Vasculogenic mimicry (VM) is one form of tumour vascularisation that describes tumour cells that have acquired endothelial-like features, endowing them with the ability to form vessel-like structures. VM networks have been postulated to be functional, enabling them to play a critical role tumour survival and metastatic disease. These de novo vessels are independent of angiogenesis processes, in which host endothelial cells form vessels from pre-existing vasculature, and instead have been shown to underpin therapeutic resistance to anti-angiogenic therapies (AATs). Although molecular data largely supported by in vitro work has immensely contributed to our understanding of VM, the spatial features that define the VM phenotype remain largely unknown and understudied. How these vessels present in space and in 3D, has, until recently, been unknown. This underpins the scarcity of in vivo evidence and subsequent imaging and spatial data which would help further illuminate VM. Therefore, the overarching goal of this dissertation was to curate a novel toolkit that enables a revolutionary approach to better capturing and understanding VM, in vivo. The toolkit has largely entailed the optimisation of a vascular perfusion assay and the integration of two state-of-the-art technologies: a 3D two-photon imaging modality and a single cell, multiplexed immune-labelling proteomics platform. The former has greatly improved our ability to confidently capture genuine VM networks in their natural environment whilst the latter has enabled a novel approach to better resolving the spatial features of these networks and VM-tagged tumours more broadly. Upon successful development, optimisation and validation of the VM toolkit, the final phase of this project was to apply it first to mouse models of VM, followed by human cancer cell line-derived mouse models, all in the triple negative breast cancer setting. The culmination of this PhD has yielded three impactful achievements. First is an optimised and validated novel toolkit that enables VM networks to be confidently and reliably captured and better understood, spatially. 3D evidence for VM can now be directly interrogated with intricate spatial technologies for further molecular and spatial characterisation. Second is the application of this toolkit to mouse models of VM, illuminating a complex vasculature across 3D models and the prominent role of anti-angiogenic pathways in these VM-tagged tumours. Third is the application of this tool kit to VM-competent human cancer cell line-derived mouse models of VM, enabling additional in vivo models to be established. In these models, genuine VM networks were captured, encapsulating some of the most convincing in vivo and 3D evidence for VM across all models supporting this PhD and arguably across much of the current VM literature. This PhD has enabled the elusive VM phenotype to be more robustly captured and comprehensively resolved spatially, using a bespoke toolkit in addition to identifying and exploring additional in vivo models of VM. These are pivotal accomplishments that will directly impact the field and enable the biological importance and relevance of this mechanism to be further supported. The implications that the toolkit developed and the insights gathered in support of this project can be clearly defined and are highlighted throughout this dissertation.