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Article

Modeling of Personalized Treatments in Colon Cancer Based on Preclinical Genomic and Drug Sensitivity Data

1
Experimental Pharmacology and Oncology Berlin-Buch GmbH (EPO), Robert-Roessle-Str. 10, 13125 Berlin, Germany
2
Comprehensive Cancer Center, Charite-Universitätsmedizin, Chariteplatz 1, 10117 Berlin, Germany
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Department of Computational Molecular Biology and Department of Vertebrate Genomics/Otto Warburg Laboratory Gene Regulation and Systems Biology of Cancer, Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany
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Alacris Theranostics GmbH, Max-Planck-Straße 3, 12489 Berlin, Germany
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Institute of Pathology, Neuropathology, and Molecular Pathology, Medical University of Innsbruck, 6020 Innsbruck, Austria
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Diagnostic & Research Center for Molecular Biomedicine, Institute of Pathology, Medical University of Graz, 8036 Graz, Austria
*
Author to whom correspondence should be addressed.
Academic Editor: Heike Allgayer
Cancers 2021, 13(23), 6018; https://doi.org/10.3390/cancers13236018
Received: 8 November 2021 / Revised: 25 November 2021 / Accepted: 25 November 2021 / Published: 30 November 2021
(This article belongs to the Special Issue Colorectal Cancers: From Present Problems to Future Solutions)
This experimental preclinical study developed a strategy to identify signatures for the personalized treatment of colon cancer focusing on target-specific drug combinations. Tumor growth inhibition was analyzed in a preclinical phase II study using 25 patient-derived xenograft models (PDX) treated with drug combinations blocking alternatively activated oncogenic pathways. Results reveal an improved response by combinatorial treatment in some defined molecular subgroups and potential alternative treatment options in KRAS- and BRAF-mutated colon cancer.
The current standard therapies for advanced, recurrent or metastatic colon cancer are the 5-fluorouracil and oxaliplatin or irinotecan schedules (FOxFI) +/− targeted drugs cetuximab or bevacizumab. Treatment with the FOxFI cytotoxic chemotherapy regimens causes significant toxicity and might induce secondary cancers. The overall low efficacy of the targeted drugs seen in colon cancer patients still is hindering the substitution of the chemotherapy. The ONCOTRACK project developed a strategy to identify predictive biomarkers based on a systems biology approach, using omics technologies to identify signatures for personalized treatment based on single drug response data. Here, we describe a follow-up project focusing on target-specific drug combinations. Background for this experimental preclinical study was that, by analyzing the tumor growth inhibition in the PDX models by cetuximab treatment, a broad heterogenic response from complete regression to tumor growth stimulation was observed. To provide confirmation of the hypothesis that drug combinations blocking alternatively activated oncogenic pathways may improve therapy outcomes, 25 models out of the well-characterized ONCOTRACK PDX panel were subjected to treatment with a drug combination scheme using four approved, targeted cancer drugs. View Full-Text
Keywords: colon cancer; personalized treatment; drug combinations colon cancer; personalized treatment; drug combinations
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MDPI and ACS Style

Keil, M.; Conrad, T.; Becker, M.; Keilholz, U.; Yaspo, M.-L.; Lehrach, H.; Schütte, M.; Haybaeck, J.; Hoffmann, J. Modeling of Personalized Treatments in Colon Cancer Based on Preclinical Genomic and Drug Sensitivity Data. Cancers 2021, 13, 6018. https://doi.org/10.3390/cancers13236018

AMA Style

Keil M, Conrad T, Becker M, Keilholz U, Yaspo M-L, Lehrach H, Schütte M, Haybaeck J, Hoffmann J. Modeling of Personalized Treatments in Colon Cancer Based on Preclinical Genomic and Drug Sensitivity Data. Cancers. 2021; 13(23):6018. https://doi.org/10.3390/cancers13236018

Chicago/Turabian Style

Keil, Marlen, Theresia Conrad, Michael Becker, Ulrich Keilholz, Marie-Laure Yaspo, Hans Lehrach, Moritz Schütte, Johannes Haybaeck, and Jens Hoffmann. 2021. "Modeling of Personalized Treatments in Colon Cancer Based on Preclinical Genomic and Drug Sensitivity Data" Cancers 13, no. 23: 6018. https://doi.org/10.3390/cancers13236018

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