• search hit 4 of 6
Back to Result List

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

Please always quote using this URN: urn:nbn:de:bvb:20-opus-193798
  • 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,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.show moreshow less

Download full text files

Export metadata

Additional Services

Share in Twitter Search Google Scholar Statistics
Metadaten
Author: Florentin Baur, Sarah L. Nietzer, Meik Kunz, Fabian Saal, Julian Jeromin, Stephanie Matschos, Michael Linnebacher, Heike Walles, Thomas Dandekar, Gudrun Dandekar
URN:urn:nbn:de:bvb:20-opus-193798
Document Type:Journal article
Faculties:Fakultät für Biologie / Theodor-Boveri-Institut für Biowissenschaften
Medizinische Fakultät / Lehrstuhl für Tissue Engineering und Regenerative Medizin
Language:English
Parent Title (English):Cancers
ISSN:2072-6694
Year of Completion:2020
Volume:12
Issue:1
Pagenumber:28
Source:Cancers 2020, 12(1), 28; https://doi.org/10.3390/cancers12010028
DOI:https://doi.org/10.3390/cancers12010028
Tag:3D tissue models; BRAF mutation; colorectal cancer; in silico simulation; stratification; targeted therapy
Release Date:2020/02/27
Date of first Publication:2019/12/20
Open-Access-Publikationsfonds / Förderzeitraum 2019
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International