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Hochdurchsatz-Sequenz-Analyse zur Identifikation von prädisponierenden sowie somatischen Mutationen bei Patienten mit Nicht-kleinzelligem Bronchialkarzinom (NSCLC) zur Vorhersage von Chemotherapie-Resistenzen


Isau,  Melanie
Cancer Genomics (Michal-Ruth Schweiger), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;
Free University Berlin, Department of Biology, Chemistry and Pharmacy;

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Isau, M. (2015). Hochdurchsatz-Sequenz-Analyse zur Identifikation von prädisponierenden sowie somatischen Mutationen bei Patienten mit Nicht-kleinzelligem Bronchialkarzinom (NSCLC) zur Vorhersage von Chemotherapie-Resistenzen. PhD Thesis, Free University, Department of Biology, Chemistry and Pharmacy, Berlin.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0026-B305-D
In this thesis, the mutation profile of somatic- and germline mutations of 23 patient-derived xenograft-models and the corresponding normal tissues of non-small-cell lung cancer patients were generated. For this, a customer target high throughput re-sequencing approach was used. However, these technologies needed to be further improved with regard to reproducibility and applicability to clinical samles and settings. It could be shown that formalin-fixed paraffin embedded tissue material can supplement fresh frozen tissues for the detection of single nucleotide variants and that solution-based enrichment experiments can be accomplished with small amounts of DNA. Finally, the question was to addressed whether the heterogeneity of a tumor is reflected by different genetic alterations, e.g. if different foci of a tumor display different genomic patterns. It could be shown that the tumor heterogeneity plays an important role mainly for the detection of copy number alterations. The results of the mutation analysis confirmed the high mutation rates that underlie lung cancer diseases. As a technical proof-of-principle experiment it could be shown that somatic mutations and somatic copy number alterations depicted a high overlap between the xenograft-tumor and the primary tumor and confirmed that patient-derived xenograft-models can be used for genetic analysis. Sensitivity tests for 23 xenografts-models were performed for different chemotherapies like Carboplatin, Gemcitabine, Paclitaxel, and Cetuximab. For these, mutations could be identified which might be responsible for intrinsic resistances. For example, the p21-activated (PAK) signaling pathway was significantly affected by somatic mutations in Carboplatin resistant tumors. In regard to Gemcitabine, many mutations within the FGFR signaling pathway were associated with a reduced sensitivity of the mice. Concerning a resistance for the EGFR inhibitor Cetuximab, the heat shock protein HSP90AB1 and the hepatocyte growth factor HGF could be identified as gene candidates transmitting a chemotherapy resistance. Functional assays were performed which provide first evidence that MAML2, CDC42BPA and KMT2D are involved in the resistance to the EGFR inhibitor Cetuximab. Since KMT2D is a histone methyltransferase, a data integration approach was used to identify its functional relevance. Interestingly, for resistant tumors with KMT2D mutations significant changes in gene expression and DNA methylation were measured. In the end, the generated datasets were integrated in a computer prediction tool (PyBios) with the aim to establish a systems biology network for therapy responses. First preliminary results in two tumors confirmed a high concordance between the chemotherapy sensitivity of the xenografts mice and in silico prediction. These modelling will now be extended to the 21 patients analyzed in this thesis. In case that the experimental data and the in silico modelling remains with a high concordance (high predictive value for the in silico modelling) this approach will have without doubt, an important consequence for the therapy of patients with non-small-cell lung cancer in the future.