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High-throughput sequencing of frozen and paraffin-embedded tumor and normal tissue

MPS-Authors
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Kerick,  M.
Cancer Genomics (Michal-Ruth Schweiger), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Timmermann,  B.
Sequencing, Max Planck Institute for Molecular Genetics, Max Planck Society;

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Schweiger,  M. R.
Cancer Genomics (Michal-Ruth Schweiger), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Citation

Kerick, M., Timmermann, B., & Schweiger, M. R. (2010). High-throughput sequencing of frozen and paraffin-embedded tumor and normal tissue. Pathologe, 31(suppl 2), 255-257. doi:10.1007/s00292-010-1377-z.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0010-7A3C-B
Abstract
Until now high-throughput sequencing of tumor samples relied on DNA isolated from fresh frozen tissues, the preparation of which, however, is relatively laborious. The use of preserved material, i.e. from tissue banks, could help to avoid this limitation and would enable the reanalysis of diverse clinical trials. So far we have shown that formalin-fixed paraffin-embedded (FFPE) tissue samples can be used for genomic re-sequencing processes. FFPE samples are amply available from surgical tumor resections and histopathological diagnosis, and comprise tissue from precursor lesions, primary tumors, and lymphogenic and/or hematogenic metastases. To generate models which predict the response to therapy, FFPE tissue also has the advantage that it is available from a variety of clinical trials. Second generation sequencing techniques are not only applicable to snap frozen and FFPE tissues for whole genome analyses but also for targeted resequencing approaches. In addition, the detection of copy number variations and mutations in FFPE tissues can be obtained within one sequencing run. The possibility of using genome-wide technologies irrespective of the mode of storage facilitates the retrieval of useful material and is a prerequisite for subsequent computational modelling approaches.