Advanced search
Start date
Betweenand

Investigation of oncogenic signaling modulators in ovarian tumors with genomic instability

Grant number: 20/11453-0
Support type:Scholarships in Brazil - Scientific Initiation
Effective date (Start): December 01, 2020
Effective date (End): November 30, 2021
Field of knowledge:Health Sciences - Medicine
Principal researcher:Fabio Albuquerque Marchi
Grantee:Karina Tiemy Oyama
Home Institution: A C Camargo Cancer Center. Fundação Antonio Prudente (FAP). São Paulo , SP, Brazil

Abstract

High-grade serous carcinoma (HGSC), the most prevalent histological type of ovarian cancer, has the lowest survival rates and is the leading cause in the world of gynecological cancer-related deaths. HGSC is characterized by the high frequency of mutations in the TP53 gene, genomic instability and few actionable targets for therapy. Despite an initial clinical response in most patients, recurrence is frequent, with low survival rates. Thus, the main clinical needs for these tumors include the identification of new therapeutic targets and a better understanding of the mechanisms leading to resistance to chemotherapy. Seeking to contribute with a different perspective on these tumors we propose in this project an in silico analysis for the identification of possible modulators (genes and proteins) of cell signaling activity in 174 HGSC, sensitive and resistant to systemic therapy, classified according to their homologous recombination deficiency profile, mutations and changes in the number of copies. With this study we hope to identify targets with therapeutic potential that can be investigated in functional experiments in the laboratory, contribute biologically with a perspective on altered signaling pathways in resistant tumors and also consolidate the learning about bioinformatics tools available for these analyses.

News published in Agência FAPESP Newsletter about the scholarship:
Articles published in other media outlets (0 total):
More itemsLess items
VEICULO: TITULO (DATA)
VEICULO: TITULO (DATA)