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A comprehensive investigation towards the indicative proteins of bladder cancer in urine: from surveying cell secretomes to verifying urine proteins Jiao Guo, Yan Ren, Guixue Hou, Bo Wen, Feng Xian, Zhen Chen, Ping Cui, Yingying Xie, Jin Zi, Liang Lin, Song Wu, Zesong Li, Lin Wu, Xiaomin Lou, and Siqi Liu J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00106 • Publication Date (Web): 04 Jun 2016 Downloaded from http://pubs.acs.org on June 13, 2016

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Journal of Proteome Research

A comprehensive investigation towards the indicative proteins of bladder cancer in urine: from surveying cell secretomes to verifying urine proteins Jiao Guo1,2, Yan Ren3, Guixue Hou1,2, Bo Wen3, Feng Xian1,2, Zhen Chen1, Ping Cui1, Yingying Xie1,2, Jin Zi3, Liang Lin3, Song Wu4, Zesong Li4, Lin Wu1,2*, Xiaomin Lou1,2*, Siqi Liu1,2,3* 1

CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of

Genomics, Chinese Academy of Sciences, Beijing, 100101, China, 2

University of Chinese Academy of Sciences, Beijing, 100049, China,

3

Proteomics Division, BGI-Shenzhen, Shenzhen, Guangdong, 518083, China,

4

Shenzhen Second People’s Hospital, Shenzhen, Guangdong, 518028, China.

*To whom correspondence should be addressed: Siqi Liu, Beijing Institute of Genomics, CAS, Beichen West Road, Beijing, 100101, China. Tel: 86-10-80485325; Fax: 86-10-80485324; E-mail: [email protected] Xiaomin Lou, Beijing Institute of Genomics, CAS, Beichen West Road, Beijing, 100101, China. Tel and Fax: 86-10-84097465; E-mail: [email protected] Lin Wu, Beijing Institute of Genomics, CAS, Beichen West Road, Beijing, 100101, China. Tel and Fax: 86-10-84097465; E-mail: [email protected]

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Abbreviations 2DE, two-dimensional gel electrophoresis; AUC, area under the curve; BCa, bladder cancer; BSA, bovine serum albumin; CAD, collision activated dissociation; CUR, curtain gas; EP, entrance potential; F-12, Nutrient Mixture F-12 Ham; FBS, fetal bovine serum; FDR, false discovery rate; GS1, ion source gas1; IAM, iodoacetamide; IHT, interface heater temperature; IS, ionspray voltage; iTRAQ, isobaric tags for relative and absolute quantitation; LDH, lactate dehydrogenase; MRM, multiple reaction monitoring; Reg-1, regenerating protein-1; ROC, receiver operating characteristic; RPMI 1640, Roswell Park Memorial Institute 1640 medium; RP, reverse phase; SDS-PAGE, sodium dodecyl sulfate–polyacrylamide gel electrophoresis; SWATH, sequential window acquisition of all theoretical spectra; TEAB, tetraethylammonium bicarbonate.

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Abstract Urine is an ideal material to study the cancer-related protein biomarkers in bladder, whereas exploration to these candidates is confronting technique challenges. Herein, we proposed a comprehensive strategy of searching the urine proteins related with bladder cancer. The strategy consists of three core combinations, screening the biomarker candidates in the secreted proteins derived from the bladder cancer cell lines and verifying them in patient urines, defining the differential proteins through twodimensional electrophoresis (2DE) and isobaric tags for relative and absolute quantitation (iTRAQ) coupled with LC MS/MS, and implementing quantitative proteomics of profiling and targeting analysis. With proteomic survey, total of 700 proteins were found their abundance of secreted proteins in cancer cell lines different from normal, while 87 proteins were identified in the urine samples. The multiple reaction monitoring （ MRM） -based quantification was adapted in verifying the bladder cancer related proteins in individual urine samples, resulting in 10 differential urine proteins linked with the cancer. Of these candidates, receiver operating characteristic analysis revealed the combination of CO3 and LDHB was more sensitive as the cancer indicator than other groups. The discovery of the bladder cancer indicators through our strategy has paved an avenue to further biomarker validation. Keywords Urine, bladder cancer, 2DE, iTRAQ, MRM, biomarker

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Introduction Bladder cancer (BCa) is a high incident cancer. The 5-year survival rate of this cancer could reach over 90% once it is detected at early phase, however, the recurrence rate is approximately 65% within 5 years and almost reaches 90% by 15 years1,2. Obviously, early diagnosis and timely intervention to BCa are beneficial to the patients. Currently the gold standard of urothelial neoplasia detection is cystoscopic examination3, which not only is limited by invasiveness and complicated operation, but also is suffered from low sensitivity and specificity, particularly for early stage tumors4. Voided urine cytology (VUC) is a noninvasive method for diagnosis of BCa, but it still encounters the similar technique obstacle such as low detection sensitivity to the low-grade BCa5. New noninvasive BCa diagnostic techniques based upon molecular level are thus urgently needed4. In recent decades, many efforts have been made to screen the disease biomarkers of BCa6. For instance, Nitta et al. found HIP/PAP was abundantly expressed in BCa, and their urinary levels were also related to BCa7. NMP22 and BTA were also found related to BCa, and a commercial kit based on them was developed for clinical use8-10. However, at present few BCa biomarkers are well and commonly accepted for disease diagnosis, while most of the biomarker candidates suffer from low sensitivity, specificity, or predictive value in clinical application6. A puzzle is still hovering in the field, how to extensively explore the BCa related proteins and to strictly verify the biomarker candidates. Bladder is an organ to collect urine excreted from kidney. As urine is closely contacted with the bladder epithelial layer and could be noninvasively collected, naturally it becomes an ideal specimen for detecting the molecules related with BCa. Proteomics 4


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techniques have been used to identify potential BCa biomarkers in urine. For instance, Tan et al. collected and pooled urine from the BCa patients, and found PLK2 as a candidate biomarker of BCa using shotgun proteomics with LC MS/MS11, while Pinero et al. claimed regenerating protein-1 (Reg-1) and keratin 10 in urine were associated with BCa through proteomic analysis with 2D-DIGE coupled with MS12. On the other hand, there are some disadvantages taking urine as a material to explore the disease biomarkers. First of all, during the MS data analysis the strong signals derived from high abundance proteins in urine cause a serious interference in distinguishing the differential proteins between patients and controls, especially for low abundance proteins. Secondarily, urine contains numerous by-products that are generated from tissue and cellular metabolism and could dilute the information of specific targets in urine. With the differences in genetic background, health condition and food intake, the protein contents in urine appear greatly diverse, from person to person. The difficulty in normalization of the urine protein abundance thus makes low efficiency in finding biomarkers in urine. One way to overcome these problems is to first screen the cancer-related proteins from secretome of cell lines and verify them in real urine samples. Lin et al. analyzed the secreted proteins of U1 and U4 BCa lines by SDS-PAGE combined with MALDI-TOF MS, and found lower abundance of pro-u-PA not only in the secreted proteins of cancer cell line but also in the urine samples from patients with high grade of BCa13. Kawanishi et al. performed proteomics analysis between a poorly invasive human BCa cell line RT112 and a highly invasive cell line T24, and found higher CXCL1 abundance in the secreted proteins of T24, and further detected the indicative signals of CXCL1 in the urines of the invasive bladder tumors14. Through secretome profiling of tumor cell lines and candidate

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verification in urine samples, several groups have demonstrated the feasibility of this approach in exploration of the BCa protein biomarkers. Several technique problems laying on this approach, nevertheless, are not well solved yet. In the cell line selection, how to choose the cell lines with typical characteristics of BCa is a primary consideration to adapt the discovery/verification strategy. In the proteomic analysis, how to take a comprehensive view to examine the differential proteins between the relative normal and disease samples is a solid base in defining more candidates related to BCa. In the quantitative proteomics, how to implement a thorough verification is crucial to harvest high quality data. Today it is well recognized that proteomic analysis is a powerful means. Also we are aware that a limited number of cancer biomarkers have been originally uncovered by proteomic analysis. There is no doubt that the disease-related proteomes are very complicated and impacted by so many factors. The improper design in proteomic experiments, however, is likely as another causal element, especially at technique developing stage for proteomics. Currently, the discovery of protein biomarkers is based upon a hypothesis that the protein abundance is regulated by disease status, in either human sample or cellular model. Comparison of the protein abundances between relative normal and disease samples is a key issue in both discovery and verification proteomics. At discovery phase, shotgun MS analysis to trypsin digestion is a common technique, and is expected to produce main peptide information. Due to partial digestion of some proteins and poor ionization of some peptides, the technique is not always to provide a satisfactory data set for qualification or quantification. Could these technique weaknesses be corrected somehow? As a traditional proteomics method, the technique of two-

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dimensional electrophoresis (2DE) coupled with MS is criticized in many aspects due to low identification rate of electrophoretic spots and poor quantification estimation. However, the technique advantage of 2DE is quite unique and incomparable with other current proteomic tools, especially in isofocusing proteins and separating multiple protein forms15. We therefore reasoned 2DE could serve compensative information to find out the BCa related proteins. At verification phase, target proteomics has become more and more acceptable for confirmation of protein profiling result rather than the analysis of immunoblotting16. The efficiency of the traditional verification using immunoassay is dependent on the quality of antibodies obtained from commercial or laboratory sources. The inconsistency of antibody resource like availability, diverse specificity and titer has resulted in low efficiency of protein biomarker verification. Multiple reaction monitoring (MRM) has emerged as a powerful means for target protein quantification17,18. In contrast to antibody-based techniques, MRM can easily verify multiple protein targets, likely over hundreds of candidates. Based upon informative quantitation signals, such as multiple transitions per peptide, multiple peptides per protein, and multiple measurements (retention time, overlays and total peak area of ions) associated with each signal, MRM can deliver accurate and reproducible data for evaluating the abundances of peptides and proteins16. Most BCa cell lines are derived from malignant bladder epithelium cells, while their secreted proteins are reasoned to exist in urine. Through the comprehensive quantification analysis to the secreted proteins generated from BCa cell lines, next inquiry is how to verify such large numbers of the candidates in human urine. Target proteomics upon MRM could provide more accurate information in quantitative analysis and enable the verification of the target proteins at large scale. Therefore, in the

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discovery/verification pipeline, we proposed the MRM-based quantification to verify the BCa related proteins in urine. Herein we present the communication to set a comprehensive pipeline up for uncovering the BCa related proteins in urine. We selected three typical cell lines, 5637, T24 and SVHUC-1, which are well characterized and accepted in BCa study19-21. The secreted proteins from the three cell lines were collected and undergone with two quantitative proteomics in parallel. With integrated bioinformatics analysis, 700 candidates were identified which displayed the significantly different abundance in the BCa from the relatively normal cell line. We further employed SWATH profiling and MRM targeting to verify the cancer related candidates in human urines collected from relatively normal people or BCa patients. The verification upon MRM signals demonstrated that 10 urine proteins emerged significant differences in their abundance between relative normal and disease samples. With the combination strategy, such as cell line and urine, iTRAQ and 2DE, and discovery and verification, we obtained a set of the BCa related proteins that are potentially valuable in next stage of biomarker validation. Materials and Methods 1. Cell culture and conditioned media collection Two human urothelial carcinoma cell lines (5637 and T24) derived from bladder carcinoma patients and one human normal urothelial cell line (SV-HUC-1) immortalized from human bladder epithelial cells, were purchased from the Type Culture Collection of the

Chinese

Academy

of

Sciences,

Shanghai,

China

(http://www.ctcccas.ac.cn/xibao/catalogue.html) which were originally obtained from the 8


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American Type Culture Collection. 5637 and T24 cells were respectively cultured in Roswell Park Memorial Institute 1640 medium (RPMI 1640, Sigma) and McCoy’s 5A Medium Modified (Sigma), and SV-HUC-1 cells were maintained in Nutrient Mixture F12 Ham (F-12, Sigma), all supplemented with 10% fetal bovine serum (FBS), penicillin (100 units/mL), and streptomycin (100 units/mL) at 37°C in a humidified atmosphere of 5% CO2.When their confluence reached 80%, the cell cultures were changed into serumfree media. Forty-eight hours later, cell culture supernatants were collected from ten dishes (80 mL) by centrifugation at 1000 g for 10 min at 4 °C in a Beckman Optima XL100 ultracentrifuge (Beckman Coulter) to remove cells and cell debris. The collected conditioned media was gradually dialyzed in a dialysis bag with a molecular weight cutoff at 3500 Da (purchased from Solarbio, Beijing) against a series of diluted NaCl solutions at 4 °C: 100 mM NaCl for 2h, 50 mM NaCl for 4h, 25 mM NaCl for 8h, 10 mM NaCl for10h, and 0 mM NaCl for 24h. The dialyzed media were lyophilized using the Maxi-dry Lyoconcentration system (Allerød, Denmark), and the resulted powder was dissolved in lysis buffer containing 8 M urea, 4% (w/v) CHAPS, 10 mM DTT, followed by centrifugation at 20,000 g. The protein concentrations were determined with Bradford protein assay. 2. Urine specimen collection For the MRM verification, 47 human urine samples were collected from Shenzhen Second People’s Hospital, including 23 samples from patients with bladder carcinomas, age range from 26 to 82 (median 57) and 24 from relative normal individuals, age range from 26 to 82 (median 54). The characteristics of the urine samples from patients and

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relative normal individuals were listed in Table S1. Informed consents had been signed and we had ethics committee approval to use these urine samples. 3. Two-dimensional electrophoresis (2DE) and image analysis The prepared secreted proteins (150 µg/gel) derived from the conditioned media of the three cell lines were individually mixed with rehydration buffer containing 8 M urea, 4% (w/v) CHAPS, 20 mM DTT, 0.5% IPGphor buffer (pH 3.0-10.0, NL), and 0.002% bromophenol blue and rehydated overnight with 18 cm (pH 3.0-10.0, NL) IPG strips. Electrofocusing was carried out at 56 kVh in an IPGphor (Amersham Biosciences, Uppsala, Sweden) at 20 °C. Prior to second-dimension electrophoresis, the electrofocused strips were reduced by 1mM DTT, 56°C for 1h and 55mM alkylatied by iodoacetamide for 45min. The treated strips were transferred and run on 12% uniform SDSpolyacrylamide gels using the Ettan DALT II system (Amersham Biosciences, Uppsala, Sweden) with a programmable power control. The separated proteins were visualized by silver staining. For each cell line, duplicated preparations were made and duplicated electrophoresis were performed for each prep. The 2DE images were acquired with an Image Scanner in transmission mode and analyzed using Image MasterTM 2D Platinum version 7.0 with manual rechecking. To get comparable data for quantitative analysis, several key parameters in the image analysis were fixed as constants, such as smooth at 2.0, mini area at 57.0, and saliency at 550.0. This experiment followed the 2DE protocol developed in our lab22. 4. Identification of 2DE Spots by MALDI-TOF MS

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All the spots on gels that could be visualized were excised by spot picker P2D1.5 (San Francisco, CA) and transferred into eppendorf tubes. The gel particles dried and rehydrated in 25 mM NH4HCO3 were reduced with 10 mM DTT at 56 °C for 1 h, and then alkylated by 55 mM iodoacetamide in the dark at room temperature for 45 min followed by a thorough process of washing and drying. Finally, the dried gel particles were incubated with 10 µL of 25 mM NH4HCO3 containing 0.05 µg/µL trypsin (Promega, Madison, USA) at 37 °C overnight. After centrifugation, the resulted supernatants were mixed with 2 µL of 1% TFA and subjected to mass spectrometry. The digestive products were loaded onto anchor chip and mixed with a matrix solution consisting of cyano-4hydroxy-cinnamic acid (4 mg/mL) in 70% acetonitrile with 0.1% TFA. After they were dried at room temperature, the spots were washed with 0.1% TFA twice to desalt, and then subjected to MALDI-TOF/TOF mass spectrometry for protein identification. Mass spectra and tandem mass spectra were obtained on UltrafleXtreme MALDI-TOF/TOF (Bruker Daltonics, Billerica, MA, USA) mass spectrometer. Positively charged ions were analyzed with the reflector mode, using delayed extraction. Typically, 100 shots were accumulated per spectrum in MS mode and 400 shots in MS/MS mode22. The spectra were processed using the FlexAnalysis 3.3 and BioTools 3.2 software tools. On the basis of mass signals, protein identification was performed with the Mascot 2.3.02 (Matrix Science, Boston, MA, USA) to search proteins against the UniprotKB/Swiss-Prot human protein database (http://www.uniprot.org). The following parameters were used for database searches: monoisotopic mass accuracy, 0.05 in iTRAQ which means incredible quantification results by iTRAQ. The proteins with multiple forms of lower molecular masses are generally considered as degradation. The quantitative evaluation upon iTRAQ

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is based on the iTRAQ intensities coupled with several peptides, and the random peptides selected for the iTRAQ quantification may not truly represent the different degradation forms of proteins. Meanwhile, the protein quantification based on the sum of the spot volume for multiple spots on 2DE is likely to indicate the total abundance contributed from varied protein forms. For the 16 proteins with multiple spots, thus we adopted the quantitative result gained from 2DE for quantitative comparison, in which all the 16 proteins were recognized significantly different between BCa and normal cells. Of the 30 proteins with single 2DE spots, majority (73%, 22/30) of these spots exhibited weak staining and were not consistently detected in the gels used for image comparison analysis. As the gel staining easily causes the experimental errors in 2DE visualization, the protein identification and quantification in these fade spots could be impacted by experimental conditions. As for these proteins identified in the single spots on 2DE, we adopted the abundance change values from iTRAQ. Based on the integrative analysis on quantification, 27 or 4 proteins were removed from the reslut of differential candidates in 2DE or iTRAQ. Besides, there were 41 proteins that could only be detected by 2DE (Fig. 4A) but not in iTRAQ, in which 40 proteins were found significantly different between 5637 vs. SV-HUC-1 or T24 vs. SV-HUC-1. Finally, a total of 700 differential proteins were defined as the BCa related proteins (Fig. 4D), which were mainly contributed from iTRAQ (92%, 647/700) and partially from 2DE (11%, 76/700). On the basis of the quantitative information above, the two methods did offer the unique and compensatory contribution to discover the BCa related proteins in this study. 4. Verification of the BCa related proteins in human urine by MRM

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Because the MRM data structure is similar to that of SWATH, the qualitative and quantitative information extracted from SWATH MS is ideal to select candidates for verification with MRM MS assays16. To evaluate whether the 700 BCa related proteins could be detected in human urine and to obtain information for establishment of MRM method, we first obtained the satisfied MS/MS signals by individually profiling the proteins in 8 urine samples (4 from BCa patients and 4 from relatively normal people) using SWATH. A total of 427 proteins (Table S8) were identified, in which the 87 (20%) proteins were overlapped with the BCa related proteins (Fig. 4D). For the sake of the proper peptides for MRM experiments from the identified peptides of the 87 urine proteins, a stringent criterion was eatablished, 1) at least two unique peptides per protein, 2) at least 4 transitions per peptide, 3) multiple peptides belonging to one protein with the same patterns of abundance change between BCa and relatively normal urines; 4) size of fragmented ions should be at least or above y3 and b3; 5) no overlap m/z between transitions and their precursors25,26,34; 6) the length of a peptide candidate is among 7-25 amino acids, and 7) no missed cleavage sites or modification on Met or Cys35,36. As a result, 31 proteins (Table S9) with 109 unique peptides were qualified for further verification. To establish the MRM method, we analyzed the 109 unique peptides with QTRAP5500 MS in a pooled urine sample collected from 23 BCa patients and 24 relative normal individuals. The MRM results demonstrated that in the urine sample, a total of 17 proteins with 41 unique peptides were qualified for the quantitative analysis (Table S10). We then implemented the MRM-based quantification to the 17 qualified proteins in the 47 individual urine samples. All the MRM signals derived from those proteins were

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satisfactorily identified in all the urine samples and normalized by BSA peptides. In addition, 18 peptides related to the BCa urines were selected to chemically synthesize corresponding peptides. After MRM assay, the MRM signals revealed that the retention time and the overlay profiles for the synthesized peptides were almost identical with that obtained from the human urines (Fig. S2). Using the statistic software of MSstats V3.2.3, the abundance ratios and the corresponding significance (p-value) between BCa and control urines were estimated (Table S11) and visualized by Volcano plot (Fig. 5A). According to the strict criteria for a differential protein with fold-change ≥ 1.5 and p-value ≤ 0.05, 6 proteins (PGBM, FINC, CADH1, PCP, CD44, CBPE) were at lower protein abundance and 4 (HBB, CO3, LDHB, ALBU) at higher abundance in the BCa urine samples compared with the control ones, whereas 7 proteins without any significant abundance change. The lower panel of Fig. 5B shows the abundance distribution of the 10 significantly differential proteins in BCa and control urine samples. Moreover, we individually and statistically scrutinized the peptide abundances of each BCa related protein in the urine samples (Fig. 5B upper panel, Table S12). All the 24 unique peptides corresponding to the 10 BCa potential biomarkers exhibited the significant difference (p ≤ 0.05) in abundance between BCa and control urine samples, and their abundance change trends were same with corresponding proteins. Hence the target proteomics revealed that the 10 urine proteins might have the indicative features for BCa. Of the 10 potential BCa biomarkers in urine, 6 were originally derived from iTRAQ, 2 were contributed from 2DE, and 2 from both two methods. We analyzed receiver operating characteristic (ROC) of the 10 potential BCa urine biomarkers with MetaboAnalyst 3.0. The area under curve (AUC) values were from 0.66 28


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to 0.84 (Table S13). Among the 10 proteins, CO3 had the highest AUC value, and its sensitivity and specificity were 87.50% and 78.26%, respectively. We further inquired to whether the 10 BCa biomarkers could be made to some combinations, which should be more sensitive and specific to indicate BCa in urine. The ROC analysis of all combinations indicated that only binary combination of CO3 and LDHB could improve the indicative efficiency over 0.84. As illustrated in Fig. 5C, AUC value of the combination C03 and LDHB reached 0.87 (95%CI, 0.763-0.972), with sensitivity of 87.00%, and specificity of 79.12%. These results implied that the biomarker combination of CO3 and LDHB was better to distinguish BCa from relative normal controls in urine samples. Discussion With concerning of the technique challenges in discovery of the BCa protein biomarkers, we designed a comprehensive approach that consisted of three key angles in technique to find out the biomarker candidates, from the secreted proteins in tumor cell lines to the urine proteins in patients, from the image-based quantification to the MS-based quantification in proteomics, and from profiling to verifying the protein candidates. A question is naturally raised how to evaluate the working efficiency of the technique combinations. Two typical cancer cell lines were selected for study of the secreted proteins, T24 and 5637. The 2DE images of the secreted proteins from the two cell lines (Fig. 2A) revealed that the secreted proteins in the T24 were very different from the 5637, while the cluster analysis of iTRAQ data divulged that the abundance of secreted proteins in the two cell lines was in a distinctive distribution pattern (Fig. 3A), suggesting each cell line might only represent a lateral information for BCa cells. In addition, the 29



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differential proteins identified by 2DE and iTRAQ were quite different (Fig. 4A), demonstrating the unique contribution of the two quantitative proteomics methods. Based upon these data, we adopted all the differential proteins between the BCa and the relatively normal cell lines as the BCa related proteins on the basis of quantitative analysis of both methods. Of the 700 differential proteins in T24 and 5637 cell lines upon the 2DE and iTRAQ analysis, we detected 87 of them in human urine using SWATH. It is well accepted that the gel-based proteomics is limited by detection sensitivity on image analysis. On the other hand, the unique advantage to recognize the protein isoforms on the 2DE images is still incomparable with other proteomic techniques. Considering that the secreted proteins are often modified, we employed 2DE analysis as a complement tool to iTRAQ in quantitative proteomics. The integrated approach to the secreted proteins offered an interesting observation, in which the quantitative differences between 2DE and iTRAQ were not iso-spot dependent on gel. Most proteins with multiple 2DE spots and relatively equal molecular masses exhibited similar quantitative comparison as elicited from iTRAQ, whereas some proteins having multiple 2DE spots with different molecular masses showed quite diverse quantification comparison from the iTRAQ data. The combination technique for quantitative proteomics upon profiling and targeting unveiled another advantage to expand the verification scale to the BCa related proteins in human urine. In contrast to the traditional verification with antibody-based assay, MRM is capable of accurate quantification as well as of multiple evaluation. As illustrated in Fig. 5A, the 17 proteins derived from differential analysis from cell lines were further verified by MRM in the individual urine samples, while the 10 urine proteins were found significantly indicative to BCa. Taking all the information delivered from the integrated

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proteomics, we attained a reasonable data set in discovery and confirmation to the urine proteins with indicative value to BCa. It is well accepted that the gel-based proteomics is limited by detection sensitivity in spot staining. Although the secreted proteins identified by 2DE (108) were much less than that by iTRAQ (1709), 2DE still provided the additional information, and 11% (76/700) of the differential proteins in cancer cell lines were obtained from 2DE. Qualitatively, 41 out of 108 proteins identified by 2DE were missed by iTRAQ (Fig4. A) in which 40 were differential proteins between BCa and relatively normal cell line. Generally, the MS/MS signals are partially inhibited in iTRAQ labeling because of the strong signals of iTRAQ tags, and the peptides recognized in iTRAQ are considerably less than that acquired from shotgun. Thus, we conducted a proteomic analysis with label-free, and identified the peptides from the same batch of secreted proteins exacted from cell culture media used in 2DE and iTRAQ. The secreted proteins (100 µg protein/cell line) from three cell lines were pooled together first and the sample preparation was exactly following the iTRAQ measurement described in experimental procedures except without labeling. And we got 5 fractions of peptides collected from 60 tubes that isolated by High-pH RP method for further lyophilization and identification. As a result, 25 of 41 proteins identified by 2DE were detected, but no correspondent peptide MS signal was found for the other 16 proteins. We further questioned whether these proteins only identified by 2DE possessed any specific peptide feature that was favorable/unfavorable to peptide detection with mass spectrometry. Since the unique peptides per protein generated from MS experiment and collected in Peptide Atlas could partially implied if a protein is relatively detectable by mass spectrometry, in the database we checked all the unique peptides related to the

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identified proteins in this study including iTRAQ and 2DE. We found that all the proteins identified in our study had their corresponding unique peptides in the database, and analyzed them in groups. As illustrated in Fig. S3, we compared the median values of the unique peptides per protein in 5 groups of proteins. The information of unique peptides was

obtained

from

Peptide

Altas

(https://db.systemsbiology.net/sbeams/cgi/PeptideAtlas/defaultBuildsPepsProts).

database The

median values of the unique peptides per protein in 5 groups were illustrated as following: 274 for the 1709 proteins identified by iTRAQ, 322 for the overlapped 67 proteins that could be identified in both iTRAQ and 2DE, 133 for the 41 proteins only identified in 2DE, 228 for the 25 supplementary proteins identified by label-free proteomics, and 97 for the 16 proteins without any LC MS/MS signals in this study. It seems obvious that the median unique peptides per protein for the iTRAQ identified proteins were much higher than those only detected in 2DE, approximately 140 peptides more, moreover, in all the groups the 16 proteins without any LC MS/MS signals in this study had the lowest unique peptides per protein. We therefore came to a deduction that the undetected proteins through iTRAQ were likely resulted from less available peptides for mass detection, whereas the 2DE technique could offer compensatory information for identifying these proteins, which was possibly benefited from the protein enrichment by 2DE spot focusing. Moreover, the integrated approach to the secreted proteins offered an interesting observation, in which the quantitative differences between 2DE and iTRAQ were not iso-spot dependent on gel. Most proteins with multiple 2DE spots and relatively equal molecular masses exhibited similar quantitative comparison as elicited from

32


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iTRAQ, whereas some proteins having multiple 2DE spots with different molecular masses showed quite diverse quantification comparison from the iTRAQ data. After surveying and verifying the BCa related proteins, total of the 10 proteins were found their abundance in the urines of BCa patients significantly different from that in the relative normal ones. We summarized from literatures the relevance of the 10 urine proteins to diseases (Table S14), and found most of these proteins were related to kidney diseases, cancers and/or inflammatory reactions, but seven of them had not been reported as BCa related. This means these candidates are generally related to disease status and tumor. As tumors in urinary system are often accompanied with inflammatory reactions, the verification results in the patient urines thus implies that the 10 BCa related proteins appear as the common indicators to the urinary disease, but do not lead to conclusion for their specific roles in this cancer only upon the proteomic evidence. As mentioned above, the 10 indicative candidates to BCa were selected through a series filtration based upon the profiling proteomics data. The verification to the 10 proteins in urine has given a proof that discovery of cellular secretomes could be developed to confirmation of urine proteins. On the other hand, this strategy was just at the initiation stage to dig more clinical information related BCa on account of the large dataset for differential proteins in the BCa cell lines. Of the 10 candidates, PGBM is a unique protein that its cancer involvement has been reported, but is not observed in the urinary disease. The proteomic evidence in this study in cellular secretome and urine proteins reached to a similar conclusion, in which the PGBM abundance was down-regulated in the secreted proteins collected from the T24 cell lines by iTRAQ, and using MRM, its abundance in patient urines was significantly less than that in the normal urine. Generally, PGBM is a

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proteoglycan with multi-domains that binds to many extracellular matrix components and cell-surface molecules. According to Hu’s study, PGBM was down-regulated in breast cancer due to overexpression of hypomethylated miR-66337, whereas Warren et al claimed that prostate cancer cells increased PGBM production in response to cytokines present in the tumor microenvironment38. For the first time, our study with the integrated proteomics demonstrated PGBM as a new candidate for denoting BCa. ROC analysis was performed to abstract the typical representatives from the 10 proteins related to BCa, and resulted in the combination of CO3 and LDHB as a better prediction to the cancer in urine. In an immunological response, complement components can detect and bind to non-self antigens or pathogens. As regards CO3, it participates in the central reaction in both classical and alternative complement pathways and mediates the local inflammatory process. Although there is no report indicating involvement of CO3 in BCa, a lot of documents support the abundance of CO3 as an indicator related to cancer, kidney disease and inflammation39-41. As a key glycolytic enzyme to catalyze the interconversion of pyruvate and lactate, lactate dehydrogenase (LDH) is widely expressed in adult somatic tissues. Similar to other glycolytic enzymes, increase of the LDH abundance was observed in different kinds of tumors42. LDH composes of a tetramer, in which the two most common subunits are the LDHA and LDHB protein. In BCa study, Liao et al took IHC and detected the down-regulated LDHB in 269 specimens with high grade urinary bladder urothelial carcinoma43. However, there is no report implying that the two common proteins, CO3 and LDHB, are specifically localized in bladder tissues or regulated in response to development of BCa. Referencing the literatures and our proteomic data, the combination of CO3 and LDHB may be meaningful to the urine

34


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samples collected from the patients with urinary disease, but not specifically indicative to BCa. In further study, it is necessary to examine this combination to an expanded urine samples with different kinds of urinary disease. Conclusions Through comprehensive survey on secretomes of the three cell lines using 2DE and iTRAQ approaches, we found 700 BCa related proteins. Ten of them were verified as potential BCa urine biomarkers in clinical samples by MRM. The combination of CO3 and LDHB could reach the AUC of 0.87 with 87.00% sensitivity and 79.12% specificity. The discovery of the bladder cancer indicators through our strategy has paved an avenue to further biomarker validation. List of supplementary components Fig. S1. iTRAQ data quality control. Fig. S2. MRM data quality control of synthesized peptides. Fig. S3. Analysis of the unique peptide numbers of the target proteins. Table S1. Clinical information of 47 urine samples (24 from normal controls and 23 from BCa patients). Table S2. Identification information of 108 proteins found by 2DE. Table S3. Secretory characteristic analysis of the 108 proteins. Table S4. 103 differential proteins from 5637 and T24 vs. SV-HUC-1 by 2DE.

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Table S5. Information of all identified proteins through iTRAQ. Table S6. 724 differential proteins quantified by 2DE and/or iTRAQ. Table S7. Information of overlapped proteins identified by both 2DE and iTRAQ. Table S8. Identification information of 427 proteins detected in urine samples by SWATH. Table S9. The list of urinary proteins screened by SWATH for MRM verification. Table S10. Transitions list of the final 17 BCa related proteins for MRM. Table S11. Quantification results of the 17 BCa related proteins by MRM. Table S12. ROC analysis results of the 10 potential BCa biomarkers. Table S13. MRM quantification results of the 10 potential BCa biomarkers in peptide level. Table S14. Disease-related information of the 10 potential BCa biomarkers summarized from references. Acknowledgment This work was supported by the National High Technology Research and Development Program of China (2012AA020206) and the National Basic Research Program of China (2014CBA02002，2014CBA02005). The mass spectrometry proteomics data by iTRAQ have

been

deposited

to

the

ProteomeXchange

Consortium

(http://proteomecentral.proteomexchange.org) via the PRIDE partner repository44 with 36


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the dataset identifier PXD003557, and the SWATH data and MRM data have been uploaded to PeptideAtlas (http://www.peptideatlas.org) with the dataset identifier PASS00814 and PASS00815. Conflict of Interests Statement: The authors declare no competing financial interest. References (1) Kirkali, Z.; Chan, T.; Manoharan, M.; Algaba, F.; Busch, C.; Cheng, L.; Kiemeney, L.; Kriegmair, M.; Montironi, R.; Murphy, W. M.; Sesterhenn, I. A.; Tachibana, M.; Weider, J. Bladder cancer: Epidemiology, staging and grading, and diagnosis. Urology 2005, 66, 4–34. (2) Siegel, R.; Ma, J.; Zou, Z.; Jemal, A. Cancer statistics, 2014. CA Cancer J. Clin. 2014, 64, 9–29. (3) Diamandis, E. P. How are we going to discover new cancer biomarkers? A proteomic approach for bladder cancer. Clin. Chem. 2004, 50, 793–795. (4) Diamandis, E. P. Tumor markers: Past, present, and future. In Tumor markers: Physiology, pathobiology, technology, and clinical applications. Diamandis, E. P.; Fritsche, H. Jr.; Lilja, H.; Chan D.; Schwartz, M., Eds.; AACC Press: Washington DC, 2002; pp 3–8. (5) Vrooman, O. P.; Witjes, J. A. Urinary Markers in Bladder Cancer. Eur. Urol. 2008, 53, 909–916.

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Mayer, G.; Gatto, L.; Campos, A.; Chalkley, R. J.; Kraus, H. J.; Albar, J. P.; MartinezBartolome, S.; Apweiler, R.; Omenn, G. S.; Martens, L.; Jones, A. R.; Hermjakob, H. ProteomeXchange provides globally coordinated proteomics data submission and dissemination. Nat. Biotechnol. 2014, 32, 223–226.

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Table 1. Protein identification based on the 2DE images Unique proteins with multiple spots (spot number)

Sample

Spots excised

Identification rates

Unique proteins identified

SV-HUC-1

160

84%

72

21 (84)

5637

142

65%

37

19 (74)

T24

154

74%

42

23 (95)

Sum

456

75%

108

41 (253)

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Table 2. Differential proteins between BCa and relative normal cell lines based on 2DE images

Cells vs. Cells

Total differential unique proteins

Differential unique proteins

Upregulated

Downregulated

Differential proteins with multiple spots (spots number)

Differential proteins coexisted in the two cell lines (sample: spots number)

Differential proteins uniquely existed in one cell line, sample: unique protein (spots number)

5637 vs. SV-HUC-1

72

20

52

24 (104)

11 (5637: 26; SV: 23)

5637: 15 (32)

T24 vs. SV-HUC-1

79

25

54

27 (128)

9 (T24: 20; SV: 26)

T24: 22 (65)

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Table 3. Differential proteins between BCa and relatively normal cell lines based on iTRAQ analysis Differential proteins

Cells vs. Cells Total

Up-regulated

Down-regulated

5637 vs. SV-HUC-1

218

106

112

T24 vs. SV-HUC-1

535

154

381

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Figure Legends Figure 1. The flowchart of the discovery/verification strategy in this study. Figure 2. The 2DE-based proteomic analysis towards the secreted proteins in BCa and relatively normal bladder epithelial cell lines. (A) The representative 2DE images of the secreted proteins in the three cell lines, SV-HUC-1, 5637 and T24, respectively. (B) The secretion and urinary property for all the proteins identified in the three cell lines by iTRAQ coupled with LC MS/MS was predicted with SignalP and SecretomeP and analyzed by three corresponding human urine databases. (C) Overlap evaluation to the differential proteins derived from 2DE analysis, 5637 vs SV-HUC-1 and T24 vs SVHUC-1. Figure 3. The iTRAQ-based proteomic analysis towards the secreted proteins in BCa and relatively normal bladder epithelial cell lines. (A) Cluster analysis of the proteins identified by iTRAQ based LC MS/MS and the corresponding abundance ratios, whose are represented by the relative fold changes (log2 (ratios)) of the tag intensity. The small panel represents the gradient of the protein abundance ratios from lower (green) to higher (red). (B) The secretion and urinary property for all the proteins identified in the three cell lines by iTRAQ coupled with LC MS/MS was predicted with SignalP and SecretomeP and analyzed by three corresponding human urine databases. (C) Overlap evaluation to the differential proteins derived from iTRAQ analysis, 5637 vs. SV-HUC-1 and T24 vs. SV-HUC-1.

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Figure 4. Integrated analysis to the quantitative proteomics from 2DE and iTRAQ. (A) Comparison of the total secretion proteins identified from the bladder cell lines by 2DE and iTRAQ. (B) Qualitative comparison of differential proteins between 5637 vs. SVHUC-1 by 2DE, T24 vs. SV-HUC-1 by 2DE, 5637 vs. SV-HUC-1 by iTRAQ and T24 vs. SV-HUC-1 by iTRAQ. (C) The secretion and urinary property for all the 724 differential proteins was predicted with SignalP and SecretomeP and analyzed by three corresponding human urinary databases. (D) Overlap evaluation to the 700 BCa related proteins and the 427 urinary proteins detected by SWATH. Figure 5. Verification of BCa related proteins using MRM. (A) Volcano plot to the urine proteins quantified by MRM. The x-axis represents the logarithmic fold changes of proteins (the protein abundance of the BCa patients (23) divided by that of the normal people (24)), and the y-axis indicates the logarithmic adjusted P values for such fold changes. (B) Paired comparison of the abundance of the peptides and proteins between normal people (green) and BCa patients (red). For peptide comparison, boxplot was taken to statistically evaluate the abundance differences between normal and disease samples for 24 peptides. For protein comparison, the distribution plot was employed to assess the abundance differences between normal and disease samples for 10 proteins derived from the 24 peptides. (C) ROC analysis for the binary combination of the urine biomarkers to BCa (CO3 and LDHB). The values of 95% confidence intervals are also presented.  

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Page Fig.511 of 56 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Journal of Proteome Research Verification

Discovery

BCa cell lines (T24 and 5637)

Relatively normal bladder epithelium cell line (SV-HUC-1)

Preparation of the secreted proteins

Determination of urine detectable BCa related proteins through SWATH

Urine from BCa patients

Urine from normal people

MRM quantification 2DE and iTRAQ quantification

Integrated analysis of differential proteins

BCa related proteins

MSstats analysis to define the verified BCa related proteins in urine

ROC analysis to identify the best combination of biomarkers


Fig. 2

Journal of Proteome Research 10 3 10 3

A. pI 3

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Page 52 of 56 10 Mr (kD) 97 66 43 31 20 14

SV-HUC-1

B.

5637 108 proteins identified by 2DE classical secreted proteins non-classical secreted proteins urinary proteins

17

4

T24 C. 5637 vs. SV-HUC-1 T24 vs. SV-HUC-1

7 25

11 44

24


48

31

T24-2 vs. 5637-2

T24-2 vs. SV-2

T24-1 vs. SV-1

5637-2 vs. SV-2

5637-1 vs. SV-1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

T24-1 vs. 5637-1

Fig. 3

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Journal B. of Proteome Research 505

171

83 146

45

1709 proteins identified by iTRAQ classical secreted proteins non-classical secreted proteins urinary proteins

278 112

369

3.0 2.0 1.0 0.0 -1.0 -2.0 -3.0

C. 5637 vs. SV-HUC-1

116


102

T24 vs. SV-HUC-1

433

Fig. 4

Journal of Proteome Research B.

A.

1 2 3 4 5 6 7 8 9 10 11 12 13 C. 14 15 16 17 18 19 20 21 22 23 24 25 26

2DE

24

110

5637 vs. SV-HUC-1 by iTRAQ

0

T24 vs. SV-HUC-1 by iTRAQ

91

12 67

5637 vs. SV-HUC-1 by 2DE T24 vs. SV-HUC-1 by 2DE

vs. iTRAQ 37

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3

3

1642

420

4

3

4

4

4 5

204

724 differential proteins classical secreted proteins

D.

non-classical secreted proteins

BCa related proteins

urine proteins

73 83

25

81

Urinary proteins

vs. identified by SWATH

14 46 613 198


87

340

up-regulated no regulation down-regulated

1.0

●

0.8

10

10 ● ● ● ●

5

●

5 0

0.6 0.4 CO3, AUC=0.84 (95%CI:0.70-0.95) LDHB, AUC=0.77 (95% CI: 0.62-0.89)

0.2

● ● ● ● ●● ●

−3 -3

B.

●

●● ●

0

CO3+LDHB, AUC=0.87 (95% CI: 0.76-0.97) Reference line, AUC=0.5

−2 −1 00 -2 -1 11 22 Log2 fold change

33

0

0.2

Log2 fold change

Log2 (area)

30 25 20 15 30 Log2 (area)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

−Log2(p-value) (p−value) -Log2

15

15

Journal of Proteome Research C.

Sensitivity

Fig. Page 55 of556 A.

25 20 15


0.4 0.6 Specificity

0.8

1.0


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for TOC only 44x23mm (300 x 300 DPI)


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A Comprehensive Investigation toward the ... - ACS Publications

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