A new computational method developed by researchers at the Biomedical Engineering Center of the University of Navarra allows locating vulnerabilities in the metabolism of the tumors that may serve to prevent the malignant cells continue to develop. The study was published in the journal Nature Communications.
Iñigo Apaolaza, first author of the paper, explains: “Tumor cells need a series of compounds to grow and survive. To make them, their genes form metabolic networks, very similar to the road networks that we use every day. What we have achieved with our algorithms and mathematical methods is identifying which genes are essential for a tumor cell to produce these compounds and thus survive.”
This algorithm, says Felipe Prosper, the group’s principal investigator and co-author of the article, “has been applied to a specific type of cancer, which is multiple myeloma –incurable today– as a way of validating if the strategy works.”
The study consisted of a series of in vitro tests on a protein called RRM1, which the algorithm pointed out as essential in the development of multiple myeloma. “Using myeloma samples, the predictive ability of the algorithm to identify whether or not RRM1 is essential for the tumor has been confirmed in 100% of cases,” said Prosper.
New therapeutic targets against cancer
“It is a tool applicable to any tumor. From the results obtained, new drugs can be developed or existing drugs can be reused to attack the disease,” said Francisco J. Planes, principal researcher. “In addition, we have not only found metabolic vulnerabilities for tumor growth, but we also have identified a list of genes which activity allows us to predict which patients could respond positively to the treatment, within the framework of personalized medicine.”
The work, which has been carried out since 2012, compared this algorithm with others designed by groups in Tel Aviv University (Israel) and at UC San Diego (USA), among others. “Comparatively our algorithm has a better behavior than those developed by these centers and adds a different approach,” said Planes.
The team is already developing a project to apply the algorithms in prostate cancer and breast cancer resistant to endocrine therapy. Additionally, the next step is the improvement of the tool itself, by extending the experimental validation of RRM1 to preclinical models.