Human gastric cancer modelling using organoids.

Seidlitz, Therese; Merker, Sebastian R; Rothe, Alexander; Zakrzewski, Falk; von Neubeck, Cläre; Grützmann, Konrad; Sommer, Ulrich; Schweitzer, Christine et al. · Gut · 2019

basic_science · Level V

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Abstract

Gastric cancer is the second leading cause of cancer-related deaths and the fifth most common malignancy worldwide. In this study, human and mouse gastric cancer organoids were generated to model the disease and perform drug testing to delineate treatment strategies. Human gastric cancer organoid cultures were established, samples classified according to their molecular profile and their response to conventional chemotherapeutics tested. Targeted treatment was performed according to specific druggable mutations. Mouse gastric cancer organoid cultures were generated carrying molecular subtype-specific alterations. Twenty human gastric cancer organoid cultures were established and four selected for a comprehensive in-depth analysis. Organoids demonstrated divergent growth characteristics and morphologies. Immunohistochemistry showed similar characteristics to the corresponding primary tissue. A divergent response to 5-fluoruracil, oxaliplatin, irinotecan, epirubicin and docetaxel treatment was observed. Whole genome sequencing revealed a mutational spectrum that corresponded to the previously identified microsatellite instable, genomic stable and chromosomal instable subtypes of gastric cancer. The mutational landscape allowed targeted therapy with trastuzumab for <i>ERBB2</i> alterations and palbociclib for <i>CDKN2A</i> loss. Mouse cancer organoids carrying <i>Kras</i> and <i>Tp53</i> or <i>Apc</i> and <i>Cdh1</i> mutations were characterised and serve as model system to study the signalling of induced pathways. We generated human and mouse gastric cancer organoids modelling typical characteristics and altered pathways of human gastric cancer. Successful interference with activated pathways demonstrates their potential usefulness as living biomarkers for therapy response testing.

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