A tool of ‘single-cell transcriptional profiling’ (scRNA seq)—gives the ability to measure how much any single human gene in the entire genome is turned ‘on’ or ‘off’ within a single cell. Utilizing scRNA seq on patient tissue samples offers the potential to identify cell populations and genes responsible for disease pathology in a highly precise manner. In diseases like TNBC where there are no treatment options outside of routine chemotherapy, scRNA seq holds the potential to make precision medicine a reality. Our laboratory has created a new method, SCALES (Spectral Correlation Analysis of Layered Evolutionary Signals), to precisely identify drivers of pathology. Preliminary results show substantial promise in precisely defining transcriptional signatures of TNBC. This study is centered around validating statistical predictions with the goal of establishing a framework to identify high-value targets for gene therapy. If successful, we believe we can develop new treatments for TNBC, reflecting a new paradigm for data-driven precision medicine.