A Targeted Mass Spectrometry Strategy for Developing Proteomic Biomarkers: A Case Study of Epithelial Ovarian Cancer.

Protein biomarkers for epithelial ovarian cancer are critical for the early detection of the cancer to improve patient prognosis and for the clinical management of the disease to monitor treatment response and to detect recurrences. Unfortunately, the discovery of protein biomarkers is hampered by the limited availability of reliable and sensitive assays needed for the reproducible quantification of proteins in complex biological matrices such as blood plasma. In recent years, targeted mass spectrometry, exemplified by selected reaction monitoring (SRM) has emerged as a method, capable of overcoming this limitation. Here, we present a comprehensive SRM-based strategy for developing plasma-based protein biomarkers for epithelial ovarian cancer and illustrate how the SRM platform, when combined with rigorous experimental design and statistical analysis, can result in detection of predictive analytes.Our biomarker development strategy first involved a discovery-driven proteomic effort to derive potential N-glycoprotein biomarker candidates for plasma-based detection of human ovarian cancer from a genetically engineered mouse model of endometrioid ovarian cancer, which accurately recapitulates the human disease. Next, 65 candidate markers selected from proteins of different abundance in the discovery dataset were reproducibly quantified with SRM assays across a large cohort of over 200 plasma samples from ovarian cancer patients and healthy controls. Finally, these measurements were used to derive a 5-protein signature for distinguishing individuals with epithelial ovarian cancer from healthy controls. The sensitivity of the candidate biomarker signature in combination with CA125 ELISA-based measurements currently used in clinic, exceeded that of CA125 ELISA-based measurements alone. The SRM-based strategy in this study is broadly applicable. It can be used in any study that requires accurate and reproducible quantification of selected proteins in a high-throughput and multiplexed fashion.

Increased DSG2 plasmatic levels identified by transcriptomic-based secretome analysis is a potential prognostic biomarker in laryngeal carcinoma.

OBJECTIVES: The tumor secretome deconvolution is a promising strategy to identify diagnostic and prognostic biomarkers. Here, transcriptomic-based secretome analysis was performed aiming to discover laryngeal squamous cell carcinomas (LSCC) biomarkers from potentially secreted proteins (PSPs). MATERIAL AND METHODS: The tumor expression profile (35 LSCC biopsies compared with surrounding normal tissues - SN) revealed 589 overexpressed genes. This gene list was used for secretome analysis based on laryngeal tumors and related secretome databases. RESULTS: Forty-nine (Laryngeal tumor secretome database) and 50 (Human Protein Atlas and Cancer Secretome Database) PSPs presented an association with worse overall survival. Specifically, DSG2 overexpression was strongly correlated with poor survival and distant metastasis. DSG2 increased expression was confirmed in the LSCC dataset (LSCC = 111; SN = 12) from TCGA. A significant association between shorter survival and DSG2 overexpression was also detected. In an independent cohort of cases, we analyzed and confirmed high protein levels of DSG2 in plasma from LSCC patients. CONCLUSION: A set of PSPs including the circulating DSG2, were associated with shorter overall survival in LSCC. DSG2 overexpression was also correlated with distant metastasis. The high plasmatic protein levels of DSG2 suggest its potential to be tested in liquid biopsies and applied as prognostic biomarker of LSCC.