Our central hypothesis is that the use of Raman spectroscopy which creates a molecular fingerprint of the tumour at single-cell resolution, in conjunction with machine learning methods, can differentiate different types of cells existent in the tumour microenvironment and provide biological insights that guide further exploration into tumour biology. This will provide an understanding of tumour biology at a level unreachable before, by measuring single cells within their microenvironment and can lead to the discovery of novel targets and biomarkers with the potential to improve the disease outcome.