@article{FecherHofmannBucketal.2016,
  author    = {Fecher, David and Hofmann, Elisabeth and Buck, Andreas and Bundschuh, Ralph and Nietzer, Sarah and Dandekar, Gudrun and Walles, Thorsten and Walles, Heike and L{\"u}ckerath, Katharina and Steinke, Maria},
  title     = {Human Organotypic Lung Tumor Models: Suitable For Preclinical \(^{18}\)F-FDG PET-Imaging},
  series = {PLoS ONE},
  volume    = {11},
  journal   = {PLoS ONE},
  number    = {8},
  doi       = {10.1371/journal.pone.0160282},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-179678},
  year      = {2016},
  abstract  = {Development of predictable in vitro tumor models is a challenging task due to the enormous complexity of tumors in vivo. The closer the resemblance of these models to human tumor characteristics, the more suitable they are for drug-development and -testing. In the present study, we generated a complex 3D lung tumor test system based on acellular rat lungs. A decellularization protocol was established preserving the architecture, important ECM components and the basement membrane of the lung. Human lung tumor cells cultured on the scaffold formed cluster and exhibited an up-regulation of the carcinoma-associated marker mucin1 as well as a reduced proliferation rate compared to respective 2D culture. Additionally, employing functional imaging with 2-deoxy-2-[\(^{18}\)F]fluoro-D-glucose positron emission tomography (FDG-PET) these tumor cell cluster could be detected and tracked over time. This approach allowed monitoring of a targeted tyrosine kinase inhibitor treatment in the in vitro lung tumor model non-destructively. Surprisingly, FDG-PET assessment of single tumor cell cluster on the same scaffold exhibited differences in their response to therapy, indicating heterogeneity in the lung tumor model. In conclusion, our complex lung tumor test system features important characteristics of tumors and its microenvironment and allows monitoring of tumor growth and -metabolism in combination with functional imaging. In longitudinal studies, new therapeutic approaches and their long-term effects can be evaluated to adapt treatment regimes in future.},
  language  = {en}
}
@article{KunzGoettlichWallesetal.2017,
  author    = {Kunz, Meik and G{\"o}ttlich, Claudia and Walles, Thorsten and Nietzer, Sarah and Dandekar, Gudrun and Dandekar, Thomas},
  title     = {MicroRNA-21 versus microRNA-34: Lung cancer promoting and inhibitory microRNAs analysed in silico and in vitro and their clinical impact},
  series = {Tumor Biology},
  volume    = {39},
  journal   = {Tumor Biology},
  number    = {7},
  doi       = {10.1177/1010428317706430},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-158399},
  year      = {2017},
  abstract  = {MicroRNAs are well-known strong RNA regulators modulating whole functional units in complex signaling networks. Regarding clinical application, they have potential as biomarkers for prognosis, diagnosis, and therapy. In this review, we focus on two microRNAs centrally involved in lung cancer progression. MicroRNA-21 promotes and microRNA-34 inhibits cancer progression. We elucidate here involved pathways and imbed these antagonistic microRNAs in a network of interactions, stressing their cancer microRNA biology, followed by experimental and bioinformatics analysis of such microRNAs and their targets. This background is then illuminated from a clinical perspective on microRNA-21 and microRNA-34 as general examples for the complex microRNA biology in lung cancer and its diagnostic value. Moreover, we discuss the immense potential that microRNAs such as microRNA-21 and microRNA-34 imply by their broad regulatory effects. These should be explored for novel therapeutic strategies in the clinic.},
  language  = {en}
}
@article{PeindlGoettlichCrouchetal.2022,
  author    = {Peindl, Matthias and G{\"o}ttlich, Claudia and Crouch, Samantha and Hoff, Niklas and L{\"u}ttgens, Tamara and Schmitt, Franziska and Pereira, Jes{\´u}s Guillermo Nieves and May, Celina and Schliermann, Anna and Kronenthaler, Corinna and Cheufou, Danjouma and Reu-Hofer, Simone and Rosenwald, Andreas and Weigl, Elena and Walles, Thorsten and Sch{\"u}ler, Julia and Dandekar, Thomas and Nietzer, Sarah and Dandekar, Gudrun},
  title     = {EMT, stemness, and drug resistance in biological context: a 3D tumor tissue/in silico platform for analysis of combinatorial treatment in NSCLC with aggressive KRAS-biomarker signatures},
  series = {Cancers},
  volume    = {14},
  journal   = {Cancers},
  number    = {9},
  issn      = {2072-6694},
  doi       = {10.3390/cancers14092176},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-270744},
  year      = {2022},
  abstract  = {Epithelial-to-mesenchymal transition (EMT) is discussed to be centrally involved in invasion, stemness, and drug resistance. Experimental models to evaluate this process in its biological complexity are limited. To shed light on EMT impact and test drug response more reliably, we use a lung tumor test system based on a decellularized intestinal matrix showing more in vivo-like proliferation levels and enhanced expression of clinical markers and carcinogenesis-related genes. In our models, we found evidence for a correlation of EMT with drug resistance in primary and secondary resistant cells harboring KRAS\(^{G12C}\) or EGFR mutations, which was simulated in silico based on an optimized signaling network topology. Notably, drug resistance did not correlate with EMT status in KRAS-mutated patient-derived xenograft (PDX) cell lines, and drug efficacy was not affected by EMT induction via TGF-β. To investigate further determinants of drug response, we tested several drugs in combination with a KRAS\(^{G12C}\) inhibitor in KRAS\(^{G12C}\) mutant HCC44 models, which, besides EMT, display mutations in P53, LKB1, KEAP1, and high c-MYC expression. We identified an aurora-kinase A (AURKA) inhibitor as the most promising candidate. In our network, AURKA is a centrally linked hub to EMT, proliferation, apoptosis, LKB1, and c-MYC. This exemplifies our systemic analysis approach for clinical translation of biomarker signatures.},
  language  = {en}
}