WNTrack – Tracking brain tumors through their exosomes: a WNT6-driven approach
Brain tumors have one of the highest mortality rates and rank 1st in average years of life lost among all tumor types. Among them, glioblastoma (GBM) is the most malignant and common tumor type in adults, with a median overall survival of ~15 months and a 5-year survival rate of only 5%. We have recently shown that WNT6, a signaling molecule of the WNT pathway, is expressed in virtually all GBM, and that its increased expression is associated with patients’ shorter overall survival and with increased GBM aggressiveness.
Besides the ineffectiveness of GBM therapy, patients’ dismal prognosis is also linked to the fact that diagnosis and tumor monitoring is technically challenging, relying on risky brain biopsy and expensive magnetic resonance imaging. In this context, several efforts have been made to use liquid biopsies for non-invasive diagnosis and monitoring of GBM, namely using circulating tumor cells and cell-free nucleic acids, but frustrating results have been obtained so far.
More recently, tumor-derived exosomes detected in patients’ blood has emerged as an appealing alternative. Exosomes are secreted by all viable tumor cells, have been found in higher levels in GBM patients, and their cargo includes nucleic acids and proteins, being globally better representations of the intratumoral heterogeneity.
Considering that WNT6 exerts its paracrine and autocrine role upon being secreted into exosomes, in this project, we intend to identify a WNT6-driven exosomal molecular signature associated with GBM aggressiveness and poor prognosis, and explore its utility as a blood-based biomarker tool for GBM patients’ management.
António J. Salgado
Bruno M. Costa
Eduarda P. Martins
Main Project Outcomes
S. Queirós, “Right ventricular segmentation in multi-view cardiac MRI using a unified U-net model”, in E. Puyol Antón et al. (eds) Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge. STACOM 2021. Lecture Notes in Computer Science, vol 13131, pp. 287-295, Springer, Cham, 2022.
“Best Paper Award in the M&Ms-2 Challenge”, by M&Ms2 Challenge organizers and the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society.