Literature DB >> 28436954

Optimized expression-based microdissection of formalin-fixed lung cancer tissue.

Markus Grafen1, Thurid R Hofmann2, Andreas H Scheel3, Julia Beck4, Alexander Emmert5, Stefan Küffer2, Bernhard C Danner5, Ekkehard Schütz4, Reinhardt Büttner3, Andreas Ostendorf1, Philipp Ströbel2, Hanibal Bohnenberger2.   

Abstract

Analysis of specific DNA alterations in precision medicine of tumors is crucially important for molecular targeted treatments. Lung cancer is a prototypic example and one of the leading causes of cancer-related deaths worldwide. One major technical problem of detecting DNA alterations in tissue samples is cellular heterogeneity, that is, mixture of tumor and normal cells. Microdissection is an important tool to enrich tumor cells from heterogeneous tissue samples. However, conventional laser capture microdissection has several disadvantages like user-dependent selection of regions of interest (ROI), high costs for dissection systems and long processing times. ROI selection in expression-based microdissection (xMD) directly relies on cancer cell-specific immunostaining. Whole-slide irradiation leads to localized energy absorption at the sites of most intensive staining and melting of a membrane covering the slide, so that tumor cells can be isolated by removing the complete membrane. In this study, we optimized xMD of lung cancer tissue by enhancing staining intensity of tumor cell-specific immunostaining and processing of the stained samples. This optimized procedure did not alter DNA quality and resulted in enrichment of mutated EGFR DNA from lung adenocarcinoma specimens after xMD. We here also introduce a quality control protocol based on digital whole-slide scanning and image analysis before and after xMD to quantify selectivity and efficiency of the procedure. In summary, this study provides a workflow for xMD, adapted and tested for lung cancer tissue that can be used for lung tumor cell dissection before diagnostic or investigatory analyses.

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Year:  2017        PMID: 28436954     DOI: 10.1038/labinvest.2017.31

Source DB:  PubMed          Journal:  Lab Invest        ISSN: 0023-6837            Impact factor:   5.662


  19 in total

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