A fundamental question in cancer biology is how cancers evolve heterogeneity and treatment resistance. The evolution trajectory of cancer is dictated by selective pressures from treatments and the tumour ecosystem. Artificial intelligence (AI) allows us to measure geographical patterns of the microenvironment in pathological samples, to study cancer habitats and niches. Current challenges are establishing multidisciplinary platforms and developing reproducible AI tools by leveraging pathology, genetic, molecular, cellular, and clinical data to improve personalized oncology.

Charles Darwin described how the intimate coexistence between flowering plants and insects leads to reciprocal evolutionary changes; this is now known as coevolution. Today, through the demolition of disciplinary barriers, AI and pathology can coevolve to create evolutionary changes and new paradigms. I will discuss our latest progress on combining AI and experimental technologies for spatial histology and omics data analysis. We aim to understand how cancer evolves within diverse environmental conditions. Our work has revealed a high level of geospatial variation in the tumour microenvironment, with profound implications for early diagnosis, biomarker development, and cancer therapeutics.