Current students


Section: Computer Science and Engineering

Major Research topic:
Novel computational methods for early diagnosis of High Grade Serous Epithelial Ovarian Cancer

Despite the efforts made in the study of treatments of High Grade Serous Epithelial Ovarian Cancer (HGS-EOC), its lethality continues to be high. This is due to the late diagnosis of the tumor: 80% of cases are diagnosed at an advanced stage. This late diagnosis is explained by the lack of early symptoms and in the failure of the screening tests and lack of specific biomarkers for disease detection.

Recent studies have shown that somatic pathogenic mutations in the TP53 gene can be detected in PAP tests performed several years (i.e., six years) before the diagnosis of HGS-EOC. This finding provides a proof of concept that early diagnosis of HGS-EOC is feasible.

The main bottlenecks in developing an assay for detection of early pathogenic somatic variants in the TP53 gene are: the limit of detection, the low sensitivity and the high error-prone rate of next generation sequencing (NGS) approaches used to scan the entire sequence of TP53 gene. The aim of the project is to develop novel computational and algorithmic methods in order to increase the sensibility and specificity of NGS based screening processes and give new opportunities for the early diagnosis of HGS-EOC.