Validation of biomarkers in temozolmide response in glioblastomas: data analysis of mRNA expression by bioinformatics methods

• Grant number: 22/16660-9
• Support Opportunities: Scholarships abroad - Research Internship - Master's degree
• Start date: April 30, 2023
• End date: October 29, 2023
• Field of knowledge: Biological Sciences - Genetics - Molecular Genetics and Genetics of Microorganisms
• Principal Investigator: Rui Manuel Vieira Reis
• Grantee: Ideli Zanesco Fontes Baptista
• Supervisor: José Javier Otero
• Host Institution: Hospital do Câncer de Barretos. Barretos , SP, Brazil
• Institution abroad: Ohio State University, United States
• Associated to the scholarship: 21/02918-1 - Validation of predictive biomarkers of survival in temozolomide treatment in glioblastoma, BP.MS

Abstract

Glioblastoma is the most common and aggressive malignant central nervous system (CNS) tumor in adults. The standard protocol is surgery, radiotherapy and adjuvant/comcomitant chemotherapy with temozolomide. The cancer cells usually have resistance mechanisms against this chemotherapy agent, commonly associated with the DNA Repair pathways. To find a way out of this problem, we investigate DNA Repair related gene expression and associated with patient's survival, aiming to find response biomarkers related to temozolomide resistance. Firstly, we performed the analysis of DNA Repair related gene expression using the DNA Damage and Repair panel (nCounter) in 120 formalin-fixed paraffin-embedded (FFPE) glioblastomas from patients treated according to standard protocol. The results of the expression profile were then associated with patient's overall survival, creating a pattern for genes with their high expression to temozolomide treatment responsiveness (p<0.05 and Fold Change >1.5 and <-1.5). Were found 32 differential expressed genes, 4 of them associated with the resistance of the treatment and worst prognosis, including the MGMT gene as expected as well as CDK7, the gene we chose to do the functional validation. The differentially expressed genes will be then validated in another glioblastoma cohort (n=100) using a custom nCounter panel. To do so we are going to implement a partnership with Ohio State University to increase the validation cohort number and to help us with the bioinformatics analysis. There, the student will learn to perform and implement machine learning technics, neural networks and in silico evaluation of the genes that were found in NanoString Analisys. (AU)