ILUMINATE – Innovative lung cancer mouse models recapitulating human Immune response and tumor-stroma exchange

ILUMINATE develops a novel platform for integrated analysis of in-vivo models for preclinical evaluation of new compounds in oncology, including innovative therapeutic approaches in oncoimmunology. The goal is to provide innovative service models that cover the entire range of in-vivo research, including planning of the study experimental set up, data analysis, and comprehensive visual presentation of results. This approach will meet the expected strong demand from oncology and oncoimmunology research and supports the public interest in efficient and transparent development of novel cancer therapies.

Relevant aspects of the human tumor microenvironment will be reconstructed by co-cultivation of human fibroblasts, immune cells, and tumor cells in immunocompromised mice, to study effects of innovative immunomodulatory therapeutic interventions. The platform comprehensively captures different cell types, labeled by multiplexed immunohistochemistry, in their spatial context. The subsequent modular analysis-workflow integrates elements of multispectral and advanced image analysis. Thus, the evaluation of new compounds reaches far beyond determining simply tumor growth rates, and provides customized approaches for individual new compounds. By developing new analysis methods, ILUMINATE facilitates discovery and evaluation of new compound classes in the development of cancer drugs.

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Partners:

Prof. Dr. med. Friedrich Feuerhake
Medizinische Hochschule Hannover
Institut für Pathologie

Dr. Julia Schüler
Oncotest GmbH
Freiburg

Contact::

Daniel Bug, Tel.: +49 (241) 80 – 22903

Funding:

Federal Ministry of Education and Research (BMBF), 2015-2018

Publications:

2017

Daniel Bug, Anne Grote, Julia Schüler, Friedrich Feuerhake and Dorit Merhof
Analyzing Immunohistochemically Stained Whole-Slide Images of Ovarian Carcinoma
In: Bildverarbeitung für die Medizin (BVM)

2017

Daniel Bug, Steffen Schneider, Anne Grote, Eva Oswald, Friedrich Feuerhake, Julia Schüler and Dorit Merhof
Context-based Normalization of Histological Stains using Deep Convolutional Features
In: MICCAI International Workshop on Deep Learning in Medical Image Analysis (DLMIA)

2016

Daniel Bug, Julia Schüler, Friedrich Feuerhake and Dorit Merhof
Multi-class single-label classification of histopathological whole-slide images
In: IEEE International Symposium on Biomedical Imaging (ISBI)

2015

Daniel Bug, Friedrich Feuerhake and Dorit Merhof
Foreground Extraction for Histopathological Whole-Slide Imaging
In: Bildverarbeitung für die Medizin (BVM)