RESUMEN
Most physical and other natural systems are complex entities composed of a large number of interacting individual elements. It is a surprising fact that they often obey the so-called scaling laws relating an observable quantity with a measure of the size of the system. Here we describe the discovery of universal superlinear metabolic scaling laws in human cancers. This dependence underpins increasing tumour aggressiveness, due to evolutionary dynamics, which leads to an explosive growth as the disease progresses. We validated this dynamic using longitudinal volumetric data of different histologies from large cohorts of cancer patients. To explain our observations we put forward increasingly-complex biologically-inspired mathematical models that captured the key processes governing tumor growth. Our models predicted that the emergence of superlinear allometric scaling laws is an inherently three-dimensional phenomenon. Moreover, the scaling laws thereby identified allowed us to define a set of metabolic metrics with prognostic value, thus providing added clinical utility to the base findings.
RESUMEN
Tumor-cell-secreted extracellular vesicles (EVs) can cross the disrupted blood-brain barrier (BBB) into the bloodstream. However, in certain gliomas, the BBB remains intact, which might limit EVs release. To evaluate the ability of tumor-derived EVs to cross the BBB, we used an orthotopic xenotransplant mouse model of human glioma-cancer stem cells featuring an intact BBB. We demonstrated that all types of tumor cells-derived EVs-apoptotic bodies, shedding microvesicles and exosomes-cross the intact BBB and can be detected in the peripheral blood, which provides a minimally invasive method for their detection compared to liquid biopsies obtained from cerebrospinal fluid (CSF). Furthermore, these EVs can be readily distinguished from total murine EVs, since they carry human-specific DNA sequences relevant for GBM biology. In a small cohort of glioma patients, we finally demonstrated that peripheral blood EVs cargo can be successfully used to detect the presence of IDH1G395A, an essential biomarker in the current management of human glioma.