TY - JOUR A1 - Loeffler-Wirth, Henry A1 - Kreuz, Markus A1 - Hopp, Lydia A1 - Arakelyan, Arsen A1 - Haake, Andrea A1 - Cogliatti, Sergio B. A1 - Feller, Alfred C. A1 - Hansmann, Martin-Leo A1 - Lenze, Dido A1 - Möller, Peter A1 - Müller-Hermelink, Hans Konrad A1 - Fortenbacher, Erik A1 - Willscher, Edith A1 - Ott, German A1 - Rosenwald, Andreas A1 - Pott, Christiane A1 - Schwaenen, Carsten A1 - Trautmann, Heiko A1 - Wessendorf, Swen A1 - Stein, Harald A1 - Szczepanowski, Monika A1 - Trümper, Lorenz A1 - Hummel, Michael A1 - Klapper, Wolfram A1 - Siebert, Reiner A1 - Loeffler, Markus A1 - Binder, Hans T1 - A modular transcriptome map of mature B cell lymphomas JF - Genome Medicine N2 - 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. KW - tumor heterogeneity KW - B cell malignancies KW - gene regulation KW - molecular subtypes KW - machine learning Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-237262 VL - 11 ER - TY - JOUR A1 - Schmid, Rafael A1 - Schmidt, Sonja K. A1 - Hazur, Jonas A1 - Detsch, Rainer A1 - Maurer, Evelyn A1 - Boccaccini, Aldo R. A1 - Hauptstein, Julia A1 - Teßmar, Jörg A1 - Blunk, Torsten A1 - Schrüfer, Stefan A1 - Schubert, Dirk W. A1 - Horch, Raymund E. A1 - Bosserhoff, Anja K. A1 - Arkudas, Andreas A1 - Kengelbach-Weigand, Annika T1 - Comparison of hydrogels for the development of well-defined 3D cancer models of breast cancer and melanoma JF - Cancers N2 - Bioprinting offers the opportunity to fabricate precise 3D tumor models to study tumor pathophysiology and progression. However, the choice of the bioink used is important. In this study, cell behavior was studied in three mechanically and biologically different hydrogels (alginate, alginate dialdehyde crosslinked with gelatin (ADA–GEL), and thiol-modified hyaluronan (HA-SH crosslinked with PEGDA)) with cells from breast cancer (MDA-MB-231 and MCF-7) and melanoma (Mel Im and MV3), by analyzing survival, growth, and the amount of metabolically active, living cells via WST-8 labeling. Material characteristics were analyzed by dynamic mechanical analysis. Cell lines revealed significantly increased cell numbers in low-percentage alginate and HA-SH from day 1 to 14, while only Mel Im also revealed an increase in ADA–GEL. MCF-7 showed a preference for 1% alginate. Melanoma cells tended to proliferate better in ADA–GEL and HA-SH than mammary carcinoma cells. In 1% alginate, breast cancer cells showed equally good proliferation compared to melanoma cell lines. A smaller area was colonized in high-percentage alginate-based hydrogels. Moreover, 3% alginate was the stiffest material, and 2.5% ADA–GEL was the softest material. The other hydrogels were in the same range in between. Therefore, cellular responses were not only stiffness-dependent. With 1% alginate and HA-SH, we identified matrices that enable proliferation of all tested tumor cell lines while maintaining expected tumor heterogeneity. By adapting hydrogels, differences could be accentuated. This opens up the possibility of understanding and analyzing tumor heterogeneity by biofabrication. KW - breast cancer KW - melanoma KW - biofabrication KW - hydrogel KW - tumor heterogeneity Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-211195 SN - 2072-6694 VL - 12 IS - 8 ER - TY - INPR A1 - Werner, Rudolf A. A1 - Ilhan, Harun A1 - Lehner, Sebastian A1 - Papp, László A1 - Zsótér, Norbert A1 - Schatka, Imke A1 - Muegge, Dirk O. A1 - Javadi, Mehrbod S. A1 - Higuchi, Takahiro A1 - Buck, Andreas K. A1 - Bartenstein, Peter A1 - Bengel, Frank A1 - Essler, Markus A1 - Lapa, Constantin A1 - Bundschuh, Ralph A. T1 - Pre-therapy Somatostatin-Receptor-Based Heterogeneity Predicts Overall Survival in Pancreatic Neuroendocrine Tumor Patients Undergoing Peptide Receptor Radionuclide Therapy T2 - Molecular Imaging and Biology N2 - Purpose: Early identification of aggressive disease could improve decision-support in pancreatic neuroendocrine tumor (pNET) patients prior to peptide receptor radionuclide therapy (PRRT). The prognostic value of intratumoral textural features (TF) determined by baseline somatostatin receptor (SSTR)-PET before PRRT was analyzed. Procedures: 31 patients with G1/G2 pNET were enrolled (G2, n=23/31). Prior to PRRT with [\(^{177}\)Lu]DOTATATE (mean, 3.6 cycles), baseline SSTR-PET/CT was performed. By segmentation of 162 (median per patient, 5) metastases, intratumoral TF were computed. The impact of conventional PET parameters (SUV\(_{mean/max}\)), imaging-based TF as well as clinical parameters (Ki67, CgA) for prediction of both progression-free (PFS) and overall survival (OS) after PRRT was evaluated. Results: Within a median follow-up of 3.7y, tumor progression was detected in 21 patients (median, 1.5y) and 13/31 deceased (median, 1.9y). In ROC analysis, the TF Entropy, reflecting derangement on a voxel-by-voxel level, demonstrated predictive capability for OS (cutoff=6.7, AUC=0.71, p=0.02). Of note, increasing Entropy could predict a longer survival (>6.7, OS=2.5y, 17/31), whereas less voxel-based derangement portended inferior outcome (<6.7, OS=1.9y, 14/31). These findings were supported in a G2 subanalysis (>6.9, OS=2.8y, 9/23 vs. <6.9, OS=1.9y, 14/23). Kaplan-Meier analysis revealed a significant distinction between high- and low-risk groups using Entropy (n=31, p<0.05). For those patients below the ROC-derived threshold, the relative risk of death after PRRT was 2.73 (n=31, p=0.04). Ki67 was negatively associated with PFS (p=0.002); however, SUVmean/max failed in prognostication (n.s.). Conclusions: In contrast to conventional PET parameters, assessment of intratumoral heterogeneity demonstrated superior prognostic performance in pNET patients undergoing PRRT. This novel PET-based strategy of outcome prediction prior to PRRT might be useful for patient risk stratification. KW - Pancreas KW - Positronen-Emissions-Tomografie KW - PET KW - neuroendocrine tumor KW - tumor heterogeneity KW - [68Ga] KW - [177Lu]-DOTATATE/-DOTATOC KW - PET/CT KW - SSTR Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-164624 UR - https://link.springer.com/article/10.1007/s11307-018-1252-5 SN - 1536-1632 N1 - This is a post-peer-review, pre-copyedit version of an article published in Molecular Imaging and Biology. The final authenticated version is available online at: http://dx.doi.org/s11307-018-1252-5 N1 - Die finale Version dieses Artikels steht unter https://doi.org/10.1007/s11307-018-1252-5 bzw. http://nbn-resolving.org/urn:nbn:de:bvb:20-opus-167168 open access zur Verfügung. ER - TY - JOUR A1 - Werner, Rudolf A. A1 - Ilhan, Harun A1 - Lehner, Sebastian A1 - Papp, László A1 - Zsótér, Norbert A1 - Schatka, Imke A1 - Muegge, Dirk O. A1 - Javadi, Mehrbod S. A1 - Higuchi, Takahiro A1 - Buck, Andreas K. A1 - Bartenstein, Peter A1 - Bengel, Frank A1 - Essler, Markus A1 - Lapa, Constantin A1 - Bundschuh, Ralph A. T1 - Pre-therapy Somatostatin-Receptor-Based Heterogeneity Predicts Overall Survival in Pancreatic Neuroendocrine Tumor Patients Undergoing Peptide Receptor Radionuclide Therapy JF - Molecular Imaging and Biology N2 - Purpose: Early identification of aggressive disease could improve decision-support in pancreatic neuroendocrine tumor (pNET) patients prior to peptide receptor radionuclide therapy (PRRT). The prognostic value of intratumoral textural features (TF) determined by baseline somatostatin receptor (SSTR)-PET before PRRT was analyzed. Procedures: 31 patients with G1/G2 pNET were enrolled (G2, n=23/31). Prior to PRRT with [\(^{177}\)Lu]DOTATATE (mean, 3.6 cycles), baseline SSTR-PET/CT was performed. By segmentation of 162 (median per patient, 5) metastases, intratumoral TF were computed. The impact of conventional PET parameters (SUV\(_{mean/max}\)), imaging-based TF as well as clinical parameters (Ki67, CgA) for prediction of both progression-free (PFS) and overall survival (OS) after PRRT was evaluated. Results: Within a median follow-up of 3.7y, tumor progression was detected in 21 patients (median, 1.5y) and 13/31 deceased (median, 1.9y). In ROC analysis, the TF Entropy, reflecting derangement on a voxel-by-voxel level, demonstrated predictive capability for OS (cutoff=6.7, AUC=0.71, p=0.02). Of note, increasing Entropy could predict a longer survival (>6.7, OS=2.5y, 17/31), whereas less voxel-based derangement portended inferior outcome (<6.7, OS=1.9y, 14/31). These findings were supported in a G2 subanalysis (>6.9, OS=2.8y, 9/23 vs. <6.9, OS=1.9y, 14/23). Kaplan-Meier analysis revealed a significant distinction between high- and low-risk groups using Entropy (n=31, p<0.05). For those patients below the ROC-derived threshold, the relative risk of death after PRRT was 2.73 (n=31, p=0.04). Ki67 was negatively associated with PFS (p=0.002); however, SUVmean/max failed in prognostication (n.s.). Conclusions: In contrast to conventional PET parameters, assessment of intratumoral heterogeneity demonstrated superior prognostic performance in pNET patients undergoing PRRT. This novel PET-based strategy of outcome prediction prior to PRRT might be useful for patient risk stratification. KW - tumor heterogeneity KW - Positronen-Emissions-Tomografie KW - PET KW - PET/CT KW - pancreas KW - SSTR KW - [177Lu]-DOTATATE/-DOTATOC KW - [68Ga] KW - neuroendocrine tumor Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-167168 SN - 1536-1632 ER -