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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

Zitieren Sie bitte immer diese URN: urn:nbn:de:bvb:20-opus-270744
  • 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 drugEpithelial-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.zeige mehrzeige weniger

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Metadaten
Autor(en): Matthias Peindl, Claudia Göttlich, Samantha Crouch, Niklas Hoff, Tamara Lüttgens, Franziska Schmitt, Jesús Guillermo Nieves Pereira, Celina May, Anna Schliermann, Corinna Kronenthaler, Danjouma Cheufou, Simone Reu-Hofer, Andreas Rosenwald, Elena Weigl, Thorsten Walles, Julia Schüler, Thomas Dandekar, Sarah Nietzer, Gudrun Dandekar
URN:urn:nbn:de:bvb:20-opus-270744
Dokumentart:Artikel / Aufsatz in einer Zeitschrift
Institute der Universität:Medizinische Fakultät / Pathologisches Institut
Fakultät für Biologie / Theodor-Boveri-Institut für Biowissenschaften
Medizinische Fakultät / Lehrstuhl für Tissue Engineering und Regenerative Medizin
Medizinische Fakultät / Comprehensive Cancer Center Mainfranken
Sprache der Veröffentlichung:Englisch
Titel des übergeordneten Werkes / der Zeitschrift (Englisch):Cancers
ISSN:2072-6694
Erscheinungsjahr:2022
Band / Jahrgang:14
Heft / Ausgabe:9
Aufsatznummer:2176
Originalveröffentlichung / Quelle:Cancers (2022) 14:9, 2176. https://doi.org/10.3390/cancers14092176
DOI:https://doi.org/10.3390/cancers14092176
Allgemeine fachliche Zuordnung (DDC-Klassifikation):6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Freie Schlagwort(e):3D lung tumor tissue models; EMT; KRAS biomarker signatures; boolean in silico models; drug resistance; invasion; stemness; targeted combination therapy
Datum der Freischaltung:08.02.2023
Datum der Erstveröffentlichung:27.04.2022
Open-Access-Publikationsfonds / Förderzeitraum 2022
Lizenz (Deutsch):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International