@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}
}