Discovery and validation of cancer biomarkers by DIA and targeted protoemics
Abstract
Targeted proteomics is the LC-MS based method, which identifies and quantifies target peptides from chromatograms extracted from MS data. It has been standard method for quantify small molecules, while it is getting popular and ultilized not only in proteomics research but also in basic life sciences and drug development. In the early period, targeted proteomics was performed by MS data acquisition with SRM/MRM and selected as Method of the Year 2012 in Nature methods. To date, by enlarging the applied research fields, various technical progresses have been achieved for improving the ability of targeted proteomics. In the acquisition mode, Q-TOF and orbitrap instruments were introduced in targeted proteomics, and HR-MRM/PRM was developed. Data Independent Acquisition (DIA, SWATH-MS) is a breakthrough methodology in targeted proteomics, which enables comprehensive peptide quantification by extracted ion chromatograms. DIA data contains huge MS data, and data processing methodologies and software have been intensively developed in laboratories and companies. Because the advantages of targeted proteomics are quantitative ability in addition to sensitivity, sample preparation and target peptide selection are also important processes to obtain reliable quantitative data. Multi-laboratory studies were performed to assess the quantitative ability of targeted proteomics in wide purposes. Targeted proteomics also became an essential method to validate the biomarkers. To improve the throughput, automated sample preparation and high-throughput LC-MS have been developed. Now, we are able to use various set of targeted proteomics system suitable for each research purpose. In this lecture, we will introduce recent technical progresses of targeted proteomics and DIA, and our latest application to biomarker research including cancer biomarkers.
Samsung Medical Center, Sungkyunkwan University School of Medicine
Title
Exosomes are Tiny vesicle but Huge impact on cancer biomarker study
Abstract
The multiple roles of extracellular vesicles (EVs) in pathogenesis have received much attention, which is valuable as the diagnostic, prognostic biomarker. A proteomics-based approach has been recognized as a useful tool for biomarker study. Serum-derived non-EVs proteins is often overlooked in EVs isolation process even though for the proteomic analysis, which can interfere with the deep protein identification of EVs. The complete removal and accurate evaluation of serum-derived proteins is the biggest challenge in those proteomics-based strategies. Here, we optimized the protocols for serum EVs isolation using polymer-based precipitation and evaluated those purity using molecular characterization and multiple reaction monitoring-mass spectrometry (MRM-MS). We established MRM assays of 15 EV proteins selected based on ExoCarta database and of them, finally selected two EV proteins, Protein 1 and 2 and Protein 3, as serum derived non-EV contaminants. Here, we performed multiple-cycles ultracentrifugation and polymeric precipitation (1 - 4 cycles) to isolate EVs from serum and evaluated those purity with EVs proteins using MRM-MS. Multiple cycle polymer-based EVs (mcpEx) isolation was very easy and convenient to remove serum albumin, 89% reduction in the first cycle and 99.9% in the fourth, without loss of EVs, while EVs concentration of protein 1 and 2 were dramatically increased after mcpEx purification. In addition, we applied this strategy to pancreatic cancer study, resulting in identification of 612 proteins using LC-MS/MS. Of them, 539 proteins were EVs proteins by matching EVpedia database (89%). In conclusion, we suggest that mcpEx is not only a more effective protocol for EVS isolation for further proteomics-based experiments, but is also useful in clinical settings.
Discovery of blood biomarker for major mental illness by high-throughput plasma proteome profiling
Abstract
Psychiatric disorders, such as schizophrenia (SZ), biopolar disorder (BD), and major depressive disorder (MDD), are severe mental illnesses associated with morbidity and life-long disability for sufferers. Because the etiology and pathophysiology of mental illnesses are diverse and complex, reliable biomarkers related to the prognosis and diagnosis of these patients remain an unmet clinical need. Recent improvement in high-throughput proteomic approaches have introduced new opportunities for a better understanding of these burdensome disorders and an efficient discovering of biomarkers. In this study, we discovered plasma protein biomarker candidates by a rapid and robust plasma proteome profiling strategy. Using optimized methods, we quantified the levels of more than 300 proteins in undepleted plasma samples of 150 patients. Finally, we identified a combined biomarker panel consisting of proteins using machine learning algorithm, such as a support vector machine. A high classification accuracy can be achieved by plasma proteomics in three major mental disorder. Our approach can be helpful for accurate clinical diagnosis of major mental illness. Further studies using larger, independent cohorts are needed to verify the role of these candidate biomarkers for diagnosis.
Serum Trace Elements and Their Associations with Breast Cancer Subgroups in Korean Breast Cancer Patients
Abstract
The relationships between serum levels of trace elements and breast cancer remain relatively unknown. In this study, we investigate serum levels of seven trace elements in Korean breast cancer patients compared to controls without breast cancer. Serum trace element levels were determined using inductively coupled plasma mass spectrometry in Korean breast cancer patients before initiation of breast cancer treatment. Korean females without breast cancer served as a control group. Trace element levels were measured in the discovery cohort (n = 287) and were validated in an independent cohort (n = 142). We further investigated possible associations between trace element levels and the presence of lymph node metastasis, distant metastasis, or triple-negative breast cancer among breast cancer patients in subgroup analyses. Serum manganese and molybdenum levels were significantly higher (p < 0.05) in breast cancer patients than in controls. Serum copper levels were significantly higher in breast cancer patients with distant metastasis, while selenium levels were significantly lower. Other trace elements were neither significantly different between breast cancer patients and controls nor between subgroups of breast cancer patients. Our study provides insights about the potential roles and impacts of trace elements through an assessment of the associations between trace elements and breast cancer.
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