Qualification and Verification of Serological Biomarker Candidates for

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Qualification and Verification of Serological Biomarker Candidates for Lung Adenocarcinoma by Targeted Mass Spectrometry Hsin-Yi Wu,† Yih-Gang Goan,‡ Ying-Hua Chang,§,∥ Yi-Fang Yang,∥ Hsiao-Jen Chang,∥ Pin-Nan Cheng,⊥ Chih-Chieh Wu,∥ Victor G. Zgoda,# Yu-Ju Chen,*,† and Pao-Chi Liao*,∥ †

Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan Division of Thoracic Surgery, Kaohsiung Veterans General Hospital, Kaohsiung 81362, Taiwan § Department of Cell and Regenerative Biology, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin 53705, United States ∥ Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan 70428, Taiwan ⊥ Department of Internal Medicine, College of Medicine, National Cheng Kung University Hospital, National Cheng Kung University, Tainan 70101, Taiwan # Institute of Biomedical Chemistry, 119121 Moscow, Russia ‡

S Supporting Information *

ABSTRACT: Lung cancer is the leading cause of cancer mortality worldwide. Although many biomarkers have been identified for lung cancer, their low specificity and sensitivity present an urgent need for the identification of more candidate biomarkers. In this study, we conducted MRM-based targeted analysis to evaluate the potential utility of a list of candidate proteins for lung cancer diagnosis. A total of 1249 transitions of 420 peptides representing 102 candidate proteins from our previous study and the literature were first screened by MRM analysis in pooled plasma samples, resulting in 78 proteins remaining in the list. Relative quantification of these 78 proteins was further performed in 60 individual plasma samples from lung adenocarcinoma patients in stages I−III and matched healthy control subjects. Ultimately, nine proteins were found to be able to distinguish patients from controls. Further combinations of five, three, and two candidate marker proteins improved the sensitivity to discriminate patients from controls and resulted in a merged AUC value of nearly 1.00 in stages I−III patients versus controls. Our results highlighted several possible markers for lung adenocarcinoma, and the proposed protein panels require further validation in a larger cohort to evaluate their potential use in clinical applications or development of therapeutics. KEYWORDS: Multiple reaction monitoring (MRM), lung adenocarcinoma, biomarkers, plasma, multimarker panel

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INTRODUCTION Lung cancer is the leading cause of cancer death, which accounts for 25% of all cancer deaths.1 Its 5 year survival rate remains much lower (at approximately 15%) than that of other cancers.2 Lung cancer is composed of two major types, small cell lung cancer (SCLC) and nonsmall cell lung cancer (NSCLC). NSCLC constitutes the majority, approximately 80%, of all diagnosed lung cancers. Among the subtypes of NSCLC, lung adenocarcinoma accounts for 50% of NSCLC cases.3 Lung cancer has a relatively higher mortality than that of other cancers, in part because symptoms are frequently absent until the cancer cells have metastasized, which is a formidable barrier to successful treatment.4,5 Early detection of lung cancer, which allows effective intervention to be made, is a promising approach to lowering its incidence and mortality rate. However, conventional diagnostic methods for lung cancer, such as chest X-rays and computed tomography (CT) © XXXX American Chemical Society

scans, are high in cost and false positive rate, leading to an urgent need for cost-effective approaches to be developed for early diagnosis and effective therapy.6,7 Proteomic strategies have provided various putative blood biomarkers to identify individuals at risk for lung cancer, such as cytokeratin-19 fragments (CYFRA 21-1), 8,9 carcinoembryonic antigens (CEA),10 cancer antigen-125 (CA-125), and neuron-specific enolase (NSE).11 However, it seems that many lung cancer protein/peptide biomarkers identified in serum overlap with other cancers and inflammatory diseases, suggesting a need to discover more lung cancer-specific biomarkers.7 Body fluids (blood, pleural effusion, etc.) that are in contact with tumors are enriched with proteins shed from cancer cells.12 Proteins secreted from cancer cells could enter the Received: November 20, 2014

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DOI: 10.1021/pr501195t J. Proteome Res. XXXX, XXX, XXX−XXX

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Journal of Proteome Research

depletion column, followed by insertion into a collection tube. The reaction was incubated at room temperature for 10 min. Eluates were collected by centrifuging the depletion column and collection tube at 10 000 rpm (8000g) for 60 s. To ensure complete depletion, the eluates were reloaded to the column and the procedure was repeated. Unbound proteins were eluted by adding 125 μL of equilibration buffer, and the eluate was combined with the flow through. The plasma samples depleted of albumin and IgG were stored at −80 °C until use.

blood circulation and have the potential to be monitored in plasma/serum. Although identification of proteins is now promising, quantification of proteins in complicated mixtures by mass spectrometry remains challenging, especially in plasma, which carries a protein abundance exceeding 10 orders of magnitude. Using antibody-based techniques, such as ELISA, for biomarker measurement could be hindered by a lack of high-quality antibodies. A quantitative approach has evolved that performs targeted analysis of representative peptides by multiple reaction monitoring (MRM) and affords multiplexing, high specificity, and a greater analytical dynamic range.13−18 In this study, by using a clinically well-characterized plasma cohort, consisting of 30 lung adenocarcinoma patients from stages I−III and corresponding paired controls, we applied MRM-based targeted proteomics to evaluate whether a subgroup of proteins previously reported to be associated with lung cancer and its metastasis could be used as potential blood biomarkers. The candidate markers that were significantly changed may be clinically used to discriminate lung cancer patients from healthy control subjects after they are extensively validated in a large-scale cohort.

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In-Solution Digestion

The protein concentration in the depleted plasma was measured by BCA protein assay. A total of 31.25 μg of protein (∼10 μL of depleted plasma) was used, which was diluted to 62.5 μL with urea (final concentration of 6 M). β-Galactosidase (10 fmol/μg) was added to each sample to function as an internal standard. The proteins were reduced by adding 2.5 μL of 1 M dithiothreitol (DTT) for 1 h at 56 °C and alkylated by adding 3.5 μL of 1 M iodoacetamide (IAA) for 1 h at room temperature in the dark, and the pH was adjusted by adding 25 μL of 0.1 M ammonium bicarbonate (ABC). The concentration of urea was diluted to 1 M before the sample was subjected to digestion. Mass spectrometry grade modified trypsin (Promega, Madison, WI, USA) was dissolved in 80 μL of resuspension buffer and 720 μL of 0.1 M ABC, resulting in a 0.025 μg/μL trypsin stock. A total of 25 μL of trypsin stock solution was added to the protein mixture. Proteolysis was performed overnight at 37 °C at a 1:50 trypsin/protein ratio. After overnight incubation, 25 μL of 0.1% TFA was added to stop the digestion. Finally, all remaining reagents from the in-solution digestion procedure were removed using C18 Omix pipet tips (100 μL, 1/PK) obtained from Agilent Technologies (Palo Alto, CA, USA). Peptide mixtures were dried and stored at −20 °C until analysis.

MATERIALS AND METHODS

Patients and Specimens

Whole blood samples from lung adenocarcinoma patients at TNM stages I−III (10 patients/stage) were collected from Veterans General Hospital−Kaohsiung (Kaohsiung, Taiwan). The patient inclusion criteria were a primiary diagnosis of lung adenocarcinoma and no prior history of cancer. The staging of the patients was performed based on the criteria released by the American Joint Committee on Cancer,19 and, as an example, one pathology report of a stage I patient is provided in Supporting Information Data S1. Whole blood samples from patients were obtained before they had surgery. Whole blood samples from healthy controls, who had no history of cancer and whose age and gender matched those of the patients, were collected from National Cheng Kung University Hospital (Tainan, Taiwan). All human subjects who fit the inclusion criteria gave informed consent in accordance with the rules and requirements of the Institutional Review Board of Veterans General Hospital−Kaohsiung (IRB approval no. AF-01-008) and National Cheng Kung University Hospital (IRB approval no. ER-100-059). All whole blood samples were collected in anticoagulant tubes (K2-EDTA-coated 10 mL tubes) and centrifuged at 3400 rpm for 10 min. The resultant plasma samples were aliquoted in small volumes (50 μL), followed by storage at −80 °C until use.

Multiple Reaction Monitoring Using Triple Quadrupole Mass Spectrometer

The peptide mixture was reconstituted in buffer A (0.1% FA in H2O) and analyzed by online nanoflow liquid chromatography tandem mass spectrometry (LC−MS/MS) on a nanoACQUITY ultraperformance LC system (Waters, Milford, MA, USA) coupled with a triple quadrupole mass spectrometer, QTRAP 5500 MS (AB SCIEX, Forster City, CA, USA). For each plasma sample, 0.5 μg of plasma protein digest spiked with internal standard peptides was injected for analysis. Peptide mixtures were separated with a 75 μm × 25 cm nanoACQUITY 1.7 μm BEH C18 column, and bound peptides were eluted at a flow rate of 500 nL/min using a gradient of 1 to 45% solvent B (0.1% FA in acetonitrile) for 102 min, which was followed by a sharp increase to 85% B within 4 min and held at 85% B for an additional 2 min before being ramped to 1% B for 7 min to equilibrate the column for the next run. The QTRAP 5500 MS instrument was interfaced with a nanospray source. The voltage for ion spray was set to 2200 V, with the declustering potential set to 80 V. Regular MRM was used to determine which transition is detectable, which was performed with a dwell time of 50 ms per transition and using unit resolution (0.7 Da fwhm) for both Q1 and Q3. The final quantitative method consisted of 203 peptides (581 transitions). Scheduled MRM was used to increase the multiplexing of the MRM assay, which scanned specific transitions based on their retention times and analyzed all of the protein candidates in one LC−MS/MS run. Approximately 150 transitions were concurrently monitored in each 5 s cycle at a minimum dwell

Sample Preparation

The protein concentration of the plasma samples was determined by BCA protein assay (Pierce, Rockford, IL). A sample pooled from 60 individuals consisting of 30 patients and 30 healthy controls was used to represent a complete library of peptides. About 300 μg of total protein from each individual sample was pooled. From the 18 mg of pooled plasma protein, 5.7 mg was taken and split into three vials. The two most abundant proteins, albumin and IgG, were removed using the ProteoPrep immunoaffinity albumin and IgG depletion kit (Sigma-Aldrich, St. Louis, MO). The depletion column was equilibrated with a low ionic strength Tris buffer at pH 7.4. A total of 25 μL of plasma (∼1900 μg of protein) was diluted to a final volume of 100 μL with the low ionic strength Tris buffer and added to the top of the packed medium bed of the B


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instructions, plasma samples needed to be diluted 2000- and 10-fold with sample diluents to measure gelsolin and galectin-1, respectively. Sample diluent consisted of a concentrated buffered protein base with preservatives. As recommended, a 10-fold dilution consisted of 40 μL of plasma sample combined with 360 μL of diluent, and a 2000-fold dilution began with adding 2 μL of plasma to 98 μL of sample diluent and was completed by adding 6 μL of this solution to 234 μL of sample diluent. Since there was no dilution factor suggested in the datasheet for the measurement of actin, cytoplasmic 1, a 6-fold dilution was adopted, which was achieved by adding 60 μL of plasma sample to 300 μL of sample diluent.

time of 33 ms/transition and a 10 min MRM detection window. For peaks with widths of 30 and 60 s, 6 and 12 data points were obtained to quantify peaks, respectively. The collision energy for each transition used in regular/scheduled MRM was calculated from the following equations: CE = 0.044 × (m/z) + 4 for (M + 2H)+ ions, CE = 0.050 × (m/z) + 3 for (M + 3H)+ ions, and CE = 0.050 × (m/z) + 2 for (M + 4H)+ ions. All formulas were derived from the results of collision energy optimization. Data Analysis

MRM raw data files (.wiff) from each sample were processed using Skyline (version 2.5). The criteria of a detectable protein candidate were as follows: (1) surrogate peptide contained at least two transitions with a signal-to-noise ratio (S/N) > 3 and (2) at least two surrogate peptides. The peak areas of transitions were examined manually to sieve out protein candidates that fit the criteria. Relative quantification of each protein candidate was determined by calculating the average peak areas of surrogate peptides because the number of surrogate peptides is different among protein candidates. The relative abundances of protein candidates in 60 individual samples were normalized by an internal standard protein (β-galactosidase) to account for variation in sample preparation and mass spectrometry. An average peak area of three internal standard peptides, DWENPGVTQLNR, APLDNDIGVSEATR, and GDFQFNISR, was used to represent the abundance of β-galactosidase.

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RESULTS

Strategy for Lung Adenocarcinoma Candidate Biomarker Validation by MRM

In order to select the plasma proteins that can be potential markers of lung adenocarcinoma, our approach has assembled an initial list of protein candidates. We first included 68 proteins that were discovered to be differentially expressed in two lung adenocarcinoma cell lines with different metastatic abilities in our previous study.21 Another 36 secreted proteins that are known to be associated with lung adenocarcinoma in the literature were also enrolled.12,22−30 Collectively, the two groups of candidates provided 102 potential markers for the current MRM assays. Our study was a Tier 3 targeted MS assay in which a labeled internal standard for each analyte targeted was not used.31 As described in Figure 1, our approach used a

Statistical Analysis

The normalized peak areas were used to compare the relative protein levels between the patients and paired healthy controls. The Wilcoxon test was used to assess the difference in biomarker expression levels between a patient and the paired control. This is a nonparametric procedure used for paired samples without the assumption of a normal population.20 The Wilcoxon test was conducted using GraphPad Prism (version 6.0; GraphPad Software, San Diego, CA, USA). A p-value of 0.05 was used as the threshold of statistical significance in this study. For the differentially expressed protein candidates, receiver operating characteristic (ROC) curves were generated with GraphPad Prism, estimating their diagnostic performance to distinguish cancer cases from healthy controls. To generate protein panels, logistic regression analyses were performed using the ENTER method within SPSS (version 20). The model takes the form y(i) = β0 + β1X1 + β2X2 + β3X3 + ... + βiXi, where y(i) indicates a dependent variable (disease condition), Xi represents an independent variable (proteins), and βi is the regression coefficient, which is the partial derivative of y(i) with respect to the various predictor variables (Xi). Once βi = 0, the corresponding protein candidate was not enrolled in the protein panel due to its minimum contribution to y(i). The classification cutoff value was set to 0.5. Combinations showing the best AUC were presented. Multiple ROC curves, combining those from protein panels and single proteins, were plotted on one graph.

Figure 1. Stepwise workflow to verify biomarkers for differential diagnosis of lung adenocarcinoma by MRM.

stepwise selection of candidates from the candidate protein list. The following steps were taken: (1) search for peptides and transitions that were suitable for MRM assay development (102 proteins), (2) monitor the selected candidates by MRM in pools of 60 samples (78 proteins), and (3) measure the relative abundance in individual plasma samples and select candidates that showed statistically significant difference between groups (nine proteins). In our work, proteotypic peptides and MRM transitions for 61 (of 102) proteins were chosen on the basis of a membrane proteome (containing 4564 proteins) identified by a Q-TOF 5600 MS (AB SCIEX) from PC9, H1975, and HeLa cells; that of the other 41 proteins was from SRMatlas.32 As for the selection criteria, doubly charged tryptic peptides were

ELISA

ELISA tests of the plasma samples were performed using commercially available ELISA kits to measure gelsolin, galectin1 (Cusabio Biotech, Suffolk, UK), and actin, cytoplasmic 1 (R&D Systems, Oxford, UK). Three proteins were measured in healthy subjects and patients in stages I−III, each consisting of 22 plasma samples. According to the manufacturer’s C


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Journal of Proteome Research preferentially selected. Peptides that had cysteine or methionine were given low priority. Two to five peptides were chosen per protein. Two or three transitions with the highest intensity were chosen per peptide. Finally, 797 and 452 transitions (269 and 151 peptides) resulted for the 102 protein candidates from our secretome study and the literature, respectively. Combined with nine transitions of three peptides from the internal standard protein, β-galactosidase, a total of 1258 transitions were determined, as listed in Supporting Information Table S1.

Table 1. Number of Selected/Detected Surrogate Peptides Per Protein Candidate

Detection of Selected Peptides and Transitions in Pooled Samples by MRM Analysis

no. of surrogate peptides/ protein

selected protein candidates

detected protein candidates

5 4 3 2 total

49 25 19 9 102

3 5 28 42 78

carried five detectable peptides among the 78 candidate proteins (as summarized in Table 1). The quantification of these three proteins using two, three, four, or five peptides showed CVs of 31.3−38.5, 30.5−35.2, 30.6−33.3, or 30.1− 32.2%, respectively, across 10 healthy controls (H2), implying that the use of three or four peptides per protein could slightly reduce the deviation of the quantification, whereas using five peptides yields only a slight increase in the precision. To check the reproducibility of the MRM, traces of each of the most intense transitions from six peptides were monitored in triplicate runs. In order to obtain portraits of high-, medium-, and low-abundance proteins, we used (A) NFPSPVDAAFR (610.81/989.49), (B) EGFGHLSPTGTTEFWLGNEK (736.35/447.22), (C) QLIVGVNK (435.77/417.25), (D) SHSSGSVLPLGELEGR (542.28/435.74), (E) ITPSYVAFTPEGER (783.89/688.33), and (F) AYSLFSYNTQGR (703.84/738.35) to represent hemopexin, fibrinogen gamma chain, elongation factor 1-alpha 2, macrophage colonystimulating factor 1, 78 kDa glucose-related protein, and serum amyloid P-component (SAP), respectively. The traces showed a CV with a peak area of 0.72−4.50% (Figure 2A−F), implying that the CV was not significantly affected by the wide range of concentrations of plasma proteins and was still reasonable for further MRM analysis. Over the triplicate runs, the intensity of the 203 peptides had CVs < 20% for 185 targets (Figure 3A). Moreover, the retention time was slightly changed, with 165 of the target peptides exhibiting CVs < 5% and all targets exhibiting CVs below 10% (Figure 3B). Further profiling of the abundance of 78 proteins in pooled plasma revealed a dynamic range for protein detection that spanned 3 orders of magnitude (Figure 3C). Additionally, our method permitted the detection of lower-abundance proteins such as SAP (indicated by the arrow in Figure 3C) whose concentration amounted to ca. 0.01−0.015 mg/mL in serum.33

To evaluate the feasibility of assessing these transitions in plasma, a sample pooled from 60 individuals (30 patients and 30 healthy persons) was used to represent a complete library of peptides. About 300 μg of protein from each individual sample was pooled, resulting in a total of 18 mg, from which 5.7 mg was removed and split into three vials. The top two most highly abundant plasma proteins (albumin and IgG) were depleted by immunoaffinity with antibodies to enhance the detection of lower-abundance proteins. An average depletion rate of 68 ± 2.03% was achieved (Supporting Information Table S2). Each sample was independently submitted to alkylation and trypsin digestion. The 423 peptides and 1258 transitions were divided into 13 different LC−MRM methods and monitored in each vial in triplicate runs. The generated results were manually inspected using Skyline software. In this study, we selected precursors/transitions by manual inspection of the raw data in Skyline. Acceptable transitions belonging to each analyte should be observed to have complete coelution or overlap with the entire set of monitored fragment ions, without interfering signals, and to have a similar proportion of relative ratios compared to that in the MS/MS spectra (adopting a dotproduct (dotp) value > 0.7 in Skyline). Proteins having two or more two identified peptides, with at least two transitions for each peptide, and transitions having a signal-to-noise ratio (S/ N) greater than 3 were accepted. This step removed a significant number of proteins, and ultimately 203 peptides (581 transitions) representing 78 protein candidates remained. In this study, we used the second-best peak function in mProphet to calculate the FDR (q-value) since we did not generate decoys before acquiring the data. The q-value for each protein is provided in Supporting Information Table S1. Among the manually selected 581 transitions, 360 transitions can be evaluated with a q-value. It was found that 42.7 and 40% of the transitions were identified with q-values < 0.001 and 0.001−0.02, respectively. About 17% of the transitions have a qvalue between 0.02 and 0.05, whereas less than 1% of the transitions had a q-value between 0.05 and 0.06 (as illustrated in Supporting Information Figure S1). The retention times of these detectable peptides and transitions that were used for the following scheduled MRM analysis are annotated in Supporting Information Table S1. Table 1 summarizes the number of selected and detected proteins with two to five surrogate peptides. In the original 102 selected protein candidates, most of them had four or five surrogate peptides (72.5%). After screening the peptides in the pooled samples, among the 78 detectable protein candidates, most of the proteins (89.7%) were had two or three observed surrogate peptides. The number of selected/detected peptides and the corresponding transitions of each protein candidate are provided in Supporting Information Table S3. In our data, three proteins, fibrinogen gamma chain, hemopexin, and alpha-1-antitrypsin,

Label-Free Analysis of 78 Proteins in 60 Plasma Samples

To evaluate the 78 candidate proteins in individual plasma samples, a relative quantitative MRM analysis was performed. Plasma samples were collected from 30 lung adenocarcinoma patients, including 10 in stage I (5 IA and 5 IB), 10 in stage II (6 IIA and 4 IIB), and 10 in stage III (7 IIIA and 3 IIIB). Demographic data of the enrolled patients is described in Table 2. Each analyzed sample was accompanied by a healthy control that was gender- and age-matched. Since 60 plasma samples took around 5 days and triplicate analyses took more than 15 days, two strategies were used to overcome instrument fluctuations occurring over the long measurement times. (a) After the analysis of each sample, MRM analysis of 25 fmole of enolase was performed. Seven peptides (three transitions per peptide) of enolase, as listed in Supporting Information Table S4, were used to monitor the instrument’s performance between individual samples. Once the intensity was lower than that listed or the retention time shifts by more than 10 D


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Figure 2. MRM traces of high-, medium-, and low-abundance proteins, represented by six surrogate peptides and their most abundant transitions.

the internal standard within each sample and each replicate for 18 batches is shown in Figure 4A, indicating that during the measurement the CV ranged from 17.2 to 25.6%. To access the variation occurring between batches of samples when they were independently analyzed, the variability in the retention time of 203 peptides over 180 individual samples was evaluated: 178 peptides with CVs < 20% were observed (Figure 4B).

min, the instrument was retuned. (b) An individual patient sample was analyzed followed by its matched control sample to reduce the run-to-run analysis or retention time shift variation. Once all 60 samples were analyzed, the repeat analyses were conducted. Given that precise label-free quantification is challenging due to the fluctuation of spray status and variation in signal intensities between runs,34 normalization was performed by using a spiked internal standard. Here, 5 fmol of Escherichia coli β-galactosidase was added into each of the 60 plasma samples. Samples were digested by trypsin and analyzed in triplicate. Three peptides, representing the standard peptide, with three transitions each (DWENPGVTQLNR: 714.67/ 884.49, 714.67/631.35, 714.67/545.19; APLDNDIGVSEATR: 486.46/563.28, 486.46/719.37, 486.46/626.28; and GDFQFNISR: 542.12/489.28, 542.12/636.35, 542.12/764.41) were monitored in 180 runs. To obtain the abundance of the internal standard in individual runs, the mean peak area of the three transitions from each of the three peptides was calculated and all of them were averaged again to provide the representative abundance of the internal standard. The CV of the intensity of

Selection of Biomarker Candidates for Diagnosing Different Stage of Lung Adenocarcinoma

The mean value of multiple peptides was used to represent the relative abundance of each protein, which, for all proteins in individual samples (n = 60), was subjected to Wilcoxon test. Nine proteins were considered to have a statistically significant difference (p < 0.05) in at least one stage compared to the healthy control (as shown in the interactive plots in Figure 5). Among them, five proteins, gelsoin, complement factor H, galectin-1, actin cytoplasmic 1, and multiple inositol polyphosphate phosphatase 1, were differentially expressed between stage I and healthy subjects, of which three were increased and E


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Figure 3. Variability of (A) intensity and (B) retention time of the 203 peptides over three repeated runs. (C) Profiling of the abundance of 78 proteins in pooled plasma samples. A low-abundance protein, serum amyloid P-component (SAP), can also be detected.

Table 2. Overview of Human Subject Data Sets demographic characteristics of enrolled subjectsa b

overall TNM stage no. of patients histological type age (mean ± SD) (year) gender (female/male)

Ic 10 adenocarcinoma 67.9 ± 9.5 5:5

IId 10 adenocarcinoma 69.2 ± 13.4 2:8

IIIe 10 adenocarcinoma 59.6 ± 11.3 6:4

a The subjects contained two sets: (1) patients and (2) gender- and age-matched healthy controls (n = 30); here, the characteristics of the patient set are shown. bDetermined using AJCC lung cancer staging.19 cConsists of 5 IA and 5 IB patients. dConsists of 6 IIA and 4 IIB patients. eConsists of 7 IIIA and 3 IIIB patients.

Figure 4. Variability of the internal standard and 203 monitored peptides. (A) CV of the intensity of the internal standard within each sample group and each replicate. (B) Variability of the retention time of 203 monitored peptides across 180 assays (60 individual plasma samples and their triplicate runs).

two were decreased in the stage I patients. Seven proteins were found to be significantly changed between stage II and healthy subjects, whereas actin cytoplasmic 1 and multiple inositol polyphosphate phosphatase 1 were increased in stage III patients (Table 3). The number of proteins that overlap

between groups is illustrated in Figure 6. Actin cytoplasmic 1 was found be able to differentiate lung adenocarcinoma from healthy subjects (gray). Interestingly, galectin-1 and gelsolin were both decreased in stages I and II patients versus control (purple), whereas multiple inositol polyphosphate phosphatase F


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Figure 5. Scatter plots depicting the distribution of the ratio of nine proteins between patients and their matched controls. Each dot in I/H1, II/H2, and III/H3 represents the peak area from each an individual patient sample at the specific stage indicated versus that from his/her matched control.

Table 3. Groups of Proteins for Differential Diagnosis of Lung Adenocarcinoma UniProt entry

protein name

median of ratiosa (patient/healthy)

p-valueb

AUC

sensitivity (%) at specificity = 95%

3.70 1.43 0.70 0.63 1.40

0.002 0.040 0.010 0.020 0.040

0.99 0.80 0.79 0.81 0.75

90 20 20 50 20

0.60 2.45 0.48 0.62 0.62 0.65 0.51

0.040 0.002 0.002 0.030 0.040 0.040 0.040

0.73 1.00 0.94 0.88 0.81 0.73 0.79

20 100 80 40 30 20 30

3.39 1.44

0.037 0.027

0.81 0.84

60 50

I (n = 10) versus H1 (n = 10) P60709 actin, cytoplasmic 1 P08603 complement factor H P09382 galectin-1 P06396 gelsolin Q9UNW1 multiple inositol polyphosphate phosphatase 1 II (n = 10) versus H2 (n = 10) P36639 7,8-dihydro-8-oxoguanine triphosphatase P60709 actin, cytoplasmic 1 P02654 apolipoprotein C-I P09382 galectin-1 P06396 gelsolin Q92823 neuronal cell adhesion molecule Q6P4A8 phospholipase B-like 1 III (n = 10) versus H3 (n = 10) P60709 actin, cytoplasmic 1 Q9UNW1 multiple inositol polyphosphate phosphatase 1 a

The median of 10 abundance ratios was derived from gender- and age-matched individuals (patient/healthy). bAll measurements were subjected to Wilcoxon test; p-value < 0.05 indicates a statistically significant difference.

1 was increased in both stages I and III versus control (green). Complement factor H was uniquely increased in stage I (pink). Another four proteins were differentially expressed in stage II patients when compared to their controls (blue). Our data suggest that these proteins may have important roles in the progression of lung adenocarcinoma. An ROC curve was constructed to further evaluate the performance of the candidates, and the area under curve (AUC) was calculated for the nine candidate markers (Table 3

and Supporting Information Figure S2). Because effective biomarkers for screening require high specificity, the sensitivity at 95% specificity was reported in addition to the AUC value (Table 3). All nine candidate markers achieved high AUC values (0.75−0.98). At 95% specificity, actin cytoplasmic 1 reached 90 and 100% sensitivities in stage I and II patients, respectively. Additionally, 80% sensitivity came from apolipoprotein C-I in discriminating stage II patients from controls. Since the sensitivity of other protein markers was 20−60% at G


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pronounced enhancement over each protein as a single candidate marker. As for differentiating stage III patients from controls, the two-marker combination panel of actin cytoplasmic 1 and multiple inositol polyphosphate phosphatase 1 also yielded a higher AUC of 0.90 over each single marker protein. Surprisingly, at 95% specificity, the combination panel provided a distinct increase in sensitivity of 80% (Figure 7C). Conclusively, using a panel of biomarkers improved the predictive performance over that with individual markers. As mentioned by Carr et al., a Tier 3 assay is not a truly quantitative analysis. Observations from these measurements will generally require additional studies to verify the findings.31 Here, we have selected a few targets for further validation. Since gelsolin and galectin-1 both show decreased levels in stages I and II patients while actin, cytoplasmic 1 showed increased expression in all three stages compared to control samples, their alterations were further validated in another set of plasma samples using commercially available ELISA kits. Demographic data of the enrolled patients is described in Supporting Information Table S6. As shown in Figure 8, the dot plots illustrate that the level of gelsolin was significantly decreased in all stages, whereas galectin-1 showed decreased levels in stages I and II. As for actin, cytoplasmic 1, its expression was elevated in stages I and II, which is consistent with the results from MRM analysis. Although its change in stage III was not significant, it also showed a slight increase compared to that in control samples. The corresponding AUC values for these three proteins are also shown in Figure 8, which ranged from 0.75 to 0.87 at different stages.

Figure 6. Venn diagram of the nine differentially expressed protein candidates showing the number of proteins that overlap between groups.

95% specificity, we assessed whether the sensitivity and specificity for lung adenocarcinoma detection could be improved by using a composite panel of these biomarker candidates. Combinations of two to five biomarker candidates according to “and” and “or” rules were tested35,36 by logistic regression performed with SPSS. The output for each disease condition is provided in Supporting Information Table S5. The combination of five proteins, gelsoin, complement factor H, galectin-1, actin cytoplasmic 1, and multiple inositol polyphosphate phosphatase 1, showed improved discriminatory power in distinguishing stage I patients from healthy subjects. The merged AUC of 1.00 surpassed that attained when any other single marker was used. In addition, the combination of the aforementioned five proteins reached 100% sensitivity at 95% specificity (Figure 7A). For discriminating stage II patients from controls, we combined three proteins, apolipoprotein C-I, galectin-1, and gelsolin, which also provided an improvement in AUC with a merged value of 1.00 (Figure 7B). Accordingly, 100% sensitivity at 95% specificity was obtained, leading to a

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DISCUSSION Although the speed with which protein identification and the number of proteins identified have been prominently improved by omics strategies, many biological questions remain unanswered due to a lack of antibodies whose quality is sufficient to allow for further validation. The emerging development of MRM analysis has relatively fulfilled the steadily rising demand for quantitative proteomic data. Comprehensive panels of biomarkers with high sensitivity and selectivity can be revealed by multiplexed MRM assays that target large numbers of proteins.37 In addition, MRM is a platform suitable for quantitative proteomic profiling of clinical samples, such as body fluids, tissues, or other biological samples,38−41 facilitating the discovery of promising biomarkers for the diagnosis of various diseases. Here, 60 plasma samples representing four groups (lung adenocarcinoma stages I, II, and III and controls) were analyzed by triple quadrupole mass spectrometry, in which 581 transitions from 78 proteins were quantified by MRM analysis. MRM allows for multiplexing and is high-throughput and low-cost compared to immunoassaybased approaches, which can be utilized for the verification of candidate markers and construction of biomarker panels. Instead of using a single peptide to represent each selected protein, for initial assay development, multiple representative peptides are monitored so that the accuracy of the quantification can be improved. As has been already proven in many types of cancers, a panel of biomarkers is likely to be capable of compensating for the insufficient sensitivity and specificity of a single marker for use in early diagnosis.42−44 For example, when PSA, an ideal biomarker for prostate cancer, is combined with thymosin beta15 (Tbeta15), higher specificity and sensitivity were generated, leading to an increased predictive power that was

Figure 7. Diagnostic performance of the five-, three-, and two-marker panels. Combination of five, three, and two candidate marker proteins improved the sensitivity in discriminating stages I−III patients from controls and resulted in a merged AUC value of nearly 1.00. H


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Figure 8. Candidate protein evaluation and confirmation by ELISA. The dot plot depicts the level of (A) gelsolin, (B) galectin-1, and (C) actin, cytoplasmic 1 in the plasma from lung cancer patients (stages I−III) and healthy control subjects. ROC curves as well as AUC values are also provided.

superior to that of PSA alone.45 In our data, the discriminating ability of panels of five, three, and two candidate markers was superior to that with any individual protein used. In previous literature, gelsolin downregulation has been commonly observed in human carcinomas of the breast, colon, prostate, bladder, and lung.46−50 Yang et al. demonstrated that higher expression of gelsolin was found to be considerably associated with poor survival in NSCLC.51 Their results also suggested that gelsolin can suppress tumor growth and is downregulated at some point during carcinogenesis, which is consistent with our observation that gelsolin showed a lower expression level in early stage (I and II) patients compared to that in healthy subjects. High expression of complement factor H (CFH) was significantly correlated with lung adenocarcinoma, and a tendency was observed for CFH-positive tumors to yield worse prognosis. They also noticed that a shorter survival time of patients with adenocarcinoma was associated with increased staining for CFH.52 Our MRM data also demonstrated that CFH has a higher expression level in stage I patients compared to that in controls. Both studies suggested that CFH might be

of great potential as a diagnostic marker for human lung adenocarcinoma. Galectin-1 was shown to be overexpressed in NSCLC cell lines. Suppression of endogenous galectin-1 in lung adenocarcinoma gave rise to reductions in cell migration, invasion, and tumor growth in mice.53 It has been reported that galectin-1 can be used in prognosis.54,55 Carlini et al. proposed that galectin-1 may be used as a potential biomarker to better predict the clinical outcome and management of NSCLC patients.56 Since the aforementioned works were performed in cell lines and tumor tissues, the decreased level of galectin-1 in blood from early stage patients (stages I and II) may result from its intracellular accumulation, which could contribute to promoting tumor growth. The lower expression level in blood has been verified by ELISA here, and the mechanism and potential application await elucidation by further studies. According to our data, actin cytoplasmic 1, also called betaactin, was differentially expressed in all stages of lung adenocarcinoma patients. Since previous work has shown it to have an altered level in squamous cell carcinoma tissue, the role of this protein in lung cancer requires further studies.57 I


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and 80% at 95% specificity were attained for stages I, II, and III, respectively, through a combination of complementary individual markers. On the basis of this evidence, these marker candidates merit further validation in larger and diverse patient and control cohorts to minimize the clinical risk factors associated with their potential use in future clinical applications.

The change in multiple inositol polyphosphate phosphatase 1 (MINPP1) seems to be a dynamic process in the progress of lung adenocarcinoma based on our results. The increase of MINPP1 in stage I patients may stem from it being employed to enhance the growth potential of tumor cells.58 MINPP1 also can be processed by lung tumor cells (H1299) and subsequently secreted into the medium and transported into lysosomes,58 which may be the reason that MINPP1 was found to be increased in the blood of stage III patients. These observations may shed light on the potential use of this candidate protein in the detection of lung adenocarcinoma. Apolipoprotein C-I (APOC1) has been found to be decreased in stage II patients, which is consistent with a previous serum proteomic profile study that also identified APOC1 as a downregulated protein in NSCLC patients using surfaceenhanced laser desorption/ionization time-of-flight mass spectroscopy (SELDI−TOF−MS).59 Although another study showed that APOC1 gradually increased from early to late stage lung cancer by immunofluorescence staining of tumor samples, no prognostic value of APOC1 was identified from the serum samples, and the enrolled subjects were not all lung adenocarcinoma patients.60 Thus, its use for diagnosis remains to be evaluated in the future. Zeng et al. performed a global lung cancer serum biomarker discovery study using LC−MS/MS and spectra counting in a set of pooled NSCLC case sera and matched controls, resulting in 49 differentially abundant candidate proteins. They found higher and lower expression levels of complement factor H and gelsolin, respectively, in NSCLC patients, which is consistent with our findings.61 However, the other 47 proteins, except for fibronectin and alpha-2HS-glycoprotein, were not included in our screening list. Recently, many proteins have been reported to be potential lung cancer serum/plasma biomarkers (including adenocarcinoma, squamous cell lung carcinoma, large cell carcinoma, and small-cell lung cancer).62 Two of them, insulin-like growth factor-binding protein-2 (IGFBP-2) and peroxiredoxin 1 (PRX1), were not found to be differentially expressed in our study. This discrepancy may due be to the different analysis approaches and diverse subject enrolling criteria used. Additionally, the rest of the reported potential biomarkers were not included in our screening list because we focused on analyzing metastasis-related secreted proteins found in our previous study. Our results have highlighted several possible markers for identifying lung adenocarcinoma at different stages, which demand further validation in a larger cohort to determine their suitability for use in clinical applications.

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ASSOCIATED CONTENT

* Supporting Information S

Table S1: Information on 1258 transitions (for 102 proteins and internal standard protein). Table S2: Depletion rate for each individual sample. Table S3: Number of selected/detected peptides and the corresponding transitions of each protein candidate. Table S4: Criteria for monitoring instrument performance by using enolase. Table S5: SPSS output for protein panels. Table S6: Demographic data of patients enrolled for EILISA assay. Figure S1: Distribution of the qvalue for 360 transitions. Figure S2: Area under the curve for the nine candidate marker proteins. Data S1: Example of a pathology report for a stage I patient. The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/pr501195t. All MRM assay files have been uploaded to Panoramaweb in the form of Skyline files and can be accessed at https://panoramaweb.org/labkey/_webdav/ NCKU%20Proteomics%20Lab/@files/.

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AUTHOR INFORMATION

Corresponding Authors

*(Y.-J.C.) Tel: +886-2-27898660; Fax: +886-2-27898534; Email: [email protected]. *(P.-C.L.) Tel: 886-6-2353535 ext 5566; Fax: 886-6-2743748; E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by Ministry of Science and Technology (MOST) and Academia Sinica (NSC99-2923-M006-001-MY3, MOST102-2325-B-006-003, MOST100-2113M-006-002-MY3, and MOST103-2113-M-006-003-MY3 to P.C.L.; 100-2628-M-001-003-MY4 and AS-102-TP-A03 to Y.J.C.). The proteomics experiments were performed by the Academia Sinica Mass Spectrometry Lab located at the Institute of Chemistry.

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REFERENCES

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CONCLUSIONS In this study, we have presented a stepwise workflow to verify candidate markers for lung adenocarcinoma. Using a limited amount of specimen, we used pooled plasma samples to effectively screen transitions suitable for MRM analysis, which resulted in 78 protein candidates. Moreover, to measure the relative abundance of numerous candidate protein biomarkers, we performed simple and robust MRM multiplex assays in individual plasma samples from 30 patients and 30 gender- and age-matched healthy donors. Conclusively, nine protein candidates were found to have a significant ability to discriminate lung adenocarcinoma patients from healthy subjects. We proposed a working panel of five, three, and two proteins that differentiated the different lung adenocarcinoma stages from healthy controls. Sensitivities of 100, 100, J


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Qualification and Verification of Serological Biomarker Candidates for

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