We report an integrated pipeline for efficient serum glycoprotein biomarker candidate discovery and qualification that may be used to facilitate cancer diagnosis and management. The discovery phase used semi-automated lectin magnetic bead array (LeMBA)-coupled tandem mass spectrometry with a dedicated data-housing and analysis pipeline; GlycoSelector (http://glycoselector.di.uq.edu.au). The qualification phase used lectin magnetic bead array-multiple reaction monitoring-mass spectrometry incorporating an interactive web-interface, Shiny mixOmics (http://mixomics-projects.di.uq.edu.au/Shiny), for univariate and multivariate statistical analysis. Relative quantitation was performed by referencing to a spiked-in glycoprotein, chicken ovalbumin. We applied this workflow to identify diagnostic biomarkers for esophageal adenocarcinoma (EAC), a life threatening malignancy with poor prognosis in the advanced setting. EAC develops from metaplastic condition Barrett's esophagus (BE). Currently diagnosis and monitoring of at-risk patients is through endoscopy and biopsy, which is expensive and requires hospital admission. Hence there is a clinical need for a noninvasive diagnostic biomarker of EAC. In total 89 patient samples from healthy controls, and patients with BE or EAC were screened in discovery and qualification stages. Of the 246 glycoforms measured in the qualification stage, 40 glycoforms (as measured by lectin affinity) qualified as candidate serum markers. The top candidate for distinguishing healthy from BE patients' group was Narcissus pseudonarcissus lectin (NPL)-reactive Apolipoprotein B-100 (p value = 0.0231; AUROC = 0.71); BE versus EAC, Aleuria aurantia lectin (AAL)-reactive complement component C9 (p value = 0.0001; AUROC = 0.85); healthy versus EAC, Erythroagglutinin Phaseolus vulgaris (EPHA)-reactive gelsolin (p value = 0.0014; AUROC = 0.80). A panel of 8 glycoforms showed an improved AUROC of 0.94 to discriminate EAC from BE. Two biomarker candidates were independently verified by lectin magnetic bead array-immunoblotting, confirming the validity of the relative quantitation approach. Thus, we have identified candidate biomarkers, which, following large-scale clinical evaluation, can be developed into diagnostic blood tests. A key feature of the pipeline is the potential for rapid translation of the candidate biomarkers to lectin-immunoassays. We report an integrated pipeline for efficient serum glycoprotein biomarker candidate discovery and qualification that may be used to facilitate cancer diagnosis and management. The discovery phase used semi-automated lectin magnetic bead array (LeMBA)-coupled tandem mass spectrometry with a dedicated data-housing and analysis pipeline; GlycoSelector (http://glycoselector.di.uq.edu.au). The qualification phase used lectin magnetic bead array-multiple reaction monitoring-mass spectrometry incorporating an interactive web-interface, Shiny mixOmics (http://mixomics-projects.di.uq.edu.au/Shiny), for univariate and multivariate statistical analysis. Relative quantitation was performed by referencing to a spiked-in glycoprotein, chicken ovalbumin. We applied this workflow to identify diagnostic biomarkers for esophageal adenocarcinoma (EAC), a life threatening malignancy with poor prognosis in the advanced setting. EAC develops from metaplastic condition Barrett's esophagus (BE). Currently diagnosis and monitoring of at-risk patients is through endoscopy and biopsy, which is expensive and requires hospital admission. Hence there is a clinical need for a noninvasive diagnostic biomarker of EAC. In total 89 patient samples from healthy controls, and patients with BE or EAC were screened in discovery and qualification stages. Of the 246 glycoforms measured in the qualification stage, 40 glycoforms (as measured by lectin affinity) qualified as candidate serum markers. The top candidate for distinguishing healthy from BE patients' group was Narcissus pseudonarcissus lectin (NPL)-reactive Apolipoprotein B-100 (p value = 0.0231; AUROC = 0.71); BE versus EAC, Aleuria aurantia lectin (AAL)-reactive complement component C9 (p value = 0.0001; AUROC = 0.85); healthy versus EAC, Erythroagglutinin Phaseolus vulgaris (EPHA)-reactive gelsolin (p value = 0.0014; AUROC = 0.80). A panel of 8 glycoforms showed an improved AUROC of 0.94 to discriminate EAC from BE. Two biomarker candidates were independently verified by lectin magnetic bead array-immunoblotting, confirming the validity of the relative quantitation approach. Thus, we have identified candidate biomarkers, which, following large-scale clinical evaluation, can be developed into diagnostic blood tests. A key feature of the pipeline is the potential for rapid translation of the candidate biomarkers to lectin-immunoassays. Biomarkers play a central role in health care by enabling accurate diagnosis and prognosis; hence there is extensive research on the identification and development of novel biomarkers. However, despite numerous biomarker publications over the years (1.Anderson N.L. Ptolemy A.S. Rifai N. The riddle of protein diagnostics: future bleak or bright?.Clin. Chem. 2013; 59: 194-197Crossref PubMed Scopus (45) Google Scholar), only a handful of new cancer biomarkers have successfully completed the journey from discovery, qualification, to verification and validation (2.Pavlou M.P. Diamandis E.P. Blasutig I.M. The long journey of cancer biomarkers from the bench to the clinic.Clin. Chem. 2013; 59: 147-157Crossref PubMed Scopus (105) Google Scholar, 3.Rifai N. Gillette M.A. Carr S.A. Protein biomarker discovery and validation: the long and uncertain path to clinical utility.Nat. Biotechnol. 2006; 24: 971-983Crossref PubMed Scopus (1369) Google Scholar, 4.Diamandis E.P. Cancer biomarkers: can we turn recent failures into success?.J. Natl. Cancer Inst. 2010; 102: 1462-1467Crossref PubMed Scopus (284) Google Scholar). One possible way to overcome this challenge is to develop an integrated biomarker pipeline that facilitates the smooth and successful transition from discovery to validation (5.Addona T.A. Shi X. Keshishian H. Mani D.R. Burgess M. Gillette M.A. Clauser K.R. Shen D. Lewis G.D. Farrell L.A. Fifer M.A. Sabatine M.S. Gerszten R.E. Carr S.A. A pipeline that integrates the discovery and verification of plasma protein biomarkers reveals candidate markers for cardiovascular disease.Nat. Biotechnol. 2011; 29: 635-643Crossref PubMed Scopus (202) Google Scholar, 6.Keshishian H. Burgess M.W. Gillette M.A. Mertins P. Clauser K.R. Mani D.R. Kuhn E.W. Farrell L.A. Gerszten R.E. Carr S.A. Multiplexed, Quantitative Workflow for Sensitive Biomarker Discovery in Plasma Yields Novel Candidates for Early Myocardial Injury.Mol. Cell. Proteomics. 2015; 14: 2375-2393Abstract Full Text Full Text PDF PubMed Scopus (145) Google Scholar, 7.Ademowo O.S. Hernandez B. Collins E. Rooney C. Fearon U. van Kuijk A.W. Tak P.P. Gerlag D.M. FitzGerald O. Pennington S.R. Discovery and confirmation of a protein biomarker panel with potential to predict response to biological therapy in psoriatic arthritis.Ann. Rheum. Dis. 2014; PubMed Google Scholar, 8.Whiteaker J.R. Zhang H. Zhao L. Wang P. Kelly-Spratt K.S. Ivey R.G. Piening B.D. Feng L.C. Kasarda E. Gurley K.E. Eng J.K. Chodosh L.A. Kemp C.J. McIntosh M.W. Paulovich A.G. Integrated pipeline for mass spectrometry-based discovery and confirmation of biomarkers demonstrated in a mouse model of breast cancer.J. Proteome Res. 2007; 6: 3962-3975Crossref PubMed Scopus (162) Google Scholar, 9.Liu N.Q. Braakman R.B. Stingl C. Luider T.M. Martens J.W. Foekens J.A. Umar A. Proteomics pipeline for biomarker discovery of laser capture microdissected breast cancer tissue.J. Mammary Gland Biol. Neoplasia. 2012; 17: 155-164Crossref PubMed Scopus (64) Google Scholar, 10.Whiteaker J.R. Lin C. Kennedy J. Hou L. Trute M. Sokal I. Yan P. Schoenherr R.M. Zhao L. Voytovich U.J. Kelly-Spratt K.S. Krasnoselsky A. Gafken P.R. Hogan J.M. Jones L.A. Wang P. Amon L. Chodosh L.A. Nelson P.S. McIntosh M.W. Kemp C.J. Paulovich A.G. A targeted proteomics-based pipeline for verification of biomarkers in plasma.Nat. Biotechnol. 2011; 29: 625-634Crossref PubMed Scopus (292) Google Scholar). The first and foremost consideration in an integrated pipeline is the sample source. In general, most of the proteomics based workflows use tissues or proximal fluids during the discovery phase, with the goal of extending the findings to plasma. Although this approach avoid the high complexity serum/plasma proteome and the associated requisite multi-dimensional sample separation in discovery stages, it often leads to failure when the candidates are not detected in plasma because of the limited sensitivity of the available analytical methods, or the absence of candidates in the plasma (11.Paulovich A.G. Whiteaker J.R. Hoofnagle A.N. Wang P. The interface between biomarker discovery and clinical validation: The tar pit of the protein biomarker pipeline.Proteomics Clin. Appl. 2008; 2: 1386-1402Crossref PubMed Scopus (172) Google Scholar). To overcome this pitfall, we have developed an integrated glycoprotein biomarker pipeline, which can simply and rapidly isolate glycosylated proteins from serum to enable high throughput analysis of differentially glycosylated proteins in discovery and qualification stages. The workflow utilizes naturally occurring glycan binding proteins, lectins, in a semi-automated high throughput workflow called lectin magnetic bead array-tandem mass spectrometry (LeMBA-MS/MS) 1 (12.Choi E. Loo D. Dennis J.W. O'Leary C.A. Hill M.M. High-throughput lectin magnetic bead array-coupled tandem mass spectrometry for glycoprotein biomarker discovery.Electrophoresis. 2011; 32: 3564-3575Crossref PubMed Scopus (33) Google Scholar, 13.Loo D. Jones A. Hill M.M. Lectin magnetic bead array for biomarker discovery.J. Proteome Res. 2010; 9: 5496-5500Crossref PubMed Scopus (41) Google Scholar). Although lectins have been well-utilized in glycobiology and biomarker discovery (14.Fanayan S. Hincapie M. Hancock W.S. Using lectins to harvest the plasma/serum glycoproteome.Electrophoresis. 2012; 33: 1746-1754Crossref PubMed Scopus (80) Google Scholar, 15.Drake R.R. Schwegler E.E. Malik G. Diaz J. Block T. Mehta A. Semmes O.J. Lectin capture strategies combined with mass spectrometry for the discovery of serum glycoprotein biomarkers.Mol. Cell. Proteomics. 2006; 5: 1957-1967Abstract Full Text Full Text PDF PubMed Scopus (194) Google Scholar, 16.Kim E.H. Misek D. E Glycoproteomics-based identification of cancer biomarkers.Int. J. Proteomics. 2011; 2011601937Crossref PubMed Google Scholar, 17.Kuzmanov U. Kosanam H. Diamandis E.P. The sweet and sour of serological glycoprotein tumor biomarker quantification.BMC Med. 2013; 11: 11-31Crossref PubMed Scopus (58) Google Scholar), the LeMBA-MS/MS workflow demonstrates several unique features. First, serum glycoproteins are isolated in a single-step using 20 individual lectin-coated magnetic beads in microplate format. Second, we have optimized the concentrations of salts and detergents for sample denaturation to avoid co-isolation of protein complexes without adversely affecting lectin pull-down efficiency. Third, a liquid handler is used for sample processing to facilitate high-throughput screening and increase reproducibility. In addition, we have optimized on-bead trypsin digestion and incorporated lectin-exclusion lists during nano-LC-MS/MS to identify nonglycosylated peptides from the isolated glycoproteins. With these innovations, LeMBA-MS/MS demonstrates nanomolar sensitivity and linearity, and applicability across species (12.Choi E. Loo D. Dennis J.W. O'Leary C.A. Hill M.M. High-throughput lectin magnetic bead array-coupled tandem mass spectrometry for glycoprotein biomarker discovery.Electrophoresis. 2011; 32: 3564-3575Crossref PubMed Scopus (33) Google Scholar). Compared with existing single, serial or multi-lectin affinity chromatography (18.Cummings R.D. Kornfeld S. Fractionation of asparagine-linked oligosaccharides by serial lectin-Agarose affinity chromatography. A rapid, sensitive, and specific technique.J. Biol. Chem. 1982; 257: 11235-11240Abstract Full Text PDF PubMed Google Scholar, 19.Yang Z. Hancock W.S. Approach to the comprehensive analysis of glycoproteins isolated from human serum using a multi-lectin affinity column.J. Chromatogr. A. 2004; 1053: 79-88Crossref PubMed Scopus (273) Google Scholar), LeMBA-MS/MS offers the capability to simultaneously screen 20 lectins in a semi-automated, high throughput manner. On the other hand, because LeMBA-MS/MS identifies the nonglycosylated peptides, it cannot be used for glycan site assignment and glycan structure elucidation (20.Drake P.M. Schilling B. Niles R.K. Braten M. Johansen E. Liu H. Lerch M. Sorensen D.J. Li B. Allen S. Hall S.C. Witkowska H.E. Regnier F.E. Gibson B.W. Fisher S.J. A lectin affinity workflow targeting glycosite-specific, cancer-related carbohydrate structures in trypsin-digested human plasma.Anal. Biochem. 2011; 408: 71-85Crossref PubMed Scopus (57) Google Scholar, 21.Li Y. Shah P. De Marzo A.M. Van Eyk J.E. Li Q. Chan D.W. Zhang H. Identification of glycoproteins containing specific glycans using a lectin-chemical method.Anal. Chem. 2015; 87: 4683-4687Crossref PubMed Scopus (28) Google Scholar, 22.Zhou H. Froehlich J.W. Briscoe A.C. Lee R.S. The GlycoFilter: a simple and comprehensive sample preparation platform for proteomics, N-glycomics and glycosylation site assignment.Mol. Cell. Proteomics. 2013; 12: 2981-2991Abstract Full Text Full Text PDF PubMed Scopus (29) Google Scholar, 23.Song E. Zhu R. Hammoud Z.T. Mechref Y. LC-MS/MS quantitation of esophagus disease blood serum glycoproteins by enrichment with hydrazide chemistry and lectin affinity chromatography.J. Proteome Res. 2014; 13: 4808-4820Crossref PubMed Scopus (72) Google Scholar). However, the main advantage of LeMBA, we believe, is as a part of an integrated translational biomarker pipeline leading to lectin immunoassays. The lack of glycan structure details is not critical for clinical translation, as exemplified by the alpha-fetoprotein-L3 (AFP-L3) test, which measures the Lens culinaris agglutinin (LCA) binding fraction of serum alpha-fetoprotein (24.Kagebayashi C. Yamaguchi I. Akinaga A. Kitano H. Yokoyama K. Satomura M. Kurosawa T. Watanabe M. Kawabata T. Chang W. Li C. Bousse L. Wada H.G. Satomura S. Automated immunoassay system for AFP-L3% using on-chip electrokinetic reaction and separation by affinity electrophoresis.Anal. Biochem. 2009; 388: 306-311Crossref PubMed Scopus (106) Google Scholar, 25.Sato Y. Nakata K. Kato Y. Shima M. Ishii N. Koji T. Taketa K. Endo Y. Nagataki S. Early recognition of hepatocellular carcinoma based on altered profiles of alpha-fetoprotein.N. Engl. J. Med. 1993; 328: 1802-1806Crossref PubMed Scopus (421) Google Scholar), and has been approved by the U.S. Food and Drug Administration for detection of hepatocellular carcinoma. In this study, we report the extension of the glycoprotein biomarker pipeline to the qualification phase with LeMBA-MRM-MS, and introduce statistical analysis pipelines GlycoSelector (http://glycoselector.di.uq.edu.au/) and Shiny mixOmics (http://mixomics-projects.di.uq.edu.au/Shiny) for the discovery and qualification phases, respectively. The utility of this integrated serum glycoprotein biomarker pipeline is demonstrated using esophageal adenocarcinoma (EAC) with unmet clinical need for an in vitro diagnostic test. EAC is a lethal malignancy of the lower esophagus with very poor 5-year survival rate of less than 25% (26.Hur C. Miller M. Kong C.Y. Dowling E.C. Nattinger K.J. Dunn M. Feuer E.J. Trends in esophageal adenocarcinoma incidence and mortality.Cancer. 2013; 119: 1149-1158Crossref PubMed Scopus (368) Google Scholar). EAC is becoming increasingly common and its incidence is associated with the prevalent precursor metaplastic condition Barrett's esophagus (BE), but with a low annual conversion rate of up to 1% (27.Spechler S.J. Souza R.F. Barrett's esophagus.N. Engl. J. Med. 2014; 371: 836-845Crossref PubMed Scopus (336) Google Scholar). A common set of risk factors are described for BE and EAC, include gastroesophageal reflux disease (GERD), obesity, male gender, and smoking (28.Reid B.J. Li X. Galipeau P.C. Vaughan T.L. Barrett's oesophagus and oesophageal adenocarcinoma: time for a new synthesis.Nat. Rev. Cancer. 2010; 10: 87-101Crossref PubMed Scopus (317) Google Scholar, 29.Rutegard M. Lagergren P. Nordenstedt H. Lagergren J. Oesophageal adenocarcinoma: the new epidemic in men?.Maturitas. 2011; 69: 244-248Abstract Full Text Full Text PDF PubMed Scopus (16) Google Scholar). The current endoscopy-biopsy based diagnosis is invasive and costly, leading to an ineffective surveillance program. A blood test employing serum biomarkers that can distinguish patients with EAC from those with either BE or healthy tissue would, potentially, change the paradigm for the way in which BE and EAC are managed in the population (30.Shah A.K. Saunders N.A. Barbour A.P. Hill M.M. Early diagnostic biomarkers for esophageal adenocarcinoma–the current state of play.Cancer Epidemiol. Biomarkers Prev. 2013; 22: 1185-1209Crossref PubMed Scopus (27) Google Scholar). Serum glycan profiling studies have shown differential expression of glycan structures between healthy, BE, early dysplastic and EAC patients (31.Gaye M.M. Valentine S.J. Hu Y. Mirjankar N. Hammoud Z.T. Mechref Y. Lavine B.K. Clemmer D.E. Ion mobility-mass spectrometry analysis of serum N-linked glycans from esophageal adenocarcinoma phenotypes.J. Proteome Res. 2012; 11: 6102-6110Crossref PubMed Scopus (41) Google Scholar, 32.Hu Y. Desantos-Garcia J.L. Mechref Y. Comparative glycomic profiling of isotopically permethylated N-glycans by liquid chromatography/electrospray ionization mass spectrometry.Rapid Commun. Mass Spectrom. 2013; 27: 865-877Crossref PubMed Scopus (40) Google Scholar, 33.Mechref Y. Hussein A. Bekesova S. Pungpapong V. Zhang M. Dobrolecki L.E. Hickey R.J. Hammoud Z.T. Novotny M.V. Quantitative serum glycomics of esophageal adenocarcinoma and other esophageal disease onsets.J. Proteome Res. 2009; 8: 2656-2666Crossref PubMed Scopus (69) Google Scholar, 34.Mitra I. Zhuang Z. Zhang Y. Yu C.Y. Hammoud Z.T. Tang H. Mechref Y. Jacobson S.C. N-glycan profiling by microchip electrophoresis to differentiate disease states related to esophageal adenocarcinoma.Anal. Chem. 2012; 84: 3621-3627Crossref PubMed Scopus (43) Google Scholar, 35.Hammoud Z.T. Mechref Y. Hussein A. Bekesova S. Zhang M. Kesler K.A. Novotny M.V. Comparative glycomic profiling in esophageal adenocarcinoma.J. Thorac. Cardiovasc. Surg. 2010; 139: 1216-1223Abstract Full Text Full Text PDF PubMed Scopus (15) Google Scholar). However, diagnostic serum glycoproteins showing differential glycosylation hence differential lectin binding remain to be discovered, making it a suitable disease model for this study. The overall biomarker study design was based on the strategy proposed by Rifai et al. (3.Rifai N. Gillette M.A. Carr S.A. Protein biomarker discovery and validation: the long and uncertain path to clinical utility.Nat. Biotechnol. 2006; 24: 971-983Crossref PubMed Scopus (1369) Google Scholar), with the current work spanning discovery and qualification of the described four-phase paradigm. Serum samples were collected as part of the Australian Cancer Study (ACS) (36.Whiteman D.C. Sadeghi S. Pandeya N. Smithers B.M. Gotley D.C. Bain C.J. Webb P.M. Green A.C. Australian Cancer Study Combined effects of obesity, acid reflux and smoking on the risk of adenocarcinomas of the oesophagus.Gut. 2008; 57: 173-180Crossref PubMed Scopus (249) Google Scholar) and Study of Digestive Health (SDH) (37.Smith K.J. O'Brien S.M. Smithers B.M. Gotley D.C. Webb P.M. Green A.C. Whiteman D.C. Interactions among smoking, obesity, and symptoms of acid reflux in Barrett's esophagus.Cancer Epidemiol. Biomarkers Prev. 2005; 14: 2481-2486Crossref PubMed Scopus (119) Google Scholar). All patients in these studies gave written, informed consent, and the studies were approved by the Human Research Ethics Committees of Queensland Institute for Medical Research, the University of Queensland, and all participating hospitals. Identical SOPs were followed for collecting samples for SDH and ACS, and processed by the same person. All 29 serum samples (Healthy-9, BE-10 and EAC-10) used for biomarker discovery phase and 79 serum samples (Healthy-20, BE-20, EAC-20 and population control-19) used for biomarker qualification study were matched by age; all selected patients were male considering the high male-dominance of EAC (29.Rutegard M. Lagergren P. Nordenstedt H. Lagergren J. Oesophageal adenocarcinoma: the new epidemic in men?.Maturitas. 2011; 69: 244-248Abstract Full Text Full Text PDF PubMed Scopus (16) Google Scholar). The samples were stored at −80 °C until use. Healthy controls were individuals with no history of esophageal cancer and no evidence of esophageal histological abnormality at the time of endoscopic sample collection. BE patients had a histologically confirmed diagnosis of Barrett's mucosa. EAC patients had histologically confirmed adenocarcinoma within the distal esophagus or gastro-esophageal junction. EAC patient sera were collected prior to the commencement of cancer treatment. Population controls were volunteers with no self-reported history of EAC or BE. Samples were randomized prior to all experiments. Table I and II describes patient information used in this study. For categorical and numerical variables related to patient information, p values were calculated using Fisher's exact test and Kruskal-Wallis test respectively.Table IClinical characteristics of the patient cohort for biomarker discovery. For categorical and numerical variables, p values were calculated using Fisher's exact test and Kruskal-Wallis test respectivelyVariablesHealthyBEEACp value (Healthy vs BE vs EAC)Sample size91010Age (Median ± S.D.)66 ± 1062 ± 1566 ± 80.9311GenderAll maleAll maleAll maleProtein concentration (μg/μl)95 ± 1985 ± 1382 ± 130.3641GastritisaAll the analyses were performed based on available patient information. Gastritis status for one BE patient was missing.1 (11.1%)1 (11.1%)1 (10.0%)1.0000Peptic ulcer3 (33.3%)2 (20.0%)3 (30.0%)0.8792Hiatus hernia0 (0.0%)4 (40.0%)6 (60.0%)0.0217Other malignancy1 (11.1%)2 (20.0%)2 (20.0%)1.0000a All the analyses were performed based on available patient information. Gastritis status for one BE patient was missing. Open table in a new tab Table IIClinical characteristics of the patient cohort for biomarker qualification. For categorical and numerical variables, p values were calculated using Fisher's exact test and Kruskal-Wallis test respectivelyVariablesHealthyBEEACp value (Healthy vs BE vs EAC)Population Controlp value (Healthy vs Pop. Control)Sample size20202019GenderAll maleAll maleAll maleAll maleAge in years (Median ± S.D.)64 ± 860 ± 861 ± 70.428362 ± 70.2793Protein concentration (μg/μl)83 ± 1078 ± 1285 ± 130.648689 ± 130.0785Reflux frequencyaAll the analyses were performed based on available patient information. Reflux frequency for one healthy patient was missing. (10 years before diagnosis)0.01080.2155 = 30)7 (35.0%)3 (15.0%)14 (70.0%)4 (21.1%)Smoking history0.61160.7813Never smoked8 (40.0%)8 (40.0%)4 (20.0%)7 (36.8%)1–29.9 pack per year8 (40.0%)9 (45.0%)10 (50.0%)6 (31.6%)30+ pack per year4 (20.0%)3 (15.0%)6 (30.0%)6 (31.6%)Alcohol consumption0.66370.8379<1 standard drink/week3 (15.0%)3 (15.0%)1 (5.0%)2 (10.5%)1–6 standard drink/week3 (15.0%)4 (20.0%)6 (30.0%)5 (26.3%)7–20 standard drink/week8 (40.0%)4 (20.0%)6 (30.0%)6 (31.6%)21+ standard drink/week6 (30.0%)9 (45.0%)7 (35.0%)6 (31.6%)a All the analyses were performed based on available patient information. Reflux frequency for one healthy patient was missing. Open table in a new tab MyOneTM Tosyl activated Dynabeads were from Life Technologies. Lectins AAL, BPL, DSA, EPHA, GNL, JAC, LPHA, MAA, NPL, SNA, STL, and UEA were from Vector Laboratories, Burlingame, CA. Modified sequencing grade trypsin was from Promega, Madison, WI. Protein assay Bradford reagent, Triton X-100, and sodium dodecyl sulfate solution were from Bio-Rad, Hercules, CA. Tris base, glycine, sodium chloride, and acrylamide/bis-acrylamide solution 40% 29:1 were from Amresco, Solon, OH. Glycerol, disodium hydrogen phosphate dihydrate, sodium dihydrogen phosphate dihydrate, calcium chloride dehydrate, and Tween-20 were from Ajax Finechem, NSW, Australia. Magnesium chloride and manganese chloride were from Univar. For quadrupole time of flight runs, acetonitrile, isocratic HPLC grade was from Scharlau and for triple quadrupole runs, acetonitrile CHROMASOLV® gradient grade was from Sigma, St. Louis, MO. Heavy labeled stable isotope-labeled standard (SIS) peptides were from Sigma. All other reagents including lectins not listed above were from Sigma unless otherwise specified. Fig. 1 represents the integrated glycoprotein biomarker discovery and qualification pipeline developed using LeMBA. The discovery phase aimed to identify changes in the lectin binding of medium to high abundance serum proteins that can distinguish between different phenotypes. To enable economic and high throughput label-free quantitation while controlling for sample processing, including tryptic digestion, we employed a nonlabeled spiked-in glycoprotein standard at the very first step of the workflow prior to denaturation (Fig. 1). Pilot experiments identified chicken ovalbumin as a suitable internal standard, with low homology to species of interest (human or mouse) that bound to all 20 lectins experimentally. Optimization experiments determined that 10 picomole ovalbumin to be added to each sample (50 μg of serum) per lectin pull-down. Depending upon the individual lectin, between 3 and 5 ovalbumin peptides (out of 7) were used for normalization (supplemental Table S1). Details about the data normalization and statistical analyses platforms GlycoSelector and Shiny mixOmics can be found in Supplemental Methods. Briefly, for discovery data, two different normalization approaches (1) based upon total ovalbumin protein intensity or (2) using individual ovalbumin peptide intensity were evaluated (supplemental Fig. S1A). There was a strong correlation between the two normalization approaches (supplemental Fig. S1B), and we selected the second normalization method for the pipeline as it gave equal weighting to each peptide. For each patient sample in discovery stage, a two-dimensional data set was generated, consisting of normalized intensity for proteins identified with each of the 20 lectin pull-down procedures. In general, glycoproteins bound several lectins, reflecting heterogeneity and multiplicity of glycosylation. GlycoSelector is a customized database with an incorporated statistical analysis pipeline coded in the R statistical programming language (38.R Core Team R: A language and environment for statistical computing.R foundation for statistical computing. 2014; Google Scholar) and integrated in PHP server-side scripting language. The pipeline is based on tools developed in mixOmics (39.Le Cao K.A. Gonzalez I. Dejean S. Rohart F. Gautier B. Monget P. Coquery J. Yao FZ. Liquet B. mixOmics: Omics data integration project.R package version 5.0–4. 2015; Google Scholar), an R package dedicated to multivariate statistical analysis of "omics" data, and includes several steps such as data normalization, sample outlier detection, multivariate statistical analysis and group binding analyses. The sample outlier detection step aims to identify possible errors in sample handling/processing (supplemental Fig. S2). As an example of its utility, sample run ID 63 shown in supplemental Fig. S2A to S2D was considered to be an outlier because of consistent anomalous results detected in all four graphical outputs. The error was at the mass spectrometry step, because when the sample was re-run after mass spectrometer re-calibration, it was no longer detected as an outlier (supplemental Fig. S2E to S2H). To determine changes in the lectin binding of individual proteins between the different conditions, GlycoSelector was designed with two parallel approaches. Firstly, Group Binding Difference analyses were performed to identify on-off changes. In addition, multivariate statistical analysis based on sparse partial least square-discriminant analysis (sPLS-DA) (40.Le Cao K.A. Boitard S. Besse P. Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems.BMC Bioinformatics. 2011; 12Crossref PubMed Scopus (487) Google Scholar) coupled with stability analysis was used to identify qualitative changes, after the exclusion of common contaminant proteins (supplemental Table S3). Based on GlycoSelector analysis, a subset of 6 lectins and 41 glycoprotein candidates were selected for independent qualification (Fig. 1). The steps included, (1) MRM-MS assay development including confirmation of linearity and reproducibility, (2) screening an independent cohort of patient samples using customized LeMBA-MRM-MS, (3) two-step data normalization (supplemental Fig. S1A and supplemental Methods), and (4) univariate and multivariate statistical analysis using Shiny mixOmics. LeMBA was performed as previously reported (12.Choi E. Loo D. Dennis J.W. O'Leary C.A. Hill M.M. High-throughput lectin magnetic bead array-coupled tandem mass spectrometry for glycoprotein bioma
Objective: The genetic complexity of schizophrenia may be compounded by the diagnostic imprecision inherent in distinguishing schizophrenia from closely related mood and substance use disorders. Further complexity may arise from studying genetically and/or environmentally diverse ethnic groups. Reported here are the ascertainment, demographic features and clinical characteristics, of a diagnostically and ethnically homogeneous schizophrenia pedigree sample from Tamil Nadu, India. Also reported is the theoretical power to detect genetic linkage in the subset of affected sibling pairs. Method: Affected sibling pair and trio pedigrees were identified by caste/ethnicity. Affected probands and siblings fulfilled DSM-IV criteria for schizophrenia or schizoaffective disorder. Results: The present sample consisted of 159 affected sibling pairs and 187 parent–offspring trios originating primarily from the Tamil Brahmin caste, but also incorporating pedigrees from genetically similar, geographically proximal caste groups. Consistent with previous studies in Tamil Nadu, a very low prevalence of affective psychoses such as schizoaffective disorder, was observed, with most affected individuals having schizophrenia (499/504). Also observed were extremely low rates of nicotine (12.4%), alcohol (1.1%) and illicit drug use (0%). Most affected individuals exhibited negative symptoms (>90%) and a severe, chronic course. All participants lived in the same geographic and climatic region and most affected individuals resided with close family members, increasing uniformity of the sociocultural environment. In affected sibling pairs, power to detect linkage to small-effect risk loci was modest, but this homogeneous sample may be enriched for loci of larger effect. Conclusions: This Indian schizophrenia sample exhibits diagnostic and ethnic homogeneity with high consistency of sociocultural environmental features. These characteristics should assist efforts to identify risk genes for schizophrenia.
The goal of this study was to identify chromosomal regions likely to contain schizophrenia susceptibility genes.A genomewide map of 310 microsatellite DNA markers with average spacing of 11 centimorgans was genotyped in 269 individuals--126 of them with schizophrenia-related psychoses--from 43 pedigrees. Nonparametric linkage analysis was used to assess the pattern of allele sharing at each marker locus relative to the presence of disease.Nonparametric linkage scores did not reach a genomewide level of statistical significance for any marker. There were five chromosomal regions in which empirically derived p values reached nominal levels of significance at eight marker locations. There were p values less than 0.01 at chromosomes 2q (with the peak value in this region at D2S410) and 10q (D10S1239), and there were p values less than 0.05 at chromosomes 4q (D4S2623), 9q (D9S257), and 11q (D11S2002).The results do not support the hypothesis that a single gene causes a large increase in the risk of schizophrenia. The sample (like most others being studied for psychiatric disorders) has limited power to detect genes of small effect or those that are determinants of risk in a small proportion of families. All of the most positive results could be due to chance, or some could reflect weak linkage (genes of small effect). Multicenter studies may be useful in the effort to identify chromosomal regions most likely to contain schizophrenia susceptibility genes.
Karyotypic analysis, loss of somatic heterozygosity, microcell fusion and cDNA transfection studies have provided compelling evidence that at least one tumour suppressor gene for melanoma resides on chromosome 6. In an attempt to further define the regions to which these putative suppressor genes map, we have carried out loss of heterozygosity (LOH) studies on DNA from 25 fresh melanoma tumours for 9 simple tandem repeat (STR) polymorphism markers spanning chromosome 6. Four samples displayed LOH or homozygosity for all markers studied, indicating that they had lost one homologue of chromosome 6. An additional 3 samples showed LOH for all markers on 6q. Furthermore, 30 melanoma cell lines, for which there were no matching somatic DNA samples, were analyzed for hemizygosity of markers on 6q. One cell line had a homozygous deletion of all markers tested and a further 12 cell lines displayed only one allele for 3 or 4 contiguous markers, indicating that most, if not all of these samples were hemizygous for the region of 6q distal to D6S87. Overall, the rate of LOH on 6q in the 55 melanoma DNAs was 35%, and there were no losses of markers on 6p without concomitant loss of markers on 6q. Two of 5 samples derived from primary melanomas showed LOH, which indicates that LOH for the melanoma suppressor gene on 6q, which maps to a region that contains the SOD2 locus, is a frequent and early event in melanoma tumorigenesis.
50 Background: Esophagectomy with preoperative chemotherapy or chemoradiotherapy are standard treatment regimens for localized esophageal adenocarcinoma (EAC). Early metabolic response to preoperative therapy (determined by FDG-PET scan) has been shown to predict histological response and survival. We present preliminary data from a randomized phase II trial of pre-operative cisplatin (C), 5-fluorouracil (F) and docetaxel ± radiotherapy based on early response to standard chemotherapy for resectable EAC. We present gene expression data for 22 patients with early FDG-PET response. Methods: Samples that were histologically proven invasive EAC and clinical stage T2/3 tumors were included in the analysis. All patients received C (day1) and F (days 1-4) chemotherapy. Early metabolic response was defined as >35% reduction in SUVmax on day 14 FDG-PET scan compared with baseline scan. Whole genome mRNA expression was obtained for pre-treatment biopsies using HumanHT-12v4 expression chips. Expression data was log2 transformed prior to robust spline normalization conducted with the lumi R package. Statistical analysis was performed in BRBarray. Results: There were 8 early PET responders and 14 early nonresponders to CF chemotherapy. Differentially expressed genes among PET responders and non-responders were identified using a random-variance t-test; 57 genes were identified as significantly differentially expressed between the two classes (p ≤ 0.001). A global test indicated significantly different expression profiles between the classes (p = 0.031). A gene classifier to predict PET response was explored and Support Vector Machines correctly classified 82% of samples with the prediction error estimated using leave-one-out cross-validation (p = 0.05). Epigenetic and apoptotic processes were significantly overrepresented in the gene list based on Gene Ontology analysis. Conclusions: This preliminary work supports a biological basis for the association between early metabolic PET response to preoperative therapy and survival in EAC patients. The list of differentially expressed genes may include potential biomarkers of treatment response and requires further investigation at the maturation of the phase II clinical trial for EAC.
'/%3e%3c/defs%3e%3cpath fill='%23012169' d='M0 0h10080v5040H0z'/%3e%3cpath stroke='%23fff' stroke-width='.6' d='m0 0 6 3m0-3L0 3' clip-path='url(%23b)' transform='scale(840)'/%3e%3cpath stroke='%23e4002b' stroke-width='.4' d='m0 0 6 3m0-3L0 3' clip-path='url(%23c)' transform='scale(840)'/%3e%3cpath stroke='%23fff' stroke-width='840' d='M2520 0v2520M0 1260h5040'/%3e%3cpath stroke='%23e4002b' stroke-width='504' d='M2520 0v2520M0 1260h5040'/%3e%3cg fill='%23fff'%3e%3cuse xlink:href='%23d' x='2520' y='3780'/%3e%3cuse xlink:href='%23a' x='7560' y='4200'/%3e%3cuse xlink:href='%23a' x='6300' y='2205'/%3e%3cuse xlink:href='%23a' x='7560' y='840'/%3e%3cuse xlink:href='%23a' x='8680' y='1869'/%3e%3cuse xlink:href='%23e' x='8064' y='2730'/%3e%3c/g%3e%3c/svg%3e)
