WO2004030521A2 - Utilisation de biomarqueurs pour detecter un rejet aigu de transplantation renale - Google Patents

Utilisation de biomarqueurs pour detecter un rejet aigu de transplantation renale Download PDF

Info

Publication number
WO2004030521A2
WO2004030521A2 PCT/US2003/031089 US0331089W WO2004030521A2 WO 2004030521 A2 WO2004030521 A2 WO 2004030521A2 US 0331089 W US0331089 W US 0331089W WO 2004030521 A2 WO2004030521 A2 WO 2004030521A2
Authority
WO
WIPO (PCT)
Prior art keywords
biomarker
molecular weight
biomarkers
molecular
ght
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/US2003/031089
Other languages
English (en)
Other versions
WO2004030521A3 (fr
Inventor
William A. Clarke
Benjamin Charles Silverman
Ernesto Pompeo Molmenti
Zhen Zhang
Daniel Wanyui Chan
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Johns Hopkins University
Original Assignee
Johns Hopkins University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Johns Hopkins University filed Critical Johns Hopkins University
Priority to AU2003275363A priority Critical patent/AU2003275363A1/en
Publication of WO2004030521A2 publication Critical patent/WO2004030521A2/fr
Anticipated expiration legal-status Critical
Publication of WO2004030521A3 publication Critical patent/WO2004030521A3/fr
Ceased legal-status Critical Current

Links

Classifications

    • 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
    • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
    • G01N33/6848—Methods of protein analysis involving mass spectrometry
    • C—CHEMISTRY; METALLURGY
    • C07—ORGANIC CHEMISTRY
    • C07K—PEPTIDES
    • C07K1/00—General methods for the preparation of peptides, i.e. processes for the organic chemical preparation of peptides or proteins of any length
    • C07K1/14—Extraction; Separation; Purification
    • C07K1/16—Extraction; Separation; Purification by chromatography
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/24—Immunology or allergic disorders
    • G01N2800/245—Transplantation related diseases, e.g. graft versus host disease
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/34—Genitourinary disorders
    • G01N2800/347—Renal failures; Glomerular diseases; Tubulointerstitial diseases, e.g. nephritic syndrome, glomerulonephritis; Renovascular diseases, e.g. renal artery occlusion, nephropathy

Definitions

  • the invention provides for Biomarkers important in the detection of acute renal transplant rejection.
  • the Biomarkers were identified by distinguishing the urine protein profile of renal transplant patients with no rejection and those with acute rejection using SELDI analysis.
  • the present invention relates the Biomarkers to a system and method in which the Biomarkers are used for the qualification of kidney transplant rejection status.
  • Acute rejection has been reported to cause a 20% reduction in the 1-year survival rate and a 4-year diminution in the projected half-life of cadaver allografts.
  • Rejection can be defined as the immunologic interaction between host and allograft in which reactivity by the former leads to a sudden deterioration in physiologic function ofthe latter.
  • Almond PS Matas A, Gillingham KJ, et al. Risk factors for chronic rejection in renal allograft recipients. Transplantation. 1993; 55: 752-756; Gulanikar AC, MacDonald AS, Sungurtekin U, et al. The incidence and impact of early rejection episodes on graft outcome in recipients of first cadaver kidney transplants. Transplantation. 1992; 53: 323-328; Lindholm A, Ohlman S, Albrechtsen D, et al.
  • renal function may not always correlate with histologic improvement ⁇ Roberti I, Reisman L. Serial evaluation of cell surface markers for immune activation after acute renal allograft rejection by urine flow cytornetry-correlation with clinical outcome. Transplantation. 2001; 71: 1317- 1320; Woodle ES, Cronin D, Newell KA, et al. Tacrolimus therapy for refractory acute renal allograft rejection. Transplantation. 1996; 62: 906; Beckingham IJ, Nicholson ML, Bell PR. Analysis of factors associated complications following renal transplant needle core biopsy. Br J Urol. 1994; 73: 13-15.).
  • Biopsy ofthe renal allograft is regarded as the standard for the diagnosis of rejection and delayed graft function.
  • percutaneous renal biopsy is costly and has associated morbidity and mortality.
  • Complications include but are not limited to pain, hematuria, arteriovenous fistulas, perirenal hematomas, injury to adjacent viscera, anuria, allograft thrombosis, sepsis, shock, allograft loss, and patient death.
  • the present invention provides sensitive and quick methods and kits that are useful for determining the kidney transplant rejection status by measuring Biomarkers ofthe present invention.
  • the measurement of these Biomarkers in patient samples provides information that diagnosticians can correlate with a probable diagnosis of kidney transplant rejection or non-rejection.
  • the Biomarkers are characterized by molecular weight and/or by other protein identities.
  • the Biomarkers can be resolved from other proteins in a sample by using a variety of fractionation techniques, e.g., chromatographic separation coupled with mass spectrometry, protein capture using immobilized antibodies or by traditional immunoassays.
  • the method of resolution involves Surface-Enhanced Laser Desorption/Ionization ("SELDI") mass spectrometry, in which the surface ofthe mass spectrometry probe comprises adsorbents that bind the Biomarkers.
  • SELDI Surface-Enhanced Laser Desorption/Ionization
  • Biomarkers 1 through 48 More specifically, fourty-eight Biomarkers were discovered and subsequently identified, in accordance with the methods described and identified and referred to as Biomarkers 1 through 48.
  • the present invention provides a method of qualifying kidney transplant rejection status in a subject comprising (a) measuring at least one Biomarker in a sample from the subject, wherein the Biomarker is selected from the group consisting Biomarkers 1 through 48 and combinations thereof, and (b) correlating the measurement with kidney transplant rejection status.
  • the measuring step comprises detecting the presence or absence of Biomarkers in the sample.
  • the measuring step comprises quantifying the amount of Biomarker(s) in the sample.
  • the measuring step comprises qualifying the type of bioBiomarker in the sample.
  • the invention also relates to methods wherein the measuring step comprises: providing a subject sample of urine or a urine derivative; fractionating proteins in the san ⁇ le on an anion exchange resin and collectine fractions that contain Biomarkers 1 through 48; and capturing Biomarkers 1 through 48 from the fractions on a surface of a substrate comprising capture reagents that bind the protein Biomarkers.
  • the substrate is a SELDI probe comprising an IMAC copper surface and wherein the protein Biomarkers are detected by SELDI.
  • the substrate is a SELDI probe comprising biospecific affinity reagents that bind Biomarkers 1 through 48 and wherein the protein Biomarkers are detected by SELDI.
  • the substrate is a microtiter plate comprising biospecific affinity reagents that bind Biomarkers 1 through 48 and the protein Biomarkers are detected by immunoassay.
  • the methods further comprise managing subject treatment based on the status determined by the method. For example, if the result of the methods ofthe present invention is inconclusive or there is reason that confirmation of status is necessary, the physician may order more tests. Alternatively, if the status indicates that altering immunosuppression is appropriate, the physician may schedule the patient for a change in immunosuppressive therapy. Furthermore, if the results show that the current treatment is appropriatel, no further management may be necessary.
  • the invention also provides for such methods where the at least one
  • Biomarker is measured again after subject management. In these instances, the step of managing subject treatment is then repeated and/or altered depending on the result obtained.
  • kidney transplant rejection status refers to the status of kidney function in the patient.
  • types of kidney transplant rejection statuses include, but are not limited to, the subject's urine ceatinine levels, the degree of immunosuppression, and the effectiveness of immunosuppressive treatment. Other statuses and degrees of each status are known in the art.
  • Biomarkers 1 through 48 Protein Biomarkers ofthe invention can be characterized in one or more of several respects. In particular, in one aspect, these Biomarkers are characterized by molecular weights under the conditions soecified herein, oarticularlv as determined by mass spectral analysis. In another aspect, the Biomarkers can be characterized by features ofthe Biomarkers' mass spectral signature such as size (including area) and/or shape ofthe Biomarkers' spectral peaks, features including proximity, size and shape of neighboring peaks, etc.
  • the Biomarkers can be characterized by affinity binding characteristics, particularly ability to binding to an LMAC copper adsorbent under specified conditions, however, other metals, e.g., nickel, may also be used.
  • Biomarkers ofthe invention may be characterized by each of such aspects, i.e. molecular weight, mass spectral signature and IMAC-Cu absorbent binding.
  • the mass accuracy of the spectral instrument is considered to be about within +/- 0.15 percent ofthe disclosed molecular weight value. Additionally, to such recognized accuracy variations ofthe instrument, the spectral mass determination can vary within resolution limits of from about 400 to 1000 m/dm, where m is mass and dm is the mass spectral peak width at 0.5 peak height. Those mass accuracy and resolution variances associated with the mass spectral instrument and operation thereof are reflected in the use ofthe term "about" in the disclosure ofthe mass of each of Biomarkers 1 through 48.
  • the present invention further provides a method of qualifying kidney transplant rejection status in a subject comprising (a) measuring at least one bioBiomarker in a sample from the subject, wherein the bioBiomarker is selected from the group consisting of Biomarkers 1 through 48 and combinations thereof, and (b) correlating the measurement with kidney transplant rejection status.
  • the measuring step comprises detecting the presence or absence of Biomarkers in the sample.
  • the measuring step comprises quantifying the amount of Biomarker(s) in the sample.
  • the measuring step comprises qualifying the type of bioBiomarker in the sample.
  • the accuracy of a diagnostic test is characterized by a Receiver Operating Characteristic curve ("ROC curve").
  • An ROC is a plot ofthe true positive rate against the false positive rate for the different possible outpoints of a diagnostic test.
  • An ROC curve shows the relationship between sensitivity and specificity. That is, an increase in sensitivity will be accompanied by a decrease in specificity. The closer the curve follows the left axis and then the top edge ofthe ROC space, the more accurate the test. Conversely, the closer the curve comes to the 45-degree diagonal ofthe ROC graph, the less accurate the test.
  • the area under the ROC is a measure of test accuracy. The accuracy ofthe test depends on how well the test separates the group being tested into those with and without the disease in question. An area under the curve (referred to as "AUC") of 1 represents a perfect test, while an area of 0.5 represents a less useful test.
  • AUC area under the curve
  • Biochip arrays useful in the invention include protein and nucleic acid arrays.
  • One or more Biomarkers are captured on the biochip array and subjected to laser ionization to detect the molecular weight ofthe Biomarkers.
  • Analysis ofthe Biomarkers is, for example, by molecular weight ofthe one or more Biomarkers against a threshold intensity that is normalized against total ion current.
  • logarithmic transformation is used for reducing peak intensity ranges to limit the number of Biomarkers detected.
  • the step of correlating the measurement ofthe Biomarkers with kidney transplant status is performed by a software classification algorithm.
  • data is generated on immobilized subject samples on a biochip array, by subjecting said biochip array to laser ionization and detecting intensity of signal for mass/charge ratio; and, transforming the data into computer readable form; and executing an algorithm that classifies the data according to user input parameters, for detecting signals that represent Biomarkers present in kidney transplant rejection patients and are lacking in non-rejection patients.
  • the biochip surfaces are, for example, ionic, anionic, comprised of immobilized nickel ions, comprised of a mixture of positive and negative ions, comprised of one or more antibodies, single or double stranded nucleic acids, proteins, peptides or fragments thereof, amino acid probes, or phage display libraries.
  • one or more ofthe Biomarkers are measured using laser desorption/ionization mass spectrometry, comprising providing a probe adapted for use with a mass spectrometer comprising an adsorbent attached thereto, and contacting the subject sample with the adsorbent, and; desorbing and ionizing the Biomarker or Biomarkers from the probe and detecting the deionized/ionized Biomarkers with the mass spectrometer.
  • the laser desorption/ionization mass spectrometry comprises: providing a substrate comprising an adsorbent attached thereto; contacting the subject sample with the adsorbent; placing the substrate on a probe adapted for use with a mass spectrometer comprising an adsorbent attached thereto; and, desorbing and ionizing the Biomarker or Biomarkers from the probe and detecting the desorbed/ionized Biomarker or Biomarkers with the mass spectrometer.
  • the adsorbent can for example be hydrophobic, hydrophilic, ionic or metal chelate adsorbent, such as, nickel or an antibody, single- or double stranded oligonucleotide, amino acid, protein, peptide or fragments thereof.
  • the methods ofthe present invention can be performed on any type of patient sample that would be amenable to such methods, e.g., blood, serum and plasma.
  • the preferred patient sample is urine.
  • a plurality of Biomarkers in a sample from the subject are measured, wherein the Biomarkers are selected from the group consisting of Biomarkers 1 through 48.
  • the plurality of Biomarkers consists of Biomarkers 3, 6, 14, 15, 16, 18, 19, 20, 21, 22, 23, 32 and 35
  • the protein Biomarkers are measured by SELDI or immunoassay.
  • kits comprising (a) a capture reagent that binds a Biomarker selected from Biomarkers 1 through 48, and combinations thereof; and (b) a container comprising at least one ofthe Biomarkers.
  • the capture reagent binds a plurality ofthe Biomarkers.
  • the plurality comprises Biomarkers 3, 6, 14, 15, 16, 18, 19, 20, 21, 22, 23, 32 and 35.
  • the capture reagent can be any type of reagent, preferably the reagent is a SELDI probe.
  • the capture reagent may also bind other known Biomarkers.
  • the kit of further comprises a second capture reagent that binds one ofthe Biomarkers that the first capture reagent does not bind.
  • kits provided by the invention comprise (a) a first capture reagent that binds at least one Biomarker selected from Biomarkers 1 through 48, and (b) a second capture reagent that binds at least one ofthe Biomarkers that is not bound by the first capture reagent.
  • at least one the capture reagent is an antibody.
  • Certain kits further comprise an MS probe to which at least one capture reagent is attached or is attachable.
  • the capture reagent comprises an immobilized metal chelate ("LMAC").
  • LMAC immobilized metal chelate
  • kits ofthe present invention further comprise a wash solution that selectively allows retention ofthe bound Biomarker to the capture reagent as compared with other Biomarkers after washing.
  • kits comprising (a) a first capture reagent that binds at least one Biomarker selected from Biomarkers 1 through 48, and (b) instructions for using the capture reagent to measure the Biomarker.
  • the capture reagent comprises an antibody.
  • some kits further comprise an MS probe to which the capture reagent is attached or is attachable.
  • the capture reagent comprises an JMAC.
  • the kits may also contain a wash solution that selectively allows retention ofthe bound Biomarker to the capture reagent as compared with other Biomarkers after washing.
  • the kit comprises written instructions for use ofthe kit for determining kidney transplant rejection status and the instructions provide for contacting a test sample with the capture reagent and measuring one or more Biomarkers retained by the capture reagent.
  • the kit also provides for a capture reagent, which is an antibody, single or double stranded oligonucleotide, amino acid, protein, peptide or fragments thereof.
  • a capture reagent which is an antibody, single or double stranded oligonucleotide, amino acid, protein, peptide or fragments thereof.
  • Measurement of one or more protein Biomarkers using the kit is by mass spectrometry or immunoassays such as an ELISA.
  • Purified proteins for detection of kidney transplant rejection and/or generation of antibodies for further diagnostic assays are also provided for
  • Figure 1 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 1 having a molecular weight of about 2.5.
  • ROC Receiver Operator Characteristic
  • FIG. A and B represent the Receiver Operator Characteristic (ROC)
  • Figure 3 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 3 having a molecular weight of about 3.4.
  • ROC Receiver Operator Characteristic
  • Figure 4 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 4 having a molecular weight of about 3.5.
  • ROC Receiver Operator Characteristic
  • Figure 5 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 5 having a molecular weight of about 3.8.
  • ROC Receiver Operator Characteristic
  • Figure 6 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 6 having a molecular weight of about 4.1.
  • Figure 7 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 7 having a molecular weight of about 4.7.
  • Figure 8 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 8 having a molecular weight of about 4.8.
  • ROC Receiver Operator Characteristic
  • Figure 9 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 9 having a molecular weight of about 5.0.
  • ROC Receiver Operator Characteristic
  • FIG. 10 A and B represent the Receiver Operator Characteristic (ROC)
  • Figure 11 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 11 having a molecular weight of about 5.6.
  • ROC Receiver Operator Characteristic
  • Figure 12 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 12 having a molecular weight of about 6.1.
  • ROC Receiver Operator Characteristic
  • Figure 13 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 13 having a molecular weight of about 6.4.
  • ROC Receiver Operator Characteristic
  • Figure 14 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 14 having a molecular weight of about 6.5.
  • ROC Receiver Operator Characteristic
  • FIG. 15 A and B represent the Receiver Operator Characteristic (ROC)
  • Figure 16 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 16 having a molecular weight of about 6.7.
  • ROC Receiver Operator Characteristic
  • Figure 17 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 17 having a molecular weight of about 6.8.
  • Figure 18 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 18 having a molecular weight of about 7.0.
  • Figure 19 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 19 having a molecular weight of about 7.1.
  • ROC Receiver Operator Characteristic
  • Figure 20 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 20 having a molecular weight of about 7.3.
  • ROC Receiver Operator Characteristic
  • FIG. 21 A and B represent the Receiver Operator Characteristic (ROC)
  • Figure 22 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 22 having a molecular weight of about 7.8.
  • ROC Receiver Operator Characteristic
  • Figure 23 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 23 having a molecular weight of about 8.0.
  • ROC Receiver Operator Characteristic
  • Figure 24 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 24 having a molecular weight of about 8.1.
  • ROC Receiver Operator Characteristic
  • Figure 25 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 25 having a molecular weight of about 9.0.
  • ROC Receiver Operator Characteristic
  • FIG. 26 A and B represent the Receiver Operator Characteristic (ROC)
  • Figure 27 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 27 having a molecular weight of about 9.3.
  • ROC Receiver Operator Characteristic
  • Figure 28 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 28 having a molecular weight of about 9.6.
  • Figure 29 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 29 having a molecular weight of about 9.7.
  • Figure 30 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 30 having a molecular weight of about 9.8.
  • ROC Receiver Operator Characteristic
  • Figure 31 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 31 having a molecular weight of about 10.0.
  • ROC Receiver Operator Characteristic
  • FIG. 32 A and B represent the Receiver Operator Characteristic (ROC)
  • Figure 33 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 33 having a molecular weight of about 10.9.
  • ROC Receiver Operator Characteristic
  • Figure 34 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 34 having a molecular weight of about 11.3.
  • ROC Receiver Operator Characteristic
  • Figure 35 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 35 having a molecular weight of about 13.4.
  • ROC Receiver Operator Characteristic
  • Figure 36 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 36 having a molecular weight of about 13.9.
  • ROC Receiver Operator Characteristic
  • FIG. 37 A and B represent the Receiver Operator Characteristic (ROC)
  • Figure 38 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 38 having a molecular weight of about 14.8.
  • ROC Receiver Operator Characteristic
  • Figure 39 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 39 having a molecular weight of about 15.1.
  • Figure 40 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 40 having a molecular weight of about 15.2.
  • Figure 41 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 41 having a molecular weight of about 16.1.
  • ROC Receiver Operator Characteristic
  • Figure 42 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 42 having a molecular weight of about 25.0.
  • ROC Receiver Operator Characteristic
  • FIG. 43 A and B represent the Receiver Operator Characteristic (ROC)
  • Figure 44 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 44 having a molecular weight of about 50.0.
  • ROC Receiver Operator Characteristic
  • Figure 45 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 45 having a molecular weight of about 50.1.
  • ROC Receiver Operator Characteristic
  • Figure 46 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 46 having a molecular weight of about 51.1.
  • ROC Receiver Operator Characteristic
  • Figure 47 A and B represent the Receiver Operator Characteristic (ROC) Curves and data for BioBiomarker 47 having a molecular weight of about 51.3.
  • ROC Receiver Operator Characteristic
  • FIG. 48 A and B represent the Receiver Operator Characteristic (ROC)
  • Figure 49 shows a sample mass spectra from nonrejection patients and rejection patients.
  • Figure 50 shows another sample mass spectra from a nonrejection patient and a rejection patient.
  • Figure 51 shows an illustrative ROC analysis of candidate biomarkers.
  • Gas phase ion spectrometer refers to an apparatus that detects gas phase ions.
  • Gas phase ion spectrometers include an ion source that supplies gas phase ions.
  • Gas phase ion spectrometers include, for example, mass spectrometers, ion mobility spectrometers, and total ion current measuring devices.
  • Gas phase ion spectrometry refers to the use of a gas phase ion spectrometer to detect gas phase ions.
  • Mass spectrometer refers to a gas phase ion spectrometer that measures a parameter that can be translated into mass-to-charge ratios of gas phase ions. Mass spectrometers generally include an ion source and a mass analyzer. Examples of mass spectrometers are time-of-flight, magnetic sector, quadrupole filter, ion trap, ion cyclotron resonance, electrostatic sector analyzer and hybrids of these. “Mass spectrometry” refers to the use of a mass spectrometer to detect gas phase ions.
  • Laser desorption mass spectrometer refers to a mass spectrometer that uses laser energy as a means to desorb, volatilize, and ionize an analyte.
  • Tudem mass spectrometer refers to any mass spectrometer that is capable of performing two successive stages of m/z-based discrimination or measurement of ions, including ions in an ion mixture.
  • the phrase includes mass spectrometers having two mass analyzers that are capable of performing two successive stages of m/z-based discrimination or measurement of ions tandem-in-space.
  • the phrase further includes mass spectrometers having a single mass analyzer that is capable of nerforming two successive stages of m z-based discrimination or measurement of ions tandem-in-time.
  • Mass analyzer refers to a sub-assembly of a mass spectrometer that comprises means for measuring a parameter that can be translated into mass-to-charge ratios of gas phase ions.
  • the mass analyzer comprises an ion optic assembly, a flight tube and an ion detector.
  • Ion source refers to a sub-assembly of a gas phase ion spectrometer that provides gas phase ions.
  • the ion source provides ions through a desorption/ionization process.
  • Such embodiments generally comprise a probe interface that positionally engages a probe in an interrogatable relationship to a source of ionizing energy (e.g., a laser desorption/ionization source) and in concurrent communication at atmospheric or subatmospheric pressure with a detector of a gas phase ion spectrometer.
  • a source of ionizing energy e.g., a laser desorption/ionization source
  • ionizing energy for desorbing/ionizing an analyte from a solid phase include, for example: (1) laser energy; (!) fast atoms (used in fast atom bombardment); (3) high energy particles generated via beta decay of radionucleides (used in plasma desorption); and (4) primary ions generating secondary ions (used in secondary ion mass spectrometry).
  • the preferred form of ionizing energy for solid phase analytes is a laser (used in laser desorption/ionization), in particular, nitrogen lasers, Nd-Yag lasers and other pulsed laser sources. "Fluence" refers to the energy delivered per unit area of interrogated image.
  • a high fluence source such as a laser, will deliver about 1 mJ / mm2 to 50 mJ / mm2.
  • a sample is placed on the surface of a probe, the probe is engaged with the probe interface and the probe surface is struck with the ionizing energy. The energy desorbs analyte molecules from the surface into the gas phase and ionizes them.
  • ionizing energy for analytes include, for example: (1) electrons that ionize gas phase neutrals; (2) strong electric field to induce ionization from gas phase, solid phase, or liquid phase neutrals; and (3) a source that applies a combination of ionization particles or electric fields with neutral chemicals to induce chemical ionization of solid phase, gas phase, and liquid phase neutrals.
  • Solid support refers to a solid material which can be derivatized with, or otherwise attached to, a capture reagent.
  • exemplary solid supports include probes, microtiter plates and chromatographic resins.
  • Probe in the context of this invention refers to a device adapted to engage a probe interface of a gas phase ion spectrometer (e.g., a mass spectrometer) and to present an analyte to ionizing energy for ionization and introduction into a gas phase ion spectrometer, such as a mass spectrometer.
  • a “probe” will generally comprise a solid substrate (either flexible or rigid) comprising a sample presenting surface on which an analyte is presented to the source of ionizing energy.
  • “Surface-enhanced laser desorption/ionization” or “SELDI” refers to a method of desorption/ionization gas phase ion spectrometry (e.g., mass spectrometry) in which the analyte is captured on the surface of a SELDI probe that engages the probe interface ofthe gas phase ion spectrometer.
  • gas phase ion spectrometer is a mass spectrometer.
  • SELDI technology is described in, e.g., U.S. patent 5,719,060 (Hutchens and Yip) and U.S. patent 6,225,047 (Hutchens and Yip).
  • SEEC Surface-Enhanced Affinity Capture
  • Adsorbent surface refers to a surface to which is bound an adsorbent (also called a “capture reagent” or an “affinity reagent”).
  • An adsorbent is any material capable of binding an analyte (e.g., a target polypeptide or nucleic acid).
  • Chroatographic adsorbent refers to a material typically used in chromatography.
  • Chromatographic adsorbents include, for example, ion exchange materials, metal chelators (e.g., nitriloacetic acid or iminodiacetic acid), immobilized metal chelates, hydrophobic interaction adsorbents, hydrophilic interaction adsorbents, dyes, simple biomolecules (e.g., nucleotides, amino acids, simple sugars and fatty acids) and mixed mode adsorbents (e.g., hydrophobic attraction/electrostatic repulsion adsorbents).
  • Biospecific adsorbent refers an adsorbent comprising a biomolecule, e.g., a nucleic acid molecule (e.g...
  • an aotamer a oolvoeptide. a polvsaccharide, a lipid, a steroid or a conjugate of these (e.g., a glycoprotein, a lipoprotein, a glycolipid, a nucleic acid (e.g., DNA)-protein conjugate).
  • the biospecific adsorbent can be a macromolecular structure such as a multiprotein complex, a biological membrane or a virus. Examples of biospecific adsorbents are antibodies, receptor proteins and nucleic acids. Biospecific adsorbents typically have higher specificity for a target analyte than chromatographic adsorbents. Further examples of adsorbents for use in SELDI can be found in U.S. Patent 6,225,047 (Hutchens and Yip, "Use of retentate chromatography to generate difference maps," May 1, 2001).
  • a SEAC probe is provided as a pre-activated surface which can be modified to provide an adsorbent of choice.
  • certain probes are provided with a reactive moiety that is capable of binding a biological molecule through a covalent bond.
  • Epoxide and carbodiimidizole are useful reactive moieties to covalently bind biospecific adsorbents such as antibodies or cellular receptors.
  • Adsorption refers to detectable non-covalent binding of an analyte to an adsorbent or capture reagent.
  • SEND Surface-Enhanced Neat Desorption
  • SEND probe. Energy absorbing molecules
  • EAM Electronic absorbing molecules
  • the phrase includes molecules used in MALDI , frequently referred to as “matrix”, and explicitly includes cinnamic acid derivatives, sinapinic acid (“SPA”), cyano-hydroxy-cinnamic acid (“CHCA”) and dihydroxybenzoic acid, ferulic acid, hydroxyacetophenone derivatives, as well as others. It also includes EAMs used in SELDI. SEND is further described in United States patent 5,719,060 and United States patent application 60/408,255, filed September 4, 2002 (Kitagawa, "Monomers And Polymers Having Energy Absorbing Moieties Of Use In Desorption ionization Of Analytes").
  • SEPAR Surface-Enhanced Photolabile Attachment and Release
  • SELDI Surface-Enhanced Photolabile Attachment and Release
  • Eluant or “wash solution” refers to an agent, typically a solution, which is used to affect or modify adsorption of an analyte to an adsorbent surface and/or remove unbound materials from the surface.
  • the elution characteristics of an eluant can depend, for example, on pH, ionic strength, hydrophobicity, degree of chaotropism, detergent strength and temperature.
  • Analyte refers to any component of a sample that is desired to be detected.
  • the term can refer to a single component or a plurality of components in the sample.
  • the "complexity" of a sample adsorbed to an adsorption surface of an affinity capture probe means the number of different protein species that are adsorbed.
  • Molecular binding partners and “specific binding partners” refer to pairs of molecules, typically pairs of biomolecules that exhibit specific binding. Molecular binding partners include, without limitation, receptor and ligand, antibody and antigen, biotin and avidin, and biotin and streptavidin.
  • Monitoring refers to recording changes in a continuously varying parameter.
  • Biochip refers to a solid substrate having a generally planar surface to which an adsorbent is attached. Frequently, the surface ofthe biochip comprises a plurality of addressable locations, each of which location has the adsorbent bound there. Biochips can be adapted to engage a probe interface and, therefore, function as probes.
  • Protein biochip refers to a biochip adapted for the capture of polypeptides.
  • Many protein biochips are described in the art. These include, for example, protein biochios produced bv Ciphersen Biosvstems (Fremont, CA), Packard BioScience Company (Meriden CT), Zyomyx (Hayward, CA) and Phylos (Lexington, MA). Examples of such protein biochips are described in the following patents or patent applications: U.S.
  • patent 6,225,047 Hutchens and Yip, "Use of retentate chromatography to generate difference maps," May 1, 2001); International publication WO 99/51773 (Kuimelis and Wagner, “Addressable protein arrays,” October 14, 1999); U.S. patent 6,329,209 (Wagner et al., “Arrays of protein-capture agents and methods of use thereof," December 11, 2001) and International publication WO 00/56934 (Englert et al., "Continuous porous matrix arrays," September 28, 2000).
  • Ciphergen Biosystems comprise surfaces having chromatographic or biospecific adsorbents attached thereto at addressable locations.
  • Ciphergen ProteinChip® arrays include NP20, H4, H50, SAX-2, WCX-2, CM-10, LMAC-3, IMAC-30, LSAX-30, LWCX-30, LMAC-40, PS-10, PS-20 and PG-20.
  • These protein biochips comprise an aluminum substrate in the form of a strip. The surface ofthe strip is coated with silicon dioxide.
  • silicon oxide functions as a hydrophilic adsorbent to capture hydrophilic proteins.
  • H4, H50, SAX-2, WCX-2, CM-10, J AC-3, JJV ⁇ AC-30, PS-10 and PS-20 biochips further comprise a functionalized, cross-linked polymer in the form of a hydrogel physically attached to the surface ofthe biochip or covalently attached through a silane to the surface ofthe biochip.
  • the H4 biochip has isopropyl functionalities for hydrophobic binding.
  • the H50 biochip has nonylphenoxy- poly(ethylene glycol)methacrylate for hydrophobic binding.
  • the SAX-2 biochip has quaternary ammonium functionalities for anion exchange.
  • the WCX-2 and CM-10 biochips have carboxylate functionalities for cation exchange.
  • the LMAC-3 and LMAC-30 biochips have nitriloacetic acid functionalities that adsorb transition metal ions, such as Cu-H- and Ni-H-, by chelation. These immobilized metal ions allow adsorption of peptide and proteins by coordinate bonding.
  • the PS-10 biochip has carboimidizole functional groups that can react with groups on proteins for covalent binding.
  • the PS-20 biochip has epoxide functional groups for covalent binding with nroteins.
  • the PS-series biochins are useful for binding biospecific adsorbents, such as antibodies, receptors, lectins, heparin, Protein A, biotin/streptavidin and the like, to chip surfaces where they function to specifically capture analytes from a sample.
  • the PG-20 biochip is a PS-20 chip to which Protein G is attached.
  • the LSAX-30 (anion exchange), LWCX-30 (cation exchange) and IMAC-40 (metal chelate) biochips have functionalized latex beads on their surfaces.
  • analytes can be detected by a variety of detection methods selected from, for example, a gas phase ion spectrometry method, an optical method, an electrochemical method, atomic force microscopy and a radio frequency method.
  • Gas phase ion spectrometry methods are described herein. Of particular interest is the use of mass spectrometry and, in particular, SELDI.
  • Optical methods include, for example, detection of fluorescence, luminescence, chemiluminescence, absorbance, reflectance, transmittance, birefringence or refractive index (e.g., surface plasmon resonance, ellipsometry, a resonant mirror method, a grating coupler waveguide method or interferometry).
  • Optical methods include microscopy (both confocal and non-confocal), imaging methods and non-imaging methods.
  • Immunoassays in various formats e.g., ELISA
  • Electrochemical methods include voltametry and amperometry methods.
  • Radio frequency methods include multipolar resonance spectroscopy.
  • Biomarker in the context ofthe present invention refers to a polypeptide (of a particular apparent molecular weight), which is differentially present in a sample taken from patients having received a kidney transplant under rejection as compared to a patient having received a kidney transplant not under rejection.
  • measuring means methods which include detecting the presence or absence of BiomarkerCs'l in the sample. Quantifying the amount of Biomarker(s) in the sample, and/or qualifying the type of bioBiomarker. Measuring can be accomplished by methods known in the art and those further described herein, including but not limited to SELDI and immunoassay. Any suitable methods can be used to detect and measure one or more ofthe Biomarkers described herein. These methods include, without limitation, mass spectrometry (e.g., laser desorption/ionization mass spectrometry), fluorescence (e.g. sandwich immunoassay), surface plasmon resonance, eilipsometry and atomic force microscopy.
  • mass spectrometry e.g., laser desorption/ionization mass spectrometry
  • fluorescence e.g. sandwich immunoassay
  • surface plasmon resonance eilipsometry
  • atomic force microscopy atomic force microscopy.
  • the phrase "differentially present” refers to differences in the quantity and/or the frequency of a Biomarker present in a sample taken from patients having received a kidney transplant.
  • the Biomarker 6 is present at an elevated level in samples of kidney transplant rejection patients compared to samples from kidney transplant non-rejection patients.
  • Biomarkers 25, 29 and 30 described herein are present at a decreased level in samples of kidney transplant rejection patients compared to samples from kidney transplant non-rejection patients.
  • a Biomarker can be a polypeptide, which is detected at a higher frequency or at a lower frequency in samples of kidney transplant rejection patients compared to samples from kidney transplant non-rejection patients.
  • a Biomarker can be differentially present in terms of quantity, frequency or both.
  • a polypeptide is differentially present between two samples if the amount of the polypeptide in one sample is statistically significantly different from the amount ofthe polypeptide in the other sample.
  • a polypeptide is differentially present between the two samples if it is present at least about 120%, at least about 130%, at least about 150%, at least about 180%, at least about 200%, at least about 300%, at least about 500%, at least about 700%, at least about 900%, or at least about 1000% greater than it is present in the other sample, or if it is detectable in one sample and not detectable in the other.
  • a polypeptide is differentially present between two sets of samples if the frequency of detecting the polypeptide in the kidney transplant rejection patients' samples is statistically significantly higher or lower than in the samples from non-rejection patients.
  • a polypeptide is Hifferenti llv nresent between the two sets of samples if it is detected at least about 120%, at least about 130%, at least about 150%, at least about 180%, at least about 200%, at least about 300%, at least about 500%, at least about 700%, at least about 900%, or at least about 1000% more frequently or less frequently observed in one set of samples than the other set of samples.
  • Diagnostic means identifying the presence or nature of a pathologic condition, i.e., kidney transplant rejection. Diagnostic methods differ in their sensitivity and specificity.
  • the "sensitivity” of a diagnostic assay is the percentage of diseased individuals who test positive (percent of "true positives”). Diseased individuals not detected by the assay are “false negatives.” Subjects who are not diseased and who test negative in the assay, are termed “true negatives.”
  • the "specificity" of a diagnostic assay is 1 minus the false positive rate, where the "false positive” rate is defined as the proportion of those without the disease who test positive. While a particular diagnostic method may not provide a definitive diagnosis of a condition, it suffices if the method provides a positive indication that aids in diagnosis.
  • test amount of a Biomarker refers to an amount of a Biomarker present in a sample being tested.
  • a test amount can be either in absolute amount (e.g., ⁇ g/ml) or a relative amount (e.g., relative intensity of signals).
  • a “diagnostic amount” of a Biomarker refers to an amount of a Biomarker in a subject's sample that is consistent with a diagnosis of kidney transplant rejection.
  • a diagnostic amount can be either in absolute amount (e.g., ⁇ g/ml) or a relative amount (e.g. , relative intensity of signals).
  • a "control amount" of a Biomarker can be any amount or a range of amount, which is to be compared against a test amount of a Biomarker.
  • a control amount of a Biomarker can be the amount of a Biomarker in a person without kidney transplant rejection.
  • a control amount can be either in absolute amount (e.g., ⁇ g/ml) or a relative amount (e.g., relative intensity of signals).
  • Antibody refers to a polypeptide ligand substantially encoded by an immunoglobulin gene or immunoglobulin genes, or fragments thereof, which specifically binds and recognizes an epitope (e.g., an antigen).
  • the recognized immunoglobulin genes include the kappa and lambda light chain constant region genes, the alpha, gamma, delta, epsilon and mu heavy chain constant region genes, and the myriad immunoglobulin variable region genes.
  • Antibodies exist, e.g., as intact immunoglobulins or as a number of well-characterized fragments produced by digestion with various peptidases. This includes, e.g., Fab' and F(ab)' 2 fragments.
  • the term "antibody,” as used herein, also includes antibody fragments either produced by the modification of whole antibodies or those synthesized de novo using recombinant DNA methodologies. It also includes polyclonal antibodies, monoclonal antibodies, chimeric antibodies, humanized antibodies, or single chain antibodies. "Fc" portion of an antibody refers to that portion of an immunoglobulin heavy chain that comprises one or more heavy chain constant region domains, CH l5 CH 2 and CH , but does not include the heavy chain variable region.
  • Managing subject treatment refers to the behavior ofthe clinician or physician subsequent to the determination of kidney transplant rejection status. For example, if the result ofthe methods ofthe present invention is inconclusive or there is reason that confirmation of status is necessary, the physician may order more tests. Alternatively, if the status indicates that altering immunosuppressive therapy is appropriate, the physician may schedule the patient for that change in treatment. Likewise, if the status is negative, e.g., late stage kidney transplant rejection or if the status is acute, no further action may be warranted. Furthermore, if the results show that treatment has been successful, no further management may be necessary.
  • the present invention provides Biomarkers generated from comparison of protein profiles from patients diagnosed with kidney transplant rejection and from patients without kidney transplant rejection, using the ProteinChip ® Biomarker
  • High-throughput protein profiling combined with effective use of bioinformatics tools provides a useful approach to screening for kidnet transplant rejection Biomarkers.
  • the system used in the present invention utilizes chromatographic ProteinChip ® Arrays to assay samples using SELDI (Surface Enhanced Laser Desorption/ionization). Proteins bound to the arrays are read in a ProteinChip ® Reader, a time-of-flight mass spectrometer.
  • SELDI Surface Enhanced Laser Desorption/ionization
  • the present invention is based upon the discovery of protein Biomarkers that are differentially present in samples of kidney transplant rejection patients and kidney transplant non-rejection patients, and the application of this discovery in methods and kits for determining kidney transplant rejection status.
  • These protein Biomarkers are found in samples from kidney transplant rejection patients at levels that are different than the levels in samples from patients without kidney transplant rejection. Accordingly, the amount of one or more Biomarkers found in a test sample compared to a control, or the presence or absence of one or more Biomarkers in the test sample provides useful information regarding the kidney transplant rejection status ofthe patient.
  • the corresponding proteins or fragments of proteins for these bioBiomarkers are represented as intensity peaks in SELDI (surface enhanced laser desorption/ionization) protein chip/mass spectra with molecular masses centered around the following values:
  • Biomarker 1 having a molecular weight of about 2.5 kD;
  • Biomarker 2 having a molecular weight of about 2.6 kD
  • Biomarker 3 having a molecular weight of about 3.4 kD ;
  • Biomarker 4 having a molecular weight of about 3.5 kD;
  • Biomarker 5 having a molecular weight of about 3.8 kD;
  • Biomarker 6 having a molecular weight of about 4.1 kD;
  • Biomarker 7 having a molecular weight of about 4.7 kD; Biomarker 8: having a molecular weight of about 4.8 kD Biomarker 9: having a molecular weight of about 5.0 kD Biomarker 10 having a molecular weight of about 5.5 kD Biomarker 11 having a molecular weight of about 5.6 kD Biomarker 12 having a molecular weight of about 6.1 kD Biomarker 13 having a molecular weight of about 6.4 kD Biomarker 14 having a molecular weight of about 6.5 kD Biomarker 15 having a molecular weight of about 6.6 kD Biomarker 16 having a molecular weight of about 6.7 kD Biomarker 17 having a molecular weight of about 6.8 kD Biomarker 18 having a molecular weight of about 7.0 kD Biomarker 19 having a molecular weight of about 7.1 kD.
  • Biomarker 20 having a molecular weight of about 7.3 kD Biomarker 21 having a molecular weight of about 7.5 kD Biomarker 22 having a molecular weight of about 7.8 kD Biomarker 23 having a molecular weight of about 8.0 kD Biomarker 24 having a molecular weight of about 8.1 kD Biomarker 25 having a molecular weight of about 9.0 kD Biomarker 26 having a molecular weight of about 9.1 kD, Biomarker 27 having a molecular weight of about 9.3 kD Biomarker 28 having a molecular weight of about 9.6 kD Biomarker 29 having a molecular weight of about 9.7 kD Biomarker 30 having a molecular weight of about 9.8 kD Biomarker 31 having a molecular weight of about 10.0 kD Biomarker 32 having a molecular weight of about 10.8 kD Biomarker 33 having a molecular weiglit of about 10.9
  • Biomarker 47 having a molecular weight of about 51.3 kD; and Biomarker 48: having a molecular weight of about 67.0 kD.
  • Biomarkers 1 through 48 also may be characterized based on affinity for an adsorbent, particularly binding to an immobilized chelate (LMAC)-Cu substrate surface under the conditions specified under ProteinChip Analysis ofthe General Comments ofthe Examples, which follow.
  • LMAC immobilized chelate
  • Samples are collected from subjects, e.g., patients who want to establish kidney transplant rejection status. Other patients include patoients who have kidney transplant rejection and the test is being used to determine the effectiveness of immunosuppressive therapy or treatment they are receiving
  • the Biomarkers can be measured in different types of biological samples.
  • the sample is preferably a biological fluid sample.
  • a biological fluid sample useful in this invention include blood, blood serum, plasma, vaginal secretions, urine, tears, saliva, etc. Because all ofthe Biomarkers are found in urine, urine is a preferred sample source for embodiments ofthe invention.
  • the sample can be prepared to enhance detectabihty ofthe Biomarkers.
  • a urine sample from the subject can be preferably fractionated by, e.g., Cibacron blue agarose chromatography and single stranded DNA affinity chromatography, anion exchange chromatography, affinity chromatography (e.g., with antibodies) and the like.
  • the method of fractionation depends on the type of detection method used. Any method that enriches for the protein of interest can be used.
  • Sample preparations, such as pre- fractionation protocols are optional and may not be necessary to enhance detectabihty of Biomarkers depending on the methods of detection used. For example, sample preparation may be unnecessary if antibodies that specifically bind Biomarkers are used to detect the presence of Biomarkers in a sample.
  • sample preparation involves fractionation ofthe sample and collection of fractions determined to contain the Biomarkers.
  • Methods of pre- fractionation include, for example, size exclusion chromatography, ion exchange chromatography, heparin chromatography, affinity chromatography, sequential extraction, gel electrophoresis and liquid chromatography.
  • the analytes also may be modified prior to detection. These methods are useful to simplify the sample for further analysis. For example, it can be useful to remove high abundance proteins, such as albumin, from blood before analysis. Examples of methods of fractionation are described in PCT/US03/00531 (incorporated herein in its entirety).
  • the sample is pre-fractionated by anion exchange chromatography.
  • Anion exchange chromatography allows pre-fractionation ofthe proteins in a sample roughly according to their charge characteristics.
  • a Q anion-exchange resin can be used (e.g., Q HyperD F, Biosepra), and a sample can be sequentially eluted with eluants having different pH's.
  • Anion exchange chromatography allows separation of biomolecules in a sample that are more negatively charged from other types of biomolecules. Proteins that are eluted with an eluant having a high pH is likely to be weakly negatively charged, and a fraction that is eluted with an eluant having a low pH is likely to be strongly negatively charged.
  • anion exchange chromatography separates proteins according to their binding characteristics.
  • the urine samples are fractionated via anion exchange chromatography. Signal suppression of lower abundance proteins by high abundance proteins presents a significant challenge to SELDI mass spectrometry.
  • Fractionation of a sample reduces the complexity ofthe constituents of each fraction. This method can also be used to attempt to isolate high abundance proteins into a fraction, and thereby reduce its signal suppression effect on lower abundance proteins.
  • Anion exchange fractionation separates proteins by their isoelectric point (pi). Proteins are comprised of amino acids, which are ambivalent-their charge changes based on the pH ofthe environment to which they are exposed. A protein's pi is the pH at which the protein has no net charge. A protein assumes a neutral charge when the pH ofthe environment is equivalent to pi ofthe protein. When the pH rises above the pi ofthe protein, the protein assumes a net negative charge. Similarly, when the pH ofthe environment falls below the pi ofthe protein, the protein has a net positive charge.
  • the urine samples were fractionated according to the protocol set forth in the Examples below to obtain the Biomarkers described herein.
  • Biomolecules in a sample can also be separated by high-resolution electrophoresis, e.g., one or two-dimensional gel electrophoresis.
  • a fraction containing a Biomarker can be isolated and further analyzed by gas phase ion spectrometry.
  • two-dimensional gel electrophoresis is used to generate two-dimensional array of spots of biomolecules, including one or more Biomarkers. See, e.g., Jungblut and Thiede, Mass Spectr. Rev. 16:145-162 (1997).
  • the two-dimensional gel electrophoresis can be performed using methods known in the art. See, e.g., Guider ed., Methods In Enzymology vol. 182.
  • biomolecules in a sample are separated by, e.g., isoelectric focusing, during which biomolecules in a sample are separated in a pH gradient until they reach a spot where their net charge is zero (i.e., isoelectric point).
  • This first separation step results in one-dimensional array of biomolecules.
  • the biomolecules in one-dimensional array is further separated using a technique generally distinct from that used in the first separation step.
  • biomolecules separated by isoelectric focusing are further separated using a polyacrylamide gel, such as polyacrylamide gel electrophoresis in the presence of sodium dodecyl sulfate (SDS- PAGE).
  • SDS-PAGE gel allows further separation based on molecular mass of biomolecules.
  • two-dimensional gel electrophoresis can separate chemically different biomolecules in the molecular mass range from 1000-200,000 Da within complex mixtures. The pi range of these gels is about 3-10 (wide range gels).
  • Biomolecules in the two-dimensional array can be detected using any suitable methods known in the art.
  • biomolecules in a gel can be labeled or stained (e.g., Coomassie Blue or silver staining).
  • the spot can be further analyzed by gas phase ion spectrometry.
  • spots can be excised from the gel and analyzed by gas phase ion spectrometry.
  • the gel containing biomolecules can be transferred to an inert membrane by applying an electric field. Then a spot on the membrane that approximately corresponds to the molecular weight of a Biomarker can be analyzed by gas phase ion spectrometry.
  • the spots can be analyzed using any suitable techniques, such as MALDI or SELDI (e.g., using ProteinChip ® array) as described herein.
  • cleaving reagents such as proteases (e.g., trypsin).
  • the digestion of biomolecules into small fragments provides a mass fingerprint ofthe biomolecules in the spot, which can be used to determine the identity of Biomarkers if desired.
  • High performance liquid chromatography can also be used to separate a mixture of biomolecules in a sample based on their different physical properties, such as polarity, charge and size.
  • HPLC instruments typically consist of a reservoir of mobile phase, a pump, an injector, a separation column, and a detector. Biomolecules in a sample are separated by injecting an aliquot ofthe sample onto the column. Different biomolecules in the mixture pass through the column at different rates due to differences in their partitioning behavior between the mobile liquid phase and the stationary phase. A fraction that corresponds to the molecular weight and/or physical properties of one or more Biomarkers can be collected. The fraction can then be analyzed by gas phase ion spectrometry to detect Biomarkers. For example, the spots can be analyzed using either MALDI or SELDI (e.g., using ProteinChip ® array) as described herein.
  • a Biomarker can be modified before analysis to improve its resolution or to determine its identity.
  • the Biomarkers may be subject to proteolytic digestion before analysis. Any protease can be used. Proteases, such as trypsin, that are likely to cleave the Biomarkers into a discrete number of fragments are particularly useful. The fragments that result from digestion function as a fingerprint for the Biomarkers, thereby enabling their detection indirectly. This is particularly useful where there are Biomarkers with similar molecular masses that might be confused for the Biomarker in question. Also, proteolytic fragmentation is useful for high molecular weight Biomarkers because smaller Biomarkers are more easily resolved by mass spectrometry.
  • biomolecules can be modified to improve detection resolution.
  • neuraminidase can be used to remove terminal sialic acid residues from glycoproteins to improve binding to an anionic adsorbent (e.g., cationic exchange ProteinChip ® arrays) and to improve detection resolution.
  • the Biomarkers can be modified by the attachment of a tag of particular molecular weight that specifically bind to molecular Biomarkers, further distinguishing them.
  • the identity ofthe Biomarkers can be further determined by matching the physical and chemical characteristics ofthe modified Biomarkers in a protein database (e.g. , SwissProt).
  • Biomarkers are preferably captured with capture reagents immobilized to a solid support, such as any biochip described herein, a multiwell microtiter plate or a resin.
  • the Biomarkers of this invention are preferably captured on SELDI protein biochips. Capture can be on a chromatographic surface or a biospecific surface. Any ofthe SELDI protein biochips comprising reactive surfaces can be used to capture and detect the Biomarkers of this invention. However, the Biomarkers of this invention bind well to immobilized metal chelates.
  • the IMAC-3 and IMAC 30 biochips which nitriloacetic acid functionalities that adsorb transition metal ions, such as Cu 1" and Ni ++ , by chelation, are the preferred SELDI biochips for capturing the Biomarkers of this invention.
  • Any ofthe SELDI protein biochips comprising reactive surfaces can be used to capture and detect the Biomarkers of this invention.
  • These biochips can be derivatized with the antibodies that specifically capture the Biomarkers, or they can be derivatized with capture reagents, such as protein A or protein G that bind immunoglobulins. Then the Biomarkers can be captured in solution using specific antibodies and the captured Biomarkers isolated on chip through the capture reagent.
  • Biomarkers such as serum
  • a sample containing the Biomarkers such as serum
  • a suitable eluant such as phosphate buffered saline.
  • phosphate buffered saline a suitable eluant
  • Biomarkers can be detected and/or measured by a variety of detection methods including for example, gas phase ion spectrometry methods, optical methods, electrochemical methods, atomic force microscopy and radio frequency methods. Using these methods, one or more Biomarkers can be detected.
  • SELDI refers to a method of desorption/ionization gas phase ion spectrometry (e.g., mass spectrometry) in which the analyte is captured on the surface of a SELDI probe that engages the probe interface.
  • gas phase ion spectrometer is a mass spectrometer. SELDI technology is described in more detail above.
  • an immunoassay can be used to detect and analyze Biomarkers in a sample. This method comprises: (a) providing an antibody that specifically binds to a Biomarker; (b) contacting a sample with the antibody; and (c) detecting the presence of a complex ofthe antibody bound to the Biomarker in the sample.
  • An immunoassay is an assay that uses an antibody to specifically bind an antigen (e.g., a Biomarker).
  • the immunoassay is characterized by the use of specific binding properties of a particular antibody to isolate, target, and/or quantify the antigen.
  • the phrase "specifically (or selectively) binds" to an antibody or “specifically (or selectively) immunoreactive with,” when referring to a protein or peptide, refers to a binding reaction that is determinative ofthe presence ofthe protein in a heterogeneous population of proteins and other biologies.
  • the specified antibodies bind to a particular protein at least two times the background and do not substantially bind in a significant amount to other proteins present in the sample.
  • Specific binding to an antibody under such conditions may require an antibody that is selected for its specificity for a particular protein.
  • polyclonal antibodies raised to a Biomarker from specific species such as rat, mouse, or human can be selected to obtain only those polyclonal antibodies that are specifically immunoreactive with that Biomarker and not with other proteins, except for polymorphic variants and alleles ofthe Biomarker. This selection may be achieved by subtracting out antibodies that cross-react with the Biomarker molecules from other species.
  • antibodies that specifically bind to a Biomarker can be prepared using any suitable methods known in the art. See, e.g., Coligan, Current Protocols in Immunology (1991); Harlow & Lane, Antibodies: A Laboratory Manual (1988); Goding, Monoclonal Antibodies: Principles and Practice (2d ed. 1986); and Kohler & Milstein, N ⁇ twre 256:495-497 (1975).
  • Such techniques include, but are not limited to, antibody preparation by selection of antibodies from libraries of recombinant antibodies in phage or similar vectors, as well as preparation of polyclonal and monoclonal antibodies by immunizing rabbits or mice (see, e.g., Huse et al, Science 246:1275-1281 (1989); Ward et al, Nature 341 :544-546 (1989)).
  • a specific or selective reaction will be at least twice background signal or noise and more typically more than 10 to 100 times background.
  • a sample obtained from a subject can be contacted with the antibody that specifically binds the Biomarker.
  • the antibody can be fixed to a solid support to facilitate washing and subsequent isolation ofthe complex, prior to contacting the antibody with a sample.
  • solid supports include glass or plastic in the form of, e.g., a microtiter plate, a stick, a bead, or a microbead.
  • Antibodies can also be attached to a probe substrate or ProteinChip ® array described above.
  • the sample is nreferablv a biological fluid sample taken from a subject.
  • biological fluid samples include blood, serum, plasma, nipple aspirate, urine, tears, saliva etc.
  • the biological fluid comprises blood serum.
  • the sample can be diluted with a suitable eluant before contacting the sample to the antibody.
  • the mixture is washed and the antibody-Biomarker complex formed can be detected.
  • This detection reagent may be, e.g., a second antibody which is labeled with a detectable label.
  • detectable labels include magnetic beads (e.g., DYNABEADSTM), fluorescent dyes, radiolabels, enzymes (e.g., horse radish peroxide, alkaline phosphatase and others commonly used in an ELISA), and colorimetric labels such as colloidal gold or colored glass or plastic beads.
  • the Biomarker in the sample can be detected using an indirect assay, wherein, for example, a second, labeled antibody is used to detect bound Biomarker-specific antibody, and/or in a competition or inhibition assay wherein, for example, a monoclonal antibody which binds to a distinct epitope ofthe Biomarker is incubated simultaneously with the mixture.
  • an indirect assay wherein, for example, a second, labeled antibody is used to detect bound Biomarker-specific antibody
  • a competition or inhibition assay wherein, for example, a monoclonal antibody which binds to a distinct epitope ofthe Biomarker is incubated simultaneously with the mixture.
  • Methods for measuring the amount of, or presence of, antibody-Biomarker complex include, for example, detection of fluorescence, luminescence, chemiluminescence, absorbance, reflectance, transmittance, birefringence or refractive index (e.g., surface plasmon resonance, ellipsometry, a resonant mirror method, a grating coupler waveguide method or interferometry).
  • Optical methods include microscopy (both confocal and non-confocal), imaging methods and non- imaging methods.
  • Electrochemical methods include voltametry and amperometry methods.
  • Radio frequency methods include multipolar resonance spectroscopy. Methods for performing these assays are readily known in the art.
  • Useful assays include, for example, an enzyme immune assay (EIA) such as enzyme-linked immunosorbent assay (ELISA), a radioimmune assay (RIA), a Western blot assay, or a slot blot assay.
  • EIA enzyme immune assay
  • ELISA enzyme-linked immunosorbent assay
  • RIA radioimmune assay
  • Western blot assay or a slot blot assay.
  • Incubation steps can vary from about 5 seconds to several hours, preferably from about 5 minutes to about 24 hours. However, the incubation time will depend upon the assay format, Biomarker, volume of solution, concentrations and the like. Usually the assays will be carried out at ambient temperature, although they can be conducted over a range of temperatures, such as 10°C to 40°C.
  • Immunoassays can be used to determine presence or absence of a Biomarker in a sample as well as the quantity of a Biomarker in a sample.
  • the amount of an antibody-Biomarker complex can be determined by comparing to a standard.
  • a standard can be, e.g., a known compound or another protein known to be present in a sample.
  • the test amount of Biomarker need not be measured in absolute units, as long as the unit of measurement can be compared to a control.
  • Biomarkers in a sample have many applications.
  • one or more Biomarkers can be measured to aid kidney transplant rejection diagnosis or prognosis.
  • the methods for detection ofthe Biomarkers can be used to monitor responses in a subject to immunosuppression treatment.
  • the methods for detecting can be used to monitor responses in a subject to immunosuppression treatment.
  • Biomarkers can be used to assay for and to identify compounds that modulate expression of these Biomarkers in vivo or in vitro.
  • the Biomarkers can be used to assay for and to identify compounds that modulate expression of these Biomarkers in vivo or in vitro.
  • Biomarkers are used to differentiate between the different stages of rejection progression, thus aiding in determining appropriate treatment.
  • the software can comprise code that converts signal from the mass spectrometer into computer readable form.
  • the software also can include code that applies an algorithm to the analysis ofthe signal to determine whether the signal represents a "peak" in the signal corresponding to a Biomarker of this invention, or other useful Biomarkers.
  • the software also can include code that executes an algorithm that compares signal from a test sample to a typical signal characteristic of "normal” and human cancer and determines the closeness of fit between the two signals.
  • the software also can include code indicating which the test sample is closest to, thereby providing a probable diagnosis.
  • multiple Biomarkers are measured.
  • the use of multiple Biomarkers increases the predictive value ofthe test and provides greater utility in diagnosis, toxicology, patient stratification and patient monitoring.
  • the process called "Pattern recognition" detects the patterns formed by multiple Biomarkers greatly improves the sensitivity and specificity of clinical proteomics for predictive medicine.
  • Subtle variations in data from clinical samples e.g., obtained using SELDI, indicate that certain patterns of protein expression can predict phenotypes such as the presence or absence of a certain stage of rejection or a positive or adverse response to immunosuppression treatments.
  • Ciphergen' s ProteinChip ® system employs an analog-to- digital converter (ADC) to accomplish this.
  • ADC analog-to- digital converter
  • the ADC integrates detector output at regularly spaced time intervals into time-dependent bins. The time intervals typically are one to four nanoseconds long.
  • the time-of-flight spectrum ultimately analyzed typically does not represent the signal from a single pulse of ionizing energy against a sample, but rather the sum of signals from a number of pulses. This reduces noise and increases dynamic range. This time-of-flight data is then subject to data processing.
  • data processing typically includes TOF-to-M/Z transformation, baseline subtraction, high frequency noise filtering.
  • TOF-to-M/Z transformation involves the application of an algorithm that transforms times-of-flight into mass-to-charge ratio (M/Z).
  • M/Z mass-to-charge ratio
  • the signals are converted from the time domain to the mass domain. That is, each time-of-flight is converted into mass-to-charge ratio, or M/Z.
  • Calibration can be done internally or externally.
  • the sample analyzed contains one or more analytes of known M/Z. Signal peaks at times-of-flight representing these massed analytes are assigned the known M/Z. Based on these assigned M/Z ratios, parameters are calculated for a mathematical function that converts times-of-flight to M/Z.
  • a function that converts times-of-flight to M/Z such as one created by prior internal calibration, is applied to a time-of-flight spectrum without the use of internal calibrants.
  • Baseline subtraction improves data quantification by eliminating artificial, reproducible instrument offsets that perturb the spectrum. It involves calculating a spectrum baseline using an algorithm that incorporates parameters such as peak width, and then subtracting the baseline from the mass spectrum.
  • a typical smoothing function applies a moving average function to each time-dependent bin.
  • the moving average filter is a variable width digital filter in which the bandwidth ofthe filter varies as a function of, e.g., peak bandwidth, generally becoming broader with increased time-of-flight. See, e.g., WO 00/70648, November 23, 2000 (Gavin et al., "Variable Width Digital Filter for Time-of-flight Mass Spectrometry").
  • Peak selection can, of course, be done by eye.
  • Peak data from one or more spectra can be subject to further analysis by, for example, creating a spreadsheet in which each row represents a particular mass spectrum, each column represents a peak in the spectra defined by mass, and each cell includes the intensity ofthe peak in that particular spectrum.
  • Various statistical or rtattern recognition aonroaches can applied to the data.
  • Ciphergen's BioBiomarker Patterns 1 Software is used to detect a pattern in the spectra that are generated. The data is classified using a pattern recognition process that uses a classification model.
  • the spectra will represent samples from at least two different groups for which a classification algorithm is sought.
  • the groups can be pathological v. non-pathological (e.g., rejection v. non-rejection), drug responder v. drug non-responder, toxic response v. non-toxic response, progressor to disease state v. non-progressor to disease state, phenotypic condition present v. phenotypic condition absent.
  • the spectra that are generated in embodiments ofthe invention can be classified using a pattern recognition process that uses a classification model.
  • data derived from the spectra e.g., mass spectra or time-of-flight spectra
  • samples such as "known samples”
  • a "known sample” is a sample that is pre-classified (e.g., rejection or non-rejection).
  • Data derived from the spectra e.g., mass spectra or time-of-flight spectra
  • a "known sample” is a sample that is pre-classified.
  • the data that are derived from the spectra and are used to form the classification model can be referred to as a "training data set”.
  • the classification model can recognize patterns in data derived from spectra generated using unknown samples.
  • the classification model can then be used to classify the unknown samples into classes. This can be useful, for example, in predicting whether or not a particular biological sample is associated with a certain biological condition (e.g., rejection v. non-rejection).
  • the training data set that is used to form the classification model may comprise raw data or pre-processed data.
  • raw data can be obtained directly from time-of-flight spectra or mass spectra, and then may be optionally "pre-processed” in any suitable manner.
  • signals above a predetermined signal-to-noise ratio can be selected so that a subset of peaks in a spectrum is selected, rather than selecting all peaks in a spectrum.
  • a predetermined number of peak "clusters" at a common value e.g., a particular time-of-flight value or mass-to-charge ratio value
  • a peak at a given mass-to-charge ratio is in less than 50% of the mass spectra in a group of mass spectra, then the peak at that mass-to-charge ratio can be omitted from the training data set.
  • Pre-processing steps such as these can be used to reduce the amount of data that is used to train the classification model.
  • Classification models can be formed using any suitable statistical classification (or "learning") method that attempts to segregate bodies of data into classes based on objective parameters present in the data.
  • Classification methods may be either supervised or unsupervised. Examples of supervised and unsupervised classification processes are described in Jain, "Statistical Pattern Recognition: A Review", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, January 2000, which is herein incorporated by reference in its entirety.
  • supervised classification training data containing examples of known categories are presented to a learning mechanism, which learns one more sets of relationships that define each ofthe known classes. New data may then be applied to the learning mechanism, which then classifies the new data using the learned relationships.
  • supervised classification processes include linear regression processes (e.g., multiple linear regression (MLR), partial least squares (PLS) regression and principal components regression (PCR)), binary decision trees (e.g., recursive partitioning processes such as CART - classification and regression trees), artificial neural networks such as backpropagation networks, discriminant analyses (e.g., Bayesian classifier or Fischer analysis), logistic classifiers, and support vector classifiers (support vector machines).
  • linear regression processes e.g., multiple linear regression (MLR), partial least squares (PLS) regression and principal components regression (PCR)
  • binary decision trees e.g., recursive partitioning processes such as CART - classification and regression trees
  • artificial neural networks such as backpropagation networks
  • discriminant analyses e.g.
  • a preferred supervised classification method is a recursive partitioning process.
  • Recursive partitioning processes use recursive partitioning trees to classify spectra derived from unknown samples. Further details about recursive partitioning processes are provided in U.S. 2002 0138208 Al (Paulse et al, "Method for analyzing mass spectra," September 26, 2002.
  • the classification models that are created can be formed using unsupervised learning methods.
  • Unsupervised classification attempts to learn classifications based on similarities in the training data set, without pre classifying the spectra from which the training data set was derived.
  • Unsupervised learning methods include cluster analyses. A cluster analysis attempts to divide the data into "clusters" or groups that ideally should have members that are very similar to each other, and very dissimilar to members of other clusters. Similarity is then measured using some distance metric, which measures the distance between data items, and clusters together data items that are closer to each other.
  • Clustering techniques include the MacQueen's K-means algorithm and the Kohonen's Self-Organizing Map algorithm.
  • the data generated from Section IN above is inputted into a diagnostic algorithm (i.e., classification algorithm as described above).
  • the classification algorithm is then generated based on the learning algorithm.
  • the process involves developing an algorithm that can generate the classification algorithm.
  • the methods ofthe present invention generate a more accurate classification algorithm by accessing a number of kidney transplant rejection and normal samples of a sufficient number based on statistical sample calculations. The samples are used as a training set of data on learning algorithm.
  • the generation ofthe classification, i.e., diagnostic, algorithm is dependent upon the assay protocol used to analyze samples and generate the data obtained in Section IN above. It is imperative that the protocol for the detection and/or measurement ofthe Biomarkers (e.g., in step IN) must be the same as that used to obtain the data used for developing the classification algorithm.
  • the assay conditions which must be maintained throughout the training and classification systems include chip type and mass spectrometer parameters, as well as general protocols for sample preparation and testing. If the protocol for the detection and/or measurement ofthe Biomarkers (step IN) is changed, the learning algorithm and classification algorithm must also change. Similarlv. if the learning algorithm and classification algorithm change, then the protocol for the detection and/or measurement of Biomarkers (step IN) must also change to be consistent with that used to generate classification algorithm. Development of a new classification model would require accessing a sufficient number of kidney transplant rejection and non-rejection samples, developing a new training set of data based on a new detection protocol, generating a new classification algorithm using the data and finally, verifying the classification algorithm with a multi-site study.
  • the classification models can be formed on and used on any suitable digital computer.
  • Suitable digital computers include micro, mini, or large computers using any standard or specialized operating system such as a Unix, WindowsTM or LinuxTM based operating system.
  • the digital computer that is used may be physically separate from the mass spectrometer that is used to create the spectra of interest, or it may be coupled to the mass spectrometer. If it is separate from the mass spectrometer, the data must be inputted into the computer by some other means, whether manually or automated.
  • the training data set and the classification models according to embodiments ofthe invention can be embodied by computer code that is executed or used by a digital computer.
  • the computer code can be stored on any suitable computer readable media including optical or magnetic disks, sticks, tapes, etc., and can be written in any suitable computer programming language including C, C++, visual basic, etc.
  • a urine sample is collected from a patient and then fractionated using an anion exchange resin as described above.
  • the Biomarkers in the sample are captured using an IMAC copper ProteinChip array.
  • the Biomarkers are then detected using SELDI.
  • the results are then entered into a computer system, which contains an algorithm that is designed using the same parameters that were used in the learning algorithm and classification algorithm to originally determine the Biomarkers.
  • the algorithm produces a diagnosis based upon the data received relating to each Biomarker.
  • the diagnosis is determined by examining the data produced from the SELDI tests with the classification algorithm that is developed using the Biomarkers.
  • the classification algorithm depends on the particulars ofthe test protocol used to detect the Biomarkers. These particulars include, for example, sample preparation, chip type and mass spectrometer parameters. If the test parameters change, the algorithm must change. Similarly, if the algorithm changes, the test protocol must change.
  • the sample is collected from the patient.
  • the Biomarkers are captured using an antibody ProteinChip array as described above.
  • the Biomarkers are detected using a biospecific SELDI test system
  • the results are then entered into a computer system, which contains an algorithm that is designed using the same parameters that were used in the learning algorithm and classification algorithm to originally determine the Biomarkers.
  • the algorithm produces a diagnosis based upon the data received relating to each Biomarker.
  • the Biomarkers are captured and tested using non-SELDI formats.
  • the sample is collected from the patient.
  • the Biomarkers are captured on a substrate using other known means, e.g., antibodies to the Biomarkers.
  • the Biomarkers are detected using methods known in the art, e.g., optical methods and refractive index. Examples of optical methods include detection of fluorescence, e.g., ELISA. Examples of refractive index include surface plasmon resonance.
  • the results for the Biomarkers are then subjected to an algorithm, which may or may not require artificial intelligence.
  • the algorithm produces a diagnosis based upon the data received relating to each Biomarker.
  • the data from the sample may be fed directly from the detection means into a computer containing the diagnostic algorithm. Alternatively, the data obtained can be fed manually, or via an automated means, into a separate computer that contains the diagnostic algorithm.
  • any Biomarker is useful in aiding in the determination of kidney transplant rejection status.
  • the selected Biomarker is measured in a subject sample using the methods described herein, e.g., capture on a SELDI biochip followed by detection by mass spectrometry. Then, the measurement is compared with a diagnostic amount or control that distinguishes kidney transplant rejection status from a non-rejection status.
  • the diagnostic amount will reflect the information herein that a particular Biomarker is up-regulated or down-regulated in a kidney transplant rejection status compared with a non-rejection status.
  • the particular diagnostic amount used can be adjusted to increase sensitivity or specificity ofthe diagnostic assay depending on the preference ofthe diagnostician. The test amount as compared with the diagnostic amount thus indicates kidney transplant rejection status.
  • preferred methods ofthe present invention comprise the measurement of more than one Biomarker.
  • the methods ofthe present invention have an AUC from ROC analysis greater than 0.50, more preferred methods have an AUC greater than 0.60, more preferred methods have an AUC greater than 0.70.
  • Especially preferred methods have an AUC greater than 0.70 and most preferred methods have an AUC greater than 0.80.
  • the UMSA algorithm is particularly useful to generate a diagnostic algorithm from test data. This algorithm is disclosed in Z. Zhang et al.,
  • the learning algorithm will generate a multivariate classification (diagnostic) algorithm tuned to the particular specificity and sensitivity desired by the operator.
  • the classification algorithm can then be used to determine kidney transplant rejection status.
  • the method also involves measuring the selected Biomarkers in a subject sample (e.g., Biomarkers 1 through 48). These measurements are submitted to the classification algorithm.
  • the classification algorithm generates an indicator score that indicates kidney transplant rej ection status.
  • the mere presence or absence of a Biomarker, without quantifying the amount of Biomarker is useful and can be correlated with a probable diagnosis of kidney transplant rejection.
  • Biomarker 15 can be more frequently detected in human kidney transplant rej ection patients than in non-rej ection patients.
  • Biomarkers 29 and 30 can be less frequently detected in human kidney transplant rejection patients than in non-rejection patients.
  • a detected presence or absence, respectively, of these Biomarkers in a subject being tested indicates that the subject has a higher probability of having kidney transplant rejection.
  • the measurement of Biomarkers can involve quantifying the Biomarkers to correlate the detection of Biomarkers with a probable diagnosis of kidney transplant rejection.
  • a control amount i.e., higher or lower than the control, depending on the Biomarker
  • the correlation may take into account the amount ofthe Biomarker or Biomarkers in the sample compared to a control amount ofthe Biomarker or
  • Biomarkers up or down regulation ofthe Biomarker or Biomarkers (e.g., in normal subjects in whom human cancer is undetectable).
  • a control can be, e.g., the average or median amount of Biomarker present in comparable samples of normal subjects in whom rejection is undetectable.
  • the control amount is measured under the same or substantially similar experimental conditions as in measuring the test amount.
  • the correlation may take into account the presence or absence ofthe Biomarkers in a test sample and the frequency of detection ofthe same Biomarkers in a control.
  • the correlation may take into account both of such factors to facilitate determination of kidney transplant rejection status.
  • the methods further comprise managing subject treatment based on the status.
  • management describes the actions ofthe physician or clinician subsequent to determining kidney transplant rejection status.
  • the physician may order more tests.
  • the status indicates that altered immunosuppression therapy is appropriate, the physician may schedule the patient for a change in therapy.
  • the result is negative, e.g., the status indicates late stage kidney transplant rejection or if the status is otherwise acute, no further action may be warranted.
  • the results show that treatment has been successful, no further management may be necessary.
  • the invention also provides for such methods where the Biomarkers (or specific combination of Biomarkers) are measured again after subject management.
  • the methods are used to monitor the status ofthe rejection, e.g., response to immunosuppression treatment. Because ofthe ease of use ofthe methods and the lack of invasiveness ofthe methods, the methods can be repeated after each treatment the patient receives. This allows the physician to follow the effectiveness of the course of treatment. If the results show that the treatment is not effective, the course of treatment can be altered accordingly. This enables the physician to be flexible in the treatment options.
  • the methods for detecting Biomarkers can be used to assay for and to identify compounds that modulate expression of these Biomarkers in vivo or in vitro.
  • the methods ofthe present invention have other applications as well.
  • the Biomarkers can be used to screen for compounds that modulate the expression ofthe Biomarkers in vitro or in vivo, which compounds in turn may be useful in treating or preventing kidney transplant rejection in patients.
  • the Biomarkers can be used to monitor the response to treatments for kidney transplant rejection
  • kits for qualifying kidney transplant rejection status wherein the kits can be used to measure the Biomarkers of the present invention.
  • the kits can be used to measure any one or more ofthe Biomarkers described herein, which Biomarkers are differentially present in samples of kidney transplant rejection patient and non-rejection patients.
  • the kits of the invention have many applications.
  • the kits can be used to differentiate if a subject has kidney transplant rejection or has a negative diagnosis, thus enabling the physician or clinician to diagnose the presence or absence of rejection.
  • the kits can also be used to monitor the patient's response to a course of treatment, enabling the physician to modify the treatment based upon the results ofthe test.
  • the kits can be used to identify compounds that modulate expression of one or more ofthe Biomarkers in in vitro or in vivo animal models for kidney transplant rejection.
  • kits comprising (a) a capture reagent that binds a Biomarker selected from Biomarkers 1 through 48, and combinations thereof; and (b) a container comprising at least one ofthe Biomarkers.
  • the capture reagent binds a plurality ofthe Biomarkers.
  • the kit of further comprises a second capture reagent that binds one of the Biomarkers that the first capture reagent does not bind.
  • kits provided by the invention comprise (a) a first capture reagent that binds at least one Biomarker selected from Biomarkers 1 through 48 and (b) a second capture reagent that binds at least one ofthe Biomarkers that is not bound by the first capture reagent.
  • at least one ofthe capture reagents is an antibody.
  • Certain kits further comprise an MS probe to which at least one capture reagent is attached or is attachable.
  • the capture reagent can be any type of reagent, preferably the reagent is a SELDI probe. In certain kits ofthe present invention, the capture reagent comprises an MAC.
  • kits comprising (a) a first capture reagent that binds at least one Biomarker selected from Biomarkers 1 through 48 and (b) instructions for using the capture reagent to measure the Biomarker.
  • the capture reagent comprises an antibody.
  • some ofthe aforesaid kits further comprise an MS probe to which the capture reagent is attached or is attachable.
  • the capture reagent comprises an IMAC.
  • Each ofthe Biomarkers identified here binds to the JJVIAC ProteinChip ® array. Therefore, one preferred embodiment ofthe present invention includes a high-throughput test for early detection of kidney transplant rejection, which analyzes a patient's sample on the IMAC ProteinChip ® array for these analytes.
  • kits as described herein comprise at least one capture reagent that binds at least one Biomarker selected from Biomarkers 1 through 48.
  • kits of the present invention further comprise a wash solution, or eluant, that selectively allows retention ofthe bound Biomarker to the capture reagent as compared with other Biomarkers after washing.
  • the kit may contain instructions for making a wash solution, wherein the combination ofthe adsorbent and the wash solution allows detection ofthe Biomarkers using gas phase ion spectrometry.
  • the kit comprises written instructions for use ofthe kit for detection of kidney transplant rejection and the instructions provide for contacting a test sample with the capture reagent and detecting one or more Biomarkers retained by the capture reagent.
  • the kit may have standard instructions informing a technician how to wash the capture reagent (e.g., probe) after a sample of urine serum contacts the capture reagent.
  • the kit may have instructions for pre-fractionating a sample to reduce complexity of proteins in the sample.
  • the kit may have instructions for automating the fractionation or other processes.
  • kits can be prepared from the materials described above, and the previous discussion of these materials (e.g., probe substrates, capture reagents, adsorbents, washing solutions, etc.) is fully applicable to this section and will not be repeated.
  • the kit may comprise a first substrate comprising an adsorbent thereon (e.g., a particle functionalized with an adsorbent) and a second substrate onto which the first substrate can be positioned to form a probe, which is removably insertable into a gas phase ion spectrometer.
  • the kit may comprise a single substrate, which is in the form of a removably insertable probe with adsorbents on the substrate.
  • the kit may further comprise a pre-fractionation spin column (e.g., Cibacron blue agarose column, anti- HSA agarose column, K-30 size exclusion column, Q-anion exchange spin column, single stranded DNA column, lectin column, etc.).
  • a pre-fractionation spin column e.g., Cibacron blue agarose column, anti- HSA agarose column, K-30 size exclusion column, Q-anion exchange spin column, single stranded DNA column, lectin column, etc.
  • kits comprises (a) an antibody that specifically binds to a Biomarker; and (b) a detection reagent.
  • a kit can be prepared from the materials described above, and the previous discussion regarding the materials (e.g., antibodies, detection reagents, immobilized supports, etc.) is fully applicable to this section and will not be repeated.
  • the kit may further comprise pre- fractionation spin columns.
  • the kit may further comprise instructions for suitable operation parameters in the form of a label or a separate insert.
  • the kit may further comprise a standard or control information so that the test sample can be compared with the control information standard to determine if the test amount of a Biomarker detected in a sample is a diagnostic amount consistent with a diagnosis of kidney transplant rejection.
  • the invention also provides an article manufacture comprising at least one capture reagent bound to at least two Biomarkers selected from Biomarkers 1 through 48.
  • articles of manufacture ofthe present invention include, but are not limited to, ProteinChip ® Arrays, probes, microtitre plates, beads, test tubes, microtubes, and any other solid phase onto which a capture reagent can be incorporated.
  • a ProteinChip Array for example, will have an adsorbent that will capture Biomarkers 1 through 48.
  • the present invention also provides a system comprising a plurality of capture reagents each of which has bound to it a different Biomarker selected from Biomarkers 1 through 48.
  • a system includes, but is not limited to, a set of ProteinChip ® Arrays, which comprise adsorbents that bind one or more of the Biomarkers selected from Biomarkers 1 through 48.
  • Examples of other systems include those in which the capture reagents are test tubes containing an antibody for each ofthe Biomarkers, either separately, or in groups.
  • One of ordinary skill in the art would readily be able to manufacture other such articles in accordance with the teachings described herein.
  • Specimens were centrifuged for 5 minutes at l,000g to remove sediment. Supernatants were aliquoted and frozen at -80°C.
  • IMAC-3 chips were pretreated with 100 mmol/L CuSO 4 and phosphate-buffered saline (PBS) at pH 7.4. H4 chips were pretreated with 50% acetonitrile. Three microliters of urine were added to each chip spot in duplicate. Chips were incubated at 37°C between applications, allowing samples to dry on the chip surface. Specimens were applied to chips in a random pattern to minimize the effects of spot-to-spot variation.
  • IMAC-3 chips were washed with PBS and H4 chips were washed with 20% acetonitrile to remove nonspecific binding components.
  • CHCA ⁇ -cyano-4-hydroxycinnamic acid
  • SPA sinapinic acid matrix solution (composed of energy-absorbing molecules) was then added to each chip spot in duplicate.
  • Protein chips were analyzed on a PBS-II mass reader (Ciphergen Biosystems, Fremont, CA) with SELDI 3.0 software. Data were collected by averaging 110 laser shots, with laser intensities and detector sensitivities optimized for each combination of chip and matrix type.
  • Mass spectra generated by SELDI mass spectrometry analysis were examined visually to select and label peaks (Fig. 50) with potential to distinguish between prerejection and rejection patients.
  • SELDI software was used to identify and label all peaks in the spectrum data by applying a threshold to signal-to-noise values. Labeled peaks were normalized to the creatinine content of each urine specimen, through division of peak intensity by creatinine concentration in g/dL.( Lemann J Jr, Doumas BT. Proteinuria in health and disease assessed by measuring the urinary protein creatinine ratio. Clin Chem. 1987; 23: 297; Yamaguchi T, Kadono K. Clinical evaluation ofthe albumin/creatinine ratio in outpatients with diabetes.
  • ROC receiver operator characteristic
  • Fig. 51 The diagnostic performance of highly ranked peaks from UMSA analysis was evaluated by receiver operator characteristic (ROC) curve analysis (Fig. 51). The ability ofthe peaks to distinguish between rejection and nonrejection patients was ranked by the area under the ROC curve (AUC). Peaks with AUCs greater than 0.6 were classified as peaks of interest, the highest ones of which (AUCs > 0.75) are considered candidate biomarkers.
  • Computer-labeled peaks were also subjected to a separate CART (Classification and Regression Tree) analysis,( Breiman L, Friedman JH, Olshen RA, et al. Classification and Regression Trees. Monterey, CA: Wadsworths & Brooks; 1984. ) implemented by Ciphergen Biomarker Patterns Software, to identify patterns of biomarkers that distinguish between patient populations.
  • CART Classification and Regression Tree

Landscapes

  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Molecular Biology (AREA)
  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • Biophysics (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Biomedical Technology (AREA)
  • Organic Chemistry (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Hematology (AREA)
  • Immunology (AREA)
  • Analytical Chemistry (AREA)
  • Urology & Nephrology (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Medicinal Chemistry (AREA)
  • Food Science & Technology (AREA)
  • Microbiology (AREA)
  • Cell Biology (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • General Physics & Mathematics (AREA)
  • Pathology (AREA)
  • Genetics & Genomics (AREA)
  • Biotechnology (AREA)
  • Investigating Or Analysing Biological Materials (AREA)

Abstract

L'invention concerne une méthode permettant de qualifier un état de rejet de transplantation rénale chez un sujet. Cette méthode consiste à : (a) mesurer au moins un des biomarqueurs de l'invention dans un échantillon prélevé sur le sujet et (b) établir une corrélation entre la mesure et l'état de rejet de transplantation rénale. L'invention concerne également des trousses permettant de qualifier un état de rejet de transplantation rénale chez un sujet.
PCT/US2003/031089 2002-10-01 2003-10-01 Utilisation de biomarqueurs pour detecter un rejet aigu de transplantation renale Ceased WO2004030521A2 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
AU2003275363A AU2003275363A1 (en) 2002-10-01 2003-10-01 Use of biomarkers for detecting acute renal transplant rejection

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US41464702P 2002-10-01 2002-10-01
US60/414,647 2002-10-01

Publications (2)

Publication Number Publication Date
WO2004030521A2 true WO2004030521A2 (fr) 2004-04-15
WO2004030521A3 WO2004030521A3 (fr) 2005-07-14

Family

ID=32069750

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2003/031089 Ceased WO2004030521A2 (fr) 2002-10-01 2003-10-01 Utilisation de biomarqueurs pour detecter un rejet aigu de transplantation renale

Country Status (2)

Country Link
AU (1) AU2003275363A1 (fr)
WO (1) WO2004030521A2 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2011017685A1 (fr) * 2009-08-07 2011-02-10 Rules-Based Medicine, Inc. Méthodes et dispositifs permettant de détecter le rejet d'une greffe rénale
US10451636B2 (en) 2014-04-09 2019-10-22 The Regents Of The University Of California Protein biomarkers for immune assessment and prediction of transplant rejection

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5762933A (en) * 1987-01-05 1998-06-09 Institut National De La Sante Et De La Recherche Medicale Method for preventing and treating graft failure in a human patient using a monoclonal antibody specific for leucocyte functional antigen LFA-1
US5679345A (en) * 1994-06-02 1997-10-21 The Johns Hopkins University Method for preventing complement-dependent rejection of organ or tissue transplants
US6187534B1 (en) * 1997-09-24 2001-02-13 Cornell Research Foundation, Inc. Methods of evaluating transplant rejection

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2011017685A1 (fr) * 2009-08-07 2011-02-10 Rules-Based Medicine, Inc. Méthodes et dispositifs permettant de détecter le rejet d'une greffe rénale
US8735080B2 (en) 2009-08-07 2014-05-27 Rules-Based Medicine, Inc. Methods and devices for detecting obstructive uropathy and associated disorders
US10451636B2 (en) 2014-04-09 2019-10-22 The Regents Of The University Of California Protein biomarkers for immune assessment and prediction of transplant rejection

Also Published As

Publication number Publication date
AU2003275363A1 (en) 2004-04-23
WO2004030521A3 (fr) 2005-07-14
AU2003275363A8 (en) 2004-04-23

Similar Documents

Publication Publication Date Title
US7605003B2 (en) Use of biomarkers for detecting ovarian cancer
AU2005230445B2 (en) Biomarkers for ovarian cancer
US20050095611A1 (en) Identification of biomarkers for detecting pancreatic cancer
US20100055690A1 (en) Prognostic biomarkers in patients with ovarian cancer
US20140155293A1 (en) Identification of biomarkers for detecting prostate cancer
US20140038837A1 (en) Biomarkers for the detection of early stage ovarian cancer
US20100068818A1 (en) Algorithms for multivariant models to combine a panel of biomarkers for assessing the risk of developing ovarian cancer
US20050170352A1 (en) Use of biomarkers to detect breast cancer
WO2006102526A2 (fr) Identification de biomarqueurs par un profilage de proteines de serum
WO2004030521A2 (fr) Utilisation de biomarqueurs pour detecter un rejet aigu de transplantation renale
HK1076489B (en) Use of biomarkers for detecting ovarian cancer

Legal Events

Date Code Title Description
AK Designated states

Kind code of ref document: A2

Designated state(s): AE AG AL AM AT AU AZ BA BB BG BR BY BZ CA CH CN CO CR CU CZ DE DK DM DZ EC EE EG ES FI GB GD GE GH GM HR HU ID IL IN IS JP KE KG KP KR KZ LC LK LR LS LT LU LV MA MD MG MK MN MW MX MZ NI NO NZ OM PG PH PL PT RO RU SC SD SE SG SK SL SY TJ TM TN TR TT TZ UA UG US UZ VC VN YU ZA ZM ZW

AL Designated countries for regional patents

Kind code of ref document: A2

Designated state(s): GH GM KE LS MW MZ SD SL SZ TZ UG ZM ZW AM AZ BY KG KZ MD RU TJ TM AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HU IE IT LU MC NL PT RO SE SI SK TR BF BJ CF CG CI CM GA GN GQ GW ML MR NE SN TD TG

121 Ep: the epo has been informed by wipo that ep was designated in this application
122 Ep: pct application non-entry in european phase
NENP Non-entry into the national phase

Ref country code: JP

WWW Wipo information: withdrawn in national office

Country of ref document: JP