Verification of the biomarker candidates for non-small-cell lung cancer using a targeted proteomics approach.

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Verification of the Biomarker Candidates for Non-SmallCell Lung Cancer Using a Targeted Proteomics Approach Yeoun Jin Kim, Katriina Sertamo, Marie-Aline Pierrard, Cédric Mesmin, Sang Yoon Kim, Marc Schlesser, Guy Berchem, and Bruno Domon J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/pr5010828 • Publication Date (Web): 16 Jan 2015 Downloaded from http://pubs.acs.org on January 30, 2015

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Journal of Proteome Research

Verification of the Biomarker Candidates for Non-Small-Cell Lung Cancer Using a Targeted Proteomics Approach Yeoun Jin Kim1, Katriina Sertamo1, Marie-Aline Pierrad1, Cédric Mesmin1, Sang Yoon Kim1, Marc Schlesser3, Guy Berchem2,3, Bruno Domon*,1 1

Luxembourg Clinical Proteomics Center, Luxembourg Institute of Health, Strassen,

Luxembourg, 2Laboratory of Experimental Hemato-Oncology, Luxembourg Institute of Health, Strassen, Luxembourg, Luxembourg,

4

Service

3

Service de Pneumologie, Centre Hospitalier de Luxembourg, d’Hémato-Cancérologie,

Centre

Hospitalier

de

Luxembourg,

Luxembourg

ABSTRACT Lung cancer, with its high metastatic potential and high mortality rate, is the worldwide leading cause of cancer-related deaths. High-throughput “omics”-based platforms have accelerated the discovery of biomarkers for lung cancer and the resulting candidates are to be evaluated for their diagnostic potential as non-invasive biomarkers. The evaluation of the biomarker candidates involves the quantitative measurement of large numbers of proteins in bodily fluids using advanced mass spectrometric techniques. In this study, a robust pipeline based on targeted proteomics was developed for biomarker verification in plasma samples and applied to verifying lung cancer biomarker candidates. Highly multiplexed LC-SRM assays for 95 potential tumor markers for non-small-cell lung cancer (NSCLC) were generated to screen plasma samples obtained from 72, early to late stage, patients. A total of seventeen proteins were verified as potent tumor markers detectable in plasma and, where available, verified by ELISAs. A novel plasma-based biomarker, zyxin, fulfilled the criteria for a potential early diagnostic marker for NSCLC.

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Keywords: lung cancer; targeted proteomics; biomarker; diagnosis, non-small-cell lung cancer; selected

reaction

monitoring;


zyxin

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INTRODUCTION

Lung cancer is the leading cause of cancer-related deaths worldwide with high metastatic potential and mortality rate. Five year survival is only around 15% mainly due to the fact that the disease is typically diagnosed only at a late stage of disease progression where surgical resection is not a standard procedure anymore

1-3

. The median age of patients at the time of diagnosis is

about 70 years, relatively higher than with other cancer types which hampers the aggressive therapeutic intervention3. Conventionally, based on pathological and etiological differences, lung cancer can be divided into two major types, small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC) 4, 5. NSCLC consisting of adenocarcinoma, squamous cell carcinoma, and large cell carcinoma, accounts for 75-85% of lung cancer cases. It arises from the epithelial cells of lung, and is considered less aggressive than SCLC which is spreading rapidly.

Despite the tremendous efforts made in biomarker studies, no blood-based tests are available to detect the presence of lung cancer early enough for treatment or to predict the outcomes in patients subjected to targeted therapies. The most widely used blood-based tumor marker screening for lung cancer include cytokeratin 19 fragment (CYFRA21-1), carcinoembryonic antigen (CEA), squamous cell carcinoma antigen (SCCA), and neuron-specific enolase (ENO2) 6, 7

. However, the level of selectivity of these markers to effectively diagnose lung cancer in

blood has not been attained. It is important to find tumor markers detectable at an early stage of the disease when the tumor cells in lung are still localized, thus allowing an effective treatment by immediate resection.

Numerous number of proteins have been already reported as being associated with lung cancer especially discovered by high-throughput “omics” platforms

8-10

. It is now a question of

evaluating these candidates as truly effective during clinical applications11. This evaluation process involves quantitative measurement of a large number of proteins in bodily fluids, often performed with immunoassays, is a time-consuming and costly task. This is in part due to the requirement of developing antibodies that warrant robust assays. Therefore, more highthroughput and multiplex-capable methods are in demand to expedite the evaluation process. Mass spectrometry-based approaches employing targeted proteomic strategies provide a sensitive and precise quantification tool, which is versatile, systematic, and scalable 3 ACS Paragon Plus Environment

12, 13

. Its


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capability of multiplexing hundreds of targets facilitates a cost-effective and rapid evaluation, thus it is especially useful during the earlier stage of the verification process. In this study, we designed a robust LC-MS pipeline that increases the throughput of the entire verification process for biomarker candidates in human plasma. The overall pipeline consists of three steps: assay design, detectability screen, and large scale verification of differential levels. The assay design refers to the generation of LC-SRM methods that measure the level of target proteins in human plasma. Selection of the representative peptides of the candidate biomarkers and the identification of the interference-free transitions of each selected peptide are performed in this step. The detectability screen refers to a series of experiments that qualifies the SRM method of the peptides to be further used in the study by confirming the presence of endogenous peptides in the samples. Usually a small set of clinical samples is used. The last step of the verification is consisted of the subsequent experiments where the qualified assays are applied in a larger number of samples in order to verify if the level of target proteins in the disease group are different from the control group.

This pipeline was applied to the verification of 95 candidate tumor markers of NSCLC, and finally led to confirm a novel biomarker candidate for early detection of NSCLC.

METHODS Sample Acquisition and Plasma Preparation Blood samples used in this study are obtained from 72 patients diagnosed with NSCLC including adenocarcinoma (stages I-IV), squamous cell carcinoma (stages I-IV), and large cell carcinoma (stages II-IV) and 30 healthy volunteers. Informed consent forms approved by the Comité National d’Ethique de Recherche (CNER) were obtained from the patients prior to sample collection. Blood samples were processed following the standard operating protocols of the Integrated BioBank of Luxembourg (IBBL) to prepare plasma aliquots.

Proteomic Sample Preparation The plasma sample per each patient was prepared in triplicate (25 µl each). The two most abundant plasma proteins, human serum albumin (HSA) and immunoglobulin G (IgG) were depleted from the plasma using multiple affinity removal spin cartridge (Agilent Technology, 4 ACS Paragon Plus Environment

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Santa Clara, CA). Briefly, plasma samples were diluted with buffer A (loading buffer, Agilent Technology) to 200 uL and loaded on to the spin cartridges followed by centrifugation at 100 g for 1 min. The cartridges were washed twice with 400 µl of buffer A. The two washes and the flow through of the load were combined. The bound proteins were eluted with buffer B (elution buffer, Agilent Technology). Total protein concentration was measured before and after depletion using the Bradford assay. Depleted plasma samples were concentrated to 100 uL with ammonium bicarbonate buffer (pH 8.0, 10% acetonitrile (ACN)) using vivaspin 3K (Sartorius AG, Goettingen, Germany). Samples were heated at 90 °C for 10 min followed by dissolving in 0.1% RapiGest (Waters, Milford, MA) for denaturation. After denaturation, proteins were reduced using 10 mM dithiothreitol (DTT) at 50 °C for 50 min, and alkylated using 25 mM iodoacetamide at room temperature in dark followed by quenching with 3 mM DTT for 30 min at room temperature. 13 µg trypsin (Promega, Sunnyvale, CA) was added to the samples and the samples were incubated at 37°C overnight after which an additional 2 µg trypsin was added for 2 hr incubation. The trypsin digests were acidified using 50 µl of 20% formic acid and desalted using C18-based cartridge. The desalted samples were dried in vacuo and reconstituted with 200 µL of 0.1% formic acid / 5% ACN. 50 fmol each stable isotope-labeled (SIL) peptides of target proteins were spiked in the samples as internal standards. 7.5 fmol each retention-timecalibration-mixture (Thermo Fisher Scientific-Pierce, Rockford, IL) were spiked as well in order to control the retention time shift during LC-MS analysis.

Selection of Biomarker Candidates and Target Peptides In order to select the lung cancer biomarker candidates, an internal database consisting of proteins previously identified in the discovery studies in plasma, differentially expressed proteins in human tissues (higher in tumor compared to distant normal tissue), and the proteins reported in lung cancer studies as potential biomarkers, was constructed. They were ranked with consideration of the incidences in the discovery studies and the availability of cross-validation assays to choose the first 95 targets. Two peptides per each protein were selected based on their uniqueness and physicochemical properties. Tryptic peptides whose sequences are related to only one protein in human protein database (Uniprot/Swissprot ver. 2012, canonical sequences) were considered as unique. Among the unique peptides only those comprising of 6-20 amino acids and with

the

hydrophobicity

factors

(HF)

calculated


using

SSRCalc

tool


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(http://hs2.proteome.ca/SSRCalc/SSRCalcX.html) ranging between 10 ~ 40 were selected. Peptides containing reactive amino acids, cysteine and methionine, were deprioritized although not excluded. Peptides containing amino acids with known post-translational modification were excluded, and with potential modifications were deprioritized. Peptides presenting the highest detectability scores in PeptideAtlas14 and CONSeQuence15 were preferred.

Synthetic Peptides and Spectral Library Stable isotope-labeled (SIL) peptides of the selected target peptides were synthesized to be used as internal standards. Stable isotope labels (13C615N4 for Arg, 13C615N2 for Lys) were incorporated at the C-terminal Arg and Lys residues. Peptide synthesis was performed by Thermo Fisher Scientific GmbH (Ulm, Germany) in crude grade (PEPotec SRM peptides). The synthesized peptides were prepared in nominal concentration of 50 pmol/uL in aqueous (5% ACN) solution. 1 pmol/uL of each synthetic peptide was subjected to LC-MS/MS analysis using LTQ-Orbitrap Velos instrument (Thermo Scientific, Bremen, Germany) using a short (10 min) linear LC gradient, and the MS/MS spectra acquired with various collision energies (nCE = 25, 30, 35) in both higher-energy collisional dissociation (HCD) and collision induced dissociation (CID) mode were recorded in an SQLite3-based database. Peptides were then pooled (groups of 30 peptides) to prepare a mixture which was subjected to LC-MS analysis using a standard LC method to acquire experimental retention times. An additional 7.5 fmol/uL retention-timecalibration mixture (Fischer Scientific) was spiked in the previous peptide mixtures before analysis to be used in quality control. The dominant precursor mass of the peptides, three most intense fragment ions from the spectral library, and corrected retention time to fit the actual analysis conditions were extracted to constitute a scheduled LC-SRM method.

LC-SRM and Data Analysis LC-SRM were performed using a TSQ vantage mass spectrometer (Thermo Scientific) as previously

described

16

.

The

raw

data

were

processed

with

Skyline

(http://proteome.gs.washington.edu/software/skyline/) to visually inspect the traces of the SRM data and to calculate the peak heights of transitions17. One quality transition per peptide was chosen to be used for further analysis. Normalized peak heights of endogenous peptides, (peak height of endogenous peptide / peak height of internal standard) × median peak heights of the 6 ACS Paragon Plus Environment

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internal standards, were used to compare the relative abundance of each peptide across the samples. For the figures 1 and 3, Savitzky–Golay method was applied to smooth the data points. For the generation of receiver operating characteristic (ROC) curves, and area under the curve (AUC) calculation, published R scripts were used

18

. Other statistical analyses were performed

using GenStat (ver.15, VSN International, Hemel Hempstead, UK).

RESULTS and DISCUSSIONS

Assay Design For the selected 95 biomarker candidates, a total of 190 peptides constituting two peptides per protein, (Table 1S in supplementary information) were synthesized with incorporation of 13C and 15

N at the C-terminal Lys or Arg residues to be used as internal standards. The LC-MS/MS data

and the physicochemical properties of each peptide were stored in an SQL-based spectral library database. The spectral library is a critical part of the robust pipeline as it facilitates the automatic generation of LC-MS methods consisting of the dominant precursor ions of target peptides, the three most-intense transitions from the selected precursor ions, and the corresponding retention times for scheduling the SRM acquisition. The retention times were further corrected based on the retention times obtained with the retention-time-calibration-mixture analyzed in advance to the assays. This provides the flexibility of using different platforms by precisely scheduling the measurements and thus increases the robustness of long-term studies.

In order to determine the optimum parameters for the spectral library, fragmentation patterns obtained by both HCD mode and CID mode of the orbitrap instrument with varying collision energies were compared with the fragmentations patterns of the same peptides by a triplequadrupole instrument operated as described in the method section. Peptide fragmentations performed with nCE 25 in HCD mode resulted in comparable intensity profiles to the triplequadrupole based fragmentations as shown in Figure 1. Thereby, the selection of the best transitions was based on the spectrum acquired in HCD mode with nCE 25.

Two LC-SRM methods were generated for the 190 pairs of peptides (endogenous and internal standard pairs) by the workflow summarized in Figure 2. 7 ACS Paragon Plus Environment


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Detectability Screen of the Peptides in Human Plasma Two SIL peptide mixtures (95 peptides per each mixture) were prepared and spiked in a small set of plasma samples (15 NSCLC and 15 control). The targeted peptide pairs were screened in the samples using two LC-SRM methods in order to verify the detectability of the endogenous peptides in plasma. The raw data were analyzed using Skyline where the transitions were visually inspected to identify detectable peptides.

Out of 190 peptides surveyed, 60 peptides corresponding to 44 proteins were verified as detectable in plasma. The success rate, the fraction of peptides successfully detected, is 32% at the peptide level and 46% at the protein level. The two patient groups (15 NSCLC vs. 15 control) used in detectability screen were matched in gender and age (+/- 5 yrs). The screening results from this matching pairs allows to already estimate differential plasma levels of each peptide in a larger set of samples. Among the analytes detected, 24 peptides corresponding to 17 proteins exhibited higher than two-folds plasma level in NSCLC compared to healthy control (success rate of 13% at the peptide level and 18% at the protein level from the initial peptides/proteins; success rate of 40% at the peptide level and 39% at the protein level from the detected peptides/proteins). The overall outcome of the detectability screen of 190 peptides in plasma is summarized in Table 2S in the supplementary information. The success rate of detectability may increase if further efforts of reducing the complexity of plasma samples are made. Increasing the number of depleted proteins and or adding a fractionation step (e.g., isoelectric focusing; highpH reversed-phase chromatography) are used for this purpose. Employing the high resolving power of mass spectrometry can also help to increase the success rate as shown in the recent development of parallel reaction monitoring (PRM) method12.

The 17 proteins that show higher plasma level in NSCLC group in this preliminary screening are alpha-actinin-1 (ACTN1), fructose-bisphosphate aldolase A (ALDOA), alpha-enolase (ENO1), filamin-A (FLNA), glucose-6-phosphate 1-dehydrogenase (G6PD), glucose-6-phosphate isomerase (GPI), endoplasmin (HSP90B1), intercellular adhesion molecule 1 (ICAM1), integrinlinked protein kinase (ILK), L-lactate dehydrogenase B chain (LDHB), moesin (MSN), 8 ACS Paragon Plus Environment

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phosphoglycerate kinase 1 (PGK1), pyruvate kinase isozymes M1/M2 (PKM2), osteopontin (SPP1), transaldolase (TALDO1), thrombospondin-1 (THBS1), and zyxin (ZYX). These proteins are the final candidates to be verified in a larger sample set. In order to increase the analytical confidence, additional peptides for these 17 proteins were selected, synthesized, and the detectability in plasma was verified in the same manner as the first set of the peptides. The additionally verified peptides were included in the final inclusion list. The final list of 42 detectable peptides (and the corresponding proteins) whose plasma levels are potentially NSCLC specific are shown in Table 1.

Verification of the Differential Levels in a Larger Set The high plasma levels of the previously screened 17 targets were further verified in the larger sample set (72 NSCLC and 30 controls). In addition to the 17 tumor marker candidates, a set of proteins that are often used for quality control of plasma analysis were included as references representing non-discriminating proteins in plasma 19. Finally, an LC-SRM method consisting in 42 peptides of the 17 target proteins, 45 peptides of 36 reference proteins, and 15 peptides of the retention time-calibration mixture, was generated and applied to screen 102 plasma samples resulting in a total of 306 LC-SRM files to be analyzed. The details of the final LC-SRM method and the information on the clinical samples are shown in supplementary information Table 3S and Table 4S, respectively. The resulting traces were visually inspected using Skyline to select one trace (most intense and free of interference) per peptide, and the peak heights of the selected traces were used for statistical analysis. The scatter plots of all target peptides and the ELISA data of a subset of the targets are presented in supplementary information Figures 1S-3S. The verification results performed in five stages are summarized in Table 2.

Novel Tumor Markers for NSCLC Detectable in Plasma The initial list of the candidate proteins was selected based on the prior knowledge of their functional association to NSCLC and the aberrant expression levels in tumor tissues. However, it is difficult to find those candidates that are also measurable in human plasma of NSCLC patients. Among the 17 verified targets, four (FLNA, MSN, TALDO1, and ZYX) have not been described previously as plasma-based tumor markers for NSCLC. More importantly, ZYX (protein name: zyxin, Uniprot ID: Q15942) showed a potential to be used for early diagnosis of NSCLC. 9 ACS Paragon Plus Environment


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Zyxin protein was analyzed using four different peptide sequences, all of which exhibited higher plasma levels in NSCLC patients as compared to the control samples. Typical LC-SRM traces of one of the zyxin peptides, FSPGAPGGSGSQPNQK, measured in two different plasma samples (control vs. NSCLC) are illustrated in Figure 3. The absence of the target traces in this control sample was clear compared to the traces measured in an NSCLC while the traces of SIL peptides were consistent in the two samples. The chromatographic traces of all four zyxin peptides are included in supplementary information Figure 4S.

Figure 4A shows a scatter plot of the intensity signals measured by antoher zyxin peptide, SPGAPGPLTLK in 102 plasma samples (30 controls and 72 NSCLC), where the clear differentiation of two groups was observed (p = 1.06×10-14). A scatter plot of the peptide ELDESLQVAER, derived from clusterin (gene name: CLU, Uniprot ID: P10909), one of the non-discriminating proteins measured concurrantly with the targets as reference proteins, is also shown as a comparison in the same data set (Figure 4B). The NSCLC group was comprised of plasma samples of patients at different clinical stages of the disease. Analysis of the zyxin values at the different clinical stages demonstrated that the levels of this zyxin peptide were already elevated at early stages of NSCLC (Figure 4C). The intensity profiles of the four zyxin peptides across the samples were highly corelated as demonstrated in Figure 4D using two peptides suggesting that zyxin at the protein level may be also detectable with other methods such as ELISA. The ELISA against zyxin performed in 142 plasma samples cross-validated our results (Figure 4E).

Zyxin in normal cell is known as a member of the focal adhesion protein family cell adhesion and cytoskeleton remodeling

21

20

involved in

. The role of zyxin in cancer has been recently

discussed as a key player in epithelial-mesenchymal transition (EMT) mechanism

22

; the

association to lung cancer was reported as a down regulator of TGF-β inducing cell motility 23; up-regulated mRNA was found in tumor-associated macrophage

21

; a peptide fragment

apparently derived from truncated zyxin has been identified in serum samples from colorectal cancer patients

24

. The expression level of zyxin corresponding to tumorigenesis or tumor mass

in human body has been quite controversial22, 25. To the best of our knowledge, the potential use 10 ACS Paragon Plus Environment

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of zyxin as a tumor biomarker in plasma for lung cancer has been verified for the first time in this study. The ROC curve generated using one of the zyxin peptides demonstrated a high specificity toward NSCLC with AUC = 0.958 in the current data set as shown in Figure 4F.

With regard to cellular functions, in the list of the initial 95 candidates, nine proteins are involved in glycolysis pathway as major glycolytic enzymes

26

. Among them, seven enzymes,

ALDOA, ENO1, ENO2, GPI, LDHB, PGK1, and PKM2, were detected in this study. In a typical cancerous microenvironment, glycolysis of tumor cells is significantly activated resulting overexpressed glycolytic enzymes

27-29

which can be released to blood

26

. It is reasonable to

hypothesize higher plasma concentration of those seven proteins in NSCLC plasma as a consequence of the malignancy. In the differential analysis of this study, six out of seven detectable enzymes were found to be elevated in plasma of NSCLC patients, only the neuron specific enolase (ENO2) result showed an opposite trend. This ENO2 result confirmed by independent ELISA experiment (supplementary information, Figure 3S) appears to be contradictory to the prior information since elevated serum ENO2 level has been used as a tumor marker for small cell lung cancer (SCLC)30. It should be noted that the specificity of ENO2 as a tumor marker has been evaluated using endocrine cell driven tumors which is the case of SCLC31. For cancers originated from epithelial cells, which is the case of NSCLC, the specificity of ENO2 or its clinical utility have not been established. Our results indicate the merits of additional validation of ENO2 with an expanded cohort including plasma samples of SCLC patients.

CONCLUSIONS A targeted proteomics-based, analytical pipeline was designed for a large scale biomarker verification and successfully applied to verifying a set of potential biomarkers for NSCLC. The peptide banking system equipped with a local spectral library of synthetic peptides facilitated automatic generation of LC-SRM methods. This feature is critical to the early-stage screening where the attrition rate of the detectablity tends to be high (68% at the peptide level in this study), and when the peptides were not fully evaluated in the prior experiments. Robust method was used by performing retention time correction based on the retention-time-calibration-



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peptides prior to the LC-SRM runs. This enables to produce consistent data throughout a long period of time where the variation of LC conditions is expected.

A total of seventeen proteins were detected without enrichment, and showed significant elevation in the plasma of NSCLC patients, representing potential tumor markers. A subset of those verified targets can be chosen as a panel to be extensively analyzed in a focused clinical study. One of the novel plasma-based tumor markers, zyxin exhibited a potential as an early diagnostic marker for NSCLC. This result was cross-validated by ELISA using an independent sample set. The multiplexed LC-SRM assays established in this study for seventeen markers are robust and potentially suited for routine analyses.


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TABLES Table1. The list of tumor marker candidates differentially detected in NSCLC. No

Gene

Accession

Protien Name

1

ACTN1

P12814

Alpha-actinin-1

2

ALDOA

P04075

Fructose-bisphosphate aldolase A

3

ENO1

P06733

Alpha-enolase

4

FLNA

P21333

Filamin-A

5

G6PD

P11413

6

GPI

P06744

7

HSP90B1

P14625

8

ICAM1

9

ILK

10

LDHB

P07195

L-lactate dehydrogenase B chain

11

MSN

P26038

Moesin

12

PGK1

P00558

Phosphoglycerate kinase 1

13

PKM2

P14618

Pyruvate kinase isozymes M1/M2

14

SPP1

P10451

Osteopontin

15

TALDO1

P37837

Transaldolase

16

THBS1

P07996

Thrombospondin-1

17

ZYX

Glucose-6-phosphate 1dehydrogenase Glucose-6-phosphate isomerase Endoplasmin

Intercellular adhesion molecule 1 Integrin-linked protein Q13418 kinase P05362

Q15942 Zyxin

PeptideSequence ETADTDTADQVMASFK IGEHTPSALAIMENANVLAR QLLLTADDR GILAADESTGSIAK DATNVGDEGGFAPNILENK GNPTVEVDLFTSK YISPDQLADLYK VTVLFAGQHIAK SPFSVAVSPSLDLSK LPQLPITNFSR LFYLALPPTVYEAVTK NSYVAGQYDDAASYQR HFVALSTNTTK TLAQLNPESSLFIIASK FQSSHHPTDITSLDQYVER GVVDSDDLPLNVSR ASVSVTAEDEGTQR DGTFPLPIGESVTVTR GMAFLHTLEPLIPR FDMIVPILEK FIIPQIVK LIAPVAEEEATVPNNK SLADELALVDVLEDK AQMVQEDLEK ALTSELANAR ALMDEVVK VLPGVDALSNI YAEAVTR APIIAVTR LDIDSPPITAR AIPVAQDLNAPSDWDSR GDSVVYGLR LVPVLSAK ALAGC[cam]DFLTISPK LLGELLQDNAK GGVNDNFQGVLQNVR FVFGTTPEDILR FTGSQPFGQGVEHATANK SPGAPGPLTLK VSSGYVPPPVATPFSSK FSPGAPGGSGSQPNQK GPPASSPAPAPK



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Table 2. A summary of the biomarker verification for NSCLC Experiment

# of proteins

# of peptides

# of clinical samples

LC-SRM assay of candidate proteins1 Detected targets in human plasma2 Detected and differential targets Final LC-SRM assay 3 Verified targets4

95 44 17 17 17

190 60 24 42 42

NA 30 30 NA 102

1

Table 1S; 2Table 2S; 3Table 3S; 4 Table 1

FIGURE LEGENDS Figure 1. Comparison of MS/MS spectra acquired on an orbitrap instrument using HCD mode (nCE = 25) and the corresponding SRM traces acquired by a triple-quadrupole instrument for peptides AGALNSNDAFVLK (13C615N2) (A) and HVVPNEVVVQR (13C615N4) (B). The three most intense fragment ions of the dominant precursor ions were selected for the transitions in SRM methods. The relative intensities by both methods are highly correlated as indicated by the dot-product (DP) > 0.99.

Figure 2. Work flow of LC-SRM method generation. Target peptides of the biomarker candidates are selected and synthesized with heavy isotopic labels. Uniformly prepared, barcoded, synthetic peptides are analyzed to generate MS/MS spectra with normalized retention times. Physicochemical properties and LC-MS/MS attributes of the peptides are stored in the spectral library. LC-SRM methods of selected peptides can be automatically generated according to the spectral library information.

Figure 3. A typical LC-SRM data verifying the relative concentration of zyxin in two different plasma samples (control vs. NSCLC). Three transitions of the endogenous zyxin peptide FSPGAPGGSGSQPNQK measured in a control sample (A) and an NSCLC sample (B). Three transitions of the stable isotope labeled FSPGAPGGSGSQPNQK peptide measured in a control (C) and an NSCLC sample (D).


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Figure 4. Verification of the differential plasma levels of zyxin protein in lung cancer samples. (A) Scatter plots of the signal intensities of the zyxin peptides SPGAPGPLTLK detected in two groups, control (n = 30) and NSCLC (n = 72). (B) Scatter plots of the peptide ELDESLQVAER derived from clusterin, one of the reference proteins for the control (n = 30) and NSCLC (n = 72) groups. (C) Scatter plots of signal intensities of the zyxin peptide SPGAPGPLTLK for the different clinical stages of NSCLC. Number of samples used in each group are n = 30 for control: n = 10 for stage I: n = 10 for stage II: n = 8 for stage III: n = 43 for stage IV. (D) Scatter plot comparing the signal intensities of two zyxin peptides SPGAPGPLTLK and VSSGYVPPPVATPFSSK. (E) ELISA results for zyxin in control (n = 22) and NSCLC (n = 122) samples. (F) ROC curve of the zyxin peptide SPGAPGPLTLK.

ABBREVIATIONS SRM: Selected reaction monitoring; NSCLC: Non-small-cell lung cancer; LC-MS: Liquid chromatography-mass spectrometry; SIL: Stable isotope-labeled; DP: Dot-product of light to heavy; ROC: Receiver operating characteristic; AUC: Area under the curve; HF: Hydrophobicity factor; CE: Collison energy; ACN: Acetonitrile; HCD: Higher-energy collisional dissociation; CID: Collision induced dissociation

COMPETING INTERESTS The authors declare that they have no competing interests.

SUPPROTING INFORMATION Supporting Information is available free of charge via http://pubs.acs.org/. This includes Table 1S, List of target proteins to be screened and their surrogate peptides; Table 2S, Summary of the detectability of the 95 targets screened in 15 control and 15 NSCLC samples; Table 3S, The final LC-SRM method; Table 4S, Sample information (30 controls and 72 NSCLC) used for verification study; Figure 1S, Scatter plots of the signal intensities of all target peptides including 42 peptides of 17 biomarker candidates; Figure 2S, Scatter plots of the signal intensities of all target peptides for the different clinical stages of NSCLC; Figure 3S, Scatter plots of the enzyme-linked immunosorbent assay (ELISA) results for targets ENO2, GPI, THBS1, ICAM1,



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PGK1, PKM2, TALDO1, and ZYX; Figure 4S, LC-SRM traces of four zyxin peptides in a typical sample set.

ACKNOWLEDGEMENTS This work was supported by PPM program funded by the Ministry for Higher Education and Research (MESR) in Luxembourg, and PEARL-CPIL program funded by the Fonds National de la Recherche Luxembourg. We thank Drs. Nikolai Goncharenko and Fay Betsou in the Integrated Biobank of Luxembourg (IBBL) for collection and handling of the clinical samples. We thank Ms. Roxane Batutu for managing clinical information, and Dr. Jan van Oostrum for critical reading of this manuscript. REFERENCES

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1 A

A G A L N S N DA F V L K 1 1015.53

80

2 901.49

y8

60

3

40

100

814.46

y7

20

Relative Abundance

Relative Abundance

80

DP = 0.99899

y9

100

0 700

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1100

40 20

0

1200

B

664.36 > 1015.53 (y9) 664.36 > 901.49 (y8) 664.36 > 814.46 (y7)

60

m/z

25.5

26.0

min

HVVPNEVVVQR 1 DP = 0.99995

950.53

y8

2

80

1049.60

y9

60 40

3

20

1148.67 0

800

900

1000

1100

1200

100

Relative Abundance

100

Relative Abundance

1 2 3 4 5 Figure 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48


80

643.37 > 950.53 (y8) 643.37 > 1049.60 (y9) 643.37 > 1148.67 (y10)

60 40 20

y10 1300

0

15.1

m/z

16.1

min

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1 2 3 4 5 Figure 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

2

Targets

Sequence: ELDESLQVAER GENE: CLU ID: P10909 Mw: 1287.63066 (Light) m/z: 649.82672 (z=2) HF: 21.68 nRT: 29.75 Conc: 50 pmol/uL

Peptide DB Peptide selection

Peptide synthesis

Peptide characterization LC-SRM method generation

LC-MS/MS

Retention time determination

nCE=35 nCE=30 nCE=25

y3

Spectra DB


y9 y5

m/z

y7


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3

Control 1100

1100

700

700

300

-100

C

NSCLC

B

Intensity

Intensity

A

ZYX: FSPGAPGGSGSQPNQK 758.36 > 641.32 (y14) 758.36 > 1056.51 (y11) 758.36 > 460.22 (b5)

300

-100

D

7500

5500

3500

1500

ZYX: FSPGAPGGSGSQPNQK[13C615N2]

3500

762.37 > 645.32 (y14) 762.37 > 1064.52 (y11) 762.37 > 460.22 (b5)

1500

-500 12.7

7500

5500

Intensity

Intensity

1 2 3 4 5 Figure 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

-500

min

17.7

12.7

min

17.7



4

A

B2.5E+5

ZYX: SPGAPGPLTLK

C

CLU: ELDESLQVAER

1.0E+4

5.0E+3

Normalized Intensity



ZYX: SPGAPGPLTLK

1.5E+4

1.5E+4

1.5E+5

P=1.06E-14

1.0E+4

5.0E+3

P=0.785 0

5.0E+4

0

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NSCLC

Control

Control

NSCLC

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II

III

IV

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D

E 300

F 1.0

ZYX: ELISA

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Sensitivity

1.5E+4 200

ng/mL

ZYX: SPGAPGPLTLK

1 2 3 4 5 Figure 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

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1.0E+4

0.6

0.4

100 5.0E+3

0.2

AUC: 0.958 R2 = 0.963 0

P=1.46E-07

ZYX: SPGAPGPLTLK

0.0

0

0

2.0E+3

4.0E+3

ZYX: VSSGYVPPPVATPFSSK

6.0E+3

Control

1.0

0.8

0.6

0.4

NSCLC

Specificity


0.2

0.0

Predictive biomarker candidates for the response of gastric cancer to targeted and cytotoxic agents.

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Targeted proteomics pipeline reveals potential biomarkers for the diagnosis of metastatic lung cancer in pleural effusion.

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Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer.

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