Open AccessIdentification of anticancer drugs for hepatocellular carcinoma through personalized genome‐scale metabolic modeling
Author(s) -
Agren Rasmus,
Mardinoglu Adil,
Asplund Anna,
Kampf Caroline,
Uhlen Mathias,
Nielsen Jens
Publication year - 2014
Publication title -
molecular systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 8.523
H-Index - 148
ISSN - 1744-4292
DOI - 10.1002/msb.145122
Subject(s) - in silico , biology , personalized medicine , hepatocellular carcinoma , computational biology , pharmacogenetics , precision medicine , cancer , bioinformatics , cancer research , gene , genotype , genetics
Genome‐scale metabolic models ( GEM s) have proven useful as scaffolds for the integration of omics data for understanding the genotype–phenotype relationship in a mechanistic manner. Here, we evaluated the presence/absence of proteins encoded by 15,841 genes in 27 hepatocellular carcinoma (HCC) patients using immunohistochemistry. We used this information to reconstruct personalized GEM s for six HCC patients based on the proteomics data, HMR 2.0, and a task‐driven model reconstruction algorithm ( tINIT ). The personalized GEM s were employed to identify anticancer drugs using the concept of antimetabolites; i.e., drugs that are structural analogs to metabolites. The toxicity of each antimetabolite was predicted by assessing the in silico functionality of 83 healthy cell type‐specific GEM s, which were also reconstructed with the tINIT algorithm. We predicted 101 antimetabolites that could be effective in preventing tumor growth in all HCC patients, and 46 antimetabolites which were specific to individual patients. Twenty‐two of the 101 predicted antimetabolites have already been used in different cancer treatment strategies, while the remaining antimetabolites represent new potential drugs. Finally, one of the identified targets was validated experimentally, and it was confirmed to attenuate growth of the HepG2 cell line.