Computational modeling of methionine cycle-based metabolism and DNA methylation and the implications for anti-cancer drug response prediction
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Mengying Zhang1, Christian Saad4, Lien Le1, Kathrin Halfter1, Bernhard Bauer4, Ulrich R. Mansmann1,2 and Jian Li1,2,3
1Institute for Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians University of München, Munich, Germany
2German Cancer Consortium (DKTK), Heidelberg, Germany
3German Cancer Research Center (DKFZ), Heidelberg, Germany
4Department of Computational Science, University of Augsburg, Augsburg, Germany
Jian Li, email: firstname.lastname@example.org
Keywords: metabolism; methylation; chemotherapy; molecular modelling; treatment prediction
Received: July 06, 2017 Accepted: July 29, 2017 Epub: February 21, 2018 Published: April 27, 2018
The relationship between metabolism and methylation is considered to be an important aspect of cancer development and drug efficacy. However, it remains poorly defined how to apply this aspect to improve preclinical disease characterization and clinical treatment outcome. Using available molecular information from Kyoto Encyclopedia of Genes and Genomes (KEGG) and literature, we constructed a large-scale knowledge-based metabolic in silico model. For the purpose of model validation, we applied data from the Cancer Cell Line Encyclopedia (CCLE) to investigate computationally the impact of metabolism on chemotherapy efficacy. In our model, different metabolic components such as MAT2A, ATP6V0E1, NNMT involved in methionine cycle correlate with biologically measured chemotherapy outcome (IC50) that are in agreement with findings of independent studies. These proteins are potentially also involved in cellular methylation processes. In addition, several components such as 3,4-dihydoxymandelate, PAPSS2, UPP1 from metabolic pathways involved in the production of purine and pyrimidine correlate with IC50. This study clearly demonstrates that complex computational approaches can reflect findings of biological experiments. This demonstrates their high potential to grasp complex issues within systems medicine such as response prediction, biomarker identification using available data resources.
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