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WO2010115077A2 - Ensembles de biomarqueurs pour l'oesophage de barrett et l'adénocarcinome de l'oesophage - Google Patents

Ensembles de biomarqueurs pour l'oesophage de barrett et l'adénocarcinome de l'oesophage Download PDF

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Publication number
WO2010115077A2
WO2010115077A2 PCT/US2010/029743 US2010029743W WO2010115077A2 WO 2010115077 A2 WO2010115077 A2 WO 2010115077A2 US 2010029743 W US2010029743 W US 2010029743W WO 2010115077 A2 WO2010115077 A2 WO 2010115077A2
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subject
sample
biomarker
eac
levels
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WO2010115077A9 (fr
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Stephen J. Meltzer
Kang (Kan), Kwisa (Takatsugu)
Fumiaki Sato
Richard R. Drake
Oliver J. Semmes
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Johns Hopkins University
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Johns Hopkins University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/56—Staging of a disease; Further complications associated with the disease

Definitions

  • the present invention relates to the field of cancer. More specifically, the present invention relates to the use of biomarkers to detect cancer. BACKGROUND OF THE INVENTION
  • EAC esophageal adenocarcinoma
  • Gastroesophageal reflux disease has long been recognized as the cause of BE. There has also been an apparent increase in the incidence of Barrett's metaplasia per se. Although endoscopic ultrasound and positron emission tomography scanning have improved preoperative staging of EAC, most tumors are detected at advanced stages, and therefore EAC remains lethal, with an overall 5 -year survival of only 10-20%. Moreover, the morbidity and mortality associated with various forms of esophagectomy remain high.
  • GASTROENTEROL. 2302-05 (2001). Forty percent or more of EAC, however, is diagnosed in subjects without previous reflux symptoms. See Lagergren et al., 340 N. ENGL. J. MED. 825-31 (1999); and Chow et al., 274 JAMA 474-77 (1995). Moreover, EAC has been estimated to develop in only 0.5%-1.0% of BE patients annually. See Jankowski et al, 122 GASTROENTEROLOGY 588-90 (2002); and O'Connor et al., 94 AM. J. GASTROENTEROL. 2037- 42 (1999). Although EAC is frequently accompanied by BE, only approximately 5% of patients presenting with EAC have an antecedent diagnosis of BE.
  • the present invention relates to the field of cancer. Specifically, the present invention relates to the use of biomarkers to detect cancer. More specifically, the biomarkers of the present invention can be used in diagnostic tests to determine, qualify, and/or assess cancer status, for example, to diagnose cancer, in an individual, subject or patient.
  • the present invention provides biomarker panels for discriminating among individuals with Barrett's Esophagus (BE), individuals with Esophageal Adenocarcinoma (EAC), and unaffected individuals (UI) (also referred to herein as normal control individuals (NC)).
  • BE Barrett's Esophagus
  • EAC Esophageal Adenocarcinoma
  • UI unaffected individuals
  • NC normal control individuals
  • the present invention can be used to distinguish between BE and EAC in an individual.
  • the biomarkers to be measured or detected in distinguishing between BE and EAC include, but are not limited to, Kininogen I, Complement C2, Teneurin-1, Talin-2, Zinc Finger Protein 532, Complement Factor B, Protein CCSMSTl, and Fibrinogen Alpha.
  • the present invention can be used to distinguish between BE and UI in an individual.
  • the biomarkers to be measured or detected in distinguishing between BE and UI include, but are not limited to, Plectin, RUN and SH3 domain- containing proteinl, SHAN2, Prothrombin, Dynein Heavy Chain 6 Axonemal, Serum Amyloid A, Kininogen 1, Centromere Protein F, and GRIP and Coiled-Coil Domain Containing Protein 2.
  • the present invention can be used to distinguish between EAC and UI in an individual.
  • the biomarkers to be measured or detected in distinguishing between EAC and UI include, but are not limited to, MIB2, STA5 A, Probable Histone-Lysine N- Methyltransferase ASHlL, Synaptic Vesicle Glycoportein 2B, Zinc Finger Protein RIf, Beta-2- Glycoprotein, and VWF.
  • biomarkers known in the relevant art may be used in combination with the biomarkers described herein including, but not limited to, HE4, LGALS3, ILlRN, TRIP13, FIGNLl, CRIPl, S100A4, EXOSC8, EXPI, BRRNl, NELF, EREG, TMEM40, and TMEMl 09.
  • the biomarkers of the present invention may be measured or detected in an appropriate sample taken from an individual, patient or subject.
  • the sample may comprise blood or other liquid samples of biological origin (including, but not limited to, serum, plasma, urine, saliva, stool and synovial fluid), solid tissue samples such as a biopsy specimen or tissue cultures or cells derived therefrom and the progeny thereof.
  • the sample is blood.
  • the sample is serum.
  • the biomarkers are detected using an immunoassay.
  • the immunoassay may be an enzyme-linked immunosorbent assay or ELISA.
  • the biomarkers are detected using mass spectrometry.
  • the mass spectrometric method may comprise matrix assisted laser desorption/ionization time-of-flight (MALDI-TOF MS or MALDI-TOF).
  • method comprises MALDI-TOF tandem mass spectrometry (MALDI-TOF MS/MS).
  • mass spectrometry can be combined with another appropriate method(s) as may be contemplated by one of ordinary skill in the art.
  • the biomarkers of the present invention can be used in diagnostic tests to determine, qualify, and/or assess other aspects of cancer status in a subject.
  • the biomarkers of the present invention can be used to determine the risk of developing cancer in a subject (e.g., UI vs. BE or BE vs. EAC).
  • the present invention provides methods for determining the stage of cancer in a subject.
  • the present invention provides methods for determining the course of disease in a subject.
  • the biomarkers of the present invention may be used to determine the effectiveness of or response to a particular cancer therapy. Based on such status, the treatment regimen of the patient may be managed more effectively.
  • the present invention provides methods for determining the therapeutic efficacy of a pharmaceutical drug.
  • kits for qualifying cancer status which kits are used to detect the biomarkers described herein.
  • the kit is provided as an ELISA kit comprising antibodies to the biomarkers of the present invention including, but not limited to, Kininogen I, Complement C2, Teneurin-1, Talin-2, Zinc Finger Protein 532, Complement Factor B, Protein CCSMSTl, Fibrinogen Alpha, Plectin, RUN and SH3 domain-containing proteinl, SHAN2, Prothrombin, Dynein Heavy Chain 6 Axonemal, Serum Amyloid A, Kininogen 1, Centromere Protein F, GRIP and Coiled-Coil Domain Containing Protein 2, MIB2, STA5 A, Probable Histone-Lysine N-Methy transferase ASHlL, Synaptic Vesicle Glycoportein 2B, Zinc Finger Protein RIf, Beta-2-Glycoprotein, and VWF.
  • the ELISA kit may comprise a solid support, such as a chip, microtiter plate (e.g., a 96- well plate), bead, or resin having biomarker capture reagents attached thereon.
  • the kit may further comprise a means for detecting the biomarkers, such as antibodies, and a secondary antibody-signal complex such as horseradish peroxidase (HRP)-conjugated goat anti-rabbit IgG antibody and tetramethyl benzidine (TMB) as a substrate for HRP.
  • HRP horseradish peroxidase
  • TMB tetramethyl benzidine
  • the kit can also comprise a washing solution or instructions for making a washing solution, in which the combination of the capture reagents and the washing solution allows capture of the biomarkers on the solid support for subsequent detection by, e.g., antibodies or mass spectrometry.
  • a kit can comprise instructions for suitable operational parameters in the form of a label or separate insert. For example, the instructions may inform a consumer about how to collect the sample, how to wash the probe or the particular biomarkers to be detected.
  • the kit can comprise one or more containers with biomarker samples, to be used as standard(s) for calibration.
  • Figure l is a schematic illustration of serum proteomic BE and EAC diagnostic and detection strategy.
  • the serum proteomic test would shift the management algorithm for Barrett's associated neoplastic progression (BN) leftward by diagnosing and endoscoping BE, dysplasia, and EAC earlier, leading to earlier and more successful treatment of EAC.
  • BN Barrett's associated neoplastic progression
  • Figure 2 is a flowchart illustrating the workflow procedures and analysis for identifying and validating the biomarkers of the present invention.
  • Figure 3 shows the MALDI-TOF/MS data from an analysis of a cohort of 65 UI and 60
  • Figure 4 is a flowchart illustrating the algorithm for proteomic panel development. From the peaks in Figure 3, a prediction model was developed using cross-validated linear discriminant analysis (LDA).
  • Figure 5 is a graph depicting the ROC curve from the UI vs. BE analysis. The ROC curve of the diagnostic model was generated using the best peak data set. The area under the ROC curve (AUROC) was 0.955. The sensitivity and specificity of the prediction model were 0.90 and 0.90, respectively.
  • LDA cross-validated linear discriminant analysis
  • Figure 6 is a graph illustrating the distribution of AUROCs from the permutation analysis.
  • the mean, median, max, and minimum AUROCs of 1000 permutation models were 0.5885, 0.6081, 0.7900, and 0.1894, respectively. Because no permuted model reached the AUROC of the prediction model developed herein (0.9947), this permutation analysis suggested that the prediction model developed herein was significantly better than the null hypothesis. Therefore, the accuracy of the peak dataset and model were extremely unlikely to have resulted from chance alone (FDR ⁇ 0.002).
  • Figure 8 is a graph showing the ELISA validation of MALDI peaks for Apolipoprotein A-I precursor (Apo-AI or ApoA-I). Apo-AI ELISA levels were significantly higher in BE than NC (normal control individuals, also may be referred to as UI or unaffected individuals) in both the 1st and 2nd sample sets (p-values in brackets).
  • FIG 9 is a graph showing the ELISA validation of MALDI peaks for Lipopolysaccharide-binding protein precursor (LBP). Similar to the Apo-AI results, LBP ELISA results were significantly higher in EAC than in NC ("UI") in both sample sets (p-values in brackets).
  • Figure 10 is a graph comparing serum Apo-AI levels against body mass index (BMI). No correlation exists between Apo-AI and BMI. In fact, no significant difference exists in BMI among all categories (EAC, BE, GERD, and UI).
  • Figure 11 illustrates the power of primary tests (NC vs BE, NC vs EAC, and BE vs EAC) as a function of true positive fraction (TPF) and false positive fraction (FPF).
  • comparing refers to making an assessment of how the proportion, level or cellular localization of one or biomarkers in a sample from a subject relates to the proportion, level or cellular localization of the corresponding one or more biomarkers in a standard or control sample. For example, “comparing” may refer to assessing whether the proportion, level, or cellular localization of one or more biomarkers in a sample from a subject is the same as, more or less than, or different from the proportion, level, or cellular localization of the corresponding one or more biomarkers in standard or control sample.
  • in reference to a parameter e.g., a modulated proportion, level, or cellular localization in the cell from a subject
  • a parameter e.g., a modulated proportion, level, or cellular localization in the cell from a subject
  • the parameter may comprise the presence, absence and/or particular amounts of one or more biomarkers of the present invention.
  • a particular set or pattern of one or more biomarkers may indicate that a subject has cancer (or correlated to a subject having cancer), in particular, EAC.
  • a particular set or pattern of one or more biomarkers may be correlated to a subject having BE (or may indicate that a subject has BE).
  • a particular set or pattern of one or more biomarkers may be correlated to a subject being unaffected.
  • correlating or "normalization” as used according to the present invention may be by any method of relating levels of expression or localization of markers to a standard valuable for the: assessment of the diagnosis, prediction of a cancer or cancer progression, assessment of efficacy of clinical treatment, identification of a tumor that may respond to a treatment, selection of a subject for a particular treatment, monitoring of the progress of treatment, and in the context of a screening assay, for the identification of an anti-EAC therapeutic.
  • the terms "individual,” “subject” or “patient” are used interchangeably herein, and refer to a mammal, particularly, a human.
  • the subject may be an individual in need of treatment or in need of diagnosis based on particular symptoms or family history.
  • the terms may refer to treatment in experimental animals, in veterinary application, and in the development of animal models for disease, including, but not limited to, rodents including mice, rats, and hamsters; and primates.
  • measuring means methods which include detecting the presence or absence of a biomarker(s) in a sample, quantifying the amount of biomarker(s) in the sample, and/or qualifying the type of biomarker(s). Measuring can be accomplished by methods known in the art and those further described herein including, but not limited to, immunoassay and mass spectrometry. The term “measuring” is used interchangeably throughout with the term “detecting.”
  • sample biological sample
  • patient sample patient sample
  • sample biological sample
  • patient sample patient sample
  • sample types obtained from an individual, subject or a patient and can be used in a diagnostic or monitoring assay.
  • a sample obtained from a patient can be divided and only a portion may be used to for diagnosis.
  • the sample, or a portion thereof can be stored under conditions to maintain sample for later analysis.
  • the definition specifically encompasses blood and other liquid samples of biological origin (including, but not limited to, serum, plasma, urine, saliva, stool and synovial fluid), solid tissue samples such as a biopsy specimen or tissue cultures or cells derived therefrom and the progeny thereof.
  • the definition also includes samples that have been manipulated in any way after their procurement, such as by centrifugation, filtration, precipitation, dialysis, chromatography, treatment with reagents, washed, or enriched for certain cell populations including tumor cells and the like.
  • the terms further encompass a clinical sample, and also include cells in culture, cell supernatants, tissue samples, organs, bone marrow, and the like.
  • a sample comprises a blood sample.
  • a serum sample is used.
  • Various methodologies of the instant invention include a step that involves comparing a value, level, feature, characteristic, property, etc. to a "suitable control,” referred to interchangeably herein as an “appropriate control” or a “control sample.”
  • a “suitable control,” “appropriate control” or a “control sample” is any control or standard familiar to one of ordinary skill in the art useful for comparison purposes.
  • a "suitable control” or “appropriate control” is a value, level, feature, characteristic, property, etc. determined in a cell, organ, or subject, e.g., a control or normal cell, organ, or subject, exhibiting, for example, normal traits.
  • the biomarkers of the present invention may be assayed for their presence in a sample from an unaffected individual (UI) or a normal control individual (NC) (both terms are used interchangeably herein).
  • a "suitable control” or “appropriate control” is a value, level, feature, characteristic, property, etc. determined prior to performing a cancer therapy on a subject.
  • a transcription rate, mRNA level, translation rate, protein level, biological activity, cellular characteristic or property, genotype, phenotype, etc. can be determined prior to, during, or after administering a cancer therapy into a cell, organ, or subject.
  • a "suitable control” or “appropriate control” is a predefined value, level, feature, characteristic, property, etc. II. Detection of Biomarkers
  • the biomarkers of the present invention may be detected by mass spectrometry, a method that employs a mass spectrometer to detect gas phase ions.
  • mass spectrometers are time-of-flight, magnetic sector, quadrupole filter, ion trap, ion cyclotron resonance, electrostatic sector analyzer, hybrids or combinations of the foregoing, and the like.
  • the mass spectrometric method comprises matrix assisted laser desorption/ionization time-of-flight (MALDI-TOF MS or MALDI-TOF).
  • method comprises MALDI-TOF tandem mass spectrometry (MALDI-TOF MS/MS).
  • mass spectrometry can be combined with another appropriate method(s) as may be contemplated by one of ordinary skill in the art.
  • MALD-TOF can be utilized with trypsin digestion and tandem mass spectrometry as described herein.
  • the mass spectrometric technique comprises surface enhanced laser desorption and ionization or "SELDI," as described, for example, in U.S. Patents No. 6,225,047 and No. 5,719,060.
  • SELDI refers to a method of desorption/ionization gas phase ion spectrometry (e.g. mass spectrometry) in which an analyte (here, one or more of the biomarkers) is captured on the surface of a SELDI mass spectrometry probe.
  • SELDI SELDI-Enhanced Desorption Mass Spectrometry
  • SEAC Surface-Enhanced Affinity Capture
  • SEND Surface-Enhanced Neat Desorption
  • Another SELDI method is called Surface-Enhanced Photolabile Attachment and Release (SEPAR), which involves the use of probes having moieties attached to the surface that can covalently bind an analyte, and then release the analyte through breaking a photolabile bond in the moiety after exposure to light, e.g., to laser light (see, U.S. Patent No. 5,719,060).
  • the biomarkers can be first captured on a chromatographic resin having chromatographic properties that bind the biomarkers.
  • a chromatographic resin having chromatographic properties that bind the biomarkers.
  • a cation exchange resin such as CM Ceramic HyperD F resin
  • this method could be preceded by fractionating the sample on an anion exchange resin before application to the cation exchange resin.
  • an immuno-chromatographic resin that comprises antibodies that bind the biomarkers
  • the biomarkers of the present invention can be detected and/or measured by immunoassay.
  • Immunoassay requires biospecific capture reagents, such as antibodies, to capture the biomarkers. Many antibodies are available commercially. Antibodies also can be produced by methods well known in the art, e.g., by immunizing animals with the biomarkers. Biomarkers can be isolated from samples based on their binding characteristics. Alternatively, if the amino acid sequence of a polypeptide biomarker is known, the polypeptide can be synthesized and used to generate antibodies by methods well-known in the art.
  • the present invention contemplates traditional immunoassays including, for example, sandwich immunoassays including ELISA or fluorescence-based immunoassays, as well as other enzyme immunoassays.
  • Nephelometry is an assay performed in liquid phase, in which antibodies are in solution. Binding of the antigen to the antibody results in changes in absorbance, which is measured.
  • a biospecific capture reagent for the biomarker is attached to the surface of an MS probe, such as a pre-activated ProteinChip array. The biomarker is then specifically captured on the biochip through this reagent, and the captured biomarker is detected by mass spectrometry.
  • the Quantikine immunoassay developed by R&D Systems, Inc. may also be used in the methods of the present invention.
  • biomarkers of the present invention may be detected by means of an electrochemicaluminescent assay developed by Meso Scale Discovery
  • Electrochemiluminescence detection uses labels that emit light when electrochemically stimulated. Background signals are minimal because the stimulation mechanism (electricity) is decoupled from the signal (light). Labels are stable, non-radioactive and offer a choice of convenient coupling chemistries. They emit light at -620 nm, eliminating problems with color quenching. See U.S. Patents No. 7,497,997; No. 7,491,540; No. 7,288,410; No. 7,036,946; No. 7,052,861; No. 6,977,722; No. 6,919,173; No. 6,673,533; No. 6,413,783; No. 6,362,011; No.
  • the biomarkers of the present invention can be detected by other suitable methods.
  • Detection paradigms that can be employed to this end include optical methods, electrochemical methods (voltametry and amperometry techniques), atomic force microscopy, and radio frequency methods, e.g., multipolar resonance spectroscopy.
  • Biochips generally comprise solid substrates and have a generally planar surface, to which a capture reagent (also called an adsorbent or affinity reagent) is attached. Frequently, the surface of a biochip comprises a plurality of addressable locations, each of which has the capture reagent bound there.
  • a capture reagent also called an adsorbent or affinity reagent
  • Protein biochips are biochips adapted for the capture of polypeptides. Many protein biochips are described in the art. These include, for example, protein biochips produced by Ciphergen Biosystems, Inc.
  • a blood sample is tested for the presence or absence of one or more biomarkers.
  • the step of collecting a sample such as a blood sample from a subject can be carried out by phlebotomy or any other suitable technique.
  • the blood sample may be further processed to provide a serum sample or other suitable blood fraction, such as plasma.
  • tissue sample may be taken and tested for the presence or absence of one or more biomarkers.
  • Tissue or cell samples can be removed from almost any part of the body.
  • biopsy methods include needle (e.g. fine needle aspiration), endoscopic, and excisional. Variations of these methods and the necessary devices used in such methods are known to those of ordinary skill in the art. III. Determination of Subject Cancer Status
  • the present invention relates to the use of biomarkers to detect cancer. More specifically, the biomarkers of the present invention can be used in diagnostic tests to determine, qualify, and/or assess cancer status, for example, to diagnose cancer, in an individual, subject or patient.
  • the present invention provides biomarker panels for discriminating among individuals with Barrett's Esophagus (BE), individuals with Esophageal Adenocarcinoma (EAC), and unaffected individuals (UI) (also referred to herein as normal control individuals (NC)).
  • BE Barrett's Esophagus
  • EAC Esophageal Adenocarcinoma
  • UI unaffected individuals
  • NC normal control individuals
  • the present invention can be used to distinguish between BE and EAC in an individual.
  • the biomarkers to be measured or detected in distinguishing between BE and EAC include, but are not limited to, Kininogen I, Complement C2, Teneurin-1, Talin-2, Zinc Finger Protein 532, Complement Factor B, Protein CCSMSTl, and Fibrinogen Alpha.
  • the present invention can be used to distinguish between BE and UI in an individual.
  • the biomarkers to be measured or detected in distinguishing between BE and UI include, but are not limited to, Plectin, RUN and SH3 domain-containing proteinl, SHAN2, Prothrombin, Dynein Heavy Chain 6 Axonemal, Serum Amyloid A, Kininogen 1, Centromere Protein F, and GRIP and Coiled-Coil Domain Containing Protein 2.
  • the present invention can be used to distinguish between EAC and UI in an individual.
  • the biomarkers to be measured or detected in distinguishing between EAC and UI include, but are not limited to, MIB2, STA5 A, Probable Histone-Lysine N-Methyltransferase ASHlL, Synaptic Vesicle Glycoportein 2B, Zinc Finger Protein RIf, Beta-2-Glycoprotein, and VWF.
  • biomarkers known in the relevant art may be used in combination with the biomarkers described herein including, but not limited to, HE4, LGALS3, ILlRN, TRIP13, FIGNLl, CRIPl, S100A4, EXOSC8, EXPI, BRRNl, NELF, EREG, TMEM40, and TMEMl 09.
  • A. Biomarker Panels The biomarkers of the present invention can be used in diagnostic tests to assess, determine, and/or qualify (used interchangeably herein) cancer status in a subject.
  • the phrase "cancer status" includes any distinguishable manifestation of the disease, including non-disease.
  • cancer status includes, without limitation, the presence or absence of cancer (e.g., distinguishing between UI and EAC in a subject ), the risk of developing cancer (e.g., distinguishing between UI and BE in a subject or distinguishing between BE and EAC in a subject), the stage of the cancer, the progress of cancer (e.g., progress of cancer or remission of cancer over time) and the effectiveness or response to treatment of cancer (e.g., clinical follow up and surveillance of BE and EAC after treatment). Based on this status, further procedures may be indicated, including additional diagnostic tests or therapeutic procedures or regimens.
  • the power of a diagnostic test to correctly predict status is commonly measured as the sensitivity of the assay, the specificity of the assay or the area under a receiver operated characteristic ("ROC") curve.
  • Sensitivity is the percentage of true positives that are predicted by a test to be positive, while specificity is the percentage of true negatives that are predicted by a test to be negative.
  • An ROC curve provides the sensitivity of a test as a function of 1 -specificity. The greater the area under the ROC curve, the more powerful the predictive value of the test.
  • Other useful measures of the utility of a test are positive predictive value and negative predictive value. Positive predictive value is the percentage of people who test positive that are actually positive. Negative predictive value is the percentage of people who test negative that are actually negative.
  • the biomarker panels of the present invention may show a statistical difference in different cancer statuses of at least p ⁇ 0.05, p ⁇ 10 "2 , p ⁇ 10 "3 , p ⁇ 10 "4 or p ⁇ 10 "5 . Diagnostic tests that use these biomarkers may show a sensitivity and specificity of at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 98% and about 100%.
  • the biomarkers are differentially present in UI (or NC), BE, and EAC, and, therefore, are useful in aiding in the determination of cancer status.
  • the biomarkers are measured in a subject sample using the methods described herein.
  • the measurement(s) may then be compared with a relevant diagnostic amount(s) or cut-off(s) that distinguish a positive cancer status from a negative cancer status.
  • the diagnostic amount(s) represents a measured amount of a biomarker(s) above which or below which a subject is classified as having a particular cancer status. For example, if the biomarker(s) is/are up-regulated compared to normal during cancer, then a measured amount(s) above the diagnostic cutoff(s) provides a diagnosis of cancer.
  • the biomarker(s) is/are down-regulated during cancer, then a measured amount(s) below the diagnostic cutoff(s) provides a diagnosis of cancer.
  • a measured amount(s) below the diagnostic cutoff(s) provides a diagnosis of cancer.
  • the particular diagnostic cut-off(s) used in an assay one can increase sensitivity or specificity of the diagnostic assay depending on the preference of the diagnostician.
  • the particular diagnostic cut-off can be determined, for example, by measuring the amount of the biomarker(s) in a statistically significant number of samples from subjects with the different cancer statuses, and drawing the cut-off to suit the desired levels of specificity and sensitivity.
  • Biomarker values may be combined by any appropriate state of the art mathematical method.
  • Well-known mathematical methods for correlating a marker combination to a disease employ methods like discriminant analysis (DA) (e.g., linear-, quadratic-, regularized-DA), Kernel Methods (e.g., SVM), Nonparametric Methods (e.g., k-Nearest- Neighbor Classifiers), PLS (Partial Least Squares), Tree-Based Methods (e.g., Logic Regression, CART, Random Forest Methods, Boosting/Bagging Methods), Generalized Linear Models (e.g., Logistic Regression), Principal Components based Methods (e.g., SIMCA), Generalized Additive Models, Fuzzy Logic based Methods, Neural Networks and Genetic Algorithms based Methods.
  • DA discriminant analysis
  • SVM Kernel Methods
  • Nonparametric Methods e.g., k-Nearest- Neighbor Classifiers
  • the method used in correlating biomarker combination of the present invention e.g. to the absence or presence of cancer is selected from DA (e.g., Linear-, Quadratic-, Regularized Discriminant Analysis), Kernel Methods (e.g., SVM), Nonparametric Methods (e.g., k-Nearest-Neighbor Classifiers), PLS (Partial Least Squares), Tree-Based Methods (e.g., Logic Regression, CART, Random Forest Methods, Boosting Methods), or Generalized Linear Models (e.g., Logistic Regression).
  • DA e.g., Linear-, Quadratic-, Regularized Discriminant Analysis
  • Kernel Methods e.g., SVM
  • Nonparametric Methods e.g., k-Nearest-Neighbor Classifiers
  • PLS Partial Least Squares
  • Tree-Based Methods e.g., Logic Regression, CART, Random Forest Methods,
  • the present invention provides methods for determining the risk of developing cancer in a subject.
  • Biomarker amounts or patterns are characteristic of various risk states, e.g., high, medium or low.
  • the risk of developing a cancer is determined by measuring the relevant biomarkers and then either submitting them to a classification algorithm or comparing them with a reference amount and/or pattern of biomarkers that is associated with the particular risk level.
  • the present invention provides methods for determining the stage of cancer in a subject.
  • Each stage of the cancer has a characteristic amount of a biomarker or relative amounts of a set of biomarkers (a pattern).
  • the stage of a cancer is determined by measuring the relevant biomarkers and then either submitting them to a classification algorithm or comparing them with a reference amount and/or pattern of biomarkers that is associated with the particular stage.
  • the present invention provides methods for determining the course of cancer in a subject.
  • Cancer course refers to changes in cancer status over time, including cancer progression (worsening) and cancer regression (improvement).
  • the amounts or relative amounts (e.g., the pattern) of the biomarkers change. For example, biomarker "X" is increased with EAC, while biomarker “Y” may be decreased in EAC. Therefore, the trend of these biomarkers, either increased or decreased over time toward cancer or non- cancer indicates the course of the disease.
  • this method involves measuring one or more biomarkers in a subject at least two different time points, e.g., a first time and a second time, and comparing the change in amounts, if any. The course of cancer is determined based on these comparisons.
  • the methods further comprise managing subject treatment based on the status.
  • Such management includes the actions of the physician or clinician subsequent to determining cancer status. For example, if a physician makes a diagnosis of BE, then a certain regime of monitoring (i.e., periodic endoscopy) would follow. A diagnosis of EAC may then require a certain cancer therapy regimen. Alternatively, a diagnosis of non-EAC might be followed with further testing to determine a specific disease that the patient might be suffering from. Also, further tests may be called for if the diagnostic test gives an inconclusive result on cancer status.
  • the present invention provides methods for determining the therapeutic efficacy of a pharmaceutical drug.
  • Therapy or clinical trials involve administering the drug in a particular regimen.
  • the regimen may involve a single dose of the drug or multiple doses of the drug over time.
  • the doctor or clinical researcher monitors the effect of the drug on the patient or subject over the course of administration. If the drug has a pharmacological impact on the condition, the amounts or relative amounts (e.g., the pattern or profile) of one or more of the biomarkers of the present invention may change toward a non-cancer profile. Therefore, one can follow the course of the amounts of one or more biomarkers in the subject during the course of treatment.
  • this method involves measuring one or more biomarkers in a subject receiving drug therapy, and correlating the amounts of the biomarkers with the cancer status of the subject.
  • One embodiment of this method involves determining the levels of one or more biomarkers at least two different time points during a course of drug therapy, e.g., a first time and a second time, and comparing the change in amounts of the biomarkers, if any.
  • the one or more biomarkers can be measured before and after drug administration or at two different time points during drug administration. The effect of therapy is determined based on these comparisons. If a treatment is effective, then one or more biomarkers will trend toward normal, while if treatment is ineffective, the one or more biomarkers will trend toward cancer indications.
  • data that are generated using samples can then be used to "train” a classification model.
  • a "known sample” is a sample that has been pre-classified.
  • the data that are used to form the classification model can be referred to as a "training data set.”
  • the training data set that is used to form the classification model may comprise raw data or pre-processed data.
  • the classification model can recognize patterns in data 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., diseased versus non-diseased).
  • 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, the teachings of which are incorporated by reference.
  • supervised classification training data containing examples of known categories are presented to a learning mechanism, which learns one or more sets of relationships that define each of the 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 back propagation 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 back propagation networks
  • discriminant analyses e.g., Bayesian classifier or Fischer analysis
  • logistic classifiers logistic classifiers
  • support vector machines support vector machines
  • Recursive partitioning processes use recursive partitioning trees to classify data derived from unknown samples. Further details about recursive partitioning processes are provided in U.S. Patent Application No. 2002 0138208 Al to Paulse et al, "Method for analyzing mass spectra.”
  • 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.
  • Suitable digital computers include micro, mini, or large computers using any standard or specialized operating system, such as a Unix, Windows® 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.
  • the training data set and the classification models according to embodiments of the 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.
  • the learning algorithms described above are useful both for developing classification algorithms for the biomarkers already discovered, or for finding new biomarkers.
  • the classification algorithms form the base for diagnostic tests by providing diagnostic values (e.g., cut-off points) for biomarkers used singly or in combination.
  • diagnostic values e.g., cut-off points
  • the present invention provides kits for qualifying cancer status, which kits are used to detect the biomarkers described herein.
  • the kit is provided as an ELISA kit comprising antibodies to the biomarkers of the present invention including, but not limited to, Kininogen I, Complement C2, Teneurin-1, Talin-2, Zinc Finger Protein 532, Complement Factor B, Protein CCSMSTl, Fibrinogen Alpha, Plectin, RUN and SH3 domain-containing proteinl, SHAN2, Prothrombin, Dynein Heavy Chain 6 Axonemal, Serum Amyloid A, Kininogen 1, Centromere Protein F, GRIP and Coiled-Coil Domain Containing Protein 2, MIB2, STA5 A, Probable Histone-Lysine N-Methy transferase ASHlL, Synaptic Vesicle Glycoportein 2B, Zinc Finger Protein RIf, Beta-2-Glycoprotein, and VWF.
  • antibodies to the biomarkers of the present invention including, but not limited to, Kininogen I, Complement C2, Teneurin-1, Talin-2, Zinc Finger Protein 532, Complement Factor B,
  • the ELISA kit may comprise a solid support, such as a chip, microtiter plate (e.g., a 96- well plate), bead, or resin having biomarker capture reagents attached thereon.
  • the kit may further comprise a means for detecting the biomarkers, such as antibodies, and a secondary antibody-signal complex such as horseradish peroxidase (HRP)-conjugated goat anti-rabbit IgG antibody and tetramethyl benzidine (TMB) as a substrate for HRP.
  • HRP horseradish peroxidase
  • TMB tetramethyl benzidine
  • the kit for qualifying cancer status may be provided as an immuno-chromatography strip comprising a membrane on which the antibodies are immobilized, and a means for detecting, e.g., gold particle bound antibodies, where the membrane, includes NC membrane and PVDF membrane.
  • the kit may comprise a plastic plate on which a sample application pad, gold particle bound antibodies temporally immobilized on a glass fiber filter, a nitrocellulose membrane on which antibody bands and a secondary antibody band are immobilized and an absorbent pad are positioned in a serial manner, so as to keep continuous capillary flow of blood serum.
  • a cancer patient can be diagnosed by adding blood or blood serum from the patient to the kit and detecting the relevant biomarkers conjugated with antibodies, specifically, by a method which comprises the steps of: (i) collecting blood or blood serum from the patient; (ii) separating blood serum from the patient's blood; (iii) adding the blood serum from patient to a diagnostic kit; and, (iv) detecting the biomarkers conjugated with antibodies.
  • the antibodies are brought into contact with the patient's blood. If the biomarkers are present in the sample, the antibodies will bind to the sample, or a portion thereof.
  • blood or blood serum need not be collected from the patient (i.e., it is already collected).
  • the sample may comprise a tissue biopsy sample.
  • the kit can also comprise a washing solution or instructions for making a washing solution, in which the combination of the capture reagents and the washing solution allows capture of the biomarkers on the solid support for subsequent detection by, e.g., antibodies or mass spectrometry.
  • a kit can comprise instructions for suitable operational parameters in the form of a label or separate insert. For example, the instructions may inform a consumer about how to collect the sample, how to wash the probe or the particular biomarkers to be detected.
  • the kit can comprise one or more containers with biomarker samples, to be used as standard(s) for calibration.
  • reaction conditions e.g., component concentrations, desired solvents, solvent mixtures, temperatures, pressures and other reaction ranges and conditions that can be used to optimize the product purity and yield obtained from the described process. Only reasonable and routine experimentation will be required to optimize such process conditions.
  • Example 1 Biomarker Panel Development
  • Figure 2 describes the workflow of experiments and analysis. Unaffected Individuals vs. Barrett's Esophagus. Two hundred twenty-one (221)
  • MALDI-TOF-MS peaks representing the expression of individual proteins or peptides were successfully detected in a cohort consisting of 65 UI and 60 (BE). See Figure 3. Using these peaks, a prediction model was developed by cross-validated linear discriminant analysis (LDA). Linear Discriminant Analysis (LDA). To build a highly accurate prediction model, linear discriminant analysis (LDA) and forward-stepwise regression were utilized to select protein peaks used in the analysis. See Figure 4. The prediction model accuracy was assessed by leave- one-out cross-validation (LOOCV). ROC curve analysis was performed using prediction values of the samples, and area under the ROC curve (AUROC) represented the accuracy of the model. Next, permutation analysis was performed. As shown in Figure 5, the area under the curve (AUROC) was 0.955. The sensitivity and specificity of the prediction model were 0.90 and 0.90, respectively.
  • LEO Linear Discriminant Analysis
  • LC-MS/MS LC-MS/MS analysis is performed with a nanoLC-2D liquid chromatography system (Eksigent Technologies, LLC, Dublin, CA) coupled on-line to a quadrupole time-of- flight mass spectrometer QStar (Applied Biosystems). Full MS scans are followed by 3 MS/MS scans of the most abundant peptide ions (in a data-dependent mode), and collision-induced dissociation is performed at a collision energy of rolling collision energy (automatically set according to the m/z of precursor, increased for 20% due to iTRAQ tags) with the ion spray voltage set to 2.2 kV.
  • Figure 7 shows the proteins identified by LC-MS/MS.
  • Data Analysis Data analysis is performed by searching MS/MS spectra against the nonredundant proteome set of Swiss-Prot (the Swiss Institute of Bioinformatics) through the Protein Pilot (Applied Biosystems) and Mascot Daemon (Matrix Science Inc. Boston, MA). For the iTRAQ data set from ProteinPilot, relative tag intensities that demonstrate a greater than 1.3 fold difference are considered significant. Mascot search may provide the possible proteins that are not detected by ProteinPilot due to the lack of tag intensity in one or more categories of ET, EN and BE.
  • ITRAQ Isobar ic Tag for Relative and Absolute Quantitation
  • iTRAQ technology for protein quantitation using mass spectrometry is a recent, powerful means of determining relative protein levels in up to four samples simultaneously. Ross et al, 3 MOL. CELL PROTEOMICS 1154-69 (2004). It has been applied to many experiments in which quantitative comparisons of protein expression among different samples are needed. Chong et al., 5 J. PROTEOME RES. 1232-40 (2006); Keshamouni et al., 5 J. PROTEOME RES. 1143-54 92006); and DeSouza et al., 4 J. PROTEOME RES. 377-86 (2005).
  • iTRAQ Reagents Multiplex Kit (Applied Biosystems, Foster City, CA) is used for the labeling of the peptides.
  • the samples are treated in parallel, the protocol comprises the following steps: reduction and cysteine blocking, digestion of proteins with trypsin, labeling of peptides with iTRAQ reagents (three different molecular weight tags; 114, 115 and 116), combining the samples to be compared, strong cation exchange (SCX) chromatography, desalting with solid phase extraction, and LC/MS/MS analysis. Briefly, dried pooled samples (50 ⁇ g of each, EN, ET and BE) are dissolved in 2OuL of Dissolution buffer and 1 ⁇ L of Denaturant is added.
  • Reducing reagent (2 ⁇ L) is added and the tubes are incubated at 60 0 C for 1 hour.
  • Cysteine Blocking reagent (1 ⁇ L) is added and incubated for another 10 minutes at room temperature.
  • Ten ⁇ l of the solution containing 5 ⁇ g of trypsin (Promega, Madison WI) is added and incubated overnight at 37 0 C.
  • the reagents are dissolved in ethanol and the contents of a vial are transferred to sample tube. Samples are labeled for 1 h at room temperature. After labeling, the sample tubes are combined and dried down to a volume of 50 ⁇ L.
  • a SCX column (PoIyLC PoIySULFOETHYL, PoIyLC Inc., Columbia, MD) is used with the flow through at 250 ⁇ l/min.
  • the peptides are eluted in gradient elution over 40 minutes using two different types of solvents. Absorbance at 214 nm is monitored and approximately 25 fractions are collected along the gradient.
  • Figures 8 and 9 show average concentration and standard deviation in UI, BE and EAC group for proteins APOAl and LBP, respectively.
  • the ELISAs shows a result consistent with iTRAQ data, suggesting the iTRAQ approach is reliable and accurate.
  • Figure 10 shows that there is no correlation between APOAl and body mass index (BMI). In fact, no significant difference existed in BMI among all categories (EAC, BE, GERD, and UI).
  • Example 2 Development of a Mass Spectrometric Serum Proteomic Panel Capable of Discriminating Between NC, BE, and EAC subjects.
  • blinded test set classifier information are obtained for bead-based Matrix Assisted Laser Desorption/Ionization Time-of- Flight Tandem Mass Spectrometry (MALDI-TOF-MS/MS) peaks representing proteins whose abundance in serum differed between NC, BE, and EAC subjects.
  • MALDI-TOF-MS/MS Matrix Assisted Laser Desorption/Ionization Time-of- Flight Tandem Mass Spectrometry
  • Magnetic bead-based sample preparation was developed by Bruker Daltonics to improve sample capacity, dynamic range, sensitivity, and flexibility of MALDI-TOF analysis.
  • trypsin digestion and LC/MS-MS liquid chromatography/tandem mass spectrometry
  • NC normal control individuals
  • UI UI or unaffected individuals
  • ELISA generalizable immunoassays
  • GLNE serum samples were divided into aliquots and stored at -8O 0 C until analyzed. Twenty microliters of serum were digested with trypsin and then processed robotically on weak cationic exchange (WCX) magnetic beads. Eluted samples were spotted in duplicate on Anchorplate 1 : 10 with CHCA matrix. They were applied in linear mode onto a Bruker Ultra-flex MALDI-TOF (Bruker Daltonics Inc., Billerica, MA) at Eastern Virginia Medical School (EVMS). MALDI-TOF Mass Spectrometry. Magnetic bead-based sample preparation was developed to improve the sample capacity, dynamic range, sensitivity, and flexibility of MALDI- TOF analysis. Zhang et al, 15 J. BIOMOL. TECH.
  • ClinProt MALDI-TOF-MS protein profiling was performed at EVMS according to the following protocol: 20 ⁇ l of trypsin-digested serum from each subject was incubated with 10 ⁇ l of WCX beads equilibrated in HEPES buffer for 10 min. Peptides bound to WCX beads were washed twice in binding buffer and eluted in 10 mM Tris pH 8.9/150 mM NaCl. Each of these steps was processed robotically on a magnetized surface by an adapted Gilson 215 liquid handling robot. A small aliquot of the eluted peptides from the beads was spotted onto an AnchorChip sample target platform (384 spots) and mixed 1 : 1 with CHCA matrix.
  • NC vs. EAC there were 44 NC and 53 CA patients. The following peaks were selected by Iasso2: y9, yl2, y22, y43, y45, y46, y51, y57, y63, y68, y81, y89, 792, y95.
  • Example 3 In an Independent Blinded GLNE Cohort, Use ELISA to Confirm the Accuracy of The Panel in Distinguishing Between NC, BE, and EAC Subjects.
  • ELISA enzyme-linked immunosorbent assay
  • the enzyme-linked immunosorbent assay (ELISA) is more sensitive and specific than Western blotting because it employs two different antibodies, each recognizing a target protein independently.
  • ELISA is more inexpensive, robust, generalizable, standardizable, and automatable than MALDI-TOF.
  • MALDI-TOF is more suitable than MALDI-TOF as a final translational platform on which to assess the biomarker panel in the clinical setting.
  • the ELISA results suggested that MALDI-TOF is a reliable method of identifying differentially abundant serum proteins.
  • ELISA Enzyme-Linked Immunosorbent Assay
  • the ELISA protocol comprises the following steps: 1) a primary antibody to the target protein is immobilized on the surface of each well of a 96-well plate; 2) 100 ⁇ l of each properly diluted serum sample or positive control (usually recombinant protein) is added to each well of the 96-well plate and incubated for 60 min at room temperature (RT); 3) the 96-well plate is washed five times; 4) horseradish peroxidase (HRP)-conjugated secondary antibody is added and incubated for 60 min at RT; 5) the 96-well plate is washed five more times; 6) TMB/peroxide substrate is added and incubated for approximately 15 min (until color develops) at RT; 7) the reaction is terminated with 0.5N sulfuric acid; and 8) the OD at a wavelength of 450nm is measured using a microplate reader.
  • HRP horseradish peroxidase
  • the current ELISA protocol may require further development, optimization, and analytical validation prior to clinical validation of the protein panel's accuracy in distinguishing among NC, BE, and EAC subjects. Indeed, using the biomarker panels described herein, the ELISAs can partition EAC from
  • TPF true positive fraction
  • Empirical ROC curves are constructed for each of the five comparisons. Secondary Objective. The proportions of low-grade and high-grade dysplastics classified as EAC, BE or NC are estimated, with 95% exact binomial distribution confidence intervals. Justification of Design. A regulated (lasso) regression function provides estimates of class membership probabilities that are robust against potential overfitting, resubstitution bias and sampling bias. Given 50 cases per class, the power of the joint hypothesis is 80% or greater if the true TPF is at least 0.88 and the true FPF is no greater than 0.12. Figure 3 displays the power of the test for various values of the TPF and FPF. It should be noted that the MALDI- TOF-MS assay achieved cross-validated sensitivity and specificity of 90% on the reference set data.
  • the performance criteria for the two applications may be different, i.e., for detecting asymptomatic BE patients, high specificity may be necessary; but for discriminating symptomatic BE patients vs. EAC, very high sensitivity may be necessary. Such criteria are taken into consideration when searching for informative peaks. Indeed, ROC values that maximize specificity in screening for BE are chosen, while values that maximize sensitivity in distinguishing BE from EAC are selected. References

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Abstract

Plusieurs biomarqueurs à base de protéines sériques, pour la détection de l'œsophage de Barrett (BE), ont été identifiés en utilisant l'analyse de désorption/ionisation laser assistée par matrice (MALDI)-temps de vol (TOF), une approche protéomique robuste. L'analyse de quantification relative et absolue par marquage isotopique (iTRAQ) a été également réalisée, non seulement pour identifier les protéines détectées par l'analyse MALDI, mais également pour confirmer et identifier d'autres protéines exprimées uniquement lors du BE et de l'adénocarcinome œsophagien (EAC). Un dosage immunoenzymatique ELISA a été réalisé pour confirmer les protéines exprimées uniquement lors du BE et de l'EAC. Le test diagnostique proposé ici est peu onéreux, non invasif et sûr, ce qui le rend approprié pour le criblage de populations entières de pays dans lesquels le BE et l'EAC se présentent, comme les Etats-Unis et de nombreux pays d'Europe de l'Ouest. Il est sensible et très spécifique, ce qui implique qu'il va détecter un grand nombre de patients avec BE et EAC sans fausse détection d'un grand nombre d'individus non affectés.
PCT/US2010/029743 2009-04-02 2010-04-02 Ensembles de biomarqueurs pour l'oesophage de barrett et l'adénocarcinome de l'oesophage Ceased WO2010115077A2 (fr)

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Cited By (8)

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WO2012123419A1 (fr) 2011-03-11 2012-09-20 Vib Vzw Molécules et procédés d'inhibition et de détection de protéines
CN102971631A (zh) * 2010-12-15 2013-03-13 株式会社凯蒂生物 类风湿性关节炎的检查方法及类风湿性关节炎检查用试剂盒
CN107430126A (zh) * 2014-11-17 2017-12-01 昆士兰大学 食管腺癌和巴雷特食管的糖蛋白生物标志物及其用途
CN110988353A (zh) * 2019-11-12 2020-04-10 晏妮 一种用于cm10芯片的重生液
JP2021501897A (ja) * 2017-11-07 2021-01-21 高麗大学校産学協力団Ｋｏｒｅａ Ｕｎｉｖｅｒｓｉｔｙ Ｒｅｓｅａｒｃｈ Ａｎｄ Ｂｕｓｉｎｅｓｓ Ｆｏｕｎｄａｔｉｏｎ Ｇｃｃ２遺伝子又はタンパク質を過発現するエクソソーム基盤肺癌診断又は予後予測用マーカー組成物
KR20210126921A (ko) * 2020-04-13 2021-10-21 고려대학교 산학협력단 Gcc2 단백질을 과발현하는 엑소좀 기반 암 진단 또는 예후 예측용 마커 조성물
CN114167059A (zh) * 2021-11-03 2022-03-11 郑州大学 一种用于食管鳞癌诊断的生物标志物及检测试剂盒
US11684595B2 (en) 2017-04-07 2023-06-27 The Trustees Of Columbia University In The City Of New York Characterization of the oral microbiome for the non-invasive diagnosis of Barrett's esophagus

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CN102971631A (zh) * 2010-12-15 2013-03-13 株式会社凯蒂生物 类风湿性关节炎的检查方法及类风湿性关节炎检查用试剂盒
CN102971631B (zh) * 2010-12-15 2015-12-16 株式会社凯蒂生物 类风湿性关节炎的检查方法及类风湿性关节炎检查用试剂盒
US9733243B2 (en) 2010-12-15 2017-08-15 Kayteebio, Co. & Ltd. Test method for rheumatoid arthritis and kit for rheumatoid arthritis test
EP3384939A1 (fr) 2011-03-11 2018-10-10 Vib Vzw Molécules et procédés pour l'inhibition et la détection de protéines
WO2012123419A1 (fr) 2011-03-11 2012-09-20 Vib Vzw Molécules et procédés d'inhibition et de détection de protéines
CN107430126A (zh) * 2014-11-17 2017-12-01 昆士兰大学 食管腺癌和巴雷特食管的糖蛋白生物标志物及其用途
US11684595B2 (en) 2017-04-07 2023-06-27 The Trustees Of Columbia University In The City Of New York Characterization of the oral microbiome for the non-invasive diagnosis of Barrett's esophagus
JP2021501897A (ja) * 2017-11-07 2021-01-21 高麗大学校産学協力団Ｋｏｒｅａ Ｕｎｉｖｅｒｓｉｔｙ Ｒｅｓｅａｒｃｈ Ａｎｄ Ｂｕｓｉｎｅｓｓ Ｆｏｕｎｄａｔｉｏｎ Ｇｃｃ２遺伝子又はタンパク質を過発現するエクソソーム基盤肺癌診断又は予後予測用マーカー組成物
CN110988353A (zh) * 2019-11-12 2020-04-10 晏妮 一种用于cm10芯片的重生液
WO2021210826A1 (fr) * 2020-04-13 2021-10-21 고려대학교 산학협력단 Composition de marqueur destiné au diagnostic ou au pronostic du cancer fondée sur un exosome à surexpression de gcc2
KR102318328B1 (ko) * 2020-04-13 2021-10-27 고려대학교 산학협력단 Gcc2 단백질을 과발현하는 엑소좀 기반 암 진단 또는 예후 예측용 마커 조성물
KR20210126921A (ko) * 2020-04-13 2021-10-21 고려대학교 산학협력단 Gcc2 단백질을 과발현하는 엑소좀 기반 암 진단 또는 예후 예측용 마커 조성물
CN114167059A (zh) * 2021-11-03 2022-03-11 郑州大学 一种用于食管鳞癌诊断的生物标志物及检测试剂盒

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