Protein expression of specific genes varies widely from cell to cell. Consequently, it is important to understand protein expression at the single-cell level in order to gain higher resolution understanding of this variation. Such data would provide valuable information about cellular functions and pathways, as well as a better understanding of cell heterogeneity and stochasticity. In particular, single-cancer cell analysis is essential to gain a more complete understanding of tumor progression and cell heterogeneity and to provide insight about rare cells in a population that could guide treatment and therapy. However, many of the current methods for studying protein expression measure average signals from a bulk sample containing thousands to millions of cells, which may not be representative of the behavior of different subpopulations of cells in the sample. In such cases, protein expression levels of a minority cell population are masked by the majority population. We demonstrate the ability to quantify protein molecules at single-cell resolution using Single Molecule Arrays (Simoa) to study cancer cells of different subtypes. We further show that with Simoa’s multiplexing capability, protein-protein interactions and changes in molecular biology at the single-cell level can be analyzed with the platform. Protein molecule profiles from single cancer cells will illuminate single-cell protein dynamics as cancer progresses.