Humans are sexually dimorphic, with sex being the most persistent difference among humans over the course of our evolutionary history. Beyond the visible sex differences that can be considered true dimorphisms, there are also sex differences at the molecular and cellular scales. The role of these biological sex differences for human health, while being increasingly recognized, have long been underappreciated and underexplored. Frequently, these differences are only recognized in sex–specific diseases, such as genitourinary diseases and cancers. However, given the evidence for sex differences in the most basic aspects of human biology, including metabolism, cellular composition, and immune activity, these differences could have consequences for the etiology and pathophysiology of a majority of diseases. It is thus essential to consider the extent to which these differences may influence the various mechanisms underlying disease processes, response to treatment, and the maintenance of health in order to better improve patient outcomes. Here we review the evidence for a broad array of biological sex differences in humans and discuss how they may relate to observed sex differences in various diseases, with an emphasis on cancer, specifically glioblastoma. We further propose that mathematical approaches can be useful for exploring the extent to which sex differences affect disease outcomes and accounting for those in the development of therapeutic strategies.
NIMG-21. SEX DIFFERENCES IN EXTREME SURVIVORSHIP AMONG PRIMARY GLIOBLASTOMA PATIENTS
Microglia are a major cellular component of gliomas, and abundant in the centre of the tumour and at the infiltrative margins. While glioma is a notoriously infiltrative disease, the dynamics of microglia and glioma migratory patterns have not been well characterized. To investigate the migratory behaviour of microglia and glioma cells at the infiltrative edge, we performed two-colour time-lapse fluorescence microscopy of brain slices generated from a platelet-derived growth factor-B (PDGFB)-driven rat model of glioma, in which glioma cells and microglia were each labelled with one of two different fluorescent markers. We used mathematical techniques to analyse glioma cells and microglia motility with both single cell tracking and particle image velocimetry (PIV). Our results show microglia motility is strongly correlated with the presence of glioma, while the correlation of the speeds of glioma cells and microglia was variable and weak. Additionally, we showed that microglia and glioma cells exhibit different types of diffusive migratory behaviour. Microglia movement fit a simple random walk, while glioma cell movement fits a super diffusion pattern. These results show that glioma cells stimulate microglia motility at the infiltrative margins, creating a correlation between the spatial distribution of glioma cells and the pattern of microglia motility.
Letter: Surgical Decision Making From Image-Based Biophysical Modeling of Glioblastoma: Not Ready for Primetime