Differential Proteomic Analysis of Renal Cell Carcinoma Tissue

ARTICLE pubs.acs.org/jpr

Differential Proteomic Analysis of Renal Cell Carcinoma Tissue Interstitial Fluid Pang-ning Teng,†,‡ Brian L. Hood,†,‡ Mai Sun,† Rajiv Dhir,§ and Thomas P. Conrads†,‡,* †

Departments of Pharmacology and Chemical Biology and §Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States

bS Supporting Information ABSTRACT: Renal cell carcinoma (RCC), the most common type of kidney cancer, currently has no biomarker of clinical utility. The present study utilized a mass spectrometry-based proteomics workflow for identifying differentially abundant proteins in RCC by harvesting shed and secreted proteins from the tumor microenvironment through sampling tissue interstitial fluid (TIF) from radical nephrectomies. Matched tumor and adjacent normal kidney (ANK) tissues were collected from 10 patients diagnosed with clear cell RCC. One-hundred thirty-eight proteins were identified with statistically significant differential abundances derived by spectral counting in tumor TIF when compared to ANK TIF. Among those proteins with elevated abundance in tumor TIF, nicotinamide n-methyltransferase (NNMT) and enolase 2 (ENO2) were verified by Western blot and selected reaction monitoring (SRM). The presence of ENO2 and thrombospondin-1 (TSP1) were verified as present and at elevated abundance in RCC patient serum samples as compared to a pooled standard control by enzyme-linked immunosorbent assay (ELISA), recapitulating the relative abundance increase in RCC as compared with ANK TIF. KEYWORDS: renal cell carcinoma, tissue interstitial fluid, proteomics, biomarker, cancer

’ INTRODUCTION Renal cell carcinoma (RCC) is the most common form of kidney cancer, accounting for 3% of all cancer diagnoses and more than 100 000 deaths worldwide each year.1 The majority of RCC cases present histologically as clear-cell, while the rest of the cases are of the papillary, chromophobe, oncocytoma, collecting duct, medullary, or unclassified cell type.2 RCC is typically asymptomatic and is most commonly diagnosed incidentally either by computed-tomography (CT) scans or when patients present in the clinic, most generally with symptoms that include blood in the urine and/or abdominal pain.3,4 The current gold standard of treatment for RCC is nephrectomy, however, due in large part because RCC is generally refractory to chemotherapy and radiotherapy, prognosis of patients with advanced stage or metastatic disease is poor.5 Arising from the observation that the survival rate for RCC patients decreases with increasing disease stage,6 as with many cancers, early detection of RCC would significantly improve patient diagnosis and outcome; however, there are currently no biomarkers to enable reliable screening for RCC.7-9 Most investigations aimed at identifying RCC-specific biomarkers have aimed at analyzing genes and proteins at the cellular or tissue level,10-14 although the candidate markers identified by these strategies may not be present or may not present with a distinct altered profile in serum, as would be necessary to be useful for noninvasive screening. Unfortunately, serum is an extremely complex biofluid containing a plethora of high-abundant proteins that hamper detection and differential quantification r 2010 American Chemical Society

of potential biomarkers, which for early stage malignancies in particular, are likely to be present at relatively low abundance. In addition, there is extensive heterogeneity among patients due to a variety of environmental and genetic influences making biomarker identification even more challenging.10,11 While potential protein biomarkers are more abundant in the tumor tissue microenvironment as compared to serum, a great number of these may not be secreted or shed into peripheral circulation, rendering development of a noninvasive assay for their presence impossible. One approach of increased focus in proteomics is to utilize proximal fluids, such as tissue interstitial fluid (TIF), for conducting candidate biomarker discovery. First described by Celis et al., TIF perfuses tissues and contains high loco-regional concentrations of secreted and shed proteins that, by virtue of their solubility, are likely to be in an active state of exchange between the tissue microenvironment and peripheral circulation.15 Tissue interstitial fluid is therefore hypothesized to be a rich sample from which biomarker discovery can be conducted and has been the central sample source for a number of differential proteomic analyses of mammary adipose and tumor tissue from patients diagnosed with breast cancer.16-18 In the present study, TIF was collected from ten patients diagnosed with clear cell RCC with matched adjacent normal kidney (ANK) samples from surgically derived radical nephrectomies. Received: October 25, 2010 Published: December 10, 2010 1333

dx.doi.org/10.1021/pr101074p | J. Proteome Res. 2011, 10, 1333–1342

Journal of Proteome Research These TIF samples were analyzed by liquid chromatographytandem mass spectrometry (LC-MS/MS) and relative abundance differences of proteins identified between RCC and ANK TIF samples were determined using a spectral counting approach.19 Several proteins were identified as significantly (p e 0.5) elevated in abundance in RCC TIF including nicotinamide n-methyltransferase (NNMT), thrompospondin-1 (TSP1), enolase 2 (ENO2), annexin A4, ferritin, CD14, galectin-1, and thyroxine-binding globulin (TBG). The relative abundances of a number of proteins of interest were verified in TIF by Western blot analysis and selected reaction monitoring (SRM). Of these, ENO2 and TSP1 were found to be elevated in the serum of patients diagnosed with clear cell RCC as compared to a normal reference serum sample, as determined by ELISA.

’ MATERIALS AND METHODS TIF and Serum Collection and Processing

Renal cell carcinoma and ANK kidney tissues were collected from ten patients diagnosed with clear cell RCC that underwent radical nephrectomy at the University of Pittsburgh Medical Center, with approval from the University of Pittsburgh IRB. Within 30 min from kidney resection, approximately 200 mg of RCC and ANK tissue from each kidney was dissected into 3 mm3 pieces, placed into a 6-well plate containing 5 mL of phosphate buffered saline (PBS) and washed for 5 min. Tissues were transferred to a microcentrifuge tube containing 1 mL of fresh PBS and incubated for 1 h at 37 °C to harvest the TIF. The TIF was transferred to a new microcentrifuge tube, centrifuged at 16 000 g for 15 min at 4 °C and supernatant was collected. Protein concentration was determined by BCA assay (Pierce, Rockford, IL) and samples were stored at -80 °C until further analysis. Sera were collected from four clear cell RCC patients and a pooled human serum sample (Sigma, cat no. S7023) was used as a normal sample. Abundant proteins from TIF and serum samples were immunodepleted using the Human-14 Multiple Affinity Removal System (MARS) (Agilent Technologies) according to the manufacturer’s protocol but using a 4 formulation of the Buffer A for sample dilution prior to depletion on the spin-columns. Phenylmethanesulfonylfluoride (PMSF) was added to the samples at a final concentration of 1 mM. Samples were concentrated to 150 μL, combined with 50 μL of the 4 Buffer A formulation and carried through the remainder of the depletion protocol. Depleted samples were buffer-exchanged into 25 mM ammonium bicarbonate using centrifugal ultrafiltration (3000 molecular weight cutoff) to a final volume of 500 μL and protein concentrations were determined by BCA. Gel Electrophoresis and In-Gel Digestion

Five μg of total protein were electrophoresed into the stacking portion of a NuPAGE Bis-Tris gel (Invitrogen). Gels were stained briefly with Simply Blue SafeStain (Invitrogen) and the single stacking gel band was excised and cut into 1 mm3 pieces. Gel pieces were destained in 25 mM ammonium bicarbonate/ 50% acetonitrile with agitation, dehydrated with acetonitrile and rehydrated and reduced with 10 mM DTT in 25 mM ammonium bicarbonate at 56 °C for 1 h. Samples were alkylated in 55 mM iodoacetamide, 25 mM ammonium bicarbonate in the dark at ambient temperature for 45 min, dehydrated with acetonitrile, rehydrated with sequencing grade porcine trypsin (Promega, 20 μg/mL) in 25 mM ammonium bicarbonate and digested at 37 °C

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for 16 h. Peptide digests were extracted with 70% acetonitrile and 5% formic acid and dried by vacuum centrifugation. Nanoflow Reversed Phase Liquid Chromatography-Tandem Mass Spectrometry

Peptide digests were resuspended in 0.1% TFA and analyzed in triplicate by reversed-phase liquid chromatography (RPLC)tandem mass spectrometry (MS/MS) using an Ultimate 3000 nanoflow LC (Dionex Corporation, Sunnyvale, CA) coupled online to an LTQ-XL (ThermoFisher Scientific, San Jose, CA). Separations were performed using 75 μm i.d. 360 μm o.d. 20 cm long fused silica capillary columns (Polymicro Technologies, Phoenix, AZ) slurry packed in house with 5 μm, 300 Å pore size C-18 silica-bonded stationary phase (Jupiter, Phenomenex, Torrance, CA). Following sample injection onto a C-18 precolumn (Dionex), the column was washed for 3 min with mobile phase A (2% acetonitrile, 0.1% formic acid) at a flow rate of 30 μL/min. Peptides were eluted using a linear gradient of 0.33% mobile phase B (0.1% formic acid in acetonitrile)/minute for 130 min, then to 95% B in an additional 15 min, all at a constant flow rate of 200 nL/min. Column washing was performed at 95% B for 15 min for all analyses after which the column was reequilibrated in mobile phase A prior to subsequent injections. The MS was operated in data-dependent MS/MS mode in which each full MS scan was followed by seven MS/MS scans performed in the linear ion trap (LIT) where the seven most abundant peptide molecular ions were selected for collisioninduced dissociation (CID) using a normalized collision energy of 35%. Data were collected over a broad precursor ion selection scan range (m/z 375-1800) and dynamic exclusion was enabled to minimize redundant selection of peptides previously selected for CID. Bioinformatic Analysis

Tandem mass spectra were searched against the UniProt human protein database (7/09 release, 58 849 entries) from the European Bioinformatics Institute (http://www.ebi.ac.uk/ integr8), using the Turbo SEQUEST algorithm in the Bioworks 3.2 software package (ThermoFisher Scientific). Search criteria were set as follows: fully tryptic with up to two missed cleavage sites, dynamic methionine oxidation (15.9949) and cysteine carboxyamidomethylation (57.0215), precursor mass tolerance of 1.4 Da and fragment ion tolerance of 0.4 Da. Peptides were considered legitimately identified if they met specific charge state and proteolytic cleavage-dependent cross correlation scores of 1.9 for [M þ H]1þ, 2.2 for [M þ 2H]2þ and 3.5 for [M þ 3H]3þ, and a minimum delta correlation of 0.08 (Supplemental Table 1, Supporting Information). A false peptide discovery rate of approximately 1.7% was determined by searching the primary tandem MS data using the same criteria against a decoy database wherein the protein sequences are reversed.20 Results were further filtered using software developed in-house, and differences in protein abundance between samples were derived by summing the total CID events that resulted in a positively identified peptide for a given protein accession across all samples (spectral counting).19 Common peptides were retained in the final data and were compiled with the protein accession that was reported by Sequest during the additional data filtering. For all proteins selected for further verification, all peptide tandem MS were manually inspected and confirmed and protein redundancy and/or isoform determinations were evaluated from the entire list of accessions for each peptide. 1334

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Journal of Proteome Research The relative protein abundance levels between RCC and ANK TIF samples were evaluated by a nonparametric Wilcoxon signed-rank where p-values where determined using Fisher’s exact test. Prior to performing the statistical analysis, proteins for inclusion were filtered based on the following criteria: proteins must have been identified from 4 out of the 5 RCC or ANK TIF sample set (80% cutoff) and proteins must have been identified with more than 2 peptides from 2 out of a total of 3 technical replicate injections for a given sample from either the RCC or ANK TIF sample set. Significant differentially abundant proteins (p < 0.05) were utilized for supervised hierarchical cluster analyses. Gene ontology analyses were conducted using Ingenuity Pathway Analysis (IPA) software (Ingenuity Systems, www.ingenuity.com). One-hundred and thirty-two out of 138 significant differential proteins successfully mapped in IPA. The IPA “Biomarker Filter” was utilized to determine whether a protein has been previously cited as present in human blood, urine or other body fluids.

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Table 1. TIF Sample Patient Cohort Information diameterb

age sample TIFd

group sex pathology gradea

(cm)

invasionc

N2, T2

50-59 M

Clear cell

2

2.6

N

N3, T3

50-59 M

Clear cell

2

6.2

N

N5, T5

80-89 F

Clear cell

3

4.1

Y

N6, T6

70-79 M

Clear cell

2

5.3

N

N7, T7

80-89 F

Clear cell

3

14.0

Y

N8, T8

50-59 M

Clear cell

2

5.8

N

N9, T9 60-69 M N11, T11 40-49 M

Clear cell Clear cell

2 4

11.3 11.0

N Y

N13, T13 50-59 F

Clear cell

3

12.0

Y

N15, T15 70-79 M

Clear cell

3

9.5

Y

Serum NHSe

Healthy

S1

70-79 M

Clear cell

4

9.5

Y

S2

50-59 M

Clear cell

2

2.2

N

Western Blot Analyses

S3

80-89 F

Clear cell

2

9.5

N

Primary antibodies used were mouse monoclonal antihuman neuron specific enolase (Abcam) and NNMT (Abcam). Secondary antibody was horseradish peroxidase-conjugated goat antimouse IgG (LþH) that was preabsorbed with serum (Pierce). The TIF protein samples (25 μg) were resolved by 1D PAGE (Invitrogen) and transferred to Immobilon-PSQ PVDF membranes (Millipore) using the Invitrogen Xcell II Blot Module according the manufacturer’s protocol. Membranes were blocked with 0.5% milk and incubated in primary antibody (1:1000 dilution) for 16 h at 4 °C. Membranes were washed with TBS with 0.1% Tween (TBST) and incubated with secondary antibody (1:75 000 dilution) for 1 h at ambient temperature. Membranes were washed with TBST and incubated with either Dura or Femto Super Signal ECL (ThermoFisher) for 5 min prior to chemiluminescent exposure.

S4

70-79 F

Clear cell

1

3.3

N

Selected Reaction Monitoring (SRM)

Peptides from eight proteins identified in this study were analyzed in triplicate by SRM on a triple quadrupole mass spectrometer (TSQ Ultra, ThermoFisher Scientific, San Jose, CA). The peptides selected for SRM must pass the following inclusion criteria: must be a proteotypic or unique peptide, precursor charge state =2 and not contain a cysteinyl or methionyl residue. CID spectra for peptides passing these inclusion criteria for SRM were manually inspected for selection of potential transition ions to monitor. Three transition ions were monitored per peptide and the MS method was designed to include SRMtriggered full scan MS/MS. These peptides were further evaluated on the TSQ Ultra by performing CID on the peptides of interest to confirm the utility of the transitions ions selected from the linear ion trap CID spectra or to select different transition ions based on the triple quadrupole CID spectra. The accepting measure included signal for all transitions, the relative ratio of signal intensity of the three transitions, and retention time. In addition, transitions must also pass the stringent criteria set in Pinpoint software (ThermoFisher Scientific Inc.) with p e 0.1 for any given file and CV e 15 for any given group. A stable isotope standard peptide was added to each TIF and serum sample and the SRM results were normalized to the peptide standard. To verify the SRM transitions from the peptides of interest, the full scan tandem mass spectrum was acquired for any precursor ion observed with an SRM transition above 3 104 count ion intensity. For SRM, q1 and q3 resolutions were set to

a

Fuhrman nuclear grade. b Greatest diameter of the neoplasm. c Carcinoma invasion. d Tumor (T), normal (N). e Normal human serum (NHS).

0.7 amu, the CID gas pressure (argon) was 1.5 mTorr and a variable collision energy (CE) dependent upon the m/z value of the precursor ion according to CE = (m/z) 0.034 þ 3. Each SRM scan width was set to 0.002 m/z units and the scan dwell time was 0.020 s. Mass chromatogram areas were obtained using Pinpoint 1.0 (ThermoFisher Scientific). A heavy-labeled synthetic peptide, FLVGPDGIPIM[Oxid]R[HeavyR] (þ2, precursor mass 670.8672 (m/z), transitions: 542.3, 924.5, 981.7) (ThermoFisher Scientific), was spiked in each TIF and serum sample. SRM data was normalized to the standard peptide and the RSD was calculated. Statistical significance between the mass chromatogram areas from RCC and ANK TIF was determined by a nonparametric Wilcoxon signed-rank test where p-values were calculated using Fisher’s exact algorithm. ELISA

The level of abundance of TSP1 in serum was measured using the Human Thrombospondin-1 Quantikine ELISA Kit (R&D Systems, Minneapolis, MN). Serum was diluted 100-fold and assayed in duplicate according to the manufacture’s protocol. Serum ENO2 levels were quantified in duplicate using the Neuron Specific Enolase ELISA kit (ALPCO Immunoassays, Salem, NH) according to the manufacture’s protocol using 10 μL of the neat serum samples.

’ RESULTS Proteomic Analysis of Renal Cell Carcinoma and Adjacent Normal Kidney TIF

Tissue interstitial fluid samples were harvested from RCC and ANK tissues from kidneys resected from patients diagnosed with clear cell RCC (Table 1 and Supplemental Figure 1, Supporting Information). From the initial discovery phase of the experiment, TIF proteins harvested from five paired RCC and ANK tissues were analyzed by LC-MS/MS for protein identification (Figure 1). It has previously been noted that TIF is likely to contain a significant number and concentration of classic serum proteins,15 therefore the present workflow employed involved depletion of the top fourteen abundant serum proteins using the 1335


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proteins identified from the technical replicate analyses for each samples were highly consistent (RSD e 20%, Supplement Table 4, Supporting Information). One-hundred thirty-eight proteins were identified with statistically significant (p e 0.05) differential abundances in spectral count in RCC when compared to ANK TIF (Supplemental Table 2, Supporting Information). A supervised hierarchical cluster analysis was performed on the proteins with significantly different abundance in RCC TIF and the heat map generated from the analysis showed similar protein profiles across five cases of RCC TIF when compared to ANK TIF (Figure 2). The differentially abundant proteins were further evaluated using IPA software to obtain gene ontology information. Sixty-four percent of proteins with higher abundance in RCC TIF were found to be localized to the plasma membrane or predicted to be extracellular (Figure 3). The top five over represented biological functions of the significant differential proteins include cancer, gastrointestinal disease, drug metabolism, lipid metabolism, and molecular transport. Using the biomarker filter analysis application in the IPA software, all of the differentially abundant proteins observed in this investigation have previously been detected in serum, urine, or other biofluids. These data strongly support the hypothesis that the TIF proteome is comprised of a high proportion of shed or secreted proteins that are likely to be found in peripheral circulation. From these analyses, several proteins were selected for biochemical verification including NNMT, TSP1, ENO2, annexin A4, ferritin, CD14, galectin-1, and TBG. Verification of Protein Abundance

Figure 1. Proteomic workflow. TIF were depleted of high abundant serum proteins using MARS-14 columns and electrophoresed into the stacking portion of a 1D-PAGE gel prior to enzymatic digestion. Samples were analyzed in triplicate by LC-MS and protein abundance differences were determined by spectral counting followed by candidate biomarker selection and verification. Numbers of verified proteins by Western blot, SRM, or ELISA are shown in parentheses.

multiple affinity removal system (MARS; Agilent Technologies),21 The present depletion protocol utilized a 4 formulation of Buffer A for the sample dilution step which eliminates the need to concentrate samples to very low volumes as originally required by the manufacturer’s protocol (8-10 μL), which can potentially result in protein aggregation and sample loss. Following depletion, samples were subjected to 1D-PAGE but only briefly electrophoresed such that the protein lysates were permitted to enter only into the stacking portion of the gel. Single gel bands containing the entire protein mixture for each sample were excised and digested in-gel with trypsin. Peptide extracts were analyzed in triplicate by nanoflow LC-MS/MS on a linear ion trap MS. A total of 539 proteins were identified with two or more peptides from RCC and/or ANK TIF with high confidence (peptide FDR e 2%). The number of peptides and

Paired RCC and ANK TIF proteins from ten patients diagnosed with clear cell RCC were subjected to Western blot analysis for NNMT and ENO2; five samples derived from the discovery set of samples for internal verification and five prospectively gathered samples naïve to proteomic analysis. Bands for NNMT and ENO2 were observed as expected at 28 and 48 kDa, respectively. Western blot results revealed that eight out of ten cases had elevated levels of NNMT and ENO2 in RCC as compared to ANK TIF (Figure 4). Furthermore, the relative intensities of the bands detected by Western blot correlate with the spectral count values observed in the LC-MS/MS discovery analysis of the TIF from the initial five patients. Peptides from a selected subset of proteins present at higher relative abundance in RCC as compared to ANK TIF were selected for verification by SRM in TIF (Table 2). For each peptide, three transition ions were monitored by SRM from immunodepleted and digested TIF and serum samples. A heavy labeled peptide was spiked in each TIF and serum sample and the total SRM peak areas were normalized to that of the labeled peptide. The normalized average fold change in protein abundance between RCC to ANK TIF was consistent for each candidate protein as compared with the spectral count-derived abundance measure (Table 3). SRM results combined from triplicate injections for each sample are shown in Supplemental Table 3 (Supporting Information). CD14, TBG, and TSP1 were also analyzed by SRM in sera and found to be at elevated abundance in clear cell RCC patients as compared to a pooled serum sample from healthy individuals (Table 4). TSP1 is used as an example here to illustrate the SRM verification in both TIF as well as in serum (Figure 5). To assess the candidate discriminatory proteins for their differential presence in a naive set of serum samples, the serum 1336



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Figure 2. Supervised hierarchical cluster analysis. Proteins determined to be significantly different in abundance between RCC TIF and ANK samples (p e 0.05) were used for a supervised hierarchical clustering analysis to highlight the similar protein abundance profiles across the five tumor samples as compared to the adjacent normals. Eight proteins including NNMT, ENO2, TSP1, galectin-1 TBG, CD14, ferritin, and annexin A4 were selected for verification. The colors in the heat map represent spectral counts that were normalized by Z-score transformation.

abundance levels of TSP1 and ENO2 were determined by ELISA in sera from four clear cell RCC patients. TSP1 was determined to be 14-fold higher in clear cell RCC patients as compared to the pooled standard control, which strongly correlates with the results by SRM (Table 5). Although ENO2 was not detected by SRM in serum, it was quantified by ELISA and found to be higher in abundance in RCC serum when compared to the pooled standard control. Table 6 summarizes the verification results in TIF and serum by Western blot, SRM, and ELISA.

’ DISCUSSION Despite advances in sample preparation and analytical instrumentation, identification of biomarkers from serum remains challenging due to the high dynamic range of concentration spanning the most and least abundant proteins in this complex proteome. These difficulties have motivated exploration of alternative clinical samples that present fewer analytical challenges from which discovery experiments can be conducted to identify candidate biomarkers. Proximal fluids, such as tissue interstitial fluid, are particularly germane for cancer biomarker discovery as it can be

Figure 3. Cellular localization of TIF proteins. IPA software was used to classify the cellular compartment of proteins with higher (dark gray) or lower (light gray) abundance in TIF as compared to adjacent normal tissue. Significantly decreased or increased abundance proteins were determined by their spectral count ratios. A substantial increase in the number of observed ECM and membrane proteins supports the notion that TIF consists of an enriched population of shed and/or secreted proteins. 1337



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Figure 4. Western blot analysis of ENO2 and NNMT in TIF. Protein abundance of ENO2 and NNMT were verified by Western blot in normal (N) and tumor (T) clear cell RCC TIF. The spectral count values for each protein determined in the first five cases used for the discovery phase of the study are shown which are confirmed by the findings from the western analysis. Images of the entire Western blot membrane are shown in Supplemental Figure 2 (Supporting Information). Protein concentrations of TIF were measured by BCA assay to ensure equal loading.

Table 2. Target Peptides and Transition Ions for SRM Analyses accession

protein

P09525

Annexin A4

P09525 P08571

Annexin A4 CD14

P09104 P02794

peptide

precursor (m/z)

transitions (m/z)

GLGTDDNTLIR

587.8

731.4, 846.4, 1004.5

GLGTDEDAIISVLAYR AFPALTSLDLSDNPGLGER

846.8 987.0

1005.6, 821.5, 708.4 1172.6, 857.4, 742.4

ENO2

YITGDQLGALYQDFVR

930.0

536.3, 827.4, 1068.5

Ferritin

NVNQSLLELHK

647.9

839.5, 1081.6, 639.4

P02794

Ferritin

IFLQDIK

438.8

616.4, 503.3, 375.2

P09382

Galectin-1

SFVLNLGK

439.3

643.4, 544.4, 431.3

P40261

NNMT

DTYLSHFNPR

625.3

533.3, 757.4, 670.3

P05543

TBG

NALALFVLPK

543.3

900.6, 603.4, 787.5

P07996

TSP1

FVFGTTPEDILR

697.9

742.4, 944.5, 1001.5

Table 3. TIF Normalized SRM Peak Area Fold Changes protein

Table 4. Serum Normalized SRM Peak Area Fold Changes a

fold change

protein

fold changea

TSP1

2.9 ( 2.9

CD14

4.5 ( 1.8

TBG CD14

1.7 ( 0.9 2.9 ( 1.7

TBG TSP1

4.3 ( 1.6 Infinity

11.7 ( 18.8

ENO2b b

NNMT

11.7 ( 22.9

Annexin A4 peptide 1b

9.9 ( 14.7

Annexin A4 peptide 2b

16.9 ( 24.7

Ferritin peptide 1

2.4 ( 2.0

Ferritin peptide 2

2.2 ( 2.0

Galectin-1

1.8 ( 1.3

a

Average normalized tumor peak area/normal peak area (T2/N2, T3/ N3, T5/N5, T6/N6, T7/N7, T8/N8, T9/N9, T11/N11, T13/N13, T15/N15). Individual peak areas are detailed in Supplemental Table 2, Supporting Information. b Statistically significant difference observed between RCC and ANK TIF (p < 0.05).

reasonably hypothesized that cancer-specific protein biomarkers will be present at relatively greater abundance in loco-regional proximity of a developing malignancy in the tumor tissue microenvironment. The present investigation sought to characterize the tumor and matching normal TIF proteomes harvested from RCC and ANK tissues to answer the question whether clear differences in protein abundances were evident and whether a subset of these could be identified in serum from RCC patients. Several proteins of interest including ENO2, TSP1, and NNMT that were increased in

Average normalized peak area of clear cell serum (S1 - S4)/normal human serum.

a

abundance in RCC TIF were confirmed in TIF and/or serum by Western blot analysis, SRM, and/or ELISA. We detected ENO2 and TSP1 with an elevated abundance in tumor TIF and observed their increase in abundance in RCC patient sera by SRM and/or ELISA when compared to pooled standard control. ENO2, also known as γ-enolase or neuralspecific enolase, is a glycolytic enzyme that has been shown to be dysregulated in Alzheimer’s and Huntington’s disease as well as elevated level in serum from patients with small cell carcinoma of the lung and neuroblastoma.22-25 Expression of ENO2 in RCC tissue has been shown to be significantly higher in tumor when compared to normal control26 and serum levels of ENO2 have been shown to be elevated in RCC patients and correlates with stage, tumor size, histological grade and disease recurrence, suggesting its potential as a candidate biomarker.27,28 The elevated level of ENO2 could be associated with the Warburg effect, where it has been previously hypothesized and experimentally noted that tumor cells exhibit a greater level of glycolysis than nontransformed cells.29 Overexpression of glycolysis-related 1338



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Figure 5. Serum and TIF verification of TSP1 by SRM. For SRM-based verification of TSP1 in TIF and serum, the TSP1 peptide 217FVFGTTPEDILR228 identified in the discovery analysis (A) was evaluated by CID in the TSQ for selection of three transition ions (B), m/z 742,4, 944.5 and 1001.8, to monitor in the assay. TSP1 levels were more abundant in tumor clear cell RCC TIF (dotted) as compared to normal (solid) (C) as well in serum (S2) from patients diagnosed with clear cell RCC (dotted) compared to the normal human serum (NHS) (solid) (D) shown by SRM analysis performed in triplicates. TSP1 level in NHS (solid) is minimal and is thus not observable in the figure. Although apparent, these retention time shifts represent less than a 1% variation across the LC gradient for the sample set.

Table 5. ELISA Determination of Serum TSP1 and ENO2 protein ENO2 TSP1 a

HNSa (ng/mL)

S1 (ng/mL)

S2 (ng/mL)

S3 (ng/mL)

S4 (ng/mL)

clear cellb (ng/mL)

fold changec

3

7

5

32

4

12

4

357

4956

7044

3264

4843

5027

14

Normal human serum (NHS). b Mean level of clear cell serum (S1-S4). c Clear cell/HNS.

genes and their products has been observed in cancers of multiple classes.30 Although not significant within the criteria utilized for the present analysis, we did observe another glycolic enzyme, pyruvate kinase M2, to be slightly elevated in abundance RCC versus ANK TIF where the mean spectral counts were observed to be 132.8 and 69.6, respectively. In addition to ENO2, TSP1 also showed elevated abundance in tumor TIF and RCC patient sera. TSP1 is an extracellular matrix protein (ECM) that has an antiangiogenic effect by binding to CD36 receptor and inducing p38 activated apoptosis in endothelial cells.31 TSP1 has shown to be inversely correlated with tumor grade and survival rate in some cancers including thyroid, colon, and bladder carcinomas.32

The abundance of NNMT was elevated in tumor TIF from clear cell RCC patients and verified by SRM and Western blot analysis. NNMT is an enzyme that is involved in xenobiotic metabolism and a wide range of cellular processes such as resistance to stress or injury and energy production.33,34 NNMT has previously been shown to be increased in abundance in clear cell RCC tissues when compared to matched normal controls and is inversely correlated to tumor size.35,36 In addition to RCC, abnormal NNMT expression has been reported in other malignancies such as gastric cancer, hepatocellular carcinoma and oral squamous cell carcinoma.37-39 NNMT expression in serum has shown to be higher in patients diagnosed with colorectal and lung cancer.40,41 1339



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Table 6. Verification Summary for Differentially Abundant Protein Candidates proteins

discoverya

Annexin A4

yd

8/10g

n

CD14

y

6/10

y (4/4 > N)h

8/10

n

5/10

n

ENO2

y

Ferritin

y

Galectin-1

y

NNMT

y

TBG TSP1

TIF WBb

e

8/10

TIF SRM (T > N)c

serum WB

f

n

serum SRM

5/10

n

8/10

n

y

5/10

y (3/4 > N)

y

6/10

y (4/4 > N)

8/10

n

serum ELISA

y

y

a

Proteins of greater abundance in RCC TIF identified from the discovery phase of the study. b Western blot analysis (WB). c Number of cases where SRM peak area of RCC TIF is greater than that of ANK TIF. d Yes. e Number of positive expression out of the total number of cases tested. f Not observed. g Number of cases where SRM peak area of RCC TIF are greater than ANK TIF out of total numbers of cases tested. h Number of cases where SRM peak area of RCC serum are greater than normal control out of total numbers of cases tested.

To our knowledge, this is the first study to differentially characterize the RCC from ANK TIF, the results of which are supported by several recent tissue-based proteomic studies of RCC including a 2-DE MALDI-TOF analysis42 and a relative quantitative characterization utilizing iTRAQ labeling and LC-MS/MS analysis.35 Differentially abundant candidates were identified by Kim et al. where a combination of NNMT, Ferritin light chain, and NSE was validated by ELISA in 88 RCC and 80 healthy plasma samples with a high predictive capacity of 0.993.42 In addition, Siu et al. reported several differentially abundant proteins included 14-3-3 protein eta, NNMT and annexin A5, which was also observed to be increased in abundance in the RCC TIF in this study.35 One limitation of characterizing TIF for the comparison of the relative protein abundance between cancer and normal tissues includes the requirement of surgical resection or biopsy of diseased and matched normal tissue. Although the samples acquired for this study utilize normally discarded tissue from full nephrectomies, it can be the case that normal tissues are limiting due to the excessive involvement of the tumor mass in the organ. Another potential downfall of using TIF is that the harvest of TIF requires incubation of tissues in PBS at 37 °C for an hour (or more), which may elicit cellular responses that may differ from the normal physiological condition. In a previous study where different buffer systems, including PBS, were compared for TIF harvest, ovarian and kidney tissues were fixed after their incubation and compared to freshly fixed tissues and no significant differences were observed.21 In addition to ECM and membrane proteins, an analysis of the “intracellular proteins” utilizing the functional annotation tool within the Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.7 suggests that 7% of these accessions are associated with the term “cytoplasmic vesicles.” The tumor microenvironment contains extracellular matrix proteins and cytokines providing a rich source for biomarker discovery that could be extracted by TIF. In this study we have identified several, differentially abundant proteins in the TIF microenvironment that may be novel candidates for potential circulating RCC biomarkers. Increased abundances of these proteins were verified not only in TIF but also in sera from patients diagnosed with RCC. Although it is acknowledged that this work represents a proof of principle set of analyses of a limited number of clinical samples, increased abundances of ENO2 and TSP1 were detectable by ELISA in serum from four RCC patients compared to a pooled normal control suggesting

that screening TIF for circulating biomarkers may be an effective strategy that can be applied to a wide range of tissues and malignancies. Based on these provocative findings, future effort will be leveraged to expand the case-control cohort toward further validation of the utility of these differentially abundant proteins.

’ ASSOCIATED CONTENT

bS

Supporting Information Supplementary figures and tables. This material is available free of charge via the Internet at http://pubs.acs.org.

’ AUTHOR INFORMATION Corresponding Author

*Thomas P. Conrads, Ph.D., 3289 Woodburn Rd, Woodburn II, Suite 375, Annandale, VA, 22003. Tel: 703-207-3357. E-mail: [email protected]. Present Addresses ‡

Women’s Health Integrated Research Center at Inova Health System, 3289 Woodburn Rd, Woodburn II, Suite 375, Annandale, VA, 22003

’ ACKNOWLEDGMENT We acknowledge the efforts of Dr. Susan E. Abbatiello who participated in the beginning phase of this work. We thank Patricia Clark, Tina Tomko, and Brandy Greenawalt from the Health Sciences Tissue Bank at the University of Pittsburgh Medical Center Shadyside Hospital for their assistance in tissue collection. In addition, we thank Christopher Vanselow, Maria VanDamme and Agilent Technologies for the 4 Buffer A formulation used in the MARS depletion protocol. We acknowledge Mary F. Lopez, Amol Prakash, and Thermo Scientific for the Pinoint Software and technical support. This work was supported by an award from the David Scaife Foundation (TPC). ’ ABBREVIATIONS RCC, renal cell carcinoma; LC-MS/MS, liquid chromatography-tandem mass spectrometry; TIF, tissue interstitial fluid; NNMT, nicotinamide n-methyltransferase; ENO2, enolase 2; TSP1, thrombospondin-1; TBG, thyroxin-binding globulin; MARS, multiple affinity removal system; PMSF, phenylmethanesulfonylfluoride; IHC, immunohistochemistry; ECM, extracellular matrix; 1340


Journal of Proteome Research ELISA, enzyme-linked immunosorbent assay; SRM, selected reaction monitoring; ANK, adjacent normal kidney.

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