Immune-oncology genetic profile associated to prediction of response to intravesical BCG treatment of bladder urotelial carcinoma

• Grant number: 19/23679-5
• Support Opportunities: Regular Research Grants
• Start date: September 01, 2020
• End date: August 31, 2022
• Field of knowledge: Health Sciences - Medicine - Medical Clinics
• Principal Investigator: Flavio Mavignier Cárcano
• Grantee: Flavio Mavignier Cárcano
• Host Institution: Hospital do Câncer de Barretos. Fundação Pio XII (FP). Barretos , SP, Brazil
• Associated researchers: Ana Carolina Laus ; João Neif Antonio Junior ; Luciane Sussuchi da Silva ; Luis Eduardo Rosa Zucca ; Rui Manuel Vieira Reis ; Wesley Justino Magnabosco

Abstract

Bladder cancer is the second most common neoplasm from urinary tract and the majority are urothelial carcinomas. The medical approach depends on the tumor invasiveness and when diagnosed as superficial tumor, the therapeutic goal is to avoid the bladder muscle invasion. Intravesical BCG vaccine after transurethral resection of the bladder tumor is the most effective therapy for non-muscle-invasive high risk disease. However, immunoregulatory mechanisms what may lead to treatment failure of BCG vaccine are not well known. The knowledge of that tumor microenvironment and of possible genetic signatures may contribute to development of more effective treatment for high-risk subset of patients. Objectives: we aim describe genetic expression profile of cellular elements from non-muscle-invasive urothelial carcinoma microenvironment whose was treated with intravesical BCG vaccine. We will look for associations between expression signatures to clinical outcomes. Methodology: It is a case-control study to evaluate three relapsing groups after intravesical BCG vaccine treatment. Primary tumors resected before BCG vaccine treatment and their cellular elements from tumor microenvironment will be analysed by genic expression using NanoString. Genetic signature data appraisal by NanoString nCounterTM will be performed by NanoStringDiff. Afterwards, dispersion parameter will be analysed by Bayesian regression and likelihood ratio test will be performed to identify gene expression differentially. (AU)