The field of personalized medicine is focused on gathering information at a pre-translational level. Although this information is useful, it is becoming increasingly apparent that post-translational expression is not always consistent with that of the DNA or mRNA. Thus to advance personalized medicine, the development of additional technologies to gather disease state information at post-translational levels are necessary. This dissertation is focused on the advancement of fluorescent polymer technology to monitor at a secretomics level. Secretomics is a subset of proteomics that is focused on secreted proteins/enzymes from the cell. To monitor these secreted proteins, polymers were synthesized from the monomer state, with a polymerizable moiety of 4-vinylbenzoic acid. This compound was conjugated to various amino acids, alcohol derivatives, fluorophores, and metalloproteinase inhibitors. The monomer composition was thus varied to generate a library of polymers capable of forming unique interactions with proteins and enzymes. This polymeric library was screened against recombinant human MMP-7, -9, and -10. The fluorescent emissions data was subsequently evaluated using statistical analysis. This analysis provided information regarding an optimal polymer formulation for MMP isozyme specific interactions. This polymer was subsequently screened against both breast and prostate cancer cell conditioned media. This media were prepared such that the polymers would only interact with secreted proteins/enzymes (i.e. secretomics). Again fluorescence emissions data were evaluated using statistical analysis (linear discriminate analysis [LDA]) to demonstrate the polymer could distinguish between the subtypes, or sub-classification of the cancerous cells. Replacement of the fluorophore with a more hydrophilic pyranine improved the water solubility of the polymer. This polymer was thus screened against both the prostate and breast cancer conditioned media and evaluated using LDA. The results demonstrate an enhanced ability to distinguish and subtype the cancer cells. This dissertation will discuss an alternative mythology for the advancement of post-translational sub-typing with the intent to advance the field of personalized medicine.