The technology of “sequential windowed acquisition of all theoretical fragment ion spectra,” known as SWATH-MS, is rapidly gaining popularity as a next generation proteomics technology for comprehensive proteome quantitation. In this chapter, we describe the use of SWATH-MS as a label-free quantitative technique in a proteomics study to identify novel serological biomarker for colorectal cancer. We compared the secreted glycoprotein profiles (glyco-secretomes) enriched from the colon adenocarcinoma cell line HCT-116 and its metastatic derivative, E1, and observed that laminin β-1 (LAMB1) was oversecreted in E1 cells. This novel oversecretion of LAMB1 was validated in colorectal cancer patient serum samples, and ROC analyses showed that LAMB1 performed better than carcinoembryonic antigen (CEA) as a clinical diagnostic biomarker for colorectal cancer. We focus here on the sample preparation methodology and data processing workflow for SWATH-MS studies.
Data-independent acquisition SWATH-MS Colorectal cancer Secretome Biomarker
This is a preview of subscription content, log in to check access.
Springer Nature is developing a new tool to find and evaluate Protocols. Learn more
Q.L. acknowledges the support of a National University of Singapore research scholarship. The authors also acknowledge the funding from the National Medical Research Council, Singapore (NMRC grant 1217/2009).
Gillet LC, Navarro P, Tate S et al (2012) Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol Cell Proteomics 11(6):O111.016717CrossRefGoogle Scholar
Liu Y, Huttenhain R, Surinova S et al (2013) Quantitative measurements of N-linked glycoproteins in human plasma by SWATH-MS. Proteomics 13(8):1247–1256CrossRefGoogle Scholar
Lin Q, Lim HS, Lin HL et al (2015) Analysis of colorectal cancer glyco-secretome identifies laminin beta-1 (LAMB1) as a potential serological biomarker for colorectal cancer. Proteomics 15(22):3905–3920CrossRefGoogle Scholar
Bray F, Ren JS, Masuyer E, Ferlay J (2013) Global estimates of cancer prevalence for 27 sites in the adult population in 2008. Int J Cancer 132(5):1133–1145CrossRefGoogle Scholar
Jemal A, Bray F, Center MM et al (2011) Global cancer statistics. CA Cancer J Clin 61(2):69–90CrossRefGoogle Scholar
van der Schouw YT, Verbeek AL, Wobbes T et al (1992) Comparison of four serum tumour markers in the diagnosis of colorectal carcinoma. Br J Cancer 66(1):148–154CrossRefGoogle Scholar
Carpelan-Holmstrom M, Haglund C, Lundin J et al (1996) Pre-operative serum levels of CA 242 and CEA predict outcome in colorectal cancer. Eur J Cancer 32A(7):1156–1161CrossRefGoogle Scholar
Carriquiry LA, Pineyro A (1999) Should carcinoembryonic antigen be used in the management of patients with colorectal cancer? Dis Colon Rectum 42(7):921–929CrossRefGoogle Scholar
Harrison LE, Guillem JG, Paty P, Cohen AM (1997) Preoperative carcinoembryonic antigen predicts outcomes in node-negative colon cancer patients: a multivariate analysis of 572 patients. J Am Coll Surg 185(1):55–59CrossRefGoogle Scholar
Moertel CG, O'Fallon JR, Go VL et al (1986) The preoperative carcinoembryonic antigen test in the diagnosis, staging, and prognosis of colorectal cancer. Cancer 58(3):603–610CrossRefGoogle Scholar
Chiu KH, Chang YH, Liao PC (2013) Secretome analysis using a hollow fiber culture system for cancer biomarker discovery. Biochim Biophys Acta 1834(11):2285–2292CrossRefGoogle Scholar
Wu HY, Chang YH, Chang YC, Liao PC (2009) Proteomics analysis of nasopharyngeal carcinoma cell secretome using a hollow fiber culture system and mass spectrometry. J Proteome Res 8(1):380–389CrossRefGoogle Scholar
Yang Z, Hancock WS (2004) Approach to the comprehensive analysis of glycoproteins isolated from human serum using a multi-lectin affinity column. J Chromatogr A 1053(1–2):79–88CrossRefGoogle Scholar