Research output per year
Research output per year
Project Details
Description
This project aims to predict patient response to radiotherapy using metabolomic strategies. Additionally, it seeks to propose an approach to optimize radiotherapy outcomes in treatment-resistant patients through the use of nanotechnology.
To achieve this, samples will be collected 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 assessing radioresistance. Samples designated for metabolomic analysis will be frozen in liquid nitrogen, stored at -80°C, and later analyzed to establish metabolomic profiles using mass spectrometry. Samples for radioresistance testing will be subcultured in the laboratory and classified based on their radioresistance through the development of survival curves.
By cross-referencing metabolomic profiles with survival curve data, the relationship between metabolic expression patterns and radioresistance will be determined, with the goal of identifying a metabolic signature that can predict radioresistant tumors.
In parallel, physical simulations will be conducted using the Geant4 platform to determine the optimal physical parameters (material, size, concentration, radiation dose delivered to the tissue) of potential nanoparticles to be used as radiosensitizers. These simulations will be based on the properties of nanoparticles that are already commercially available or under experimental development for this or other purposes. Finally, samples from patients exhibiting radioresistant profiles will be selected, and the enhancement of radiotherapy effectiveness will be tested in vitro by comparing survival curves of samples with and without nanoparticles.
To achieve this, samples will be collected 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 assessing radioresistance. Samples designated for metabolomic analysis will be frozen in liquid nitrogen, stored at -80°C, and later analyzed to establish metabolomic profiles using mass spectrometry. Samples for radioresistance testing will be subcultured in the laboratory and classified based on their radioresistance through the development of survival curves.
By cross-referencing metabolomic profiles with survival curve data, the relationship between metabolic expression patterns and radioresistance will be determined, with the goal of identifying a metabolic signature that can predict radioresistant tumors.
In parallel, physical simulations will be conducted using the Geant4 platform to determine the optimal physical parameters (material, size, concentration, radiation dose delivered to the tissue) of potential nanoparticles to be used as radiosensitizers. These simulations will be based on the properties of nanoparticles that are already commercially available or under experimental development for this or other purposes. Finally, samples from patients exhibiting radioresistant profiles will be selected, and the enhancement of radiotherapy effectiveness will be tested in vitro by comparing survival curves of samples with and without nanoparticles.
Objective
To describe and explore the potential relationship between metabolomic profiles and patterns of radioresistance in thyroid tumors and gliomas, as well as their possible sensitization through the use of nanotechnology
| Status | Finished |
|---|---|
| Effective start/end date | 28/01/19 → 28/01/22 |
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
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SDG 3 Good Health and Well-being
Keywords
- Metabolomics
- thyroid cancer
- astrocytic tumors
- radiotherapy
- nanoparticles
Mederi Institutes
- Brain, Head and Neck Institute
Fingerprint
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
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Abstract P4-6.008: Integrating Molecular Profiling into Glioma Diagnosis: Implications of the WHO-CNS5-2021 Classification
Patiño Aldana, A. F., Echeverría, O., Chinchilla, A. M., Contreras, N., Peralta, C., Caballero, N., Tovar, H. V., Palacio, J. M., Uribe, V., Mineo, M., Velandia, F., Silgado, D., Castillo, W. M. R., Méndez, A. O. O. & Fonseca Mendoza, D. J., 7 Apr 2025. 1 p.Research output: Contribution to conference › Abstract
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Integrating molecular profiling into glioma diagnosis: implications of the WHO-CNS5-2021 classification of adult-type diffuse gliomas in Colombian patients
Echeverría, O., Patiño-Aldana, A. F., Chinchilla, A. M., Contreras, N., Peralta, S., Caballero, N., Tovar, H. V., Palacio, J. M., Uribe, V., Mineo-Pachón, M., Velandia, F., Riveros Castillo, W. M., Valero, N., Silgado-Guzmán, D. F., Ondo-Mendez, A. & Fonseca-Mendoza, D. J., 30 Nov 2025, In: Frontiers in neurology. 16, p. 1-12 12 p.Research output: Contribution to journal › Article › peer-review
1 Scopus citations