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Glioblastoma brain cancer invasion: how different subclasses move through the brain

Grundy T1,2, De Leon E1, Stringer B3, Day B3, Boyd A3, Cooper-White J4 and O’Neill GM1,2 1Children’s Cancer Research Unit, The Children’s Hospital at Westmead, Sydney, NSW, Australia, 2Discipline of Paediatrics and Child Health, University of Sydney, Australia, 3Brain Cancer Research Unit & Leukaemia Foundation Research Unit, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia and 4Tissue Engineering and Microfluidics Laboratory, Australian Institute for Bioengineering and Nanotechnology, University of Queensland, St. Lucia, Queensland, Australia


Many types of solid tumours are detected by physical palpation and it has been revealed that tumour stiffness (evidenced by the ability to distinguish the tumour from surrounding tissue by touch), is in fact a significant factor in the promotion of cancer malignancy (increased invasion). Because this changes our understanding of the cancer microenvironment and opens completely new avenues for treatment, this mechanism is now being studied in a wide variety of tumours, including glioblastoma (GBM). However, there are conflicting reports over whether GBM are rigidity-dependent (induced to invade in response to increasing external tissue stiffness) or rigidity-independent (invade irrespective of local tissue rigidity). Since GBM are now known to constitute a group of molecularly and prognostically distinct subclasses, we hypothesized that the conflicting reports may be due to different responses between the subclasses. To address this hypothesis, we analysed the rigidity-response of primary GBM lines representative of proneural, neural and mesenchymal subclasses, based on cluster analysis of microarray expression data as previously reported. Cells were plated on poly-acrylamide gels of defined rigidity, corresponding to the reported range of Young’s modulus values for brain tissue (0.2, 1.0 and 8.0 kPa) and 50 kPa (~stiffness of fibrotic tissue). Time-lapse imaging and cell tracking revealed that proneural cell migration speed is rigidity-dependent, mesenchymal cell migration speed exhibits an intermediate response and neural cell migration is rigidity-independent. Moreover, we show corresponding differences in actin stress fibre formation correlating with the differences in rigidity responses. Our data therefore reveals that GBM subclasses display different responses to the external mechanical environment that may determine invasive capacity. While the ability to stratify GBM patients into different risk groups can inform treatment decisions, the even greater promise of such stratification is if we can find ways that will specifically target the different patient groups. Our data has revealed subclass specific invasion mechanisms that may provide a key to new approaches to successfully treat GBM from different subclasses.

Format: Oral communication

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