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  • Challenge: Making the integration of computational biology in cancer research routine

This project applies combined spatiotemporal statistical approaches to provide quantifiable classifications of qualitative observations made by pathologists and analyse spatial molecular phenotyping data. This will allow Oxford researchers to develop new methods to compare relative distributions of tumour infiltrating leukocyte subclasses and other markers, such as microenvironmental hypoxia. This is being applied first in deep phenotyping ‘omics and clinical data from patients in relatively small clinical trials, for example, oesophageal cancer patients receiving chemo(radio)-immunotherapy, before validation in larger sample sets. Interrogating multifaceted data will provide an agile, adaptable framework for identifying new biomarkers, stratifying patient response and informing improved treatment strategies at a patient-specific level.