Tumor heterogeneity in glioblastoma: An integrative approach
Tumor heterogeneity describes the fact that, even for the same type of tumor, cells can show distinct genomic and phenotypic profiles. This phenomenon occurs both between tumours (inter- tumour heterogeneity) and within tumors (intra-tumor heterogeneity). This heterogeneity introduces significant challenges in designing effective treatment strategies that are valid for the tumor as a whole and not only for a specific cellular type within the tumor. Research into understanding and characterizing heterogeneity is necessary to design more refined treatment strategies that incorporate knowledge of heterogeneity to yield higher efficacy. Specifically, glioblastoma has always been known for its complexity, thus the name “multiforme”. This is one of tumors in which heterogeneity has been recognized at many different levels to be one of the reasons of the lack of response or relapse after therapies. Understanding this complexity at the different levels requires the integration of the biological information coming from a broad variety of sources including molecular biology (ohmics), cellular biology/pathology (immunohistochemistry), imaging (functional imaging), and also accounting for the space and time factors. Putting together all of this information into useful models of the disease may open the way for the design of more efficient treatments. Mathematical models have the potential to integrate all this information into simplified yet useful non-linear representations of the tumor’s behavior that can be later used as in-silico platforms to optimize therapies at the personalized level.
James S. Mc Donnell Foundation (USA) (2015-2016)
Universidad de Castilla-La Mancha
Número de investigadores