@article{LoefflerWirthKreuzHoppetal.2019,
  author    = {Loeffler-Wirth, Henry and Kreuz, Markus and Hopp, Lydia and Arakelyan, Arsen and Haake, Andrea and Cogliatti, Sergio B. and Feller, Alfred C. and Hansmann, Martin-Leo and Lenze, Dido and M{\"o}ller, Peter and M{\"u}ller-Hermelink, Hans Konrad and Fortenbacher, Erik and Willscher, Edith and Ott, German and Rosenwald, Andreas and Pott, Christiane and Schwaenen, Carsten and Trautmann, Heiko and Wessendorf, Swen and Stein, Harald and Szczepanowski, Monika and Tr{\"u}mper, Lorenz and Hummel, Michael and Klapper, Wolfram and Siebert, Reiner and Loeffler, Markus and Binder, Hans},
  title     = {A modular transcriptome map of mature B cell lymphomas},
  series = {Genome Medicine},
  volume    = {11},
  journal   = {Genome Medicine},
  doi       = {10.1186/s13073-019-0637-7},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-237262},
  year      = {2019},
  abstract  = {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.},
  language  = {en}
}
@article{SchmidSchmidtHazuretal.2020,
  author    = {Schmid, Rafael and Schmidt, Sonja K. and Hazur, Jonas and Detsch, Rainer and Maurer, Evelyn and Boccaccini, Aldo R. and Hauptstein, Julia and Teßmar, J{\"o}rg and Blunk, Torsten and Schr{\"u}fer, Stefan and Schubert, Dirk W. and Horch, Raymund E. and Bosserhoff, Anja K. and Arkudas, Andreas and Kengelbach-Weigand, Annika},
  title     = {Comparison of hydrogels for the development of well-defined 3D cancer models of breast cancer and melanoma},
  series = {Cancers},
  volume    = {12},
  journal   = {Cancers},
  number    = {8},
  issn      = {2072-6694},
  doi       = {10.3390/cancers12082320},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-211195},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}
@unpublished{WernerIlhanLehneretal.2018,
  author    = {Werner, Rudolf A. and Ilhan, Harun and Lehner, Sebastian and Papp, L{\´a}szl{\´o} and Zs{\´o}t{\´e}r, Norbert and Schatka, Imke and Muegge, Dirk O. and Javadi, Mehrbod S. and Higuchi, Takahiro and Buck, Andreas K. and Bartenstein, Peter and Bengel, Frank and Essler, Markus and Lapa, Constantin and Bundschuh, Ralph A.},
  title     = {Pre-therapy Somatostatin-Receptor-Based Heterogeneity Predicts Overall Survival in Pancreatic Neuroendocrine Tumor Patients Undergoing Peptide Receptor Radionuclide Therapy},
  series = {Molecular Imaging and Biology},
  journal   = {Molecular Imaging and Biology},
  issn      = {1536-1632},
  doi       = {https://doi.org/10.1007/s11307-018-1252-5},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-164624},
  year      = {2018},
  abstract  = {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.},
  subject      = {Positronen-Emissions-Tomografie},
  language  = {en}
}
@article{WernerIlhanLehneretal.2018,
  author    = {Werner, Rudolf A. and Ilhan, Harun and Lehner, Sebastian and Papp, L{\´a}szl{\´o} and Zs{\´o}t{\´e}r, Norbert and Schatka, Imke and Muegge, Dirk O. and Javadi, Mehrbod S. and Higuchi, Takahiro and Buck, Andreas K. and Bartenstein, Peter and Bengel, Frank and Essler, Markus and Lapa, Constantin and Bundschuh, Ralph A.},
  title     = {Pre-therapy Somatostatin-Receptor-Based Heterogeneity Predicts Overall Survival in Pancreatic Neuroendocrine Tumor Patients Undergoing Peptide Receptor Radionuclide Therapy},
  series = {Molecular Imaging and Biology},
  journal   = {Molecular Imaging and Biology},
  issn      = {1536-1632},
  doi       = {10.1007/s11307-018-1252-5},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-167168},
  year      = {2018},
  abstract  = {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.},
  subject      = {Positronen-Emissions-Tomografie},
  language  = {en}
}