Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling
Journal article, 2014

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. 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.

FATTY-ACID OXIDATION

hepatocellular carcinoma

GLOBAL

genome-scale metabolic models

INHIBITION

CARNITINE

CANCER METABOLISM

antimetabolites

MEDICINE

HUMAN PROTEIN ATLAS

personalized medicine

CELLS

ADULTS

RECONSTRUCTION

proteome

Author

Rasmus Ågren

Chalmers, Chemical and Biological Engineering, Life Sciences, System Biology

Adil Mardinoglu

Chalmers, Chemical and Biological Engineering, Life Sciences, System Biology

A. Asplund

Uppsala University

C. Kampf

Uppsala University

M. Uhlen

Royal Institute of Technology (KTH)

Jens B Nielsen

Chalmers, Chemical and Biological Engineering, Life Sciences, System Biology

Molecular Systems Biology

1744-4292 (ISSN)

Vol. 10 3 A721

Subject Categories

Cell and Molecular Biology

Immunology in the medical area

Pharmacology and Toxicology

Infrastructure

C3SE (Chalmers Centre for Computational Science and Engineering)

Areas of Advance

Life Science Engineering (2010-2018)

DOI

10.1002/msb.145122

More information

Latest update

2/28/2018