Targeted In-Gel MRM: A Hypothesis Driven Approach for Colorectal Cancer Biomarker Discovery in Human Feces Ching-Seng Ang†,‡ and Edouard C. Nice*,†,‡,§ Ludwig Institute for Cancer Research, Melbourne Tumour Biology Branch, The University of Melbourne, and Department of Biochemistry, Monash University, Melbourne, Australia Received January 18, 2010
Colorectal cancer (CRC) is the second most common cause of cancer-related deaths in both men and women. The fecal occult blood test is currently the first line method for CRC screening but has an unacceptably low sensitivity and specificity. Improved screening tests are therefore urgently required for early stage CRC screening. We have described a hypothesis-driven approach for a rapid biomarker discovery process whereby selected proteins previously implicated as colorectal cancer-associated proteins (CCAP), which can potentially be shed into the feces from a colorectal tumor, are targeted for excision from 1D-SDS-PAGE based on their predicted molecular weight followed by directed identification and relative quantification using multiple reaction monitoring (MRM). This approach can significantly reduce the time for clinical assay development with the added advantage that many proteins will have been validated by previous in vitro and/or in vivo studies. Sixty potential CCAPs were selected from the literature and appropriate MRM conditions were established for measurement of proteotypic peptides. Nineteen of these proteins were detected in the feces from a patient with colorectal cancer. Relative quantitation of these 19 CCAP across 5 CRC patients and 5 healthy volunteers were carried out, revealing hemoglobin, myeloperoxidase, S100A9, filamin A and L-plastin to be present only in the feces of CRC patients. Keywords: MRM • biomarkers • colorectal cancer • fecal proteomics • 18O labeling • comparative proteomics
Introduction
invasive screening tests for early stage colon cancer when therapy is most likely to be effective.
Colorectal cancer (CRC) is the second most common internal cancer in both men and women with an annual worldwide incidence of approximately 1 million cases and an annual mortality of around 655 000.1 Unfortunately, 30-50% of patients have metastases at presentation,2 when prognosis is very poor with a 5-year survival of less than 10%. By comparison, if the tumor is detected early while still localized (Dukes Stage A), 5-year survival following surgical resection of the tumor is greater than 90%.2 Early methods of detection are therefore urgently required. Current screening methods include the fecal occult blood test (FOBT), flexible sigmoidoscopy and colonoscopy. Immuno FOBT, which uses antibody-based methods to screen for the presence of blood in stool samples, is cheap and noninvasive but has an unacceptable low sensitivity and specificity of (52.6% and 87.2%, respectively).3 New biomarkers, or panels of biomarkers, are therefore urgently required to develop more sensitive, reliable and specific non- or minimally
A frequently used method of biomarker discovery involves comparison of samples from diseased patients and normal controls with the identification of proteins that are either upor down-regulated in the disease situation using massspectrometric-based techniques (comparative proteomics).4 This usually involves labeling one of the sample sets with a stable isotope (eg 2D DIGE, ICAT, ITRAQ or 18O5-9) and comparing the intensities of the resultant peptide signatures. Alternatively label-free quantitation such as spectral counting and multiple reaction monitoring (MRM) based relative/ absolute quantitation can be used.10-12 MRM, also known as selective reaction monitoring (SRM), which uses a triple quadrupole mass spectrometer (QQQ) to monitor both intact peptide mass and one or more specific fragment ions of that peptide over the course of an LC-MS experiment, is rapidly becoming the method of choice for quantitative and/or directed analysis of proteins and peptides.12-15 Instead of scanning over a large range of nonrelevant masses, QQQ spectrometers can be programmed to scan specific preset mass-charge (m/z) ratios, thus offering a high duty cycle facilitating identification and quantification of low-level components in complex biological samples such as plasma, urine or feces. The combination of chromatographic retention time, peptide mass, and characteristic fragment mass eliminates ambiguities in peptide identification resulting in a quantification range of 4-5 orders
* Corresponding author Prof. Edouard Nice, Head, Clinical Biomarker Discovery and Validation, Dept. of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria 3800, Australia. E-mail: ed.nice@ med.monash.edu.au. † Ludwig Institute for Cancer Research, Melbourne Tumour Biology Branch. ‡ The University of Melbourne. § Monash University.
4346 Journal of Proteome Research 2010, 9, 4346–4355 Published on Web 07/24/2010
10.1021/pr100509e
2010 American Chemical Society
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Targeted In-Gel MRM for Biomarker Discovery 16
of magnitude. The sensitivity for direct analysis is typically low nanogram/milliliter which can be extended to the picogram/milliliter range by the use of simple chromatographic enrichment techniques.13,17 The use of fecal samples offers a number of significant advantages for CRC biomarker detection and analysis as collection is noninvasive, requires no unpleasant bowel preparation, and can be undertaken at home with a simple collection kit without the need for trained staff or a formal health care visit. Unlike sigmoidoscopy, the contents reflect the full length of the colorectum18 and may contain tumor-related proteins and peptides present due to leakage, secretion or exfoliation.18 Importantly potential biomarkers may be present at relatively elevated levels in feces compared with serum.19 While the potential for using fecal proteomics to identify cancer biomarkers has been recognized for some time18,20,21 there are relatively few relevant publications to date. Recently a largescale shotgun metaproteomics approach designed to identify microbial proteins in the human gut also identified a number of human proteins present due to contamination of the bacterial cell fraction which had been generated by differential centrifugation.22 We have previously demonstrated the applicability of fecal proteomics for the identification of colon cancer-associated proteins and peptides (CCAP) in murine fecal samples using the C57BL/ApcMin+ mouse model of colorectal cancer.23 In that study, 115 proteins of murine origin were identified with 75% of the identified proteins predicted to be extracellular or associated with the plasma membrane, a useful attribute of a potential CRC biomarker.23 The success of the murine fecal proteomics studies promoted us to investigate a hypothesis-driven approach for a rapid biomarker discovery process whereby selected proteins potentially present in fecal samples, and previously implicated as colorectal cancer associated proteins (CCAP) from published literature, were targeted for excision based on their predicted molecular weight on SDS PAGE with subsequent identification and relative quantification using MRM. Compared to traditional comparative proteomics approaches where the entire sample has to be compared, this approach has the advantage of both prefractionation of the complex sample and specific directed targeting of only a small subset of proteins of interest. Selection of specific proteins that have previously been proposed as biomarkers can significantly decrease the time to qualify a large number of biomarkers with the added advantage that many of those would have already been validated by previous in vitro and in vivo studies. We have investigated the potential of this hypothesis-driven approach by demonstrating recovery and identification of proteins from 1D-SDS-PAGE at the anticipated molecular weight with linearity of the response in a QQQ MS using known concentrations of a protein standard spiked into human fecal extracts. 60 potential colorectal cancer associated proteins were selected from the literature and appropriate MRM conditions established for related proteotypic peptides. Using this approach in an initial study, 19 of these proteins were detected in a fecal sample from a patient with colorectal cancer following separation on 1D-SDS-PAGE including CCAPs such as protocadherin 24, carcinoembryonic antigen 6, S100 calcium binding proteins (S100A6, A8 and A9), profilin-1, L-plastin and selenium binding protein. Relative MRM quantitation of these 19 colon cancer associated proteins was then undertaken using fecal samples from 5 CRC patients and 5 healthy individuals. Myeloperoxidase, hemoglobin, protein S100A9, filamin A and L-plastin were found
to be present only in the feces of the CRC patient suggesting them to be potentially useful markers for further large scale investigations. The results for three of these proteins, hemoglobin, protocadherin-24 and myeloperoxidase, were validated using 18O generated internal standards showing the applicability of this rapid quantitation approach.
Materials and Methods Collection of Human Fecal Samples. This study was approved by the Institutional Human Research Committee of the Royal Melbourne Hospital and informed consent was obtained from the participants. Samples were obtained from colorectal cancer patients following diagnosis at colonoscopy and prior to surgical resection of their tumors or from volunteers who had recently undergone colonoscopy and had no evidence of disease. Two of the patients were Dukes stage A (CRC patients 1 and 5), 1 patient was Dukes Stage C (CRC patient 2) and 2 patients were Dukes Stage D (CRC patients 3 and 4). Samples were collected under SOP as follows: patients or volunteers were instructed to empty their bladder, and then flush the toilet. A cellulose biodegradable sample collection sheet (Bayer, Pymble, NSW, Australia) was then placed in the toilet bowl to retain the stool sample. Once the stool was collected, an aliquot (approximately 10 g) was recovered using a plastic spatula and placed into a 120 mL sample collection tube (Techno-Plas Pty Ltd., St Marys, S. Australia), which was immediately dispatched to the laboratory. Samples were received within 2 h of defecation and stored at -70 °C prior to proteomic analysis. Extraction of Fecal Samples. Small aliquots (100-200 mg) were carefully removed from the frozen fecal samples using a scalpel, placed in 1.5 mL Eppendorf tubes and manually disrupted using 5 volumes (w/v) of 0.15% (v/v) trifluoracetic acid (TFA) using a polypropylene tissue grinder (Bel-Art product, Danbury, CT) designed to fit the contours of the Eppendorf tube. Samples were centrifuged at 13 000 rpm at 4 °C in a Heraeus Fresco Biofuge (Heraeus Instruments, Hanau, Germany) and the soluble supernatant removed for subsequent analysis. 1D-SDS PAGE and In-Gel Digestion. Proteins (equivalent to 3 mg wet fecal sample) were separated on precast NuPAGE 12% gels (Invitrogen) and stained overnight with Commassie Brilliant Blue G-250 (Sigma). Eleven individual 6 mm bands were then excised along the length of the gel. Destaining of the excised bands was carried out three times with a solution of 50% acetonitrile and 25 mM ammonium bicarbonate (ABC) buffer followed by dehydration with 100% acetonitrile. Reduction was carried out by incubating the dehydrated gel cubes with 10 mM DTT in 25 mM ABC buffer for 60 min at 56 °C. The reduction solution was then replaced with 55 mM of iodoacetamide in 25 mM ABC, and the sample was incubated for 45 min in the dark. The gel samples were then washed twice in 50 mM ABC and dehydrated with 100% acetonitrile. Modified sequencing-grade trypsin (Promega) at a concentration of 5 µg/mL in 25 mM ABC buffer was added and digestion was carried out overnight at 37 °C and terminated by addition of TFA to a final concentration of 0.1% v/v. The supernatant was then transferred to a protein LowBind tube (Eppendorf). The gel was then re-extracted with 50 µL of 50% acetonitrile in 0.1% TFA followed by sonication using a 80W sonicator (Bransonic, Pequannock, NJ) for 5 min. The process was repeated and all supernatants were pooled and lyophilized using a freeze-dryer (Thermo Savant). Comparative Analysis of CRC and Normal Fecal Extracts. Fifteen microliter aliquots of extracted fecal samples from 5 CRC patients and 5 normal volunteers were separated using Journal of Proteome Research • Vol. 9, No. 9, 2010 4347
research articles 1D-SDS PAGE, and the bands were excised and reduced with DTT, alkylated with iodoacetamide and digested with trypsin as described above. The freeze-dried samples were then rehydrated in 30 µL buffer containing 50 fmol/µL yeast enolase digest (Waters MassPREEP) made up in 3% acetonitrile/0.1% TFA. 18 O Labeling. 18O labeling was carried out as described previously.9 Briefly, the two gel lanes were processed as described above followed by reduction and alkylation. Following alkylation the gel cubes were washed 3 times in ABC buffer, followed by dehydration twice in 100% ACN for 10 min. The gel cubes were then further dried under centrifugation for 90 min using a Speedvac (Thermo Savant). Digestion was carried out in 60 µL solution per gel section containing 2 µg of sequence grade modified trypsin (Promega) and 1/2 strength ABC buffer made up in either H216O or H218O (H218O, >97% purity, Sigma) for 20 h at 37 °C. After digestion, the peptides were twice extracted from the gel using a solution of 50% acetonitrile/0.1% TFA in their respective water (H216O/H218O), with sonication. The pooled extract was boiled for 5 min to inactivate the trypsin followed by freeze-drying for 48 h. The freeze-dried samples were then resuspended in 20 µL of 3% acetonitrile/0.1% TFA in their respective water (H216O/H218O) and mixed immediately prior to analysis. MRM Analysis. MRM was carried out on an Agilent 6410 triple quadrupole mass spectrometer coupled to a 1200 nanoflow HPLC-ChipMS system (Agilent). Separation was achieved using an Agilent HPLC protein chip (C18 Zorbax, 300 Å, 75 µm × 43 mm column) and eluted in 0.1% formic acid using the following acetonitrile gradient: 0-2 min (3%), 2-45 min (3-30%), 45-50 min (30-50%), 50-55 min (50-80%). The flow rate was 300 nL/min. Sample injection volume was 1 µL. Drying gas for the mass spectrometer was set at a flow rate of 4 L/min at 300 °C. Ultrahigh-purity nitrogen was used as the nebulizing gas (40 psi) and the capillary voltage was set at 1850 V. The Q1 and Q3 quadrupoles were set at unit resolution (0.7 fwhm). The starting point for collision energies for each transition were based on theoretical values calculated from the equation CE ) 0.036 * (m/z) - 4.8 for (M + 2H+) ions prior to optimization using PeptideOptimizer (Agilent). Blanks were included between injections to ensure no cross contamination between samples Data analysis was carried out using the Agilent Mass Hunter Quantitative Analysis software (version B.01.04 build 1.4.126.0) using the parameterless integrator with a 15 point Gaussian smoothing. Peptides which were unique to the protein of interest were selected for the MRM assays. The MRM transitions were derived from the MS/MS data of selected peptides using y-series ions selected for their relative high signal intensities (Supplementary Table 1, Supporting Information). Synthetic peptides corresponding to the proteotypic peptides selected for MRM analysis were purchased from JPT Peptide Technologies GmbH, Berlin, Germany for validation. Up to 8 separate transition ions were monitored for each peptide precursor to increase the confidence of identification, taken together with the characteristic nanoHPLC retention times of the transition ions and their relative product ion intensities.
Results and Discussion The pipeline for our hypothesis driven approach is illustrated in Figure 1. The goal of this investigation was to see whether potential CRC biomarkers could be detected in fecal samples based on their molecular weight on 1D-SDS-PAGE and their MRM-MS characteristics. To show proof of principal 60 CCAPs 4348
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Figure 1. Pipeline for targeted MRM analysis.
(Supplementary Table 2, Supporting Information) were chosen from the literature using the following criteria (i) They had previously been shown to be present in feces and/or (ii) they were elevated in serum of CRC patients and (iii) they were shown to be present in colon and intestinal tissues or cell lines and (iv) they were proposed as potential cancer biomarkers in the past 10 years. In an initial experiment, a human fecal extract from a patient who had been diagnosed with CRC at colonoscopy was prefractionated on a 1D SDS-PAGE gel to resolve the dynamic range of proteins present in the complex biological sample. The use of the ionic detergent SDS helps maintain solubility during separation.24 The expected molecular weights of the selected proteins were then recorded and matched to the anticipated migration distance on the gel based on linear regression analysis of the protein standards. Gel bands from the appropriate region were excised and subjected to in-gel tryptic digestion. Potential proteotypic peptides for the MRM assays were derived from existing publically available databases (GPMdb,25 PeptideAtlas,26 MRMaid27), in house databases or predicted using PeptideSieve28 and PeptideSelector (Agilent Spectrum Mill); the latter concurrently checks for uniqueness of the peptide with the SwissProt database. Once a list of proteotypic peptides had been identified, the associated fragment ions were selected based on prediction of the relative signal intensities using information derived from GPMdb, MRMAtlas, PeptideSelector and Zhang Kinetic model.29 Additionally empirical rules for peptide fragmentation including the “mobile proton” hypothesis together with peptide composition and peptide conformation30-32 were taken into account. An initial MRM screen of the 60 selected CCAPs was made to check for their detectability in the fecal sample together with their characteristic retention time on nanoHPLC and peptide suitability (interference ions, S/N, fragment ion intensities etc) of the proteotypic peptides. Validation of detected proteins was achieved using the corresponding proteotypic synthetic peptides which showed equivalence based on peptide elution time and relative intensities of the multiple product ions33 (Supplementary Table 1, Supporting Information). Candidate peptides were then subjected to an additional round of optimization using PeptideOptimizer. Here the collision energy for each peptide was optimized during chromatographic separation by applying at least 5 different collision energies for each fragment
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Figure 2. Targeted in-gel MRM. (A) Trypsin Inhibitor (TI) standards (234-3764 ng). (B) Fecal extract of CRC patient to which TI has been added as indicated. The box shows the region taken for targeted in-gel MRM. (C) MRM analysis was performed on the excised region of the gel for the proteotypic peptide GIGTIISSPYR. b, recovery of TI standard from gel (ex panel A); [, recovery of TI standard spiked in fecal extract of a CRC patient (panel B, boxed region). Error bars represents standard deviation (n ) 3).
ion during a single MRM experiment. This eliminated the need for infusion or flow injection analysis methods that are both time and sample consuming and which can be complicated by ion suppression and adduct formation in highly complex biological samples. Following optimization, the dwell time for all transitions was programmed to ensure a minimum of 15 data points across a peak. Recovery of a Protein Standard Using 1D-SDS-PAGE-Based Targeted Quantitation. The recovery and linearity of response for proteins recovered from 1D-SDS PAGE at their anticipated molecular weight followed by MRM analysis was investigated using a protein standard (Trypsin Inhibitor from Glycine max). A 2-fold dilution series ranging from 3744 to 234 ng was constructed (Figure 2A). The lowest protein amount selected was similar to the levels of calprotectin found in fecal samples from CRC patients (180-680 ng/mg).35 In our fecal assays (see Materials and Methods), we are routinely loading equivalent to 3 mg feces per assay. The region of the SDS-PAGE gel ((3 mm) corresponding to the apparent molecular weight of trypsin inhibitor (∼24 KDa) was then excised and in-gel digested as described in Materials and Methods. Two peptides, GIGTIISSPYR (582.2 f 609.3/ 722.4/835.5) and VSDDEFNNYK (615.7 f 814.4/929.4/1044.4) were selected for MRM analysis based on the presence of Pro, Gly, Asp and Glu residues that should result in preferential cleavage of the peptide bonds, resulting in intense fragment ions for quantitation.30,31 Following resuspension of the tryptic peptides and nanoLC-MRM analysis, an excellent linear response was observed for both peptides (GIGTIISSPYR (R2 ) 0.999, n ) 3, (Figure 2C)) and VSDDEFNNYK (R2 ) 0.997, n ) 3, data not shown)). Recovery of a Protein Standard in Complex Sample Matrix Using 1D-SDS-PAGE-Based Targeted Quantitation. To show that the method can be applied to a complex sample matrix, a similar dilution series was constructed in a 0.15%TFA fecal extract from a CRC patient obtained postcolonoscopy and prior to resection of the tumor (Figure 2B). Trypsin Inhibitor from Glycine max was chosen as the test protein because it is unlikely
to be present in human samples. The appropriate region was excised from the SDS-PAGE gel, extracted, digested and analyzed by nanoLC-MRM using the same peptides and peptide transitions described above. In agreement with the data generated following recovery of the pure protein from SDS-PAGE, a similar linear response for both peptides GIGTIISSPYR (R2 ) 0.988, n ) 3. Figure 2C) and VSDDEFNNYK (R2 ) 0.993, n ) 3, data not shown) was observed with comparable signal intensities indicating minimal ion suppression or loss in peptide extraction efficiency even in the complex biological sample. It should be noted that, based on the measured abundance of ∼2 * 106 for the Q3 ion (Figure 2C), this methodology has the potential to detect proteins by at least 2 orders of magnitude lower based on the results in the following sections. Targeted Identification of CCAPs in a Fecal Sample from a CRC Patient. In a preliminary experiment, the fecal extract from a CRC patient (patient 4, Dukes Stage D) was extracted and separated by 1D-SDS PAGE gel as described in Materials and Methods. The gel lane was evenly divided into 11 sections (corresponding to 6 mm slices) using a custommade stencil (Figure 3A). The MRM method was programmed to screen for the 60 selected CCAPs in the SDS PAGE fractions, maintaining a minimum of 15 data points for each peptide. To investigate potential degradation of the proteins in feces due to the proteolytic environment,35 fractions from the gel slice corresponding to the anticipated molecular weight (( 3 mm) to the bottom of the gel were also screened (for example if myeloperoxidase (expected MW of 83869) is expected to be present in fraction 6 of the gel, the MRM assay was carried out from fractions 6 to 11). From the starting list of the 60 proteins previously proposed to be CRC biomarkers based on their reported altered expression in serum, cancer tissues or feces, we were able to unambiguously identify 19 of them in this fecal extract (Figure 3B, Supplementary Table 1). These proteins were identified from a minimum of 5 transitions, clear differentiation from noise (S/N > 10) and absence of any interfering peaks. The identity was confirmed using the corresponding synthetic peptides Journal of Proteome Research • Vol. 9, No. 9, 2010 4349
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Figure 3. Targeted in-gel MRM of 20 proteins in a fecal extract. (A) 1D-SDS PAGE gel from a CRC patient. Each gel was sectioned into 11 fractions as indicated for directed MRM-analysis. (B) Proteins identified in each fraction from the CRC sample. (C) Corresponding lanes from 5 CRC patients and 5 normal volunteers were taken for directed comparative analysis (see Table 1).
which showed similar peptide elution time and relative intensities of the multiple product ions (Supplementary Table 1, Supporting Information). Of the 19 proteins identified, only 3 (DPPIV, Filamin C and SBP1) appeared to be unstable in the feces as evidenced by their presence in multiple gel fractions at lower apparent molecular weight than predicted from their composition. Some of the detected CCAPs (S100A8, S100A9, hemoglobin and CEA) have been previously shown to be resistant to degradation based on their stability in stool samples even after up to 3 day’s storage at room temperature.35,36 The identification of CCAPs that are resistant to degradation is a useful criterion for the validation of these proteins as potential fecal biomarkers. It is interesting to note that 10 of these proteins: L-plastin, filamin A. filamin C, cathepsin S, hemoglobin alpha, hemoglobin beta, S100A6, profilin 1, selenium binding protein and Lysosome-associated membrane glycoprotein 2 were not identified in the data of Verberkmoes et al22 who used a shot gun proteomics approach on a LTQ-Orbitrap MS to identify human distal gut microbiota in bacterial cells isolated from fecal samples from two healthy female twin volunteers, but who also identified more than 500 human proteins presumably present due to contamination of their bacterial cell fractions. This might reflect both differences between normal and diseased samples 4350
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as well as differences between a discovery based approach and directed targeting of CCAPs. Targeted MRM Analysis for the Relative Quantitation of CCAPs in Fecal Extracts from 5 CRC Patients and 5 Normal Volunteers. We next extended these studies, using fecal extracts from 5 CRC patients and 5 normal volunteers, to demonstrate the potential of targeted MRM for the relative quantitation of CCAPs in clinical samples (Table 1). Fecal extracts from the CRC patients and normal volunteers were loaded onto a 1D-SDS-PAGE gel for direct comparison (Figure 3C). Sample loads were normalized to the fecal wet weight (3 mg/sample). Visual analysis of the individual bands revealed significant differences in the SDS-PAGE profiles from the different cancer and normal samples. In the case of human stool samples, as we have shown in our study on mouse fecal proteomics,23 the sample will include a number of proteins from the microbiota. These are known to be highly varied between individuals.37 However, these proteins are not individually present in high abundance and do not interfere with the detection of the CCAP. Directed multiplexed MRM comparison was performed across the 10 samples on gel slices corresponding to the anticipated molecular weight of the proteins of interest determined from the initial targeted approach (see Figure 3B). The optimized MRM transitions derived for the 16 CCAPs (Supplementary Table 1, Supporting Information) that had been shown
protein
Uni-Prot acc no
1
P27487
2
3 +
4 +
5 +
1 +
2
P68871
+
+
+
+
+
+
4 +
5
P80188
P12273
Q9BYE9 P05109
P06702
Neutrophil gelatinaseassociated lipocalin
Prolactin-inducible protein
Protocadherin 24 Protein S100A8
Protein S100A9
+
+
+ +
+
+
+
+
+ +
+
+
+
+
+ +
+
+
+
+
+
+
+
+
+
Q14315
P06703
Filamin C
Protein S100A6
+
+
+
+
Potential CCAPs based on literature and not previously detected in fecal samples Cathepsin S P25774 + + Filamin A P21333 + + + + +
P05164
Myeloperoxidase
+
+
+
+
+
+
+
4133-97024
range CRCb 2004-29491
range normb
8522 1183-20283
3965-454814
5105 Not detected
Not detected
2890-6936 3902
Not detected
Not detected 1951-5248
Not detected
LQDAEIAR
11438-353134
64851
Not compared due to protein degradation
GIDSDASYPYK YGGDEIPFSPYR
DLQNFLK
10645 3407 7647-18001 9301-424318
1972-372262 2333-11961
IANVFTNAFR TFVPGCQPGEFTLGNIK
ELGICPDDAAVIPIK YTACLCDDNPK EFYSASVAEDAAK ALNSIIDVYHK
1427-207796
VVLEGGIDPILR
1854 -3571
Not detected Not detected Not detected
75079-9.71 × 107 59662-1.14 × 108 86337-1.37 × 108
VGAHAGEYGAEALER EFTPPVQAAYQK VNVDEVGGEALGR 2964-10555
Not detected
14180-1.14 × 108
MFLSFPTTK
ILGLTDSVTEVR 2620-68212 2124-23939 Not compared due to protein degradation
VAQNPFSIQVIR
peptide
Potential CCAPs based on literature and have also been previously identified in shotgun proteomics experiment CEA6 P40199 + + + + + + + + EVLLLAHNLPQNR
Hemoglobin Beta
+
3
norm volunteera
Proteins expected to be present in feces of CRC sample based on positive FOBT Hemoglobin alpha P69905 + + + + +
Dipeptidyl peptidase 4
Colon associated proteins (brush border enzymes) Sucrase-isomaltase, P14410 + intestinal
CRC patienta,c comments
Filamins are one of the actin-binding proteins which stabilize the three-dimensional actin filaments, linking them to cellular membranes, thus playing an important role in effecting changes in the actin cytoskeleton.58 Filamin A has been implicated together with CEA in tumor cell migration40 Elevated S100-A6 has been implicated in CRC59
Fecal concentrations of S100A8/9 have been found to be elevated in patients with colorectal neoplasia55 The high levels of fecal calprotectin seen in patients with CRC are likely to be due to polymorphonuclear cell infiltration of the tumor followed by shedding into the intestinal lumen56 which it a potential candidate as a stool biomarker.50,57
Neutrophil gelatinase-associated lipocalin has a role in regulating cellular growth and is highly upregulated in a large number of cancer cells with expression suggested to be a suppressor of metastasis52-54
Blood-based CEA assays are currently in routine clinical use to monitor CRC recurrence. CEA has been shown by ELISA to be elevated in faecal samples from patients with CRC compared to controls or patients with benign gastrointestinal disorders.19 CEA6 has been shown by IHC to be elevated in colorectal cancer49,50 Myeloperoxidase, an enzyme present in the lysosomes of neutrophils. is useful in the evaluation of the presence and degree of inflammation in normal mucusa and dysplastic crypts as indicators of colorectal cancer risk51
ImmunoFOBT (faecal occult blood test), which measures human hemoglobin in faecal samples, is currently used as the first line diagnosis in CRC18,48
Table 1. Potential CCAPs Identified Following Targeted In-Gel MRM with Relative Quantitation between Feces from 5 CRC Patients and 5 Normal Volunteers
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+ + +
+
+ +
P07739 Profilin-1
+ P13473 Lysosome-associated membrane glycoprotein 2
+
+ + P13796 L-plastin
+
+
+
5 4 3 2 Q13228
1 Uni-Prot acc no protein
Selenium-binding protein 1
a + indicates peptide has been identified in sample. b CRC patient 1- Dukes Stage A; CRC patient 2 - Dukes Stage C; CRC patient 3- Dukes Stage D; CRC patient 4 - Dukes Stage D; CRC patient 5- Dukes Stage A. c Range represents the normalized average intensities of Q3 used for quantitation (Table 1 highlighted in bold, n ) 3) (Supplementary Table 3, Supporting Information).
SSFYVNGLTLGGQK
22309
1197-18772 6253-20228 CNSLSTLEK + +
5 4 3 2 1
norm volunteera CRC patienta,c
Table 1. Continued 4352
Ang and Nice
Expression of L-plastin is implicated in cancer invasion and proliferation41 and have been shown to correlate with cancer progression with moderate to strong expression seen in stage III and IV tumor tissues60 LAMP2 was expressed more intensively in the epithelium of colorectal neoplasms, than in normal mucosa61 with highly metastatic colon cancer cells expressing more LAMP2 on the cell surface. Not detected 2614-49156 QFVTATDVVR
range normb peptide
range CRCb
Not compared due to protein degradation
comments
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Journal of Proteome Research • Vol. 9, No. 9, 2010
in the preliminary experiment to be stable and present in a unique gel slice were used (Figure 3B, Table 1). For relative quantitation, three fragment ions were selected for each peptide, and the most intense ion was used for quantitation (highlighted in boldsSupplementary Table 1, Supporting Information). These experiments were performed on 3 replicate samples (Supplementary Table 3, Supporting Information). Blanks were included between runs to minimize cross contamination. As an additional control, the freeze-dried tryptic peptides were resuspended in buffer containing 50 fmol/µL yeast enolase digest to allow for data normalization for any potential run-to-run variation from sample loading or machine response. There was excellent chromatographic retention time reproducibility between the cancer and normal samples and absence of any major interfering peaks, facilitating accurate relative quantitation between the two samples as calculated by the ratio of their abundance (Supplementary Table 3, Supporting Information). Out of the 16 CCAP that were selected for relative quantitation across the 5 CRC and 5 normal samples, myeloperoxidase, hemoglobin, protein S100A9, filamin A and L-plastin were found to be present only in the feces of CRC patients. As expected from its use in the standard FOBT screening, hemoglobin was only identified in the feces from the CRC patients. All 5 CRC patients had been found to be FOBT positive using the immunoFOBT assay, further validating this hypothesis driven methodology. Protein S100A9 and myeloperoxidase (both neutrophil derived proteins) have been used previously as a stool based marker for intestinal inflammatory and damage.36,38 The identification of these inflammatory related proteins is also consistent with studies implicating long-term risk of colorectal cancer with intestinal inflammatory manifestations.39 To evaluate the potential of these inflammatory markers as part of a CRC diagnostic biomarker panel, comparative studies including group of patients with benign colon diseases will have to be carried out. Although the utility of the inflammatory proteins as CRC markers requires further confirmation, the presence of high levels of inflammatory proteins in feces would nevertheless warrant further colonoscopic investigation to identify other underlying pathologies. Interestingly two other proteins detected in our assay, filamin A and L-plastin (actin binding proteins) have both been implicated in tumor cell migration.40,41 These proteins, to the best of our knowledge, have not been previously been identified in feces and it is therefore very tempting to speculate that these two actin binding proteins could be shed into the intestinal lumen at much higher level during tumor growth or metastasis and could be a potential marker for CRC. However, more studies using much larger sample populations are required to test this hypothesis and also the utility of the current combined panel of five fecal markers for the discrimination of colorectal cancer. 18 O Labeling to Validate the Direct LC-MRM Approach. 18 O labeling was used to investigate both run to run variation and also confirm the relative quantitation obtained from the MRM analysis. 18O labeling methodology is a mass labeling technique whereby global internal standards are generated.7 During 18O labeling, all peptides (except the C-terminal peptide of the protein) in the presence of H218O and trypsin will exchange the C-terminal 16O with 18O resulting in a 4 Da mass increase while maintaining a similar HPLC retention time. In an initial experiment using MRM to determine the percentage incorporation during 18O labeling, no residual unlabeled peptides were detected. This is consistent with earlier reported data
Targeted In-Gel MRM for Biomarker Discovery
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Figure 4. Comparison of directed and 18O-labeled MRM quantitation made on fecal samples from a CRC patient (patient 4) and a normal volunteer (volunteer 3). (A) Hemoglobin Beta peptide VNVDEVGGEALGR. (B) Myeloperoxidase peptide IANVFTNAFR (C): Protocadherin 24 peptide EFYSASVAEDAAK (Panels (I) EIC for MRM transitions, (II) direct LC-MRM comparison, (III) 18O-labeled comparison, (IV) MS/MS spectrum of transitions selected for 18O-labeled peptides, showing the additional 4 Da mass increase for all y-series ions and (V) MS/MS spectrum of the endogenous peptide).
on the efficient and complete incorporation of 18O onto the C-terminus of tryptic peptides with high purity H218O (>97%) and complete drying of gel pieces prior to digestion and high concentrations of trypsin are used to drive the reaction.9,42 Tryptic peptides from both the CRC and the normal samples are mixed together (CRC samples labeled with 18O) immediately prior to injection. Three of the CCAP that had been identified by the targeted MRM approach (Hemoglobin Beta, Protocadherin 24 and Myeloperoxidase) were chosen for this study and compared for CRC patient 4 and normal volunteer 3. Similar elution times but different precursor (+2 Da for [M + 2H]2+) and fragment masses (+4 Da for all y-series ions) were observed for the CRC samples compared to the normal control (Figure 4). Good agreement was also seen when comparing the relative ratios obtained with the 18O labeling compared with the direct LC-MRM approach (Figure 4 panels (ii) and (iii)). In the case of hemoglobin and myeloperoxidase, only the 18O labeled samples from the CRC patient gave a positive signal in agreement with the results of the direct LC-MRM approach.
response from the tumor, we were able to identify 19 of these proteins (32.2%) in a fecal sample from a CRC patient without performing any prior global profiling experiments,. The other proteins may, of course, not be present at detectable levels in the samples analyzed, or may not run at their anticipated molecular weight due to either post-translational modification or sample stability. Relative quantitation, using MRM analysis, between fecal samples from 5 CRC patients and 5 normal volunteers was performed on 16 of these proteins which had been shown to be stable in fecal samples. The relative quantitation was further confirmed on three of these CCAPs using 18 O-labeling methodology. Interestingly, we observed 5 proteins to be present at significant levels only in the feces of CRC patients showing the potential of this hypothesis-driven method for identifying potential biomarkers prior to using the same MRM technology for subsequent validation using much larger patient numbers to reveal whether the selected proteins have improved sensitivity and/or selectivity to those currently in clinical use (e.g., FOBT).13
Conclusion
The data presented herein have shown the potential for a hypothesis-driven approach for the rapid identification and relative quantitation of CCAPs in the feces of CRC patients. This directed process could easily be applied to other biological fluids such as serum, urine, semen, etc. or other disease states. This hypothesis-driven approach could also be adapted for other biological studies including pathway analysis, quantification of immuno-affinity enriched proteins, degredomics or analysis of protein complexes.
The limited specificity of many ELISAs, and in many cases limited antibody availability for assay development and validation, coupled with recent advances in MS technology, is stimulating the development of quantitative multiple reaction monitoring (MRM) MS technologies for protein and peptide analysis.43-45 These assays, which are specific, quantitative, rapid to develop and readily multiplexed, are gaining widespread use for the discovery and validation of new diagnostic products.13,46,47 In our study, from an initial list of 60 potential marker proteins selected from the literature that could potentially be shed into the feces or result from a pathological
Acknowledgment. C.S.A. and E.C.N. were supported, in part, by research grant 433620 from the Cancer Council of Victoria and a grant from the Department of Innovation, Journal of Proteome Research • Vol. 9, No. 9, 2010 4353
research articles Industry and Regional Development, Australia. We are also grateful for the assistance of Michael Harold with the clinical sample collection.
Supporting Information Available: Supplementary Table 1, Potential CCAPs identified following targeted in-gel MRM. The signature peptides, peptide mass (Q1, m/z), fragment mass (Q3, m/z) and optimized collision energies (CE) unique to the protein are shown. Extracted ion chromatograms showing retention time and fragmentation spectrum data for endogenous and synthetic peptides are also shown. Supplementary Table 2: List of 60 proteins selected for targeted ingel MRM analysis. Supplementary Table 3: Relative comparison of proteins from 5 CRC and 5 normal feces. This material is available free of charge via the Internet at http://pubs.acs.org. References (1) Jemal, A.; Siegel, R.; Ward, E.; Hao, Y.; Xu, J.; Murray, T.; Thun, M. J. Cancer statistics, 2008. CA. Cancer J. Clin. 2008, 58 (2), 71– 96. (2) Etzioni, R.; Urban, N.; Ramsey, S.; McIntosh, M.; Schwartz, S.; Reid, B.; Radich, J.; Anderson, G.; Hartwell, L. The case for early detection. Nat. Rev. Cancer 2003, 3 (4), 243–52. (3) Nakazato, M.; Yamano, H.; Matsushita, H.; Sato, K.; Fujita, K.; Yamanaka, Y.; Imai, Y. Immunologic fecal occult blood test for colorectal cancer screening. Japan Med. Assoc. J. 2006, 49 (5), 203– 207. (4) Simpson, R. J.; Bernhard, O. K.; Greening, D. W.; Moritz, R. L. Proteomics-driven cancer biomarker discovery: looking to the future. Curr. Opin. Chem. Biol. 2008, 12 (1), 72–7. (5) Tonge, R.; Shaw, J.; Middleton, B.; Rowlinson, R.; Rayner, S.; Young, J.; Pognan, F.; Hawkins, E.; Currie, I.; Davison, M. Validation and development of fluorescence two-dimensional differential gel electrophoresis proteomics technology. Proteomics 2001, 1 (3), 377–96. (6) Gygi, S. P.; Rist, B.; Gerber, S. A.; Turecek, F.; Gelb, M. H.; Aebersold, R. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat. Biotechnol. 1999, 17 (10), 994–9. (7) Yao, X.; Freas, A.; Ramirez, J.; Demirev, P. A.; Fenselau, C. Proteolytic 18O labeling for comparative proteomics: model studies with two serotypes of adenovirus. Anal. Chem. 2001, 73 (13), 2836– 42. (8) Ross, P. L.; Huang, Y. N.; Marchese, J. N.; Williamson, B.; Parker, K.; Hattan, S.; Khainovski, N.; Pillai, S.; Dey, S.; Daniels, S.; Purkayastha, S.; Juhasz, P.; Martin, S.; Bartlet-Jones, M.; He, F.; Jacobson, A.; Pappin, D. J. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol. Cell. Proteomics 2004, 3 (12), 1154–69. (9) Ang, C. S.; Veith, P. D.; Dashper, S. G.; Reynolds, E. C. Application of 16O/18O reverse proteolytic labeling to determine the effect of biofilm culture on the cell envelope proteome of Porphyromonas gingivalis W50. Proteomics 2008, 8 (8), 1645–60. (10) Pang, J. X.; Ginanni, N.; Dongre, A. R.; Hefta, S. A.; Opitek, G. J. Biomarker discovery in urine by proteomics. J. Proteome Res. 2002, 1 (2), 161–9. (11) Lehmann, U.; Wienkoop, S.; Tschoep, H.; Weckwerth, W. If the antibody fails--a mass western approach. Plant J. 2008, 55 (6), 1039–46. (12) Kuzyk, M. A.; Smith, D.; Yang, J.; Cross, T. J.; Jackson, A. M.; Hardie, D. B.; Anderson, N. L.; Borchers, C. H. MRM-based, multiplexed, absolute quantitation of 45 proteins in human plasma. Mol. Cell. Proteomics 2009, 8 (8), 1860–77. (13) Zolg, J. W.; Langen, H. How industry is approaching the search for new diagnostic markers and biomarkers. Mol. Cell. Proteomics 2004, 3 (4), 345–54. (14) Faca, V.; Krasnoselsky, A.; Hanash, S. Innovative proteomic approaches for cancer biomarker discovery. BioTechniques 2007, 43 (3), 281-3, 285. 279. (15) Anderson, L.; Hunter, C. L. Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol. Cell. Proteomics 2006, 5 (4), 573–88. (16) Picotti, P.; Rinner, O.; Stallmach, R.; Dautel, F.; Farrah, T.; Domon, B.; Wenschuh, H.; Aebersold, R. High-throughput generation of selected reaction-monitoring assays for proteins and proteomes. Nat. Methods 2010, 7 (1), 43–6.
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