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Lung cancer is currently the leading cause of cancer related mortality due to late diagnosis and limited treatment intervention. Non-coding RNAs are not translated into proteins and have emerged as fundamental regulators of gene expression. Recent studies reported that microRNAs and long non-coding RNAs are involved in lung cancer development and progression. Moreover, they appear as new promising non-invasive biomarkers for early lung cancer diagnosis. Here, we highlight their potential as biomarker in lung cancer and present how bioinformatics can contribute to the development of non-invasive diagnostic tools. For this, we discuss several bioinformatics algorithms and software tools for a comprehensive understanding and functional characterization of microRNAs and long non-coding RNAs.
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.
The investigation of the biodistribution profile of a cell-based medicinal product is a pivotal prerequisite to allow a factual benefit-risk assessment within the non-clinical to clinical translation in product development. Here, a qPCR-based method to determine the amount of human DNA in mouse DNA was validated according to the guidelines of the European Medicines Agency and the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. Furthermore, a preclinical worst-case scenario study was performed in which this method was applied to investigate the biodistribution of 2 x 10\(^6\) intravenously administered, genetically modified, blood outgrowth endothelial cells from hemophilia A patients after 24 h and 7 days. The validation of the qPCR method demonstrated high accuracy, precision, and linearity for the concentration interval of 1:1 x 10\(^3\) to 1:1 x 10\(^6\) human to mouse DNA. The application of this method in the biodistribution study resulted in the detection of human genomes in four out of the eight investigated organs after 24 h. After 7 days, no human DNA was detected in the eight organs analyzed. This biodistribution study provides mandatory data on the toxicokinetic safety profile of an actual candidate cell-based medicinal product. The extensive evaluation of the required validation parameters confirms the applicability of the qPCR method for non-clinical biodistribution studies.