Mass spectrometry offers the most robust platform to discover and characterize new diagnostic, prognostic, and therapeutic biomarkers for ovarian cancer across all molecular classes. Moreover, a systems biology approach will allow the underlying biology of ovarian cancer to be understood. This presentation will discuss the challenges specific to the study of epithelial ovarian cancer (EOC) in humans and how these challenges have directed our thinking, in terms of the development of model organisms and mass spectrometry-based bioanalytical strategies. First, to augment the human model, we developed the domestic hen model of spontaneous EOC, which allowed us to longitudinally sample the rapid onset and progression of the disease in a controlled environment. Second, we developed bioanalytical tools to characterize structurally challenging analytes that are critical to a systems-level analysis. To increase the electrospray response of N-linked glycans, perform stable-isotope relative quantification, and semi-automated data analysis, we synthesized novel hydrophobic tagging reagents (INLIGHTTM). Third, we developed a novel ionization technique for tissue imaging of lipids and metabolites. This unique model organism has and continues to provide new insights into the biology of ovarian cancer; combined with other –OMICS data obtained through these novel bioanalytical approaches, we will understand the origin of ovarian cancer and ultimately translate that knowledge to humans. The second part of the presentation will detail the characterization of SOD1 derived from patients diagnosed with sporadic ALS. The level of molecular detail (100% sequence coverage) using bottom-up proteomics and intact protein measurements allowed us to discover novel attributes of SOD1 including the incorporation of a nonprotein amino acid, novel nonsynonymous SNPs as well as low abundant PTM’s (<1% occupancy) pertaining to oxidative stress. New materials for the characterization and quantification of novel SOD1 proteoforms will also be presented. Finally, computational modeling of these proteoforms suggests that they have a significant impact on the structure of SOD1 and the progression of motor neuron diseases.