Hannu-Pekka Schukov, Matthias Nees, Janne Heikkilä, Malin Åkerfelt, Sean Robinson, Raija Sormunen, Neslihan Bayramoglu, Johannes Virtanen, Juho Kannala, Mika Kaakinen, Mervi Toriseva, Ville Härmä, Lauri Eklund, Turku Centre for Biotechnology, University of Turku-Åbo Academy University, University of Turku, Laboratoire de Biologie à Grande Échelle (BGE - UMR S1038), Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), VTT Technical Research Centre of Finland (VTT), Machine Vision Group (MVG), University of Oulu, Center for Machine Vision Research (CMV), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)
// Malin Akerfelt 1, 2 , Neslihan Bayramoglu 3 , Sean Robinson 4, 5, 6, 7 , Mervi Toriseva 1, 2, 8 , Hannu-Pekka Schukov 8 , Ville Harma 2 , Johannes Virtanen 2 , Raija Sormunen 9 , Mika Kaakinen 10 , Juho Kannala 3 , Lauri Eklund 10 , Janne Heikkila 3 , Matthias Nees 1, 2 1 Turku Centre for Biotechnology, University of Turku, Turku, FI-20520, Finland 2 VTT Technical Research Centre of Finland, Turku, FI-20521, Finland 3 Centre for Machine Vision Research, University of Oulu, Oulu, FI-90014, Finland 4 Department of Mathematics and Statistics, University of Turku, Turku, FI-20014, Finland 5 University Grenoble Alpes, iRTSV-BGE, Grenoble, F-38000, France 6 CEA, iRTSV-BGE, Grenoble, F-38000, France 7 INSERM, BGE, Grenoble, F-38000, France 8 Institute of Biomedicine, University of Turku, Turku, FI-20520, Finland 9 Biocenter Oulu and Department of Pathology, University of Oulu and Oulu University Hospital, Oulu, FI-90220, Finland 10 Oulu Center for Cell-Matrix Research, Biocenter Oulu and Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, FI-90014, Finland Correspondence to: Matthias Nees, e-mail: matthias.nees@btk.fi Keywords: 3D co-culture, cancer associated fibroblast (CAF), phenotypic screening, invasion, focal adhesion kinase (FAK) Received: March 21, 2015 Accepted: August 24, 2015 Published: September 03, 2015 ABSTRACT Cancer-associated fibroblasts (CAFs) constitute an important part of the tumor microenvironment and promote invasion via paracrine functions and physical impact on the tumor. Although the importance of including CAFs into three-dimensional (3D) cell cultures has been acknowledged, computational support for quantitative live-cell measurements of complex cell cultures has been lacking. Here, we have developed a novel automated pipeline to model tumor-stroma interplay, track motility and quantify morphological changes of 3D co-cultures, in real-time live-cell settings. The platform consists of microtissues from prostate cancer cells, combined with CAFs in extracellular matrix that allows biochemical perturbation. Tracking of fibroblast dynamics revealed that CAFs guided the way for tumor cells to invade and increased the growth and invasiveness of tumor organoids. We utilized the platform to determine the efficacy of inhibitors in prostate cancer and the associated tumor microenvironment as a functional unit. Interestingly, certain inhibitors selectively disrupted tumor-CAF interactions, e.g. focal adhesion kinase (FAK) inhibitors specifically blocked tumor growth and invasion concurrently with fibroblast spreading and motility. This complex phenotype was not detected in other standard in vitro models. These results highlight the advantage of our approach, which recapitulates tumor histology and can significantly improve cancer target validation in vitro .