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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.
Background: Approximately every third surgical patient is anemic. The most common form, iron deficiency anemia, results from persisting iron‐deficient erythropoiesis (IDE). Zinc protoporphyrin (ZnPP) is a promising parameter for diagnosing IDE, hitherto requiring blood drawing and laboratory workup.
Study design and methods: Noninvasive ZnPP (ZnPP‐NI) measurements are compared to ZnPP reference determination of the ZnPP/heme ratio by high‐performance liquid chromatography (ZnPP‐HPLC) and the analytical performance in detecting IDE is evaluated against traditional iron status parameters (ferritin, transferrin saturation [TSAT], soluble transferrin receptor–ferritin index [sTfR‐F], soluble transferrin receptor [sTfR]), likewise measured in blood. The study was conducted at the University Hospitals of Frankfurt and Zurich.
Results: Limits of agreement between ZnPP‐NI and ZnPP‐HPLC measurements for 584 cardiac and noncardiac surgical patients equaled 19.7 μmol/mol heme (95% confidence interval, 18.0–21.3; acceptance criteria, 23.2 μmol/mol heme; absolute bias, 0 μmol/mol heme). Analytical performance for detecting IDE (inferred from area under the curve receiver operating characteristics) of parameters measured in blood was: ZnPP‐HPLC (0.95), sTfR (0.92), sTfR‐F (0.89), TSAT (0.87), and ferritin (0.67). Noninvasively measured ZnPP‐NI yielded results of 0.90.
Conclusion: ZnPP‐NI appears well suited for an initial IDE screening, informing on the state of erythropoiesis at the point of care without blood drawing and laboratory analysis. Comparison with a multiparameter IDE test revealed that ZnPP‐NI values of 40 μmol/mol heme or less allows exclusion of IDE, whereas for 65 μmol/mol heme or greater, IDE is very likely if other causes of increased values are excluded. In these cases (77% of our patients) ZnPP‐NI may suffice for a diagnosis, while values in between require analyses of additional iron status parameters.