Journal Article

Identification of anticancer drugs for hepatocellular carcinoma through personalized genome‐scale metabolic modeling

Mardinoglu, Adil, Kampf, Caroline, Nielsen, Jens, Asplund, Anna, Agren, Rasmus, Uhlen, Mathias

2014

Overview

Genome‐scale metabolic models (GEMs) 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 GEMs for six HCC patients based on the proteomics data, HMR 2.0, and a task‐driven model reconstruction algorithm (tINIT). The personalized GEMs 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 GEMs, 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. Synopsis Personalized GEMs for six hepatocellular carcinoma patients are reconstructed using proteomics data and a task‐driven model reconstruction algorithm. These GEMs are used to predict antimetabolites preventing tumor growth in all patients or in individual patients. The presence of proteins encoded by 15,841 genes in tumors from 27 HCC patients is evaluated by immunohistochemistry. Personalized GEMs for six HCC patients and GEMs for 83 healthy cell types are reconstructed based on HMR 2.0 and the tINIT algorithm for task‐driven model reconstruction. 101 antimetabolites are predicted to inhibit tumor growth in all patients. Antimetabolite toxicity is tested using the 83 cell type‐specific GEMs. An l ‐carnitine analog inhibits the proliferation of HepG2 cells. Graphical Abstract Personalized GEMs for six hepatocellular carcinoma patients are reconstructed using proteomics data and a task‐driven model reconstruction algorithm. These GEMs are used to predict antimetabolites preventing tumor growth in all patients or in individual patients.

• Publisher: Nature Publishing Group UK,EMBO Press,European Molecular Biology Organization,Springer Nature
• Subject: ADULTS / Algorithms / Antimetabolites / Antineoplastic Agents - therapeutic use / Antineoplastic drugs / Antitumor agents / CANCER METABOLISM / Carcinoma, Hepatocellular - drug therapy / Carcinoma, Hepatocellular - pathology / CARNITINE / Cell growth / CELLS / Computer Simulation / Drug Discovery / Drugs / EMBO17 / EMBO28 / FATTY-ACID OXIDATION / Gems / Genome, Human / genome-scale metabolic models / Genomes / Genotypes / GLOBAL / Hepatocellular carcinoma / HUMAN PROTEIN ATLAS / Humans / Immunohistochemistry / INHIBITION / Liver cancer / Liver Neoplasms - drug therapy / Liver Neoplasms - pathology / MEDICINE / Metabolism / Metabolites / Models, Biological / Patients / personalized medicine / Phenotypes / Precision Medicine / Proteins / proteome / Proteomics / RECONSTRUCTION / Target recognition / Toxicity / Tumors