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Quantitative Proteomic Analysis Identifies AHNAK (Neuroblast Differentiation-associated Protein AHNAK) as a Novel Candidate Biomarker for Bladder Urothelial Carcinoma Diagnosis by Liquid-based Cytology

2018; Elsevier BV; Volume: 17; Issue: 9 Linguagem: Inglês

10.1074/mcp.ra118.000562

ISSN

1535-9484

Autores

Hyebin Lee, Kwangsoo Kim, Jongmin Jacob Woo, Joonho Park, Hyeyoon Kim, Jeong Yong Lee, Hye‐Yeon Kim, Youngsoo Kim, Kyung Chul Moon, Ji Young Kim, In Ae Park, Bo Bae Shim, Ji Hye Moon, Dohyun Han, Han Suk Ryu,

Tópico(s)

Advanced Proteomics Techniques and Applications

Resumo

Cytological examination of urine is the most widely used noninvasive pathologic screen for bladder urothelial carcinoma (BLCA); however, inadequate diagnostic accuracy remains a major challenge. We performed mass spectrometry-based proteomic analysis of urine samples of ten patients with BLCA and ten paired patients with benign urothelial lesion (BUL) to identify ancillary proteomic markers for use in liquid-based cytology (LBC). A total of 4,839 proteins were identified and 112 proteins were confirmed as expressed at significantly different levels between the two groups. We also performed an independent proteomic profiling of tumor tissue samples where we identified 7,916 proteins of which 758 were differentially expressed. Cross-platform comparisons of these data with comparative mRNA expression profiles from The Cancer Genome Atlas identified four putative candidate proteins, AHNAK, EPPK1, MYH14 and OLFM4. To determine their immunocytochemical expression levels in LBC, we examined protein expression data from The Human Protein Atlas and in-house FFPE samples. We further investigated the expression of the four candidate proteins in urine cytology samples from two independent validation cohorts. These analyses revealed AHNAK as a unique intracellular protein differing in immunohistochemical expression and subcellular localization between tumor and non-tumor cells. In conclusion, this study identified a new biomarker, AHNAK, applicable to discrimination between BLCA and BUL by LBC. To our knowledge, the present study provides the first identification of a clinical biomarker for LBC based on in-depth proteomics. Cytological examination of urine is the most widely used noninvasive pathologic screen for bladder urothelial carcinoma (BLCA); however, inadequate diagnostic accuracy remains a major challenge. We performed mass spectrometry-based proteomic analysis of urine samples of ten patients with BLCA and ten paired patients with benign urothelial lesion (BUL) to identify ancillary proteomic markers for use in liquid-based cytology (LBC). A total of 4,839 proteins were identified and 112 proteins were confirmed as expressed at significantly different levels between the two groups. We also performed an independent proteomic profiling of tumor tissue samples where we identified 7,916 proteins of which 758 were differentially expressed. Cross-platform comparisons of these data with comparative mRNA expression profiles from The Cancer Genome Atlas identified four putative candidate proteins, AHNAK, EPPK1, MYH14 and OLFM4. To determine their immunocytochemical expression levels in LBC, we examined protein expression data from The Human Protein Atlas and in-house FFPE samples. We further investigated the expression of the four candidate proteins in urine cytology samples from two independent validation cohorts. These analyses revealed AHNAK as a unique intracellular protein differing in immunohistochemical expression and subcellular localization between tumor and non-tumor cells. In conclusion, this study identified a new biomarker, AHNAK, applicable to discrimination between BLCA and BUL by LBC. To our knowledge, the present study provides the first identification of a clinical biomarker for LBC based on in-depth proteomics. Urothelial carcinoma of the bladder is a disease with high morbidity and mortality (1Fitzmaurice C. Allen C. Barber R.M. Barregard L. Bhutta Z.A. Brenner H. Dicker D.J. Chimed-Orchir O. Dandona R. Dandona L. Fleming T. Forouzanfar M.H. Hancock J. Hay R.J. Hunter-Merrill R. Huynh C. Hosgood H.D. Johnson C.O. Jonas J.B. Khubchandani J. Kumar G.A. Kutz M. Lan Q. Larson H.J. Liang X. Lim S.S. Lopez A.D. MacIntyre M.F. Marczak L. Marquez N. Mokdad A.H. Pinho C. Pourmalek F. Salomon J.A. Sanabria J.R. Sandar L. Sartorius B. Schwartz S.M. Shackelford K.A. Shibuya K. Stanaway J. Steiner C. Sun J. Takahashi K. Vollset S.E. Vos T. Wagner J.A. Wang H. Westerman R. Zeeb H. Zoeckler L. Abd-Allah F. Ahmed M.B. Alabed S. Alam N.K. Aldhahri S.F. Alem G. Alemayohu M.A. Ali R. Al-Raddadi R. Amare A. Amoako Y. Artaman A. Asayesh H. Atnafu N. Awasthi A. Saleem H.B. Barac A. Bedi N. Bensenor I. Berhane A. Bernabe E. Betsu B. Binagwaho A. Boneya D. Campos-Nonato I. Castaneda-Orjuela C. Catala-Lopez F. Chiang P. Chibueze C. Chitheer A. Choi J.Y. Cowie B. Damtew S. das Neves J. Dey S. Dharmaratne S. Dhillon P. Ding E. Driscoll T. Ekwueme D. Endries A.Y. Farvid M. Farzadfar F. Fernandes J. Fischer F. G/Hiwot TT. Gebru A Gopalani S. Hailu A. Horino M. Horita N. Husseini A. Huybrechts I. Inoue M. Islami F. Jakovljevic M. James S. Javanbakht M. Jee S.H. Kasaeian A. Kedir M.S. Khader Y.S. Khang Y.H. Kim D. Leigh J. Linn S. Lunevicius R. El Razek H.M. Malekzadeh R. Malta D.C. Marcenes W. Markos D. Melaku Y.A. Meles K.G. Mendoza W. Mengiste D.T. Meretoja T.J. Miller T.R. Mohammad K.A. Mohammadi A. Mohammed S. Moradi-Lakeh M. Nagel G. Nand D. Le Nguyen Q. Nolte S. Ogbo F.A. Oladimeji K.E. Oren E. Pa M. Park E.K. Pereira D.M. Plass D. Qorbani M. Radfar A. Rafay A. Rahman M. Rana S.M. Soreide K. Satpathy M. Sawhney M. Sepanlou S.G. Shaikh M.A. She J. Shiue I. Shore H.R. Shrime M.G. So S. Soneji S. Stathopoulou V. Stroumpoulis K. Sufiyan M.B. Sykes B.L. Tabares-Seisdedos R. Tadese F. Tedla B.A. Tessema G.A. Thakur J.S. Tran B.X. Ukwaja K.N. Uzochukwu B.S. Vlassov V.V. Weiderpass E. Wubshet Terefe M. Yebyo H.G. Yimam H.H. Yonemoto N. Younis M.Z. Yu C. Zaidi Z. Zaki M.E. Zenebe Z.M. Murray C.J. Naghavi M. Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-years for 32 Cancer Groups, 1990 to 2015: A Systematic Analysis for the Global Burden of Disease Study.JAMA Oncol. 2016; 3: 524-548Google Scholar). Urinary cytology represents the 'gold standard' for surveillance of urothelial carcinoma diagnosis and recurrence; however, the diagnostic accuracy of the test has been doubted owing to considerable variability in its diagnostic positive prediction rate (31–72%) for identification of patients with bladder cancer (2Wiener H.G. Vooijs G.P. van't Hof-Grootenboer B. Accuracy of urinary cytology in the diagnosis of primary and recurrent bladder cancer.Acta Cytologica. 1993; 37: 163-169PubMed Google Scholar, 3Raitanen M.P. Aine R. Rintala E. Kallio J. Rajala P. Juusela H. Tammela T.L. Differences between local and review urinary cytology in diagnosis of bladder cancer. An interobserver multicenter analysis.Eur. Urol. 2002; 41: 284-289Abstract Full Text Full Text PDF PubMed Scopus (176) Google Scholar). In addition, inter-observer variability had a range of 38–65% across institutions (4Karakiewicz P.I. Benayoun S. Zippe C. Ludecke G. Boman H. Sanchez-Carbayo M. Casella R. Mian C. Friedrich M.G. Eissa S. Akaza H. Huland H. Hedelin H. Rupesh R. Miyanaga N. Sagalowsky A.I. Marberger M.J. Shariat S.F. Institutional variability in the accuracy of urinary cytology for predicting recurrence of transitional cell carcinoma of the bladder.BJU Int. 2006; 97: 997-1001Crossref PubMed Scopus (122) Google Scholar). To overcome these drawbacks, several molecular tests have been developed; however, used alone, the overall specificity and sensitivity of these tests are like those of cytology, because of the lack of simultaneous assessment of cytological appearance (5Moonen P.M. Merkx G.F. Peelen P. Karthaus H.F. Smeets D.F. Witjes J.A. UroVysion compared with cytology and quantitative cytology in the surveillance of non-muscle-invasive bladder cancer.Eur. Urol. 2007; 51 (discussion 1280): 1275-1280Abstract Full Text Full Text PDF PubMed Scopus (96) Google Scholar). Recent rapid advances in proteomic technologies, including computational algorithms and biochemical techniques, have enabled quantitative evaluation of novel diagnostic markers to determine their levels in tumor tissues (6Hughes C.S. McConechy M.K. Cochrane D.R. Nazeran T. Karnezis A.N. Huntsman D.G. Morin G.B. Quantitative Profiling of Single Formalin Fixed Tumour Sections: proteomics for translational research.Sci. Reports. 2016; 6: 34949Crossref PubMed Scopus (74) Google Scholar, 7Tyanova S. Albrechtsen R. Kronqvist P. Cox J. Mann M. Geiger T. Proteomic maps of breast cancer subtypes.Nat. Commun. 2016; 7: 10259Crossref PubMed Scopus (186) Google Scholar). In-depth proteomic analyses of clinically available urine specimens have been performed in several previous studies (8Frantzi M. Latosinska A. Fluhe L. Hupe M.C. Critselis E. Kramer M.W. Merseburger A.S. Mischak H. Vlahou A. Developing proteomic biomarkers for bladder cancer: towards clinical application.Nat. Rev. Urol. 2015; 12: 317-330Crossref PubMed Scopus (68) Google Scholar). Urine is a useful source of proteins for biomarker discovery and comprehensive assessment, because it is readily available, can be obtained by non-invasive collection methods, and enables disease monitoring; however, the proteins in urine can originate from various types of cells or secretions, such as blood cells, epithelial cells from the glomerulus or urinary tract, and can include a mixture of benign and malignant cells and excreted plasma, which can contribute to misinterpretation and misleading results. In contrast, cytological urine specimens almost exclusively contain epithelial cells from the lining of the urinary tract, which are selected under microscopic examination. Therefore, genomic or proteomic information obtained from cytological preparations is expected to exclusively reflect the molecular landscape in urothelial cells and be suitable for identification of novel biomarkers. In this study, for the first time, we employed MS-based in-depth proteomics to identify novel biomarkers in voided urine cytology samples collected by the liquid-based method, which has technical advantages (9Hoda R.S. Non-gynecologic cytology on liquid-based preparations: A morphologic review of facts and artifacts.Diagnostic Cytopathol. 2007; 35: 621-634Crossref PubMed Scopus (116) Google Scholar). To discover suitable biomarkers, we designed an integrative workflow, including comparative analyses of results from a cytological proteomic platform with those in a public transcriptomic database, and from an in-house generated formalin fixed paraffin embedded (FFPE) 1The abbreviations used are:FFPEformalin fixed paraffin embeddedACNacetonitrileAHNAKneuroblast differentiation-associated protein AHNAKAUCthe area under the curveBLCAbladder urothelial carcinomaBULbenign urothelial lesionCVcoefficient of variationDEGdifferentially expressed geneDEPdifferentially expressed proteinEPPK1EpiplakinFDRfalse discovery rateiBAQintensity based absolute quantificationLBCliquid-based cytologyMYH14Myosin-14NPVNegative predictive valueOLFM4Olfactomedin-4PPVPositive predictive valueROCthe receiver operating characteristicsTCGAthe cancer genome atlasWHO/ISUPWorld Health Organization/the International Society of Urologic Pathology. 1The abbreviations used are:FFPEformalin fixed paraffin embeddedACNacetonitrileAHNAKneuroblast differentiation-associated protein AHNAKAUCthe area under the curveBLCAbladder urothelial carcinomaBULbenign urothelial lesionCVcoefficient of variationDEGdifferentially expressed geneDEPdifferentially expressed proteinEPPK1EpiplakinFDRfalse discovery rateiBAQintensity based absolute quantificationLBCliquid-based cytologyMYH14Myosin-14NPVNegative predictive valueOLFM4Olfactomedin-4PPVPositive predictive valueROCthe receiver operating characteristicsTCGAthe cancer genome atlasWHO/ISUPWorld Health Organization/the International Society of Urologic Pathology.-based proteomic experiment, followed by immunostaining validation in two independent liquid-based cytology cohorts. formalin fixed paraffin embedded acetonitrile neuroblast differentiation-associated protein AHNAK the area under the curve bladder urothelial carcinoma benign urothelial lesion coefficient of variation differentially expressed gene differentially expressed protein Epiplakin false discovery rate intensity based absolute quantification liquid-based cytology Myosin-14 Negative predictive value Olfactomedin-4 Positive predictive value the receiver operating characteristics the cancer genome atlas World Health Organization/the International Society of Urologic Pathology. formalin fixed paraffin embedded acetonitrile neuroblast differentiation-associated protein AHNAK the area under the curve bladder urothelial carcinoma benign urothelial lesion coefficient of variation differentially expressed gene differentially expressed protein Epiplakin false discovery rate intensity based absolute quantification liquid-based cytology Myosin-14 Negative predictive value Olfactomedin-4 Positive predictive value the receiver operating characteristics the cancer genome atlas World Health Organization/the International Society of Urologic Pathology. All pathologic specimens enrolled in this study were collected from the Seoul National University Hospital biorepository operated by the department of pathology. A discovery set consisting of a total of 20 voided urine cytology samples was collected from 10 patients with primary bladder urothelial carcinoma and 10 with benign urothelial lesion as a negative control. Separately, six FFPE urinary bladder tissue samples from three patients each with bladder urothelial carcinoma and benign urothelial lesion (previously diagnosed with cystitis cystica) were included for comparative proteomic analysis. For immunocytochemical validation of selected proteomic biomarkers, an independent cohort of 140 voided urine liquid-based cytology samples, containing both urothelial carcinoma and normal cells, were collected. All cases were histologically confirmed, using samples obtained within one month before corresponding surgical examination. All slides were reviewed by two experienced urologic pathologists and classified according to the WHO/ISUP system for surgical biopsy (10Epstein J.I. Amin M.B. Reuter V.R. Mostofi F.K. The World Health Organization/International Society of Urological Pathology consensus classification of urothelial (transitional cell) neoplasms of the urinary bladder. Bladder Consensus Conference Committee.Am. J. Surg. Pathol. 1998; 22: 1435-1448Crossref PubMed Scopus (1394) Google Scholar) and the Paris system for liquid-based cytology (11Barkan G.A. Wojcik E.M. Nayar R. Savic-Prince S. Quek M.L. Kurtycz D.F. Rosenthal D.L. The Paris System for Reporting Urinary Cytology: The Quest to Develop a Standardized Terminology.Acta Cytologica. 2016; 60: 185-197Crossref PubMed Scopus (133) Google Scholar), respectively. The clinicopathologic features are presented in Table I.Table IClinicopathologic characteristics of the patients in cohort of bladder urothelial carcinomasBladder urothelial carcinoma (BLCA)Benign urothelial lesion (BUL)Gender (%, n) Male100%10/10100%10/10Age at diagnosis (years) 50–6020%2/1030%3/10 60–7020%2/1010%1/10 7060%6/1060%6/10Pathologic diagnosis Invasive urothelial carcinoma, high grade40%4/10 Papillary urothelial carcinoma, high grade60%6/10 Urothelial papilloma30%3/10Cystitis cystica70%7/10Stroma invasion Present90%9/10Pathologic stage pTis10%1/10 pT160%6/10 pT230%3/10AJCC stage 0is10%1/10 I60%6/10 II30%3/10Treatment Transurethral resection36%4/10100%10/10 Cystectomy9%1/10 Nephroureterectomy9%1/10 No treatment18%2/10 Adjuvant chemotherapy+cystectomy9%1/10 Cystectomy+ureterectomy9%1/10Status Follow up loss30%3/10 No evidence of disease recurrence30%3/10100%10/10 Local recurrence30%3/10 Dead of complication10%1/10 Open table in a new tab All liquid-based cytology slides were previously scanned with an Aperio AT2 Digital Whole Slide Scanner (Leica Biosystems, IL), and the number of cells initially screened by pathologists, and counted using an Aperio ImageScope (Leica Biosystems) with Aperio's nuclear algorithm (Leica Biosystems). The study protocol was approved by the Institutional Review Board at Seoul National University Hospital (IRB no. 1602-150-747). All urine samples were fixed with BD CytoRichTM Clear Preservative Fluid (BD Diagnostics-TriPath Imaging, Burlington, NC) and prepared using the SurePath liquid-based preparation method according to the manufacturer's instructions (12Lee C.H. Chung S.Y. Moon K.C. Park I.A. Chung Y.R. Ryu H.S. A Pilot Study Evaluating Fine-Needle Aspiration Cytology of Clear-Cell Renal Cell Carcinoma: Comparison of Ancillary Immunocytochemistry and Cytomorphological Characteristics of SurePath Liquid-Based Preparations with Conventional Smears.Acta Cytologica. 2015; 59: 239-247Crossref PubMed Scopus (6) Google Scholar). Briefly, the samples were collected in individual 20-ml specimen containers based on the midstream clean catch method, which is widely-used in daily practice. Each 12-ml sample was transferred to a 50 ml tube (BD Prepstain™ system). The supernatant was discarded and 10 ml of preservative fluid (BD CytoRich™ Clear) was added. After being vortexed for 15 ± 5 s and left static for a minimum of 30 min, the contents were transferred to a 12-ml tubes and centrifuged for 5 min at 600 g (3240 rpm). The urine samples were stored in a refrigerator (4 °C) without freezing for less than 5 min on average until further preparation. After samples were taken out from the refrigerator, the entire amount of each unfixed urine sample was transferred to a centrifuge tube and centrifuged for 5 min at 600 × g (3240 rpm). The supernatant fluid was decanted and vortexed for 15 ± 5 s at room temperature to homogenize the sample, followed by loading into a 12-ml centrifuge tube holder onto the BD Prepstain™ system for processing. BD SurePath™ PreCoat slides with settling chambers were placed on the slide rack in the same position as the tubes in the centrifuge tube holder. The NON-GYN program was run in the instrument. In the Prepstain™ system, 500 μl of buffered distilled water (Sigma-Aldrich, Cat #T6664; pH 8.0) was added to each 12-ml centrifuge tube. finally, 300 μl was aspirated from each sample and added to the corresponding slide (supplemental Fig. S1). Individual liquid-based cytology and FFPE tissue sections of 13 mm diameter and 10 μm thickness were scraped for each case to collect well-preserved populations of stained or unstained cells in individual Eppendorf tubes. Cell pellets were lysed with 100 μl of SDS extraction buffer (4% SDS; 100 mm Tris, pH 7.4; and 1 mm TCEP). Samples were lysed by sonication and boiling at 95 °C for 30 min. Proteins were digested using the filter-aided sample preparation procedure, as previously described (13Han D. Moon S. Kim Y. Kim J. Jin J. Kim Y. In-depth proteomic analysis of mouse microglia using a combination of FASP and StageTip-based, high pH, reversed-phase fractionation.Proteomics. 2013; 13: 2984-2988PubMed Google Scholar). Briefly, 50 μl of samples were mixed with 0.2 ml 8 m urea in 0.1 m Tris/HCl, pH 8.5, loaded onto a 30 k spin filter (EMD Millipore, Billerica, MA). Buffer was exchanged with urea solution by centrifugation. Reduced cysteines were alkylated with iodoacetamide solution in darkness at room temperature for 30 min. An additional 50 mm ammonium bicarbonate was added to exchange the urea solution. Finally, proteins were digested at 37 °C overnight with trypsin at an enzyme to protein ratio of 1:100. After an overnight incubation, the filtration unit was transferred to new collection tubes, followed by centrifugation for 20 min. Peptides that were retained in the filtration units were eluted with 50 μl 0.5 m NaCl to enhance the yield of digested protein. The resultant supernatants were acidified with 1% TFA. FFPE sections (10 μm) were incubated twice in xylene for 5 min, followed by 100% (v/v) ethanol twice for 3 min. Sections were then hydrated twice in 85% (v/v) ethanol for 1.5 min, and distilled water for 3 min. Tissue samples were then scraped off the slides into microfuge tubes, and extraction buffer (4% SDS; 1 mm TCEP; and 0.3 m Tris, pH 8.0) added. After sonication, samples were incubated at 95 °C for 2 h. Extracted proteins were precipitated by adding chilled acetone at a volume ratio of 1:5 buffer to acetone, followed by incubation at −20 °C for 16 h. After washing with 200 μl of chilled acetone, protein pellets were collected by centrifugation at 15,000 rpm for 10 min and air-dried. Protein concentrations were measured using a bicinchoninic acid reducing agent compatible kit (Thermo Fisher Scientific Inc., Rockford, IL). Protein (100 μg per sample) was digested using the filter-aided sample preparation procedure, as described above. Eluted peptides were desalted using C18 Stage Tips, as previously described (14Rappsilber J. Mann M. Ishihama Y. Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips.Nature Protocols. 2007; 2: 1896-1906Crossref PubMed Scopus (2589) Google Scholar). C18 Empore disk membranes (3 m, Bracknell, UK) were packed into the bottom of 200 μl yellow pipette tips. POROS 20 R2 reversed-phase media (Applied Biosystems, Foster City, CA) was dissolved in 1 ml MeOH and 100 μl of the mixture loaded separately into the tip for two rounds of filtration with MeOH. Packed microcolumns were washed three times with 100 μl of MeOH and 100% acetonitrile (ACN) consecutively and equilibrated three times with 100 μl 0.1% TFA, by applying air pressure from a syringe. After samples were loaded, microcolumns were washed three times with 100 μl 0.1% TFA, and peptides subsequently eluted with 100 μl of a series of elution buffers containing 40%, 60%, and 80% ACN in 0.1% formic acid. Finally, all eluates were dried in a vacuum centrifuge and stored at −80 °C until LC-MS/MS analysis. LC-MS/MS analysis was performed using a Q Exactive Plus Hybrid Quadrupole-Orbitrap mass spectrometer (Thermo Fisher Scientific Inc.), coupled to an Ultimate 3000 RSLC system (Dionex, Sunnyvale, CA) via a nano electrospray source, as previously described (13Han D. Moon S. Kim Y. Kim J. Jin J. Kim Y. In-depth proteomic analysis of mouse microglia using a combination of FASP and StageTip-based, high pH, reversed-phase fractionation.Proteomics. 2013; 13: 2984-2988PubMed Google Scholar, 15Han D. Jin J. Woo J. Min H. Kim Y. Proteomic analysis of mouse astrocytes and their secretome by a combination of FASP and StageTip-based, high pH, reversed-phase fractionation.Proteomics. 2014; 14: 1604-1609Crossref PubMed Scopus (61) Google Scholar), with some modifications. Peptide samples were separated on a two-column system, consisting of a trap column and an analytic column (75 μm × 50 cm) with a 120 min gradient from 7% to 32% acetonitrile at 300 nl/min and analyzed by mass spectrometry. Column temperature was maintained at 60 °C using a column heater. Survey scans (350 to 1650 m/z) were acquired with a resolution of 70,000 at m/z 200. A top-20 method was used to select precursor ions with an isolation window of 1.2 m/z. MS/MS spectra were acquired at an HCD-normalized collision energy of 30, with a resolution of 17,500, at m/z 200. The maximum ion injection times for the full scan and MS/MS scan were 20 and 100 ms, respectively. The detailed data analysis process is described in supplemental Materials and Methods. Mass spectra were processed using MaxQuant version 1.5.3.1 (16Tyanova S. Temu T. Cox J. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics.Nature Protocols. 2016; 11: 2301-2319Crossref PubMed Scopus (1899) Google Scholar). MS/MS spectra were searched against the Human Uniprot protein sequence database (December 2014, 88,657 entries) using the Andromeda search engine (17Cox J. Neuhauser N. Michalski A. Scheltema R.A. Olsen J.V. Mann M. Andromeda: a peptide search engine integrated into the MaxQuant environment.J. Proteome Res. 2011; 10: 1794-1805Crossref PubMed Scopus (3469) Google Scholar). Primary searches were performed using a 6-ppm precursor ion tolerance for total protein level analysis. The MS/MS ion tolerance was set to 20 ppm. Cysteine carbamidomethylation was set as a fixed modification. N-acetylation of protein and oxidation of methionine were set as variable modifications. Enzyme specificity was set to full tryptic digestion. Peptides with a minimum length of six amino-acids and up to two missed cleavages were considered. The required false discovery rate (FDR) was set to 1% at the peptide, protein, and modification level. To maximize the number of quantification events across samples, we enabled the 'Match between Runs' option on the MaxQuant platform. For label-free quantification, the Intensity Based Absolute quantification (iBAQ) algorithm (18Schwanhausser B. Busse D. Li N. Dittmar G. Schuchhardt J. Wolf J. Chen W. Selbach M. Global quantification of mammalian gene expression control.Nature. 2011; 473: 337-342Crossref PubMed Scopus (4091) Google Scholar) was used as a part of the MaxQuant platform. Briefly, iBAQ values calculated by MaxQuant are raw intensities divided by the number of theoretical peptides. Thus, iBAQ values are proportional to the molar quantities of the proteins. All statistical analyses were performed using Perseus software (19Tyanova S. Temu T. Sinitcyn P. Carlson A. Hein M.Y. Geiger T. Mann M. Cox J. The Perseus computational platform for comprehensive analysis of (prote)omics data.Nature Methods. 2016; 13: 731-740Crossref PubMed Scopus (3574) Google Scholar). For quantitative analysis of iBAQ cytology data, we first filtered out proteins with at least 20 quantified values in each group. Missing values were imputed on the basis of a normal distribution (width = 0.15, down-shift = 1.8) to simulate signals of low abundance proteins. Finally, data were normalized using width adjustment, which subtracts the medians and scales all values in a sample to have equal interquartile ranges (20Deeb S.J. Tyanova S. Hummel M. Schmidt-Supprian M. Cox J. Mann M. Machine Learning-based Classification of Diffuse Large B-cell Lymphoma Patients by Their Protein Expression Profiles.Mol. Cell. Proteomics. 2015; 14: 2947-2960Abstract Full Text Full Text PDF PubMed Scopus (63) Google Scholar). For normalization, the first, second and third quartile (q1, q2, and q3) are calculated from the distribution of all iBAQ values. The second quartile that is the median is subtracted from each value to center the distribution. Then, we divide by the width in an asymmetric way. All values that are positive after subtraction of the median are divided by values that calculated from (q3–q2) while all negative values are divided by values that calculated from (q2–q1). For quantitative analysis of iBAQ bladder urothelial carcinoma FFPE data, we first filtered out proteins with at least 3 quantified values in each group. Missing values were imputed by normal distribution as described above. The iBAQ values of each protein were normalized against the sum of quantitative values in individual runs. For pairwise comparison of proteomes, two-sided t-tests were performed using permutation-based FDR and a significance level of 5%. In case of FFPE data, a protein was considered statistically significant if its fold change was ≥ 2 and if it had an FDR ≤ 0.05. For bladder urothelial carcinoma RNA sequencing data, we downloaded the level 3 RNA sequencing version 2 data set from TCGA with upper quartile normalized RSEM count estimates from Broad Institute GDAC FireBrowse (TCGA data version 20160128, http://firebrowse.org/). The RNA sequencing version 2 dataset were produced on the Illumina HiSeq 2000 platform and processed by the algorithms of MapSplice for aligning sequenced reads and RSEM for quantifying the gene expression levels. For the data normalization between samples, gene expression levels are scaled by upper-quartile normalization method. Among the data downloaded from TCGA, there are 408 bladder urothelial carcinoma samples which include 19 benign urothelial lesion-matched bladder urothelial carcinoma samples. Proteins identified in the comparative proteomic analysis as differentially abundant between benign urothelial lesion and bladder urothelial carcinoma in the liquid-based cytology cohort were aligned to transcripts expressed in the benign urothelial lesion and bladder urothelial carcinoma cohort data in FFPE-based quantitative proteomic analyses to compare the cytology proteome profiles of bladder urothelial carcinoma samples. Finally, the external public repository, TCGA data portal, was employed for comparative bioinformatics analyses. Bladder urothelial carcinoma RNA sequencing data were sourced independently for comparative analysis with protein expression data obtained using MS-based proteomic assays to evaluate reliable ancillary biomarkers for bladder urothelial carcinoma diagnosis using liquid-based cytology and FFPE samples (supplemental Fig. S2). To select immunoreactive markers, protein expression value data and criteria for antibodies listed from comparative analyses were obtained from The Human Protein Atlas data set, a public repository of immunohistochemistry data (6Hughes C.S. McConechy M.K. Cochrane D.R. Nazeran T. Karnezis A.N. Huntsman D.G. Morin G.B. Quantitative Profiling of Single Formalin Fixed Tumour Sections: proteomics for translational research.Sci. Reports. 2016; 6: 34949Crossref PubMed Scopus (74) Google Scholar, 21Uhlen M. Fagerberg L. Hallstrom B.M. Lindskog C. Oksvold P. Mardinoglu A. Sivertsson A. Kampf C. Sjostedt E. Asplund A. Olsson I. Edlund K. Lundberg E. Navani S. Szigyarto C.A. Odeberg J. Djureinovic D. Takanen J.O. Hober S. Alm T. Edqvist P.H. Berling H. Tegel H. Mulder J. Rockberg J. Nilsson P. Schwenk J.M. Hamsten M. von Feilitzen K. Forsberg M. Persson L. Johansson F. Zwahlen M. von Heijne G. Nielsen J. Ponten F. Proteomics. Tissue-based map of the human proteome.Science. 2015; 347: 1260419Crossref PubMed Scopus (7332) Google Scholar). Immunohistochemical expressio

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