Determination of metabolomic profiles of radio-sensitivity and optimization of radiotherapy using nanoparticles in thyroid and glioma tumors: a translational approach.

Project: Research project

Project Details


This project seeks to predict, through the use of metabolomic strategies, the patient's response to radiotherapy treatment. Additionally, to propose a strategy to optimize the response of radiotherapy in patients resistant to treatment, through the use of nanotechnology. To achieve this, samples will be obtained from patients with brain tumors or differentiated thyroid cancer. Each tumor tissue sample will be divided into two parts, one for metabolomic studies and the other for the evaluation of radio-resistance. The samples for metabolomics will be frozen in liquid nitrogen, stored at -80°C and subsequently analyzed to establish metabolomic profiles by mass spectrometry. The samples for radio-resistance will be sub-cultivated in the laboratory and will be
will classify according to their radius-resistance by developing survival curves. Crossing the information of the metabolomic profile and the survival curves, the relationship between metabolic expression profiles and radio-resistance will be established in order to determine the metabolic footprint that allows identifying a patient with a radioresistant tumor.

At the same time, physical simulations will be carried out on the Geant4 platform to establish the optimal physical parameters (material, size, concentration, radiation dose delivered to the tissue) of the possible nanoparticles to be used as radiosensitizers, based on the characteristics of nanoparticles already marketed or undergoing experimentation for this or other purposes. Finally, samples will be selected from patients who have shown radioresistance profiles and the degree of increase in the effectiveness of radiotherapy will be tested in vitro, comparing the survival curves of the sample with and without nanoparticles.

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Effective start/end date3/20/193/30/21

Main Funding Source

  • Internal