US20100105571A1 - Protein Signature/Markers for the Detection of Adenocarcinoma - Google Patents

Protein Signature/Markers for the Detection of Adenocarcinoma Download PDF

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US20100105571A1
US20100105571A1 US12/593,448 US59344808A US2010105571A1 US 20100105571 A1 US20100105571 A1 US 20100105571A1 US 59344808 A US59344808 A US 59344808A US 2010105571 A1 US2010105571 A1 US 2010105571A1
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proteins
serum
test sample
binding agent
sample
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Carl Arne Krister Borrebaeck
Lars Bertil Christer Wingren
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Immunovia AB
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • G01N33/57438—Specifically defined cancers of liver, pancreas or kidney

Definitions

  • the present invention relates to methods for diagnosis of pancreatic adenocarcinoma, and biomarkers and arrays for use in the same.
  • Pancreatic ductal adenocarcinoma is the most lethal malignancy by anatomic site, with >30,000 new cases and deaths annually in the United States alone, and with a 5-year survival of 3-5%. This extreme mortality is due to the lack of effective early diagnostic methods (5) and to poor efficacy of existing therapies for advanced disease. Even the patients (10-20%) diagnosed with a surgically resectable tumor, ultimately die of recurrent and metastatic disease. An increased ability to detect and predict cancer is therefore crucial for individual patient management.
  • Antibody microarray technology (3) has the potential to provide a highly multiplexed analysis (6,7) and has been suggested as the technology platform that eventually will deliver a defined protein signature, i.e. a combination of serum proteins that distinguish cancer from normal patients.
  • Microarray technology has now matured to the point were the initial hurdles have been overcome and minute amounts of proteins in complex proteomes can be analyzed (8-12).
  • gene expression profiling of cancer has only demonstrated the ability to predict time of survival in few cases (1,2) and no combination of serum proteins has so far been associated with any of the above clinical parameters.
  • a serum sample analysis that can predict survival time would allow a more individualized cancer therapy. This has been emphasized for e.g. pancreatic adenocarcinomas, where no tumor-specific markers exist—although most patients will have an elevated CA 19-9 at time of diagnosis, individual prognostic markers have shown to be inconclusive (4). Furthermore, non-invasive approaches, such as computed tomography, is not sufficiently sensitivity to detect small cancers, whereas e.g. endoscopic ultrasonography can be used to survey at-risk individuals for pancreatic lesions (5).
  • the inventors have now developed a proteomic approach to prognostic diagnosis of cancer and identified the first sets of serum biomarkers for detection of pancreatic cancer and for predicting survival.
  • the invention provides a method for determining the presence of pancreatic adenocarcinoma in an individual comprising the steps of:
  • protein signature we include the meaning of a combination of the presence and/or amount of serum proteins present in an individual having cancer and which can be distinguished from a combination of the presence and/or amount of serum proteins present in an individual not afflicted with cancer (such as pancreatic adenocarcinoma)—i.e. a normal, or healthy, individual.
  • the presence and/or amount of certain serum proteins present in a test sample may be indicative of the presence of cancer, such as pancreatic adenocarcinoma, in an individual.
  • the relative presence and/or amount of certain serum proteins in a single test sample may be indicative of the presence of cancer, such as pancreatic adenocarcinoma, in an individual.
  • the individual is a human, but may be any mammal such as a domesticated mammal (preferably of agricultural or commercial significance including a horse, pig, cow, sheep, dog and cat).
  • a domesticated mammal preferably of agricultural or commercial significance including a horse, pig, cow, sheep, dog and cat.
  • the method of the first aspect of the invention further comprises the steps of:
  • the presence and/or amount in the test sample of the one or more proteins measured in step (b) is significantly different (i.e. statistically different) from the presence and/or amount in the control sample of the one or more proteins measured in step (b).
  • significant difference between the presence and/or amount of a particular protein in the test and control samples may be classified as those where p ⁇ 0.05.
  • the method of the first aspect comprises measuring the presence and/or amount in the test sample of all of the proteins defined in Table 1—i.e. all 19 of the proteins in Table 1.
  • the method of the first aspect may comprise measuring the presence and/or amount in the test sample of 1 or 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10 or 11 or 12 or 13 or 14 or 15 or 16 or 17 or 18 or 19 of the proteins defined in Table 1.
  • the method of the first aspect comprises measuring the presence and/or amount in the test sample of Rantes and/or Eotaxin and/or EI and/or TNF-b(1) and/or TNF-b(2) and/or GLP-1 and/or VEGF and/or IL-13 and/or CD40.
  • the invention provides a method for determining the survival time of an individual afflicted with pancreatic adenocarcinoma comprising the steps of:
  • the method according to the second aspect further comprises the steps of:
  • the test sample By comparing the presence and/or amount of the selected one or more proteins in the test sample and the control sample, it is possible to determine the survival time of the individual afflicted with pancreatic adenocarcinoma. For example, if the test sample has the same (i.e. identical) or substantially similar or significantly similar presence and/or amount of the selected one or more proteins as a control sample from a patient known to have a survival time of more than 24 months, the test sample will be determined as a sample from a patient having a survival time of more than 24 months. Other such comparisons will be understood by a person skilled in the art of diagnostics.
  • the method of the second aspect comprises measuring the presence and/or amount in the test sample of all of the proteins defined in Table 2—i.e. all 22 of the proteins in Table 2.
  • the method of the first aspect may comprise measuring the presence and/or amount in the test sample of 1 or 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10 or 11 or 12 or 13 or 14 or 15 or 16 or 17 or 18 or 19 or 20 or 21 or 22 of the proteins defined in Table 2.
  • the method of the first aspect comprises measuring the presence and/or amount in the test sample of CD40 ligand and/or mucine and/or IL-16 and/or Rantes and/or Eotaxin and/or MCP-4 and/or IL-11 and/or TNF-b and/or IL-1ra and/or MCP-3 and/or IL-1a and/or IL-3 and/or C3 and/or LDL (1) and/or LDL (2) and/or Lewis Y.
  • the first aspect of the invention provides a method wherein step (b) and/or step (d) is performed using a first binding agent capable of binding to the one or more proteins.
  • the second aspect of the invention provides a method wherein step (ii) and/or step (iv) is performed using a first binding agent capable of binding to the one or more proteins.
  • Binding agents can be selected from a library, based on their ability to bind a given motif, as discussed below.
  • At least one type, more typically all of the types, of the binding molecules may be an antibody or fragments or variants thereof.
  • a fragment may contain one or more of the variable heavy (V H ) or variable light (V L ) domains.
  • V H variable heavy
  • V L variable light
  • the term antibody fragment includes Fab-like molecules (Better et al (1988) Science 240, 1041); Fv molecules (Skerra et al (1988) Science 240, 1038); single-chain Fv (ScFv) molecules where the V H and V L partner domains are linked via a flexible oligopeptide (Bird et al (1988) Science 242, 423; Huston et al (1988) Proc. Natl. Acad. Sci. USA 85, 5879) and single domain antibodies (dAbs) comprising isolated V domains (Ward et al (1989) Nature 341, 544).
  • antibody variant includes any synthetic antibodies, recombinant antibodies or antibody hybrids, such as but not limited to, a single-chain antibody molecule produced by phage-display of immunoglobulin light and/or heavy chain variable and/or constant regions, or other immunointeractive molecule capable of binding to an antigen in an immunoassay format that is known to those skilled in the art.
  • At least one type, more typically all of the types, of the binding molecules is an aptamer.
  • the molecular libraries may be expressed in vivo in prokaryotic (Clackson et al, 1991, op. cit.; Marks et al, 1991, op. cit.) or eukaryotic cells (Kieke et al, 1999, Proc Natl Acad Sci USA, 96(10):5651-6) or may be expressed in vitro without involvement of cells (Hanes & Pluckthun, 1997 , Proc Natl Acad Sci USA 94(10):4937-42; He & Taussig, 1997 , Nucleic Acids Res 25(24):5132-4; Nemoto et al, 1997, FEBS Lett, 414(2):405-8).
  • binding molecules may involve the use of array technologies and systems to analyse binding to spots corresponding to types of binding molecules.
  • the first binding agent is an antibody or a fragment thereof; more preferably, a recombinant antibody or fragment thereof.
  • the antibody or fragment thereof is selected from the group consisting of: scFv; Fab; a binding domain of an immunoglobulin molecule.
  • variable heavy (V H ) and variable light (V L ) domains of the antibody are involved in antigen recognition, a fact first recognised by early protease digestion experiments. Further confirmation was found by “humanisation” of rodent antibodies. Variable domains of rodent origin may be fused to constant domains of human origin such that the resultant antibody retains the antigenic specificity of the rodent parented antibody (Morrison et al (1984) Proc. Natl. Acad. Sci. USA 81, 6851-6855).
  • variable domains that antigenic specificity is conferred by variable domains and is independent of the constant domains is known from experiments involving the bacterial expression of antibody fragments, all containing one or more variable domains.
  • variable domains include Fab-like molecules (Better et al (1988) Science 240, 1041); Fv molecules (Skerra et al (1988) Science 240, 1038); single-chain Fv (ScFv) molecules where the V H and V L partner domains are linked via a flexible oligopeptide (Bird et al (1988) Science 242, 423; Huston et al (1988) Proc. Natl. Acad. Sci.
  • ScFv molecules we mean molecules wherein the V H and V L partner domains are linked via a flexible oligopeptide.
  • antibody fragments rather than whole antibodies
  • the smaller size of the fragments may lead to improved pharmacological properties, such as better penetration of solid tissue.
  • Effector functions of whole antibodies, such as complement binding, are removed.
  • Fab, Fv, ScFv and dAb antibody fragments can all be expressed in and secreted from E. coli , thus allowing the facile production of large amounts of the said fragments.
  • the antibodies may be monoclonal or polyclonal. Suitable monoclonal antibodies may be prepared by known techniques, for example those disclosed in “Monoclonal Antibodies: A manual of techniques”, H Zola (CRC Press, 1988) and in “Monoclonal Hybridoma Antibodies: Techniques and applications”, J G R Hurrell (CRC Press, 1982), both of which are incorporated herein by reference.
  • the invention provides methods wherein the one or more proteins in the test sample is labelled with a detectable moiety.
  • the first aspect provides a method wherein the one or more proteins in the control sample is labelled with a detectable moiety.
  • the one or more proteins in the first and/or second control sample is labelled with a detectable moiety.
  • detecttable moiety we include the meaning that the moiety is one which may be detected and the relative amount and/or location of the moiety (for example, the location on an array) determined.
  • Detectable moieties are well known in the art.
  • a detectable moiety may be a fluorescent and/or luminescent and/or chemiluminescent moiety which, when exposed to specific conditions, may be detected.
  • a fluorescent moiety may need to be exposed to radiation (i.e. light) at a specific wavelength and intensity to cause excitation of the fluorescent moiety, thereby enabling it to emit detectable fluorescence at a specific wavelength that may be detected.
  • the detectable moiety may be an enzyme which is capable of converting a (preferably undetectable) substrate into a detectable product that can be visualised and/or detected. Examples of suitable enzymes are discussed in more detail below in relation to, for example, ELISA assays.
  • the detectable moiety may be a radioactive atom which is useful in imaging. Suitable radioactive atoms include 99m Tc and 123 I or scintigraphic studies. Other readily detectable moieties include, for example, spin labels for magnetic resonance imaging (MRI) such as 123 I again, 131 I, 111 In, 19 F, 13 C, 15 N, 17 O, gadolinium, manganese or iron.
  • MRI magnetic resonance imaging
  • the agent to be detected (such as, for example, the one or more proteins in the test sample and/or control sample described herein and/or an antibody molecule for use in detecting a selected protein) must have sufficient of the appropriate atomic isotopes in order for the detectable moiety to be readily detectable.
  • the radio—or other labels may be incorporated into the agents of the invention (i.e. the proteins present in the samples of the methods of the invention and/or the binding agents of the invention) in known ways.
  • the binding moiety is a polypeptide it may be biosynthesised or may be synthesised by chemical amino acid synthesis using suitable amino acid precursors involving, for example, fluorine-19 in place of hydrogen.
  • Labels such as 99m Tc, 123 I, 186 Rh, 188 Rh and 111 In can, for example, be attached via cysteine residues in the binding moiety.
  • Yttrium-90 can be attached via a lysine residue.
  • the IODOGEN method (Fraker et al (1978) Biochem. Biophys. Res.
  • the accompanying Examples provide an example of methods and detectable moieties for labelling agents of the invention (such as, for example, proteins in the sample of the methods of the invention and/or binding molecules) for use in the methods of the invention.
  • labelling agents of the invention such as, for example, proteins in the sample of the methods of the invention and/or binding molecules
  • the detectable moiety is selected from the group consisting of: a fluorescent moiety; a luminescent moiety; a chemiluminescent moiety; a radioactive moiety; an enzymatic moiety.
  • the first aspect provides a method wherein step (b) and/or step (d) is performed using an array.
  • step (ii) and/or step (iv) is performed using an array.
  • Arrays per se are well known in the art. Typically they are formed of a linear or two-dimensional structure having spaced apart (i.e. discrete) regions (“spots”), each having a finite area, formed on the surface of a solid support.
  • An array can also be a bead structure where each bead can be identified by a molecular code or colour code or identified in a continuous flow. Analysis can also be performed sequentially where the sample is passed over a series of spots each adsorbing the class of molecules from the solution.
  • the solid support is typically glass or a polymer, the most commonly used polymers being cellulose, polyacrylamide, nylon, polystyrene, polyvinyl chloride or polypropylene.
  • the solid supports may be in the form of tubes, beads, discs, silicon chips, microplates, polyvinylidene difluoride (PVDF) membrane, nitrocellulose membrane, nylon membrane, other porous membrane, non-porous membrane (e.g. plastic, polymer, perspex, silicon, amongst others), a plurality of polymeric pins, or a plurality of microtitre wells, or any other surface suitable for immobilising proteins, polynucleotides and other suitable molecules and/or conducting an immunoassay.
  • PVDF polyvinylidene difluoride
  • nitrocellulose membrane nitrocellulose membrane
  • nylon membrane other porous membrane
  • non-porous membrane e.g. plastic, polymer, perspex, silicon, amongst others
  • a plurality of polymeric pins e.g. plastic, polymer, perspex, silicon, amongst others
  • microtitre wells e.g. plastic, polymer, perspex, silicon,
  • the array is a microarray.
  • microarray we include the meaning of an array of regions having a density of discrete regions of at least about 100/cm 2 , and preferably at least about 1000/cm 2 .
  • the regions in a microarray have typical dimensions, e.g., diameters, in the range of between about 10-250 ⁇ m, and are separated from other regions in the array by about the same distance.
  • the array may also be a macroarray or a nanoarray.
  • binding molecules discussed above
  • the skilled person can manufacture an array using methods well known in the art of molecular biology.
  • the first aspect of the invention provides a method wherein step (b) and/or step (d) is performed using an assay comprising a second binding agent capable of binding to the one or more proteins, the second binding agent having a detectable moiety.
  • step (ii) and/or step (iv) is performed using an assay comprising a second binding agent capable of binding to the one or more proteins, the second binding agent having a detectable moiety.
  • Binding agents are described in detail above.
  • the second binding agent is an antibody or a fragment thereof; typically a recombinant antibody or fragment thereof.
  • the antibody or fragment thereof is selected from the group consisting of: scFv; Fab; a binding domain of an immunoglobulin molecule. Antibodies are described in detail above.
  • the invention provides a method wherein the detectable moiety is selected from the group consisting of: a fluorescent moiety; a luminescent moiety; a chemiluminescent moiety; a radioactive moiety; an enzymatic moiety.
  • the detectable moiety is selected from the group consisting of: a fluorescent moiety; a luminescent moiety; a chemiluminescent moiety; a radioactive moiety; an enzymatic moiety. Examples of suitable detectable moieties for use in the methods of the invention are described above.
  • Preferred assays for detecting serum or plasma proteins include enzyme linked immunosorbent assays (ELISA), radioimmunoassay (RIA), immunoradiometric assays (IRMA) and immunoenzymatic assays (IEMA), including sandwich assays using monoclonal and/or polyclonal antibodies.
  • ELISA enzyme linked immunosorbent assays
  • RIA radioimmunoassay
  • IRMA immunoradiometric assays
  • IEMA immunoenzymatic assays
  • sandwich assays are described by David et al in U.S. Pat. Nos. 4,376,110 and 4,486,530, hereby incorporated by reference.
  • Antibody staining of cells on slides may be used in methods well known in cytology laboratory diagnostic tests, as well known to those skilled in the art.
  • the assay is an ELISA (Enzyme Linked Immunosorbent Assay) which typically involve the use of enzymes which give a coloured reaction product, usually in solid phase assays. Enzymes such as horseradish peroxidase and phosphatase have been widely employed. A way of amplifying the phosphatase reaction is to use NADP as a substrate to generate NAD which now acts as a coenzyme for a second enzyme system. Pyrophosphatase from Escherichia coli provides a good conjugate because the enzyme is not present in tissues, is stable and gives a good reaction colour. Chemi-luminescent systems based on enzymes such as luciferase can also be used.
  • ELISA Enzyme Linked Immunosorbent Assay
  • Vitamin biotin Conjugation with the vitamin biotin is frequently used since this can readily be detected by its reaction with enzyme-linked avidin or streptavidin to which it binds with great specificity and affinity.
  • the invention provides an array for determining the presence of pancreatic adenocarcinoma in an individual comprising one or more binding agent according to the invention.
  • the one or more binding agent is capable of binding to all of the proteins defined in Table 1.
  • the invention provides an array for determining the survival time of an individual afflicted with pancreatic adenocarcinoma comprising one or more binding agent according to the invention.
  • the one or more binding agent is capable of binding to all of the proteins defined in Table 2.
  • Arrays suitable for use in the methods of the invention are discussed above.
  • the invention provides the use of an array in the methods of the first and/or second aspects of the invention.
  • the invention provides the use of one or more proteins selected from the group defined in Table 1 as a diagnostic marker for determining the presence of pancreatic adenocarcinoma in an individual.
  • a diagnostic marker for determining the presence of pancreatic adenocarcinoma in an individual is used as a diagnostic marker for determining the presence of pancreatic adenocarcinoma in an individual.
  • the invention provides the use of one or more proteins selected from the group defined in Table 2 as a diagnostic marker for determining the survival time of an individual afflicted with pancreatic adenocarcinoma. It is preferred that all of the proteins defined in Table 2 are used as a diagnostic marker for determining the survival time of an individual afflicted with pancreatic adenocarcinoma.
  • kits for determining the presence of pancreatic adenocarcinoma comprising:
  • the invention provides a kit for determining the presence of pancreatic adenocarcinoma comprising:
  • kits for determining the survival time of an individual afflicted with pancreatic adenocarcinoma comprising:
  • the invention provides a kit for determining the survival time of an individual afflicted with pancreatic adenocarcinoma comprising:
  • FIG. 1 Detection of pancreatic adenocarcinomas by serum protein expression analysis, using recombinant antibody microarrays.
  • the two-way hierarchical clustering was based on 19 serum biomarkers that were significantly (p ⁇ 0.05) differentially expressed in cancer vs. normal individuals, using a training set composed of 28 serum samples.
  • FIG. 2 Identification of a predictive serum protein biomarker signature, discriminating between two patient cohorts of short ( ⁇ 12 months) vs. long (>24 months) survivors.
  • a A Receiver Operator Curve (ROC) area as a function of the number of analytes included in a predictive signature, which clearly demonstrates that the two cohorts of survivors could be well discriminated using a signature >29 analytes;
  • b The ROC area of a predictive serum biomarker signature, based on 29 antibody identified analytes;
  • a SVM was trained with the biomarker signature chosen by the training set.
  • test set consisting of 10 randomly chosen patients (samples marked with *) was then classified, using the SVM Prediction Value; (d) A heat map based on the 22 non-redundant serum proteins in the predictive signature.
  • the columns represents cancer patients, where blue is long (>24 months) survivors and red is short ( ⁇ 12 months) survivors. See legend to FIG. 1 c for color coding.
  • FIG. 3 Principle of the recombinant antibody microarray technology.
  • the driving force behind oncoproteomics is to identify protein signatures that are associated with a particular malignancy.
  • a protein signature based on 22 non-redundant analytes, discriminating between cancer and healthy patients. The specificity and sensitivity were predicted to be 99.7 and 99.9%, respectively.
  • a protein signature consisting of 19 protein analytes was defined that had the potential to predicted survival amongst cancer patients. This novel predictor signature distinguished between patients having ⁇ 12 months or >24 months survival time and suggests new possibilities in individualized medicine.
  • the present study describes an affinity proteomic approach to prognostic diagnosis of cancer based on a recombinant antibody microarray, utilizing array adapted recombinant scFv fragments (12,13).
  • array adapted recombinant scFv fragments (12,13).
  • the results demonstrated that an array of antibody fragments, specific for immunoregulatory proteins, can discriminate between human serum proteomes derived from either cancer patients or healthy individuals.
  • scFv 129 human recombinant scFv antibody fragments against 60 different proteins mainly involved in immunoregulation
  • n-CoDeR library (13) and kindly provided by BioInvent International AB (Lund, Sweden).
  • each antigen was recognized by up to four different scFv fragments.
  • All scFv antibodies were produced in 100 ml E. coli cultures and purified from expression supernatants, using affinity chromatography on Ni-NTA agarose (Qiagen, Hilden, Germany). Bound molecules were eluted with 250 mM imidazole, extensively dialyzed against PBS, and stored at 4° C., until further use.
  • the protein concentration was determined by measuring the absorbance at 280 nm (average concentration 210 ⁇ g/ml, range 60-1090 ⁇ g/ml). The purity was evaluated by 10% SDS-PAGE (Invitrogen, Carlsbad, Calif., USA).
  • Serum Samples In total, 44 serum samples supplied by Swiss South General Hospital (Sweden) and Lund University Hospital (Lund, Sweden) were included in this study. 24 serum samples (PA1-PA30) were collected from patients with pancreatic cancer at the time of diagnosis. 20 serum samples (N1-N20) (no clinical symptoms) were collected from healthy donors. Patient demographics are shown in Table 4. All samples were aliquoted and stored at ⁇ 80° C., following standard operating procedures.
  • serum samples were labeled using previously optimized labeling protocols for serum proteomes (9, 14, 15). All serum samples were biotinylated using the EZ-Link Sulfo-NHS-LC-Biotin (Pierce, Rockford, Ill., USA). 50 ⁇ l serum aliquots were centrifuged at 16.000 ⁇ g for 20 minutes at 4 ° C. and diluted 1:45 in PBS, resulting in a concentration of about 2 mg/ml. The samples were then biotinylated by adding Sulfo-NHS-biotin to a final concentration of 0.6 mM for 2 h on ice, with careful Vortexing every 20 minute.
  • Enzyme linked immunosorbent assay The serum concentration of 4 protein analytes (MCP-3, IL-4, IL-5 and IL-13) were measured in all samples, using commercial ELISA kits (Quantikine, R&D Systems, Minneapolis, Minn., USA). The measurements were performed according to the recommendations provided by the supplier.
  • the antibodies were spotted onto black polymer MaxiSorb microarray slides (NUNC A/S, Roskilde, Denmark), resulting in an average of 5 fmol scFv per spot (range 1.5-25 fmol). Eight replicates of each scFv clone were arrayed to ensure adequate statistics. In total, 160 antibodies and controls were printed per slide orientated in two columns with 8 ⁇ 80 antibodies per column. To assist the alignment of the grid during the quantification a row containing Cy5 conjugated streptavidin (2 ⁇ g/ml) was spotted for every tenth row. A hydrophobic pen (DakoCytomation Pen, DakoCytomation, Glostrup, Denmark) was used to draw a hydrophobic barrier around the arrays.
  • the arrays were blocked with 500 ⁇ l 5% (w/v) fat-free milk powder (Semper AB, Sundbyberg, Sweden) in PBS overnight. All incubations were conducted in a humidity chamber at room temperature. The arrays were then washed four times with 400 ⁇ l 0.05% Tween-20 in PBS (PBS-T), and incubated with 350 ⁇ l biotinylated serum, diluted 1:10 (resulting in a total serum dilution of 1:450) in 1% (w/v) fat-free milk powder and 1% Tween in PBS (PBS-MT) for 1 h.
  • the arrays were washed four times with 400 ⁇ l PBS-T and incubated with 350 ⁇ l 1 ⁇ g/ml Alexa-647 conjugated streptavidin, diluted in PBS-MT for 1 h. Finally, the arrays were washed four times with 400 ⁇ l PBS-T, dried immediately under a stream of nitrogen gas and scanned with a confocal microarray scanner (ScanArray Express, Perkin Elmer Life & Analytical Sciences) at 5 ⁇ m resolution, using six different scanner settings. The ScanArray Express software V2.0 (Perkin Elmer Life & Analytical Sciences) was used to quantify the intensity of each spot, using the fixed circle method.
  • ScanArray Express Perkin Elmer Life & Analytical Sciences
  • the local background was subtracted and to compensate for possible local defects, the two highest and the two lowest replicates were automatically excluded and each data point represents the mean value of the remaining four replicates.
  • the coefficient of correlation for intra-assays was >0.99 and for inter-assays >0.96, respectively.
  • Chip-to-chip normalization of the data sets was performed, using a semi-global normalization approach, conceptually similar to the normalization developed for DNA microarrays. Thus, the coefficient of variation (CV) was first calculated for each analyte and ranked. Fifteen % of the analytes that displayed the lowest CV-values over all samples were identified, corresponding to 21 analytes, and used to calculate a chip-to-chip normalization factor.
  • CV coefficient of variation
  • the Sammon map was performed using Euclidean distance in the space of all 129 analytes.
  • Supervised classification was done with a Support Vector Machine (SVM) using a linear kernel (16-18).
  • the cost of constraints violation (the parameter C in the SVM) was fixed to 1, which is the default value in the R function svm, and no attempt was done to tune it. This absence of parameter tuning was chosen to avoid overfitting and to make the classification procedure easier to understand.
  • the output of the SVM on a test sample is a SVM decision value, which is the signed distance to the hyperplane.
  • FIGS. 1C and 2C the split into training and test set was done randomly once and kept fixed from thereon.
  • FIG. 1C and 2C the split into training and test set was done randomly once and kept fixed from thereon.
  • a leave-one-out cross validation procedure is used. For every number K between 1 and 129 the following procedure was carried out. For a training set, i.e., all samples except one, the K highest ranked analytes with a Wilcoxon test were chosen, and a SVM was trained with those K analytes. A SVM decision value was then calculated for the left out sample with this classifier. As is common practice, this was done for all samples in the leave-one-out cross validation.
  • FIG. 2A shows the ROC area as a function of K.
  • Pancreatic ductal adenocarcinoma is a cancer with poor prognosis and improved diagnostic tool facilitating the clinical decision making would significantly benefit the patients.
  • One approach to improved diagnosis is to identify a set of biomarkers that can detect cancer and that also is predict clinical outcome. Consequently, to be able to identify a protein signature linked to pancreatic cancer with high sensitivity, we have designed the first large-scale microarray ( FIG. 1A ) based on 129 recombinant antibody fragments (12, 14, 15), directed against 60 serum proteins, mainly of immunoregulatory nature (Table 2). In this study, labeled sera from 24 pancreatic cancer patients and 20 healthy patients were incubated on the antibody microarrays, which subsequently were quantified, using a confocal scanner.
  • the microarray data was displayed in an unsupervised Sammon plot based on all antibodies and two distinct populations could clearly be distinguished ( FIG. 1B ). This indicated the existence of a clear difference between the cancer and the normal proteomes, in relation to the serum analytes analyzed by the microarray.
  • SVM Support Vector Machine
  • the decision value is the output of the predictor, and samples with a prediction value above (below) a threshold are predicted to be pancreatic carcinomas (healthy).
  • the threshold parameterizes the trade-off between sensitivity and specificity and is often, but not always, set to zero.
  • the 24 pancreatic carcinoma samples obtained decision values in the interval from 0.30 to 1.93, and the healthy samples in the interval from ⁇ 1.84 to ⁇ 0.30.
  • a threshold value of zero, or any other value between ⁇ 0.30 and 0.30 the sensitivity and specificity is 100% in our data set.
  • a SVM was trained with the biomarker signature chosen by the training set and the test set could then be classified, as shown in FIG. 2C . All patients surviving ⁇ 12 months were correctly classified, using a SVM prediction value of ⁇ 0, which was considered the most important classification. One long-term survivor was miss-classified.
  • the 29 most significant analytes separating long and short term survivors among all 23 patients in a Wilcoxon test corresponds to 22 non redundant serum proteins (7 of the 29 analytes were duplicates but defined by different antibody clones).
  • This novel predictor signature represented by 22 non-redundant proteins, and the differential analyte response displayed by short and long survivors, respectively, are shown as a heat map in FIG. 2D .
  • cytokines such as IL-1a, IL-3, IL-8, and IL-11 were upregulated in short term survivors, while Rantes, IL-16, IL-4 and eotaxin were mostly upregulated in long term survivors ( FIG. 2D ). The significance of this remains to be validated but it could possibly indicating a more active T-cell compartment in the latter population.
  • Antibody microarrays as a tool in affinity proteomics, have evolved over the last several years from a promising tool to an approach that is starting to deliver promising results in oncoproteomics (3, 12, 20, 21).
  • the main focus in these endeavors is to detect cancer at an early stage, to predict tumor relapse and treatment resistance, or to select patients for a particular treatment regime (3).
  • This is in particular important for cancers with poor prognosis, which is also intrinsic to pancreatic cancer since it rapidly metastasize to e.g. lymph nodes, lungs, peritoneum (4, 23) and is difficult to diagnose at an early stage.
  • the ability of a biomarker signature to distinguish between different carcinomas or between cancer and inflammation has so far been difficult to achieve (for review see ref. 3) (20).
  • the distance to the hyperplane is called the prediction or decision value ( FIG. 2C ).
  • the hyperplane and, thus, the classification of groups, were found by using our training set.
  • the performance of the classifier was then estimated by subsequently utilizing a test set, where no overlap between the training and test set was allowed.
  • a data set can randomly be split into different training and a test set, which are then used to train and test the classifier, respectively.
  • the drawback of this is that the final result depends on the split into training and test set. Consequently, we used cross validation as the procedure of making several splits of our data set and used the average performance of the test sets as a measure of the accuracy of data classification.
  • the test set contains one sample and the training set contains the rest.
  • the performance of the SVM can be measured by the ROC curve and, in particular, the area under the ROC curve.
  • the normal and pancreatic carcinoma samples were remarkably well separated, since the SVM classified all samples correctly with a gap between the two groups. Extrapolation of the decision values gave very high sensitivity (99.9%) and specificity (99.3%), showing that it would take hundreds of samples to get one misclassification.
  • pancreatic cancer associated biomarker signature had, however, only eotaxin, IL-5 and IL-13 in common with fourteen biomarkers found as a result of a bacterial infection, associated with another gastrointestinal cancer (12), which indicated that the pancreatic signature was not related to general inflammation.
  • this signature was not similar to biomarkers found in systemic lupus erythematosus, an autoimmune disorder with a significant inflammatory component (Wingren et al., manuscript in preparation). The signature was also completely different from what Orchekowski et al.
  • our present cancer signature contained a number of over-expressed TH2 cytokines (IL-4, -5, -10 and -13), whereas classical TH1 cytokines (IL-12 and TNF-b) were down-regulated, which also was in agreement with the study of Belone et al., who showed that TGF-b and IL-10 were up-regulated in pancreatic cancer sera (27). These authors also showed that blood-derived monocytes from pancreatic cancer patients were primed to develop a TH2-like response rather than a TH1-like response, with increased expression of IL-4 and decreased expression of IL-12.

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CN109564221A (zh) * 2016-05-10 2019-04-02 免疫媒介有限公司 方法、阵列及其用途

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