Our central hypothesis is that the use of Raman spectroscopy which creates a molecular fingerprint of the tumor at single-cell resolution, in conjunction with machine learning methods, can differentiate different types of cells existent in the tumor microenvironment and provide biological insights that guide further exploration into tumor biology. This will provide an understanding of tumor 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.