Multiplexed Homogeneous Proximity Ligation Assays for High-throughput Protein Biomarker Research in Serological Material
2011; Elsevier BV; Volume: 10; Issue: 4 Linguagem: Inglês
10.1074/mcp.m110.004978
ISSN1535-9484
AutoresMartin Lundberg, Stine Buch Thorsen, Erika Assarsson, Andrea Villablanca, Bonnie Robin Tran, Nick Gee, Mick Knowles, Birgitte Sander Nielsen, E. González Couto, Roberto Martín‐Hernández, Olle Nilsson, Christian Fermér, Jörg Schlingemann, Ib Jarle Christensen, Hans Jørgen Nielsen, Björn Ekström, Claes Andersson, Mats Gustafsson, Nils Brünner, Jan Stenvang, Simon Fredriksson,
Tópico(s)Monoclonal and Polyclonal Antibodies Research
ResumoA high throughput protein biomarker discovery tool has been developed based on multiplexed proximity ligation assays in a homogeneous format in the sense of no washing steps. The platform consists of four 24-plex panels profiling 74 putative biomarkers with sub-pm sensitivity each consuming only 1 μl of human plasma sample. The system uses either matched monoclonal antibody pairs or the more readily available single batches of affinity purified polyclonal antibodies to generate the target specific reagents by covalently linking with unique nucleic acid sequences. These paired sequences are united by DNA ligation upon simultaneous target binding forming a PCR amplicon. Multiplex proximity ligation assays thereby converts multiple target analytes into real-time PCR amplicons that are individually quantified using microfluidic high capacity qPCR in nano liter volumes. The assay shows excellent specificity, even in multiplex, by its dual recognition feature, its proximity requirement, and most importantly by using unique sequence specific reporter fragments on both antibody-based probes. To illustrate the potential of this protein detection technology, a pilot biomarker research project was performed using biobanked plasma samples for the detection of colorectal cancer using a multivariate signature. A high throughput protein biomarker discovery tool has been developed based on multiplexed proximity ligation assays in a homogeneous format in the sense of no washing steps. The platform consists of four 24-plex panels profiling 74 putative biomarkers with sub-pm sensitivity each consuming only 1 μl of human plasma sample. The system uses either matched monoclonal antibody pairs or the more readily available single batches of affinity purified polyclonal antibodies to generate the target specific reagents by covalently linking with unique nucleic acid sequences. These paired sequences are united by DNA ligation upon simultaneous target binding forming a PCR amplicon. Multiplex proximity ligation assays thereby converts multiple target analytes into real-time PCR amplicons that are individually quantified using microfluidic high capacity qPCR in nano liter volumes. The assay shows excellent specificity, even in multiplex, by its dual recognition feature, its proximity requirement, and most importantly by using unique sequence specific reporter fragments on both antibody-based probes. To illustrate the potential of this protein detection technology, a pilot biomarker research project was performed using biobanked plasma samples for the detection of colorectal cancer using a multivariate signature. The quest for early detection of cancer leads us to retroactively mine biobanked plasma samples in hopes of finding better protein biomarkers and biomarker combinations (1Schrohl A.S. Würtz S. Kohn E. Banks R.E. Nielsen H.J. Sweep F.C. Brünner N. Banking of biological fluids for studies of disease-associated protein biomarkers.Mol. Cell Proteomics. 2008; 7: 2061-2066Abstract Full Text Full Text PDF PubMed Scopus (86) Google Scholar, 2Anderson L. Candidate-based proteomics in the search for biomarkers of cardiovascular disease.J. Physiol. 2005; 563: 23-60Crossref PubMed Scopus (316) Google Scholar, 3Templin M.F. Stoll D. Schrenk M. Traub P.C. Vöhringer C.F. Joos T.O. Protein microarray technology.Trends Biotechnol. 2002; 20: 160-166Abstract Full Text Full Text PDF PubMed Scopus (480) Google Scholar, 4Polanski M. Anderson N.L. A list of candidate cancer biomarkers for targeted proteomics.Biomark. Insights. 2007; 1: 1-48PubMed Google Scholar). Such development of serological biomarkers find vast uses not only for disease detection but also patient monitoring and in general research applications. Thus, identification and employment of multiprotein signatures is a promising and natural step in modern molecular medicine. Novel technologies to enable such studies require several features such as multiplexing, low sample consumption, high sensitivity and of course good immunoassay performance in general. In particular, one needs to extract as much high quality putative biomarker data as possible from a single sample collection with good clinical background information without consuming the entire sample. Multiplexed immunoassays performed on planar arrays or beads are limited in multiplexing capacity by the inherent antibody cross-reactivity which leads to extensive effort devoted to assay development and optimization (5Ellington A.A. Kullo I.J. Bailey K.R. Klee G.G. Antibody-based protein multiplex platforms: technical and operational challenges.Clin. Chem. 2009; 56: 186-193Crossref PubMed Scopus (242) Google Scholar). As each detector antibody in these standard assays carry the same reporter fluorophore, any nonspecific binding of a detector antibody will give rise to false positive signals. In contrast, the proximity ligation assay (PLA) 1The abbreviations used are:PLAproximity ligation assayCRCcolorectal cancerELISAenzyme linked immunosorbent assayEIAenzyme immuno assayqPCRquantitative real-time polymerase chain reactionPEphycoerethyrinAPCallophycocyaninGFPgreen fluorescent proteinCEAcarcinoembryonic antigen. utilizes pairs of target specific antibodies linked with DNA strands forming so called proximity probe pairs, which upon simultaneous and pair wisebinding to their respective analyte in a homogeneous solution enables an enzymatic ligation reaction. Thereby, a new PCR amplicon is formed composed of both reporter sequences of the proximity probes (6Fredriksson S. Gullberg M. Jarvius J. Olsson C. Pietras K. Gústafsdóttir S.M. Ostman A. Landegren U. Protein detection using proximity-dependent DNA ligation assays.Nat. Biotechnol. 2002; 20: 473-477Crossref PubMed Scopus (1058) Google Scholar, 7Gullberg M. Gústafsdóttir S.M. Schallmeiner E. Jarvius J. Bjarnegård M. Betsholtz C. Landegren U. Fredriksson S. Cytokine detection by antibody-based proximity ligation.Proc. Natl. Acad. Sci. U.S.A. 2004; 101: 8420-8424Crossref PubMed Scopus (299) Google Scholar). This reporter molecule reflects the identity of the protein through sequence encoding in multiplex and its amount corresponds to the protein analyte concentration. Previous smaller scale reports have shown that the homogeneous proximity ligation assay is a promising tool for such studies (8Fredriksson S. Dixon W. Ji H. Koong A.C. Mindrinos M. Davis R.W. Multiplexed protein detection by proximity ligation for cancer biomarker validation.Nat Methods. 2007; 4: 327-329Crossref PubMed Scopus (156) Google Scholar, 9Fredriksson S. Horecka J. Brustugun O.T. Schlingemann J. Koong A.C. Tibshirani R. Davis R.W. Multiplexed proximity ligation assays to profile putative plasma biomarkers relevant to pancreatic and ovarian cancer.Clin. Chem. 2008; 54: 582-589Crossref PubMed Scopus (84) Google Scholar, 10Chang S.T. Zahn J.M. Horecka J. Kunz P.L. Ford J.M. Fisher G.A. Le Q.T. Chang D.T. Ji H. Koong A.C. Identification of a biomarker panel using a multiplex proximity ligation assay improves accuracy of pancreatic cancer diagnosis.J. Transl. Med. 2009; 7: 105Crossref PubMed Scopus (50) Google Scholar). Here we report the further development in both throughput, multiplexing, and especially assess the immunoassay performance in greater detail. When building content in multiplexed immunoassays the availability of binding reagents for target proteins are often a limiting step. Because PLA can use both matched monoclonal antibodies or just a single batch of an affinity purified polyclonal antibody raised against the whole native antigen split in two aliquots, the potential repertoire of multiplex PLAs should be greater than for conventional multiplex assays. We show herein that multiplex PLA panels can be developed without the need for extensive antibody selection, optimization, and reselection. In this report we have used new antibody-oligonucleotide conjugation chemistry, previously not used for PLA, to build four 24-plex assays including spike-in standard controls, and validated their performance in human diseased and control plasma samples. Quantification of the PLA reaction products using high throughput nanoliter microfluidic real-time PCR enabled rapid putative biomarker profiling of 74 cases of colorectal cancer versus 74 matched control samples using all four panels. proximity ligation assay colorectal cancer enzyme linked immunosorbent assay enzyme immuno assay quantitative real-time polymerase chain reaction phycoerethyrin allophycocyanin green fluorescent protein carcinoembryonic antigen. Subjects undergoing a sigmoidoscopy or colonoscopy either following symptoms consistent with colorectal cancer (CRC) or patients attending surveillance programs because of hereditary CRC (hereditary nonpolyposis colorectal cancer and familial adenomatous polyposis) were included in a cross sectional study. A total of 5165 subjects were included (11Nielsen H.J. Brünner N. Frederiksen C. Lomholt A.F. King D. Jørgensen L.N. Olsen J. Rahr H.B. Thygesen K. Hoyer U. Laurberg S. Christensen I.J. Plasma tissue inhibitor of metalloproteinases-1 (TIMP-1): A novel biological marker in the detection of primary colorectal cancer. Protocol outlines of the Danish-Australian endoscopy study group on colorectal cancer detection.Scand J Gastroenterol. 2008; 43: 242-248Crossref PubMed Scopus (38) Google Scholar, 12Nielsen H.J. Brünner N. Jørgensen L.N. Olsen J. Rahr H.B. Thygesen K. Hoyer U. Laurberg S. Stieber P. Blankenstein M.A. Davis G. Dowell B.L. Christensen I.J. Plasma TIMP-1 and CEA in detection of primary colorectal cancer: Aprospective, population based study of 4,509 high risk individuals.Scand. J. Gastroenterol. 2010; 46: 60-69Crossref PubMed Scopus (59) Google Scholar) and according to the Helsinki II Declaration, oral and written consent was given from each subject. The study was approved by The Regional Ethical Committee (KF 01–080/03). A case-control study was designed for the present study by randomly choosing 74 biobanked stage I–IV (13O'Connell J.B. Maggard M.A. Ko C.Y. Colon cancer survival rates with the new American Joint Committee on Cancer sixth edition staging.J. Natl. Cancer Inst. 2004; 96: 1420-1425Crossref PubMed Scopus (1282) Google Scholar) CRC samples and 74 age and gender matched individuals with no pathological findings by endoscopy and/or no self reported diseases or medication. Main clinical characteristics of the samples included in the study are presented in Table I.Table IClinical characteristics for the 74 CRC patients included in the study. AJCC: American Joint Committee on Cancer (13O'Connell J.B. Maggard M.A. Ko C.Y. Colon cancer survival rates with the new American Joint Committee on Cancer sixth edition staging.J. Natl. Cancer Inst. 2004; 96: 1420-1425Crossref PubMed Scopus (1282) Google Scholar)CharacteristicsSubjects, n (%)Gender Female36 (49) Male38 (51)Age group 40–493 (4) 50–5910 (14) 60–6917 (23) 70–7924 (32) 80–8919 (26) 90–991 (1)Cancer stageAJCC classification I8 (11) II30 (40) III16 (22) IV15 (20) Not specified5 (7) Open table in a new tab EDTA blood samples were collected from all subjects at time of endoscopy following standard operating procedure (11Nielsen H.J. Brünner N. Frederiksen C. Lomholt A.F. King D. Jørgensen L.N. Olsen J. Rahr H.B. Thygesen K. Hoyer U. Laurberg S. Christensen I.J. Plasma tissue inhibitor of metalloproteinases-1 (TIMP-1): A novel biological marker in the detection of primary colorectal cancer. Protocol outlines of the Danish-Australian endoscopy study group on colorectal cancer detection.Scand J Gastroenterol. 2008; 43: 242-248Crossref PubMed Scopus (38) Google Scholar). The samples were centrifuged at 2500 × g for 10 min at 4 °C. Following centrifugation the plasma was aspirated, aliquoted and stored at −80 °C. In a literature search target assays were selected using criteria such as potential biomarkers for CRC, general cancer markers, availability of appropriate antibodies, and reported plasma levels. A total of 80 antibody pairs against both high and low abundant proteins in plasma were selected for configuration of the multiplex panels 1–4 (see Table II). The few instances of antibody-oliognucleotide conjugation failure resulted in removal of these assays. In addition, antibodies against green fluorescent protein (GFP), phycoerythrin (PE), and allophycocyanin (APC) were included as internal control standards along with an oligonucleotide amplicon. The majority were affinity-purified polyclonal antibodies raised against the whole native recombinant proteins except for the monoclonal antibodies CEA, CA19–9, CA 15.3, CA242, and CA125. More detailed information about antibody and antigens sources can be found in supplemental Table S1.Table IIConfiguration of the four panels used for multiplex PLA biomarker screeningPanel1234Dilution1:11:11:101:100Sequence 1Carbonic Anhydrase IXSurvivinSpondin-2/MidinClusterin 2MCP-1 (CCL2)DesminEPCAMNo assay 3MesothelinHGF R/cMETNSE (enolase 2)sP-selectin 4IL-6IL-17Transaldolase 1MMP-2 5No assayIL-23No assayMMP-9 6oit1/FAM3DFoxP3Galectin-3Tenascin-C 7CEACAM 5 pAbCEACAM 5 pAbTIMP1TIMP1 8VEGFCEACAM1EGFRCRP 9No assayTGFb1TFF3PAI-1/Serpin 10No assayTGFb3u-PAsVCAM/CD106 11CEA mAbKLK3 (PSA)CEA mAbIGF-I 12CA125Cystatin SNTransferrin Rec. (CD71)Cathepsin B 13Her4ProlactinCA 15.3Angiogenin 14HGF/SFDcR3CA19–9IGF2 15FABP2IL8Tropomyosin (CH1)Tetranectin 16Oligo PCRaInternal control used for normalization and reaction quality control.Oligo PCRaInternal control used for normalization and reaction quality control.Oligo PCRaInternal control used for normalization and reaction quality control.Oligo PCRaInternal control used for normalization and reaction quality control. 17APCaInternal control used for normalization and reaction quality control.APCaInternal control used for normalization and reaction quality control.APCaInternal control used for normalization and reaction quality control.APCaInternal control used for normalization and reaction quality control. 32GFPaInternal control used for normalization and reaction quality control.GFPaInternal control used for normalization and reaction quality control.GFPaInternal control used for normalization and reaction quality control.GFPaInternal control used for normalization and reaction quality control. 35IL8CTGFOPNCystatin C 38PEaInternal control used for normalization and reaction quality control.PEaInternal control used for normalization and reaction quality control.PEaInternal control used for normalization and reaction quality control.PEaInternal control used for normalization and reaction quality control. 45Her2GDNFMIFIGFBP-1 51Her3FractalkineNo assayGRP78/HSPA5/BiP 53TNFaMIP-1SLPIKallikrein 11 57IL-1aCA 242S100A8YKL-40a Internal control used for normalization and reaction quality control. Open table in a new tab The 40-mer 3′ and 5′ oligonucleotide sequences used for the antibody conjugation consisted of a central 20 bp connector specific sequence, denoted universal, and a flanking 20 bp sequence for primer targeting in PCR amplification and quantitative PCR (see Fig. 1, supplemental Table S2). To generate the variable primers and probe sequences, a selection from a library containing more than 2000 unique DNA sequences was evaluated by performing in silico testing to limit cross-hybridization and formation of hairpin loops using Zuker mFold (data not shown). A set of 62 sequences passed the in silico screen as suitable for conjugation from which 24 were used in this study. Connector oligonucleotide, the 24 oligonucelotide pair sequences, forward primers (PreAmp-F, qPCR-F) and reverse primers (PreAmp-R, qPCR-R) were obtained from Biomers(GmbH, Germany) (See supplemental Table S2 for more detailed information). In order to manage and conveniently share project data, a web-based laboratory information management system (LIMS), developed in Java language, and based on a relational database management system (MySQL), was developed (Integromics S.L., Madrid, Spain). The LIMS data model was designed within the EU-funded FP7 project PROACTIVE. The resulting LIMS consists of interconnected entities, and allows efficiently collecting, storing, and recalling the generated high throughput data, together with parameters characterizing the experiment, as well as relevant clinical information about the patient sample (supplemental Fig. S7). Data visualization tools, implemented using the freely available Google Chart Tools API (Application Programming Interface), were used for primary quality controls (supplemental Fig. S8) and provided insight into patterns in high-dimensional data (supplemental Fig. S9). Proximity probes were prepared by linking a single batch of affinity purified polyclonal antibody or matched monoclonal antibody pairs to 3′-hydroxyl free and 5′-phosphate free 40-mer oligonucleotide sequences thereby forming unique amplicons representing each target protein. The antibody-oligonucleotide conjugates included in the biomarker panels were generated by Innova Biosciences (Cambridge, UK) using their Lightning-LinkTM technology. Conjugation quality was analyzed by SDS-PAGE and a few failures resulted in four empty assays because of antibody formulations incompatible with conjugation. Prior to evaluation of PLA panels, the proximity probes were pooled together with a probe mix diluent (Olink Bioscience, Uppsala, Sweden) at a final concentration of 2 nm and stored at 4 °C. The 24-plex assays for each panel were evaluated for performance with respect to sensitivity, dynamic range, linearity of dilution and recovery in plasma. Dilution series of antigen ranging from 2 pm to 200 pm in 1 μl samples were prepared in PLA buffer (Olink Bioscience, available upon request) and assayed in multiplex to assess sensitivity and dynamic range. The incubations of sample and Proximity-probes totaled 4 μl and was performed in a 96 well plate tightly sealed with a plastic film at 4 °C over night. For each dilution series a control sample without antigen was included to set the background level. Recovery values were determined by spiking 20 pm antigen in 90% plasma samples. Furthermore, antibody cross-reactive events were examined in buffer by comparing 24-plex antigen mixes to 4 or 5 antigen submixes and specificity was also assessed in a complex sample matrix containing 100% chicken plasma. A linearity of dilution experiment was performed to evaluate possible inhibitory factors present in plasma as well as to ensure that the analytes measured were within their linear range. Two fivefold dilutions were prepared in two plasma samples for each panel and the starting concentration of the plasma was 100% in panel 1 and 2, 10% in panel 3 and 1% in panel 4. For those assays where the signals were not reduced sufficiently by simple dilution, free antibody titrations were performed in order to set the assay within the linear range of its standard curve, that is below the inherent hook-effect of this homogeneous immunoassay (6Fredriksson S. Gullberg M. Jarvius J. Olsson C. Pietras K. Gústafsdóttir S.M. Ostman A. Landegren U. Protein detection using proximity-dependent DNA ligation assays.Nat. Biotechnol. 2002; 20: 473-477Crossref PubMed Scopus (1058) Google Scholar, 7Gullberg M. Gústafsdóttir S.M. Schallmeiner E. Jarvius J. Bjarnegård M. Betsholtz C. Landegren U. Fredriksson S. Cytokine detection by antibody-based proximity ligation.Proc. Natl. Acad. Sci. U.S.A. 2004; 101: 8420-8424Crossref PubMed Scopus (299) Google Scholar). Depending on the panel examined, the plasma concentration was 100% (Panel 1 and 2), 10% (Panel 3) or 1% (Panel 4) in the PLA analyses, a total of 148 plasma samples were analyzed. Multiplex PLA was performed by mixing 1 μl sample with plasma dilution buffer (Olink Bioscience, available upon request) at a 1:1 ratio and an internal control standard spike-in mix of 200 pm GFP, 20 pm PE, 40 pm APC, and 40fM of a DNA sequence (oligo PCR) to asses reaction quality. The mixture was then incubated for 20 min at RT. Next, 2 μl of proximity probe mixture containing 1× Probe Mix Diluent (Olink Bioscience, available upon request), 1% bovine serum albumin and 0.1% Triton X-100 was added to 2 μl of each prediluted sample followed by an incubation at 4 °C over night to allow the probes to bind to the analytes. Next, ligation of the bound probes was performed by incubating 96 μl of reaction mixture containing 1× T4 ligation buffer supplemented with 100 nm connector oligonucleotide and 0.006 units of T4 DNA ligase (Fermentas, Burlington, Ontario) with 4 μl of probed samples for 10 min at 37 °C followed by heat inactivation at 65 °C for 10 min. To digest the connector oligonucleotide, 1 Unit of uracil-DNA excision mix (Epicenter Madison, WI, USA) was added and incubated at 37 °C for 20 min and heat inactivated at 70 °C for 10 min. Finally, pre-amplification was performed in a total volume of 25 μl by mixing 20 μl of ligated product with 5 μl pooled PCR mix (1× PCR buffer (Invitrogen), 15 mm MgCl2 (Invitrogen), 1 mm dNTP (Invitrogen), 0.2 μm of each forward and reverse Pre-AMP primer in 24-plex (See supplemental Table S2), 7.5 units Platinum Taq polymerase (Invitrogen)) for 17 cycles that included an initial incubation for 10 min at 95 °C followed by 2 cycles for 15 s at 95 °C, 10 min at 46 °C, 2 min at 60 °C, and 15 cycles for 15 s at 95 °C, 2 min at 54 °C for and 2 min at 60 °C. The products were finally diluted fivefold in 1× Tris-EDTA buffer prior to detection by real-time PCR. The qPCR reactions were performed either on an ABI 9700 HT Fast (Applied Biosystems) instrument or the BioMarkTM micro fluidic system from Fluidigm. Regardless of instrument used, each protein assay used separate qPCR reactions with individual primer pairs (Frw and Rew qPCR primer, see supplemental Table S2). For the 9700 HT Fast system the diluted DNA products were first incubated for 30 min at 37 °C in a mixture containing1.4 × Fast Universal Master Mix (Applied Biosystems), dH2O and 0.05 Units of uracil-DNA excision mix (Epicenter) for partial digestion of primers used in the pre-amplification step to reduce interference during the qPCR. The q-PCR reaction was performed on a 384-well plate by transferring 7 μl of the pre-amplified sample mix and 3 μl of 3 μm primer (Biomers), dH2O and 0.8 μm TaqMan probe (Applied Biosystems) to a total sample volume of 10 μl per well. The thermal cycler program was initiated with 5 min at 95 °C followed by for 45 cycles for 15 s at 95 °C and for 1 min at 60 °C. The Fluidigm system required priming of the 48 × 48 Dynamic Chip array prior to use by injecting 300 μl control line fluid into each accumulator on the chip and run on an integrated fluidic circuit controller for 20 min performed according to manufacturers protocol. Next, an assay mix containing 1× assay loading reagent (Fluidigm, San Francisco, CA) and 2.5 μm of TaqMan probe (Applied Biosystems, Foster City, CA) was prepared and mixed with 9 μm of each primer pair. A sample master mix consisting of 1.4× sample loading reagent, 1.6× of Fast Universal Master Mix (Applied Biosystems) and 0.001 Units of uracil-DNA excision mix was prepared and mixed together with 1.8 μl diluted DNA products to a total volume of 6.5 μl. Thereafter, 5 μl of the assay mix and sample mix were added to their respective wells on the chip and run using the integrated fluidic circuit controller software for 60 min in order to load the samples and the assays into the chip. Each assay was run in duplicates and the micro fluidic compartment was loaded with 9 nL sample mix with 1 nL assay mix. Finally, the chip was run on the Biomark instrument running for 5 min at 95 °C followed by 40 cycles for 15 s at 95 °C and for 1 min at 60 °C. The qPCR data was analyzed with the RQ Manager 1.2 software (Applied Biosystems) or BioMark system software (Fluidigm). Thereafter, the recorded Ct values were converted to a linear scalefrom a log2-scale providing an estimation of the number of amplicons, X = 2^(30-Ct), subtracting the Ct value of the analyzed point from the threshold number for fluorescence detection theoretically assuming a single amplicon molecule. The data of each individual sample was normalized against its spike-in control GFP value before further statistical analysis. Recovery values were calculated by the formula [% Recovery = Amplicons (Spiked Ag)−Amplicons (Plasma)/Amplicons (Ag)−Amplicons (buffer)]. Sensitivity was estimated for all assays by the formula [Sensitivity = Concentration of spike-in closest to background (Molar)/2 d^Ct where dCt] is the signal over background level. For quality assessment and validation of the PLA technology to an orthogonal standard method the protein level for TIMP-1, CEA, CA242, IL-8, SLPI, and VEGF were determined by ELISA. TIMP-1 was measured by a rigorously validated in-house assay (14Holten-Andersen M.N. Murphy G. Nielsen H.J. Pedersen A.N. Christensen I.J. Høyer-Hansen G. Brünner N. Stephens R.W. Quantitation of TIMP-1 in plasma of healthy blood donors and patients with advanced cancer.Br. J. Cancer. 1999; 80: 495-503Crossref PubMed Scopus (128) Google Scholar). The concentration of the other analytes was measured by commercially available ELISA kits; CEA (IBL, Hamburg, Germany), CA242 (Fujirebio Diagnostics, Gothenburg, Sweden), IL8, SLPI, and VEGF (R&D Systems, Minneapolis, MN) according to the manufacturers recommendations. Prior to ELISA measurements, the plasma samples were diluted in order to yield values within the linear range of the standard curve for each assay. The protein levels were determined using a PowerWave x1 microplate reader (Bio-Tek Instruments, Germany) measuring TIMP-1 and CA242 at 405 nm and CEA, IL-8, VEGF, and SLPI at 450 nm. All samples were determined in duplicate and the mean values were used for statistical analysis. Samples with intra-assay CV>10% were re-analyzed. A four-parameter-fitted standard curve was generated using KC4TM Software (Bio-Tek Instruments, Germany), by which the concentrations of the six biomarkers were calculated. For univariate analysis serological markers levels were given in log-scale and used for further calculations. The distribution was analyzed for each biomarker by Quantile-Quantile plots (Q-Q) to verify normal distribution which is a prerequisite for the statistical analyses by t test. For the descriptive statistics median, minimum, maximum, actual mean, 95% confidence interval, and standard deviation were calculated. The statistical analyses were based on a case-control design and differences were compared using a paired t test. p values below 0.05 were considered significant. The correlation of PLA data to ELISA was calculated using standard procedure Spearman's rank correlation coefficient. All univariate data analyses were performed using the SAS (v 9.1, SAS Institute, USA) and GraphPad Prism (v 4.03 GraphPad Software, USA). Multivariate analysis for the potential of detecting CRC was performed using random forest classifier design as implemented in the random Forest (15Liaw A. Wiener M. Classification and regression by random forest.R. News. 2002; 2: 18-22Google Scholar) package in R (16R Development Core Team R: A language and environment forstatistical computing.3-900051-07-0 R Foundation for Statistical Computing, Vienna, Austria2009URL http://www.R-project.orgGoogle Scholar) and in-house scripts. As a crude filter to eliminate uninformative assays, prior to the analysis, assays with a variance of the raw Ct value not larger than either the variance of PE or GFP in the corresponding panel were removed. Binary classifiers of healthy versus CRC samples were then built using the remaining 54 assays using repeated hold-out of matched samples. In particular, 15 hold-out matched cases were selected at random and a random forest classifier was induced using the remaining 59 cases. Variable importance as measured by the mean decrease gini index (17Calle M.L. Urrea V. Letter to the Editor: Stability of random forest importance measure.Briefings Bioinformatics. 2010; 12 (7–10): 86-90Crossref PubMed Scopus (229) Google Scholar) was computed and the 15 hold-out cases were used as an external test set, providing an unbiased error rate estimate. The random selection was repeated 15 times, each repetition producing an error rate estimate and estimates of variable importance. The expected error rate was then estimated as the mean of the error rate in the 15 repetitions. In addition, following the procedure introduced by Nadeau and Bengio (18Nadeau C. Bengio Y. Inference for the generalization error.Machine Learning. 2003; 52: 239-281Crossref Scopus (589) Google Scholar), a conservative estimate of the variance of the estimated expected error rate was computed by first splitting the 74 cases into two partitions and then estimating the expected error rate as above in each partition. This was repeated 10 times, and the mean variance between the two partitions was used as a conservative estimate of the variance of the estimated expected error rate. Finally a conservative confidence interval for the expected error rate was determined under the assumption of normality using this variance estimate (18Nadeau C. Bengio Y. Inference for the generalization error.Machine Learning. 2003; 52: 239-281Crossref Scopus (589) Google Scholar). The comparison between multivariate and univariate classification was produced by building and estimating the expected error rate, as described above, for random forest classifiers built using only a single assay. The principle of homogenous multiplex proximity ligation assay has previously been described in detail (8Fredriksson S. Dixon W. Ji H. Koong A.C. Mindrinos M. Davis R.W. Multiplexed protein detection by proximity ligation for cancer biomarker validation.Nat Methods. 2007; 4: 327-329Crossref PubMed Scopus (156) Google Schola
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