Novel approaches for glioblastoma treatment: Focus on tumor heterogeneity, treatment resistance, and computational tools

Abstract

Background: Glioblastoma (GBM) is a highly aggressive primary brain tumor. Currently, the suggested line of action is the surgical resection followed by radiotherapy and treatment with the adjuvant temozolomide, a DNA alkylating agent. However, the ability of tumor cells to deeply infiltrate the surrounding tissue makes complete resection quite impossible, and, in consequence, the probability of tumor recurrence is high, and the prognosis is not positive. GBM is highly heterogeneous and adapts to treatment in most individuals. Nevertheless, these mechanisms of adaption are unknown.

Recent findings: In this review, we will discuss the recent discoveries in molecular and cellular heterogeneity, mechanisms of therapeutic resistance, and new technological approaches to identify new treatments for GBM. The combination of biology and computer resources allow the use of algorithms to apply artificial intelligence and machine learning approaches to identify potential therapeutic pathways and to identify new drug candidates.

Conclusion: These new approaches will generate a better understanding of GBM pathogenesis and will result in novel treatments to reduce or block the devastating consequences of brain cancers.

Keywords: artificial intelligence; biomarkers; cancer stem cells; gap junctions; tunneling nanotubes (TNTs).