Assessment of tumor biomarkers for prognostic and prediction of non-Hodgkin's lymphoma through a metabolomics approach

• Grant number: 14/10734-4
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: August 01, 2014
• End date: July 31, 2015
• Field of knowledge: Physical Sciences and Mathematics - Chemistry - Analytical Chemistry
• Agreement: Coordination of Improvement of Higher Education Personnel (CAPES)
• Principal Investigator: Ana Valéria Colnaghi Simionato
• Grantee: Adriana Zardini Buzatto
• Host Institution: Instituto de Química (IQ). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil

Abstract

Cancer is the cause of one in every eight deaths in the world and a 58% increase in the number of deaths is predicted until 2030. Non-Hodgkin's lymphomas are tumors that develop in tissues of the lymphatic system and present high cellular and genetic variability. The diagnosis for the disease is achieved through pathological exams, but its prognostic and the prediction of patient's response to the currently available treatments are uncertain. It is vital that the prognosis and the prediction of treatment response are accurate and precise to ensure that the patients receive a proper treatment, which results in better life quality and increase in survival period, as well as reduction of toxic effects from drugs. In this context, the research for tumor biomarkers for prognostic and prediction of non-Hodgkin's lymphoma is essential for the affected population. Metabolomics is the study of metabolites present in a biological sample and is highly representative of the transient state of the patient; therefore, it is a proper alternative for biomarkers research. This work aims the search of tumor biomarkers for prognostic and prediction of non-Hodgkin's lymphoma through a metabolomics approach. Blood serum and urine samples from diseased patients will be collected before every chemotherapy cycle and analyzed by UHPLC-MS, GC-MS and CE-MS, techniques that are complementary and guarantee a wide coverage on the many metabolite classes. After data treatment by multivariate statistical analysis, the putative biomarkers will be validated and may aid the choice of treatment in a personalized medicine approach. (AU)