The biomedical engineering team at Ceit-IK4 is tackling research projects that contribute to the fight against cancer

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Ceit-IK4 engineers from different areas of specialisation are making progress in the on-going fight against cancer in collaboration with doctors from Clínica Universidad de Navarra and with biologists and pharmacists from the Center for Biomedical Engineering at the University of Navarra (CBIO) and CIMA.

A group of bioinformatics researchers from the Genomics Group at Ceit-IK4 are working on a number of cancer-related research projects in collaboration with doctors, biologists and biochemists from CIMA.

Their primary objective is to understand and detect the genetic alterations that occur in cancer patients.

To that end, they have built a software program that detects the alterations that occur in gene structure via a process known as “alternative splicing”. Since these modifications can lead to changes in gene functions, being able to discover these alterations in cancer patients will help researchers to better understand cancer evolution.

The challenge lies in the fact that because people have about 22,000 genes, mathematical and statistical formulas are needed to model the alterations and assign them a statistical indicator that confirms the results. This approach has yielded positive outcomes, as in some of the tests researchers were able to find a “splicing event” that is related to the evolution of Glioblastoma Multiforme, a very aggressive brain tumour.

Running in parallel to the above project, another team of engineers from the Bioinformatics Group at Ceit-IK4 is studying cancer metabolism. Given the growing evidence that cancer cells undergo metabolic reprogramming in order to increase their ability to proliferate, correctly regulating cell metabolism has great therapeutic potential. To help explore that potential, the research team is developing computational models of prostate cancer, breast cancer, and certain cancers of the blood using high resolution molecular data in order to generate new treatment hypotheses that can then be tested experimentally.

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