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• W2750544041 abstract "Oral cancer is one of the most common cancers worldwide, and there are currently no biomarkers approved for aiding its management. Although many potential oral cancer biomarkers have been discovered, very few have been verified in body fluid specimens in parallel to evaluate their clinical utility. The lack of appropriate multiplexed assays for chosen targets represents one of the bottlenecks to achieving this goal. In the present study, we develop a peptide immunoaffinity enrichment-coupled multiple reaction monitoring-mass spectrometry (SISCAPA-MRM) assay for verifying multiple reported oral cancer biomarkers in saliva. We successfully produced 363 clones of mouse anti-peptide monoclonal antibodies (mAbs) against 36 of 49 selected targets, and characterized useful mAbs against 24 targets in terms of their binding affinity for peptide antigens and immuno-capture ability. Comparative analyses revealed that an equilibrium dissociation constant (KD) cut-off value < 2.82 × 10−9 m could identify most clones with an immuno-capture recovery rate >5%. Using these mAbs, we assembled a 24-plex SISCAPA-MRM assay and optimized assay conditions in a 25-μg saliva matrix background. This multiplexed assay showed reasonable precision (median coefficient of variation, 7.16 to 32.09%), with lower limits of quantitation (LLOQ) of <10, 10–50, and >50 ng/ml for 14, 7 and 3 targets, respectively. When applied to a model saliva sample pooled from oral cancer patients, this assay could detect 19 targets at higher salivary levels than their LLOQs. Finally, we demonstrated the utility of this assay for quantification of multiple targets in individual saliva samples (20 healthy donors and 21 oral cancer patients), showing that levels of six targets were significantly altered in cancer compared with the control group. We propose that this assay could be used in future studies to compare the clinical utility of multiple oral cancer biomarker candidates in a large cohort of saliva samples. Oral cancer is one of the most common cancers worldwide, and there are currently no biomarkers approved for aiding its management. Although many potential oral cancer biomarkers have been discovered, very few have been verified in body fluid specimens in parallel to evaluate their clinical utility. The lack of appropriate multiplexed assays for chosen targets represents one of the bottlenecks to achieving this goal. In the present study, we develop a peptide immunoaffinity enrichment-coupled multiple reaction monitoring-mass spectrometry (SISCAPA-MRM) assay for verifying multiple reported oral cancer biomarkers in saliva. We successfully produced 363 clones of mouse anti-peptide monoclonal antibodies (mAbs) against 36 of 49 selected targets, and characterized useful mAbs against 24 targets in terms of their binding affinity for peptide antigens and immuno-capture ability. Comparative analyses revealed that an equilibrium dissociation constant (KD) cut-off value < 2.82 × 10−9 m could identify most clones with an immuno-capture recovery rate >5%. Using these mAbs, we assembled a 24-plex SISCAPA-MRM assay and optimized assay conditions in a 25-μg saliva matrix background. This multiplexed assay showed reasonable precision (median coefficient of variation, 7.16 to 32.09%), with lower limits of quantitation (LLOQ) of <10, 10–50, and >50 ng/ml for 14, 7 and 3 targets, respectively. When applied to a model saliva sample pooled from oral cancer patients, this assay could detect 19 targets at higher salivary levels than their LLOQs. Finally, we demonstrated the utility of this assay for quantification of multiple targets in individual saliva samples (20 healthy donors and 21 oral cancer patients), showing that levels of six targets were significantly altered in cancer compared with the control group. We propose that this assay could be used in future studies to compare the clinical utility of multiple oral cancer biomarker candidates in a large cohort of saliva samples. Oral cavity cancer, one of the most common cancers worldwide, accounts for more than 10,000 deaths annually (1.Siegel R. Naishadham D. Jemal A. Cancer statistics, 2013.CA. 2013; 63: 11-30Google Scholar, 2.Jemal A. Bray F. Center M.M. Ferlay J. Ward E. Forman D. Global cancer statistics.CA. 2011; 61: 69-90Google Scholar). These cancers can occur at different locations in the oral cavity, including the tongue, buccal area, gingiva, lip, floor of mouth, and hard palate. The main risk factors for oral cavity cancer include alcohol and tobacco use, betel quid chewing, and viral infections (3.Hashibe M. Brennan P. Chuang S.C. Boccia S. Castellsague X. Chen C. Curado M.P. Dal Maso L. Daudt A.W. Fabianova E. Fernandez L. Wunsch-Filho V. Franceschi S. Hayes R.B. Herrero R. Kelsey K. Koifman S. La Vecchia C. Lazarus P. Levi F. Lence J.J. Mates D. Matos E. Menezes A. McClean M.D. Muscat J. Eluf-Neto J. Olshan A.F. Purdue M. Rudnai P. Schwartz S.M. Smith E. Sturgis E.M. Szeszenia-Dabrowska N. Talamini R. Wei Q. Winn D.M. Shangina O. Pilarska A. Zhang Z.F. Ferro G. Berthiller J. Boffetta P. Interaction between tobacco and alcohol use and the risk of head and neck cancer: pooled analysis in the International Head and Neck Cancer Epidemiology Consortium.Cancer Epidemiol. Biomarkers Prevention. 2009; 18: 541-550Crossref PubMed Scopus (791) Google Scholar, 4.D'Souza G. Agrawal Y. Halpern J. Bodison S. Gillison M.L. Oral sexual behaviors associated with prevalent oral human papillomavirus infection.J. Infectious Dis. 2009; 199: 1263-1269Crossref PubMed Scopus (447) Google Scholar, 5.Wen C.P. Tsai M.K. Chung W.S. Hsu H.L. Chang Y.C. Chan H.T. Chiang P.H. Cheng T.Y. Tsai S.P. Cancer risks from betel quid chewing beyond oral cancer: a multiple-site carcinogen when acting with smoking.Cancer Causes Control. 2010; 21: 1427-1435Crossref PubMed Scopus (88) Google Scholar, 6.Chen Y.J. Chang J.T. Liao C.T. Wang H.M. Yen T.C. Chiu C.C. Lu Y.C. Li H.F. Cheng A.J. Head and neck cancer in the betel quid chewing area: recent advances in molecular carcinogenesis.Cancer Sci. 2008; 99: 1507-1514Crossref PubMed Scopus (246) Google Scholar). The World Health Organization predicts that the incidence of oral cavity cancer will continue to increase worldwide for the next several decades, especially in Asia, because of distinct cultural practices such as betel-quid chewing (6.Chen Y.J. Chang J.T. Liao C.T. Wang H.M. Yen T.C. Chiu C.C. Lu Y.C. Li H.F. Cheng A.J. Head and neck cancer in the betel quid chewing area: recent advances in molecular carcinogenesis.Cancer Sci. 2008; 99: 1507-1514Crossref PubMed Scopus (246) Google Scholar, 7.Krishna Rao S.V. Mejia G. Roberts-Thomson K. Logan R. Epidemiology of oral cancer in Asia in the past decade–an update (2000–2012).Asian Pacific J. Cancer Prevention. 2013; 14: 5567-5577Crossref PubMed Scopus (306) Google Scholar). The incidence of oral cavity cancer in Taiwan has increased over the past two decades; between 1996 and 2009, the age-standardized incidence in males reached 24.64/100,000 annually, which is among the highest in the world (8.Lian Ie B. Tseng Y.T. Su C.C. Tsai K.Y. Progression of precancerous lesions to oral cancer: results based on the Taiwan National Health Insurance Database.Oral Oncol. 2013; 49: 427-430Crossref PubMed Scopus (62) Google Scholar). The majority (∼90%) of oral cavity cancer cases are oral squamous cell carcinomas (OSCCs) 1The abbreviations used are: OSCC, oral squamous cell carcinoma; LC-MRM-MS, liquid-chromatography-multiple-reaction-monitoring-MS; SIS, stable isotope-coated peptides; SISCAPA-MRM, stable isotope standards with capture by anti-peptide antibodies coupled with multiple reaction monitoring MS; SPR, surface plasmon resonance. 1The abbreviations used are: OSCC, oral squamous cell carcinoma; LC-MRM-MS, liquid-chromatography-multiple-reaction-monitoring-MS; SIS, stable isotope-coated peptides; SISCAPA-MRM, stable isotope standards with capture by anti-peptide antibodies coupled with multiple reaction monitoring MS; SPR, surface plasmon resonance., which are quite locally aggressive and are characterized by a moderate locoregional recurrence rate and relatively poor survival, with a 5-year overall survival rate of ∼60% (9.Funk G.F. Karnell L.H. Robinson R.A. Zhen W.K. Trask D.K. Hoffman H.T. Presentation, treatment, and outcome of oral cavity cancer: a National Cancer Data Base report.Head Neck. 2002; 24: 165-180Crossref PubMed Scopus (307) Google Scholar, 10.Liao C.T. Chang J.T. Wang H.M. Ng S.H. Hsueh C. Lee L.Y. Lin C.H. Chen I.H. Huang S.F. Cheng A.J. Yen T.C. Analysis of risk factors of predictive local tumor control in oral cavity cancer.Ann. Surgical Oncol. 2008; 15: 915-922Crossref PubMed Scopus (211) Google Scholar, 11.Scully C. Bagan J.V. Recent advances in Oral Oncology.Oral Oncol. 2007; 43: 107-115Crossref PubMed Scopus (41) Google Scholar). Despite recent advances in therapeutic modalities and strategies, OSCC has maintained one of the lowest 5-year survival rates among all major cancers (1.Siegel R. Naishadham D. Jemal A. Cancer statistics, 2013.CA. 2013; 63: 11-30Google Scholar, 2.Jemal A. Bray F. Center M.M. Ferlay J. Ward E. Forman D. Global cancer statistics.CA. 2011; 61: 69-90Google Scholar). This low rate is at least partly a reflection of the fact that ∼50% of OSCC patients present with late stage (stage III and IV) tumors (12.Prasad V. Goldstein J.A. US News and World Report cancer hospital rankings: do they reflect measures of research productivity?.PloS One. 2014; 9: e107803Crossref PubMed Scopus (26) Google Scholar). Most cases of OSCC develop from potentially malignant oral disorders characterized by visible changes in oral mucosa (13.Hsue S.S. Wang W.C. Chen C.H. Lin C.C. Chen Y.K. Lin L.M. Malignant transformation in 1458 patients with potentially malignant oral mucosal disorders: a follow-up study based in a Taiwanese hospital.J. Oral Pathol. Med. 2007; 36: 25-29Crossref PubMed Scopus (185) Google Scholar, 14.van der Waal I. Schepman K.P. van der Meij E.H. Smeele L.E. Oral leukoplakia: a clinicopathological review.Oral Oncol. 1997; 33: 291-301Crossref PubMed Scopus (235) Google Scholar). Currently, visual inspection of oral mucosa combined with pathological examination of lesion showing morphological alternation and/or colored change is the most common strategy for oral cancer detection. However, determining which oral lesions warrant referral to a specialist for further histological confirmation is often challenging for first-line health workers, mainly because early-stage oral cancer is largely indistinguishable from certain benign or inflammatory disorders; moreover, submucous fibrosis may complicate detection. Therefore, identifying effective biomarkers that can serve as more objective molecular tools represents an urgent need for aiding early detection and/or management of OSCC. Currently, no biomarkers that meet this need are approved by official health agencies in endemic areas (15.ADaskalaki Informatics in Oral Medicine: Advanced Techniques in Clinical and Diagnostic Technologies. Medical Information Science Reference, Hershey PA2010: 17-26Google Scholar). Over the past two decades, numerous protein biomarker candidates implicated in the carcinogenesis of OSCC have been discovered through analyses of DNA, RNA and/or protein expression in different specimens, such as cultured cells, tissue, plasma/serum, and saliva (16.Yu C.J. Chang K.P. Chang Y.J. Hsu C.W. Liang Y. Yu J.S. Chi L.M. Chang Y.S. Wu C.C. Identification of guanylate-binding protein 1 as a potential oral cancer marker involved in cell invasion using omics-based analysis.J. Proteome Res. 2011; 10: 3778-3788Crossref PubMed Scopus (63) Google Scholar, 17.Banerjee A.G. Bhattacharyya I. Vishwanatha J.K. Identification of genes and molecular pathways involved in the progression of premalignant oral epithelia.Mol. Cancer Therapeut. 2005; 4: 865-875Crossref PubMed Scopus (54) Google Scholar, 18.He Q.Y. Chen J. Kung H.F. Yuen A.P. Chiu J.F. Identification of tumor-associated proteins in oral tongue squamous cell carcinoma by proteomics.Proteomics. 2004; 4: 271-278Crossref PubMed Scopus (145) Google Scholar, 19.Ralhan R. Desouza L.V. Matta A. Chandra Tripathi S. Ghanny S. Datta Gupta S. Bahadur S. Siu K.W. Discovery and verification of head-and-neck cancer biomarkers by differential protein expression analysis using iTRAQ labeling, multidimensional liquid chromatography, and tandem mass spectrometry.Mol. Cell. Proteomics. 2008; 7: 1162-1173Abstract Full Text Full Text PDF PubMed Scopus (187) Google Scholar). However, very few of these candidates have been verified in parallel in clinical specimens (plasma/serum and/or saliva) to test and compare their clinical utility. The lack of careful validation that such biomarker candidates warrant and the task of generating appropriate reagents/technology platforms for multiplexed quantification assays of selected targets represent two obvious bottlenecks to the successful translation of biomarker candidates into clinical use (20.Anderson N.L. The clinical plasma proteome: a survey of clinical assays for proteins in plasma and serum.Clin. Chem. 2010; 56: 177-185Crossref PubMed Scopus (390) Google Scholar, 21.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 (1367) Google Scholar). Multiplexed quantification assays for verification of protein biomarker candidates in clinical specimens require high precision, reproducibility, and sensitivity. Some enzyme-linked immunosorbent assay (ELISA)-based multiplexed assays have been successfully applied to quantify multiple targets in clinical specimens (22.Shimada Y. Tabeta K. Sugita N. Yoshie H. Profiling biomarkers in gingival crevicular fluid using multiplex bead immunoassay.Arch. Oral Biol. 2013; 58: 724-730Crossref PubMed Scopus (37) Google Scholar, 23.Chang K.P. Chang Y.T. Liao C.T. Yen T.C. Chen I.H. Chang Y.L. Liu Y.L. Chang Y.S. Yu J.S. Wu C.C. Prognostic cytokine markers in peripheral blood for oral cavity squamous cell carcinoma identified by multiplexed immunobead-based profiling.Clin. Chim. Acta. 2011; 412: 980-987Crossref PubMed Scopus (30) Google Scholar), but the availability of specific antibodies against selected targets, the inconsistency of quantification results using different antibodies and false-positive or -negative interference caused by some endogenous antibodies, such as autoantibodies and heterophilic antibodies, create additional roadblocks in the biomarker verification pipeline (24.Degn S.E. Andersen S.H. Jensen L. Thiel S. Jensenius J.C. Assay interference caused by antibodies reacting with rat kappa light-chain in human sera.J. Immunol. Methods. 2011; 372: 204-208Crossref PubMed Scopus (11) Google Scholar, 25.Nishii N. Takasu M. Kojima M. Hachisu T. Wakabayashi K. Iwasawa A. Maeda S. Ohba Y. Kitagawa H. Presence of anti-insulin natural autoantibodies in healthy cats and its interference with immunoassay for serum insulin concentrations.Domestic Animal Endocrinol. 2010; 38: 138-145Crossref PubMed Scopus (9) Google Scholar, 26.Sehlin D. Sollvander S. Paulie S. Brundin R. Ingelsson M. Lannfelt L. Pettersson F.E. Englund H. Interference from heterophilic antibodies in amyloid-beta oligomer ELISAs.J. Alzheimer's Dis. 2010; 21: 1295-1301Crossref PubMed Scopus (49) Google Scholar, 27.Borromeo V. Berrini A. Gaggioli D. Secchi C. Heterophile antibody interference in a solid phase sandwich immunoassay for detection of equine growth hormone in plasma.Veterinary Immunol. Immunopathol. 2007; 115: 1-9Crossref PubMed Scopus (5) Google Scholar). In the past decade, a multiplexed and targeted quantification technique based on peptide quantification using liquid chromatography coupled multiple-reaction-monitoring mass spectrometry (LC-MRM-MS) approach has been described for quantifying dozens of proteins in a single assay with acceptable precision in different biofluids, such as plasma (28.Kuzyk M.A. Smith D. Yang J. Cross T.J. Jackson A.M. Hardie D.B. Anderson N.L. Borchers C.H. Multiple reaction monitoring-based, multiplexed, absolute quantitation of 45 proteins in human plasma.Mol. Cell. Proteomics. 2009; 8: 1860-1877Abstract Full Text Full Text PDF PubMed Scopus (444) Google Scholar, 29.Abbatiello S.E. Schilling B. Mani D.R. Zimmerman L.J. Hall S.C. MacLean B. Albertolle M. Allen S. Burgess M. Cusack M.P. Gosh M. Hedrick V. Held J.M. Inerowicz H.D. Jackson A. Keshishian H. Kinsinger C.R. Lyssand J. Makowski L. Mesri M. Rodriguez H. Rudnick P. Sadowski P. Sedransk N. Shaddox K. Skates S.J. Kuhn E. Smith D. Whiteaker J.R. Whitwell C. Zhang S. Borchers C.H. Fisher S.J. Gibson B.W. Liebler D.C. MacCoss M.J. Neubert T.A. Paulovich A.G. Regnier F.E. Tempst P. Carr S.A. Large-scale interlaboratory study to develop, analytically validate and apply highly multiplexed, quantitative peptide assays to measure cancer-relevant proteins in plasma.Mol. Cell. Proteomics. 2015; 14: 2357-2374Abstract Full Text Full Text PDF PubMed Scopus (135) Google Scholar, 30.Li X.J. Hayward C. Fong P.Y. Dominguez M. Hunsucker S.W. Lee L.W. McLean M. Law S. Butler H. Schirm M. Gingras O. Lamontagne J. Allard R. Chelsky D. Price N.D. Lam S. Massion P.P. Pass H. Rom W.N. Vachani A. Fang K.C. Hood L. Kearney P. A blood-based proteomic classifier for the molecular characterization of pulmonary nodules.Sci. Transl. Med. 2013; 5: 207ra142Crossref PubMed Scopus (152) Google Scholar), urine (31.Chen Y.T. Chen H.W. Domanski D. Smith D.S. Liang K.H. Wu C.C. Chen C.L. Chung T. Chen M.C. Chang Y.S. Parker C.E. Borchers C.H. Yu J.S. Multiplexed quantification of 63 proteins in human urine by multiple reaction monitoring-based mass spectrometry for discovery of potential bladder cancer biomarkers.J. Proteomics. 2012; 75: 3529-3545Crossref PubMed Scopus (122) Google Scholar, 32.Percy A.J. Yang J. Hardie D.B. Chambers A.G. Tamura-Wells J. Borchers C.H. Precise quantitation of 136 urinary proteins by LC/MRM-MS using stable isotope labeled peptides as internal standards for biomarker discovery and/or verification studies.Methods. 2015; 81: 24-33Crossref PubMed Scopus (37) Google Scholar), cerebrospinal fluid (33.Wildsmith K.R. Schauer S.P. Smith A.M. Arnott D. Zhu Y. Haznedar J. Kaur S. Mathews W.R. Honigberg L.A. Identification of longitudinally dynamic biomarkers in Alzheimer's disease cerebrospinal fluid by targeted proteomics.Mol. Neurodegener. 2014; 9: 22Crossref PubMed Scopus (100) Google Scholar), and saliva (34.Yu J.S. Chen Y.T. Chiang W.F. Hsiao Y.C. Chu L.J. See L.C. Wu C.S. Tu H.T. Chen H.W. Chen C.C. Liao W.C. Chang Y.T. Wu C.C. Lin C.Y. Liu S.Y. Chiou S.T. Chia S.L. Chang K.P. Chien C.Y. Chang S.W. Chang C.J. Young J.D. Pao C.C. Chang Y.S. Hartwell L.H. Saliva protein biomarkers to detect oral squamous cell carcinoma in a high-risk population in Taiwan.Proc. Natl. Acad. Sci. U.S.A. 2016; 113: 11549-11554Crossref PubMed Scopus (77) Google Scholar). In this MRM-MS technique, specific transitions of precursor/product ions are selected for detection using triple quadrupole MS instruments (or QTRAP operating in triple quadrupole mode). The coefficients of variation (CVs) for target protein quantification using well-designed, scheduled MRM coupled with stable isotope-coded standard (SIS) peptides can be less than 15% (35.Addona T.A. Abbatiello S.E. Schilling B. Skates S.J. Mani D.R. Bunk D.M. Spiegelman C.H. Zimmerman L.J. Ham A.J. Keshishian H. Hall S.C. Allen S. Blackman R.K. Borchers C.H. Buck C. Cardasis H.L. Cusack M.P. Dodder N.G. Gibson B.W. Held J.M. Hiltke T. Jackson A. Johansen E.B. Kinsinger C.R. Li J. Mesri M. Neubert T.A. Niles R.K. Pulsipher T.C. Ransohoff D. Rodriguez H. Rudnick P.A. Smith D. Tabb D.L. Tegeler T.J. Variyath A.M. Vega-Montoto L.J. Wahlander A. Waldemarson S. Wang M. Whiteaker J.R. Zhao L. Anderson N.L. Fisher S.J. Liebler D.C. Paulovich A.G. Regnier F.E. Tempst P. Carr S.A. Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma.Nat. Biotechnol. 2009; 27: 633-641Crossref PubMed Scopus (862) Google Scholar). Protein concentrations in unfractionated plasma in the mid to high nanogram per milliliter range have been reported with high reproducibility within and across laboratories and instrument platforms (28.Kuzyk M.A. Smith D. Yang J. Cross T.J. Jackson A.M. Hardie D.B. Anderson N.L. Borchers C.H. Multiple reaction monitoring-based, multiplexed, absolute quantitation of 45 proteins in human plasma.Mol. Cell. Proteomics. 2009; 8: 1860-1877Abstract Full Text Full Text PDF PubMed Scopus (444) Google Scholar, 35.Addona T.A. Abbatiello S.E. Schilling B. Skates S.J. Mani D.R. Bunk D.M. Spiegelman C.H. Zimmerman L.J. Ham A.J. Keshishian H. Hall S.C. Allen S. Blackman R.K. Borchers C.H. Buck C. Cardasis H.L. Cusack M.P. Dodder N.G. Gibson B.W. Held J.M. Hiltke T. Jackson A. Johansen E.B. Kinsinger C.R. Li J. Mesri M. Neubert T.A. Niles R.K. Pulsipher T.C. Ransohoff D. Rodriguez H. Rudnick P.A. Smith D. Tabb D.L. Tegeler T.J. Variyath A.M. Vega-Montoto L.J. Wahlander A. Waldemarson S. Wang M. Whiteaker J.R. Zhao L. Anderson N.L. Fisher S.J. Liebler D.C. Paulovich A.G. Regnier F.E. Tempst P. Carr S.A. Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma.Nat. Biotechnol. 2009; 27: 633-641Crossref PubMed Scopus (862) Google Scholar). To further improve the quantitation limit for targeted peptides, researchers have developed a platform that combines immuno-affinity enrichment using antipeptide antibodies and MRM-MS, known as “SISCAPA-MRM” (stable isotope standards with capture by anti-peptide antibodies coupled with multiple reaction monitoring MS) or “immuno-MRM” (36.Anderson N.L. Anderson N.G. Haines L.R. Hardie D.B. Olafson R.W. Pearson T.W. Mass spectrometric quantitation of peptides and proteins using Stable Isotope Standards and Capture by Anti-Peptide Antibodies (SISCAPA).J. Proteome Res. 2004; 3: 235-244Crossref PubMed Scopus (694) Google Scholar, 37.Whiteaker J.R. Zhao L. Anderson L. Paulovich A.G. An automated and multiplexed method for high throughput peptide immunoaffinity enrichment and multiple reaction monitoring mass spectrometry-based quantification of protein biomarkers.Mol. Cell. Proteomics. 2010; 9: 184-196Abstract Full Text Full Text PDF PubMed Scopus (274) Google Scholar). Recent studies have demonstrated the high precision quantification of human plasma proteins using the automated SISCAPA-MRM (38.Razavi M. Leigh Anderson N. Pope M.E. Yip R. Pearson T.W. High precision quantification of human plasma proteins using the automated SISCAPA Immuno-MS workflow.Nat. Biotechnol. 2016; 33: 494-502Crossref PubMed Scopus (44) Google Scholar), as well as acceptable reproducibility across independent laboratories for assaying plasma proteins (39.Kuhn E. Whiteaker J.R. Mani D.R. Jackson A.M. Zhao L. Pope M.E. Smith D. Rivera K.D. Anderson N.L. Skates S.J. Pearson T.W. Paulovich A.G. Carr S.A. Interlaboratory evaluation of automated, multiplexed peptide immunoaffinity enrichment coupled to multiple reaction monitoring mass spectrometry for quantifying proteins in plasma.Mol. Cell. Proteomics. 2012; 11 (M111 013854)Abstract Full Text Full Text PDF PubMed Scopus (151) Google Scholar). The feasibility of assembling a single multiplexed assay for measuring more than 100 target peptides in clinical plasma samples was also manifested (40.Ippoliti P.J. Kuhn E. Mani D.R. Fagbami L. Keshishian H. Burgess M.W. Jaffe J.D. Carr S.A. Automated microchromatography enables multiplexing of immunoaffinity enrichment of peptides to greater than 150 for targeted MS-based assays.Anal. Chem. 2016; 88: 7548-7555Crossref PubMed Scopus (30) Google Scholar). High-affinity anti-peptide antibodies have played a key role in the successful development of these multiplex assays. As noted above, very few candidate OSCC biomarkers have been verified using multiplexed assays in clinical specimens. Thus, development of effective multiplexed assays for potential OSCC biomarkers should greatly facilitate the future development of clinically useful biomarkers for OSCC detection and/or management. In this study, we report the production and characterization of anti-peptide mouse monoclonal antibodies (mAbs) and the subsequent successful development of a 24-plex SISCAPA-MRM assay for quantifying multiple OSCC candidate biomarkers in saliva samples. In a further application, we used this assay to detect and quantify target proteins in saliva samples from healthy controls and OSCC patients. The purpose of this study is to establish a multiplexed and automated SISCAPA-MRM assay for verification of OSCC biomarker candidates in the saliva samples. The overall study design is shown in Fig. 1, which includes selection of 49 candidate proteins and their proteotypic peptides (one peptide for one protein), production of anti-peptide mouse mAbs, screening of useful mAbs, establishment and optimization of the multiplex SISCAPA-MRM assay, and application of the optimized assay to measure candidate proteins in a small set of clinical saliva samples for evaluating the applicability of the developed assay. For screening of useful mAbs, analyses of binding kinetics (using surface plasmon resonance (SPR) technique) and peptide-capture ability (using SISCAPA-MS platform) were performed to characterize the produced mAbs. To assess whether the binding kinetics reflects the peptide-capturing ability of mAbs, the following statistical analyses were employed. Correlation between equilibrium binding constant (KD) and peptide-capture ability of the produced mAbs were analyzed using Spearman's correlation. The power of kinetic constants (ka, kd, and KD) to predict mAbs with higher peptide-capture ability was estimated by the receiver operating characteristic (ROC) curve analysis using SPSS statistical software. Two kinds of magnetic beads and three different elution solutions were applied to optimize the 24-plex SCISCAPA-MRM assay, and the performance characteristics (the linearity of the response curves, and values for LOD, LLOQ, coefficient of variation (CV), and accuracy for all 24 targets) of the optimized assay were evaluated in a background of saliva digest (25 μg protein). Finally, two model saliva samples (pooled from 20 healthy donors and 20 OSCC patients, respectively) and another set of 41 individual saliva samples (20 healthy donors and 21 OSCC patients) were used to evaluate the applicability of the developed assay for quantifying all 24 targets. Three process repeats were performed independently (from digestion to the final LC-MRM/MS step) for each of the model/individual clinical samples, and significance of differences of the quantified target protein levels between two groups (healthy donors and OSCC patients) were analyzed by the Mann Whitney test. In addition, we prepared another model saliva sample (pooled from seven OSCC patients) to evaluate the intra- and interday reproducibility of the 24-plex SISCAPA-MRM assay as well as the stability of the targets in unprocessed or trypsin-digested saliva sample during storage at different conditions (−80, 4, and 25 °C) for various time periods (1–7 or 1–14 days). Each sample was assayed in triplicate (i.e. three process repeats). Our study design for establishing and testing a 24-plex SISCAPA-MRM assay with internal standards for accurate quantification of selected biomarker candidates in a small set of clinical saliva samples belongs to a Tier 2 analysis. A total of 49 candidate OSCC biomarkers were selected from review of >1,400 papers related to OSCC or head-and-neck cancer published between 1995 and 2012 in the PubMed database and our previous studies of OSCC/head-and-neck cancer biomarkers using genomic and proteomic approaches (16.Yu C.J. Chang K.P. Chang Y.J. Hsu C.W. Liang Y. Yu J.S. Chi L.M. Chang Y.S. Wu C.C. Identification of guanylate-binding protein 1 as a potential oral cancer marker involved in cell invasion using omics-based analysis.J. Proteome Res. 2011; 10: 3778-3788Crossref PubMed Scopus (63) Google Scholar, 41.Chang K.P. Kao H.K. Yen T.C. Chang Y.L. Liang Y. Liu S.C. Lee L.Y. Kang C.J. Chen I.H. Liao C.T. Yu J.S. Overexpression of macrophage inflammatory protein-3alpha in oral cavity squamous cell carcinoma is associated with nodal metastasis.Oral Oncol. 2011; 47: 108-113Crossref PubMed Scopus (34) Google Scholar, 42.Chang J.T. Chan S.H. Lin C.Y. Lin T.Y. Wang H.M. Liao C.T. Wang T.H. Lee L.Y. Cheng A.J. Differentially expressed genes in radioresistant nasopharyngeal cancer cells: gp96 and GDF15.Mol. Cancer Therapeut. 2007; 6: 2271-2279Crossref PubMed Scopus (80) Google Scholar, 43.Chang K.P. Yu J.S. Chien K.Y. Lee C.W. Liang Y. Liao C.T. Yen T.C. Lee L.Y. Huang L.L. Liu S.C. Chang Y.S. Chi L.M. Identification of PRDX4 and P4HA2 as metastasis-associated proteins in oral cavity squamous cell carcinoma by comparative tissue proteomics of microdissected specimens using iTRAQ technology.J. Proteome Res. 2011; 10: 4935-4947Crossref PubMed Scopus (75) Google Scholar, 44.Chiu C.C. Lin C.Y. Lee L.Y. Chen Y.J. Lu Y.C. Wang H.M. Liao C.T. Chang J.T. Cheng A.J. Molecular chaperones as a common set of proteins that regulate the invasion phenotype of head and neck cancer.Clin. Cancer Res. 2011; 17: 4629-4641Crossref PubMed Scopus (49) Google Scholar, 45.Lin C.Y. Lin T.Y. Wang H.M. Huang S.F. Fan K.H. Liao C.T. Chen I.H. Lee L.Y. Li Y.L. Chen Y.J. Cheng A.J. Chang J.T. GP96 is over-expressed in oral cavity cancer and is a poor prognostic indicator for patients receiving radiotherapy.Radiat. Oncol. 2011; 6: 136Crossref PubMed Scopus (16) Google Scholar, 46.Lin T.Y. Chang J.T. Wang H.M. Chan S.H. Chiu C.C. Lin C.Y. Fan K.H. Liao C.T. Chen I.H. Liu T.Z. Li H.F. Cheng A.J. Proteomics of the radioresistant p" @default.
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• W2750544041 date "2017-10-01" @default.
• W2750544041 modified "2023-10-09" @default.
• W2750544041 title "Development of a Multiplexed Assay for Oral Cancer Candidate Biomarkers Using Peptide Immunoaffinity Enrichment and Targeted Mass Spectrometry" @default.
• W2750544041 cites W1532594816 @default.
• W2750544041 cites W1571689846 @default.
• W2750544041 cites W1863578670 @default.
• W2750544041 cites W1964074906 @default.
• W2750544041 cites W1964753682 @default.
• W2750544041 cites W1968398558 @default.
• W2750544041 cites W1972891644 @default.
• W2750544041 cites W1974500649 @default.
• W2750544041 cites W1975746647 @default.
• W2750544041 cites W1976026580 @default.
• W2750544041 cites W1979744503 @default.
• W2750544041 cites W1980931467 @default.
• W2750544041 cites W1981187883 @default.
• W2750544041 cites W1990950439 @default.
• W2750544041 cites W1997008541 @default.
• W2750544041 cites W2004114046 @default.
• W2750544041 cites W2004182185 @default.
• W2750544041 cites W2009455674 @default.
• W2750544041 cites W2014076617 @default.
• W2750544041 cites W2017792346 @default.
• W2750544041 cites W2027872382 @default.
• W2750544041 cites W2031059274 @default.
• W2750544041 cites W2031488930 @default.
• W2750544041 cites W2035037428 @default.
• W2750544041 cites W2035349279 @default.
• W2750544041 cites W2036760453 @default.
• W2750544041 cites W2043276694 @default.
• W2750544041 cites W2045847786 @default.
• W2750544041 cites W2046350954 @default.
• W2750544041 cites W2047322941 @default.
• W2750544041 cites W2051143564 @default.
• W2750544041 cites W2059441089 @default.
• W2750544041 cites W2066368211 @default.
• W2750544041 cites W2069304635 @default.
• W2750544041 cites W2070210884 @default.
• W2750544041 cites W2071102786 @default.
• W2750544041 cites W2082568125 @default.
• W2750544041 cites W2088051571 @default.
• W2750544041 cites W2090069859 @default.
• W2750544041 cites W2091906225 @default.
• W2750544041 cites W2094229819 @default.
• W2750544041 cites W2094602322 @default.
• W2750544041 cites W2096642770 @default.
• W2750544041 cites W2097161379 @default.
• W2750544041 cites W2099277710 @default.
• W2750544041 cites W2099320651 @default.
• W2750544041 cites W2104451599 @default.
• W2750544041 cites W2105743998 @default.
• W2750544041 cites W2109100622 @default.
• W2750544041 cites W2109876169 @default.
• W2750544041 cites W2121060043 @default.
• W2750544041 cites W2122863289 @default.
• W2750544041 cites W2123768348 @default.
• W2750544041 cites W2126925889 @default.
• W2750544041 cites W2129771181 @default.
• W2750544041 cites W2141091417 @default.
• W2750544041 cites W2142484230 @default.
• W2750544041 cites W2145353373 @default.
• W2750544041 cites W2145406398 @default.
• W2750544041 cites W2148104731 @default.
• W2750544041 cites W2148800999 @default.
• W2750544041 cites W2148818765 @default.
• W2750544041 cites W2152331748 @default.
• W2750544041 cites W2153831689 @default.
• W2750544041 cites W2159247973 @default.
• W2750544041 cites W2160061958 @default.
• W2750544041 cites W2162896172 @default.
• W2750544041 cites W2166579846 @default.
• W2750544041 cites W2170791918 @default.
• W2750544041 cites W2172080431 @default.
• W2750544041 cites W2186136307 @default.
• W2750544041 cites W2413428715 @default.
• W2750544041 cites W2437913534 @default.
• W2750544041 cites W2521198002 @default.
• W2750544041 doi "https://doi.org/10.1074/mcp.ra117.000147" @default.
• W2750544041 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5629267" @default.
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