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Personal models reveal potential antimetabolites to prevent hepatocellular cancer growth

Individual genetic models produced 101 antimetabolites that may be effective in inhibiting tumor growth in hepatocellular cancer patients, according to new data.

Using the Task-Driven Integrative Network Inference for Tissues (tINIT), researchers in Sweden reconstructed personalized genome-scale metabolic models (GEMs) for six of 27 patients with hepatocellular cancer (HCC) to identify anticancer therapies using antimetabolites. Researchers initially examined protein expression patterns encoded by 15,841 genes in 27 patients with HCC. They found 4,936 proteins identifiable in six patients and healthy hepatocytes. The functionality and assessment of 83 healthy cell type-specific GEMs predicted antimetabolite toxicities, and the GEMs were reconstructed with the tINIT algorithm. Researchers then applied the tINITs to the Human Metabolic Reaction database to construct models based on data produced in the Human Protein Atlas.

Of the 101 antimetabolites found effective in preventing tumor growth in all patients, 46 were specific to individual patients. Twenty-two metabolites currently are used in various cancer treatments, according to the study.

Researchers wrote that personalized models ranged from 4,690 to 4,957 reactions and 1,715 genes to 2,025 genes. In all, 5,405 reactions and 2,361 genes were shared among all models, and 4,212 reactions and 1,324 genes were present in the six personalized HCC models. Differences in reactions varied between 356 and 610, whereas similarities between them ranged from 4,437 to 4,699. Larger model differences in the number of genes was observed (between 392 and 524), and a 16% to 22% difference was observed between the personalized models among 2,361 shared genes in all the models.

“With this approach, we can find and evaluate new potential drugs, some that could be used for general treatment of HCC, and others that are highly specific for each HCC patient,” Adil Mardinoglu, PhD,of the Systems Biology group at Chalmers University of Technology, said in a press release. “We can also predict false-positive drug targets that would not be effective in all patients. This would lead to more targeted and efficient cancer treatment.”

Disclosure: The researchers report no relevant financial disclosures.