Connecting cancer pathways to tumor engines: A stratification tool for colorectal cancer combining human in vitro tissue models with boolean in silico models

Baur F, Nietzer SL, Kunz M, Saal F, Jeromin J, Matschos S, Linnebacher M, Walles H, Dandekar T, Dandekar G (2020)

Publication Type: Journal article

Publication year: 2020

Journal

Cancers MDPI

Book Volume: 12

Article Number: 28

Journal Issue: 1

DOI: 10.3390/cancers12010028

Abstract

To improve and focus preclinical testing, we combine tumor models based on a decellularized tissue matrix with bioinformatics to stratify tumors according to stage-specific mutations that are linked to central cancer pathways. We generated tissue models with BRAF-mutant colorectal cancer (CRC) cells (HROC24 and HROC87) and compared treatment responses to two-dimensional (2D) cultures and xenografts. As the BRAF inhibitor vemurafenib is—in contrast to melanoma—not effective in CRC, we combined it with the EGFR inhibitor gefitinib. In general, our 3D models showed higher chemoresistance and in contrast to 2D a more active HGFR after gefitinib and combination-therapy. In xenograft models murine HGF could not activate the human HGFR, stressing the importance of the human microenvironment. In order to stratify patient groups for targeted treatment options in CRC, an in silico topology with different stages including mutations and changes in common signaling pathways was developed. We applied the established topology for in silico simulations to predict new therapeutic options for BRAF-mutated CRC patients in advanced stages. Our in silico tool connects genome information with a deeper understanding of tumor engines in clinically relevant signaling networks which goes beyond the consideration of single drivers to improve CRC patient stratification.

Authors with CRIS profile

Meik Kunz Lehrstuhl für Medizinische Informatik

Involved external institutions

Universitätsklinikum Würzburg DE Germany (DE) Julius-Maximilians-Universität Würzburg DE Germany (DE) Universitätsmedizin Rostock DE Germany (DE)

How to cite

APA:

Baur, F., Nietzer, S.L., Kunz, M., Saal, F., Jeromin, J., Matschos, S.,... Dandekar, G. (2020). Connecting cancer pathways to tumor engines: A stratification tool for colorectal cancer combining human in vitro tissue models with boolean in silico models. Cancers, 12(1). https://doi.org/10.3390/cancers12010028

MLA:

Baur, Florentin, et al. "Connecting cancer pathways to tumor engines: A stratification tool for colorectal cancer combining human in vitro tissue models with boolean in silico models." Cancers 12.1 (2020).

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