Brain Tumors

SOX genes are developmental regulators with functions in the instruction of cell fate and maintenance of progenitor’s identity during embryogenesis. They play additional roles during tissue homeostasis and regeneration in adults particularly in the Central Nervous System (CNS). In the last years a growing number of evidences has shown that mutations and dysfunction of SOX factors are implicated in several human diseases, including a variety of cancers. In this review, we will summarize the current knowledge about SOX family in CNS tumors and their role in the origin and maintenance of the subpopulation of cancer stem cells in these tumors.

Background. Endoplasmic reticulum (ER) stress results from protein misfolding imbalance and has been postulated as a therapeutic strategy. ER stress activates the unfolded protein response which leads to a complex cellular response, including the upre-gulation of aberrant protein degradation in the ER, with the goal of resolving that stress. O 6-methylguanine DNA methyltransferase (MGMT), N-methylpurine DNA glycosylase (MPG), and Rad51 are DNA damage repair proteins that mediate resistance to temozolomide in glioblastoma. In this work we sought to evaluate whether ER stress-inducing drugs were able to downmodulate DNA damage repair proteins and become candidates to combine with temozolomide.

Glioblastoma is the most frequent malignant intra-cranial tumour. Magnetic resonance imaging is the modality of choice in diagnosis, aggressiveness assessment, and follow-up. However, there are examples where it lacks diagnostic accuracy. Magnetic resonance spectroscopy enables the identification of molecules present in the tissue, providing a precise metabolomic signature. Previous research shows that combining imaging and spectroscopy information results in more accurate outcomes and superior diagnostic value. This study proposes a method to combine them, which builds upon a previous methodology whose main objective is to guide the extraction of sources. To this aim, prior knowledge about class-specific information is integrated into the methodology by setting the metric of a latent variable space where Non-negative Matrix Factorisation is performed. The former methodology, which only used spectroscopy and involved combining spectra from different subjects, was adapted to use selected areas of interest that arise from segmenting the T2-weighted image. Results showed that embedding imaging information into the source extraction (the proposed semi-supervised analysis) improved the quality of the tumour delineation, as compared to those obtained without this information (unsupervised analysis). Both approaches were applied to pre-clinical data, involving thirteen brain tumour-bearing mice, and tested against histopathological data. On results of twenty-eight images, the proposed Semi-Supervised Source Extraction (SSSE) method greatly outperformed the unsupervised one, as well as an alternative semi-supervised approach from the literature, with differences being statistically significant. SSSE has proven successful in the delineation of the tumour, while bringing benefits such as 1) not constricting the metabolomic-based prediction to the image-segmented area, 2) ability to deal with signal-to-noise issues, 3) opportunity to answer specific questions by allowing researchers/radiologists define areas of interest that guide the source extraction, 4) creation of an intra-subject model and avoiding contamination from inter-subject overlaps, and 5) extraction of meaningful, good-quality sources that adds interpretability, conferring validation and better understanding of each case.