PO-167 Metabolomics biomarkers in breast cancer tumours treated with neoadjuvant therapy
| Autor: | Angelo Gámez-Pozo, Lucía Trilla-Fuertes, Mariana Díaz-Almirón, Andrea Zapater-Moros, M. Ferrer-Gomez, R López Vacas, E. Espinosa, Pilar Zamora, Guillermo Prado-Vázquez, J.A. Fresno Vara |
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| Rok vydání: | 2018 |
| Předmět: |
Oncology
Cancer Research medicine.medical_specialty Chemotherapy business.industry Metabolite medicine.medical_treatment Omics medicine.disease chemistry.chemical_compound Basal (phylogenetics) Metabolomics Breast cancer chemistry Internal medicine medicine Biomarker (medicine) business Neoadjuvant therapy |
| Zdroj: | ESMO Open. 3:A292 |
| ISSN: | 2059-7029 |
| DOI: | 10.1136/esmoopen-2018-eacr25.689 |
| Popis: | Introduction Breast cancer is one of the most prevalent cancers in the world. Traditionally, early breast cancer treatment is based on surgery and, after surgery, hormone treatment or chemotherapy. However, the neoadjuvant treatment is increasingly used. Metabolomics is the most recent ‘omics’ which allows quantify metabolites into blood patient samples. Coupled with computational analyses it could be possible to study differential metabolomics patterns and associate them with neoadjuvant response. Material and methods Blood plasma samples from patients with breast cancer treated with neoadjuvant chemotherapy were used to perform metabolomics experiments. One sample before the treatment (basal) and one sample after the chemotherapy (post-treatment) were analysed and clinical data regarding response (complete response or partial response) was also collected. Metabolomics experiments were performed using liquid chromatography coupled with mass-spectrometry.). Bayesian network and class comparison analyses were used to establish differential metabolic patterns between conditions. Additionally, a response prediction model based on logistic regression was build using metabolomics data from basal samples. Results and discussions A network showing the relationships between metabolites was build. Comparing metabolite measurements between complete response and partial response tumours in basal samples, 19 metabolites showed a differential quantification between both types of responses. Moreover, one of these metabolites is linoleic acid, previously described as a biomarker of complete response in neoadjuvant treatment in breast cancer. Using these 19 differential metabolites, a response predictive model was build. According to this model, it is possible to predict response to neoadjuvant treatment based on the amount of one metabolite, still only identified by its mass and charge. On the other hand, comparing basal and post-treatment samples, the network showed differential metabolomics patterns. These differential metabolites could be used as predictive biomarkers of response. Conclusion This study is a proof of concept that using a new ‘omics’ technique such as metabolomics in blood samples, coupled with computational analyses, it is possible to identify differential metabolomics patterns between complete and partial response or basal and post-treatment samples and design predictive models of response These results could facilitate in the future the implementation of blood-based tests into the clinical routine. |
| Databáze: | OpenAIRE |
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