A "bottom-up" proteomics approach and a two-dimensional (strong cation exchange followed by reversed-phase) LC-MS/MS strategy on a linear ion trap (LTQ) were utilized to identify and compare expressions of extracellular and membrane-bound proteins in the conditioned media of three breast cell lines (MCF-10A, BT474, and MDA-MB-468). Proteomics analysis of the media identified in excess of 600, 500, and 700 proteins in MCF-10A, BT474, and MDA-MB-468, respectively. We successfully identified the internal control proteins, kallikreins 5, 6, and 10 (ranging in concentration from 2 to 50 μg/liter) in MDA-MB-468 conditioned medium as validated by ELISA and confidently identified Her-2/neu in BT474 cells. Subcellular localization was determined based on Genome Ontology terms for all the 1,139 proteins of which 34% were classified as extracellular and membrane-bound. Proteomics analysis of MDA-MB-468 cell lysate demonstrated that only 5% of all identified proteins were extracellular. This confirmed our hypothesis that examining the CM of cell lines, as opposed to the cell lysates, leads to a significant enrichment in secreted proteins. Tissue specificity, functional classifications, and spectral counting were performed. Elafin, a protease inhibitor, identified in the conditioned media of BT474 and MDA-MB-468 and the three kallikreins (KLK5, KLK6, and KLK10) were validated using an immunoassay on various serum and biological samples. Some of the secreted proteins identified have established roles in breast cancer development (cell growth, differentiation, and metastasis) and/or are linked to early onset breast cancer. Our approach to mining for low abundance molecules could identify proteins in various stages of breast cancer development. Many of the identified proteins are potentially useful to investigate as circulating serum breast cancer biomarkers.

Detection of lung cancer at an early stage is necessary for successful therapy and improved survival rates. We performed a bottom-up proteomics analysis using a two-dimensional LC-MS/MS strategy on the conditioned media of four lung cancer cell lines of different histological backgrounds (non-small cell lung cancer: H23 (adenocarcinoma), H520 (squamous cell carcinoma), and H460 (large cell carcinoma); small cell lung cancer: H1688) to identify secreted or membrane-bound proteins that could be useful as novel lung cancer biomarkers. Proteomics analysis of the four conditioned media allowed identification of 1,830 different proteins (965, 871, 726, and 847 from H1688, H23, H460, and H520, respectively). All proteins were assigned a subcellular localization, and 38% were classified as extracellular or membrane-bound. We successfully identified the internal control proteins (also detected by ELISA), kallikrein-related peptidases 14 and 11, and IGFBP2. We also identified known or putative lung cancer tumor markers such as squamous cell carcinoma antigen, carcinoembryonic antigen, chromogranin A, creatine kinase BB, progastrin-releasing peptide, neural cell adhesion molecule, and tumor M2-PK. To select the most promising candidates for validation, we performed tissue specificity assays, functional classifications, literature searches for association to cancer, and a comparison of our proteome with the proteome of lung-related diseases and serum. Five novel lung cancer candidates, ADAM-17, osteoprotegerin, pentraxin 3, follistatin, and tumor necrosis factor receptor superfamily member 1A were preliminarily validated in the serum of patients with lung cancer and healthy controls. Our results demonstrate the utility of this cell culture proteomics approach to identify secreted and shed proteins that are potentially useful as serological markers for lung cancer.

Current ovarian cancer biomarkers are inadequate because of their relatively low diagnostic sensitivity and specificity. There is a need to discover and validate novel ovarian cancer biomarkers that are suitable for early diagnosis, monitoring, and prediction of therapeutic response. We performed an in-depth proteomics analysis of ovarian cancer ascites fluid. Size exclusion chromatography and ultrafiltration were used to remove high abundance proteins with molecular mass ≥30 kDa. After trypsin digestion, the subproteome (≤30 kDa) of ascites fluid was determined by two-dimensional liquid chromatography-tandem mass spectrometry. Filtering criteria were used to select potential ovarian cancer biomarker candidates. By combining data from different size exclusion and ultrafiltration fractionation protocols, we identified 445 proteins from the soluble ascites fraction using a two-dimensional linear ion trap mass spectrometer. Among these were 25 proteins previously identified as ovarian cancer biomarkers. After applying a set of filtering criteria to reduce the number of potential biomarker candidates, we identified 52 proteins for which further clinical validation is warranted. Our proteomics approach for discovering novel ovarian cancer biomarkers appears to be highly efficient because it was able to identify 25 known biomarkers and 52 new candidate biomarkers that warrant further validation.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 2019 (COVID-19). While molecular-based testing is used to diagnose COVID-19, serologic testing of antibodies specific to SARS-CoV-2 is used to detect past infection.