Project 467051

Current cancer therapies, such as chemotherapy, are largely non-specific and cause systemic side effects. Modern advances in cancer therapeutics involve the development of personalized smart drugs to specifically target different cancers, while reducing toxicity to healthy cells. One branch of this is nanomedicine, which involves nanoparticle drug carriers that infiltrate the tumour and selectively target cancer cells. Currently, design of nanoparticle therapeutics are limited by our understanding of factors that impact their accumulation in tumours. The aim of this work is to investigate how structural features within the tumour affect nanoparticle delivery, which will aid in improved design and allow for identification of tumours that will have the greatest response to nanotherapeutics. 3D imaging will be used to visualize the complex structure of the tumour environment and analyze integral components, such as blood vessels and immune cells. Images will then be processed and analyzed to categorize the heterogeneous tumoural network into regions, termed neighbourhoods, depending on the combination of unique structural features present. Nanoparticles will be quantified within each neighbourhood and accumulation patterns will be correlated with certain structural features. This correlation can then be used to predict how nanoparticles will behave in other tumours depending on tumour characteristics. Using the classifications developed through this work, we can begin to expand our understanding of the impact of tumour structural features on nanoparticle accumulation, which is the first step towards developing a predictive algorithm to optimize selection of therapeutics for difficult to treat cancers.