reported treatment‐emergent adverse events (TEAEs); 21.9% had TEAEs that were assessed by the treating physician as drug‐related. Incidences of behavioral AEs before (3‐month baseline) and after brivaracetam initiation in patients who withdrew levetiracetam were 19.2% and 8.0%, respectively (5.0% and 7.7% in patients who withdrew other AEDs). Significance Brivaracetam was effective and well‐tolerated in patients who required a change in AED drug regimen and initiated adjunctive brivaracetam in German clinical practice.
Background: Germinal center-derived B cell lymphomas are tumors of the lymphoid tissues representing one of the most heterogeneous malignancies. Here we characterize the variety of transcriptomic phenotypes of this disease based on 873 biopsy specimens collected in the German Cancer Aid MMML (Molecular Mechanisms in Malignant Lymphoma) consortium. They include diffuse large B cell lymphoma (DLBCL), follicular lymphoma (FL), Burkitt’s lymphoma, mixed FL/DLBCL lymphomas, primary mediastinal large B cell lymphoma, multiple myeloma, IRF4-rearranged large cell lymphoma, MYC-negative Burkitt-like lymphoma with chr. 11q aberration and mantle cell lymphoma.
Methods: We apply self-organizing map (SOM) machine learning to microarray-derived expression data to generate a holistic view on the transcriptome landscape of lymphomas, to describe the multidimensional nature of gene regulation and to pursue a modular view on co-expression. Expression data were complemented by pathological, genetic and clinical characteristics.
Results: We present a transcriptome map of B cell lymphomas that allows visual comparison between the SOM portraits of different lymphoma strata and individual cases. It decomposes into one dozen modules of co-expressed genes related to different functional categories, to genetic defects and to the pathogenesis of lymphomas. On a molecular level, this disease rather forms a continuum of expression states than clearly separated phenotypes. We introduced the concept of combinatorial pattern types (PATs) that stratifies the lymphomas into nine PAT groups and, on a coarser level, into five prominent cancer hallmark types with proliferation, inflammation and stroma signatures. Inflammation signatures in combination with healthy B cell and tonsil characteristics associate with better overall survival rates, while proliferation in combination with inflammation and plasma cell characteristics worsens it. A phenotypic similarity tree is presented that reveals possible progression paths along the transcriptional dimensions. Our analysis provided a novel look on the transition range between FL and DLBCL, on DLBCL with poor prognosis showing expression patterns resembling that of Burkitt’s lymphoma and particularly on "double-hit" MYC and BCL2 transformed lymphomas.
Conclusions: The transcriptome map provides a tool that aggregates, refines and visualizes the data collected in the MMML study and interprets them in the light of previous knowledge to provide orientation and support in current and future studies on lymphomas and on other cancer entities.