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WO2003057014A2 - Biomarqueurs destines a la detection du cancer de l'ovaire - Google Patents

Biomarqueurs destines a la detection du cancer de l'ovaire Download PDF

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Publication number
WO2003057014A2
WO2003057014A2 PCT/US2003/000531 US0300531W WO03057014A2 WO 2003057014 A2 WO2003057014 A2 WO 2003057014A2 US 0300531 W US0300531 W US 0300531W WO 03057014 A2 WO03057014 A2 WO 03057014A2
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Prior art keywords
marker
markers
molecular weight
biomarkers
protein
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PCT/US2003/000531
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English (en)
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WO2003057014A9 (fr
WO2003057014A3 (fr
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Daniel W. Chan
Zhen Zheng
Alex Jaideep Rai
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John Hopkins University
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Priority to AU2003235749A priority Critical patent/AU2003235749A1/en
Priority to US10/500,838 priority patent/US20050214760A1/en
Publication of WO2003057014A2 publication Critical patent/WO2003057014A2/fr
Publication of WO2003057014A9 publication Critical patent/WO2003057014A9/fr
Publication of WO2003057014A3 publication Critical patent/WO2003057014A3/fr

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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
    • G01N33/57449—Specifically defined cancers of ovaries
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K39/00—Medicinal preparations containing antigens or antibodies
    • A61K39/395—Antibodies; Immunoglobulins; Immune serum, e.g. antilymphocytic serum
    • A61K39/44—Antibodies bound to carriers

Definitions

  • the invention provides inter alia for new biomarkers useful for measuring the ovarian cancer status of a subject.
  • Ovarian carcinoma represents one of such insidious and aggressive cancers. It is the most lethal gynecologic malignancy in women with 23,400 new cases and 13,900 deaths expected in 2001.
  • CA125 is the best characterized serological tumor marker for advanced epithelial ovarian cancers.
  • its use as a population-based screening tool for early detection and diagnosis of ovarian cancer is hindered by its low sensitivity and specificity.
  • pelvic and more recently vaginal sonography has been used to screen high-risk patients, neither technique has the sufficient sensitivity and specificity to be applied to the general population.
  • the present invention provides, for the first time, novel protein markers that are differentially present in the samples of human cancer patients and in the samples of control subjects.
  • the present invention also provides sensitive and quick methods and kits that can be used as an aid for diagnosis of human cancer by detecting these novel markers.
  • the measurement of these markers, alone or in combination, in patient samples provides information that a diagnostician can correlate with a probable diagnosis of human cancer or a negative diagnosis (e.g., normal or disease-free). All the markers are characterized by molecular weight.
  • the markers can be resolved from other proteins in a sample by using a variety of fractionation techniques, e.g., chromatographic separation coupled with mass spectrometry, or by traditional immunoassays.
  • the method of resolution involves Surface-Enhanced Laser Desorption/Ionization ("SELDI”) mass spectrometry, in which the surface of the mass spectrometry probe comprises adsorbents that bind the markers.
  • SELDI Surface-Enhanced Laser Desorption/Ionization
  • comparative protein profiles are generated using the ProteinChip Biomarker System from patients diagnosed with ovarian serous carcinoma and from patients without known neoplastic diseases.
  • a subset of biomarkers was selected based on collaborative results from supervised analytical methods.
  • Preferred analytical methods include the Classification And Regression Tree (CART) (see, L. Breiman et al., Classification and /Regression Trees: Wadsworth & Brooks, Monterey, CA 1994), implemented in Biomarker Pattern Software V4.0 (BPS) (Ciphergen, CA), and the Unified Maximum Separability Analysis (UMSA) procedure(see Z. Zhang et al., Proc of Critical Assessment of Techniques for Microarray Data analysis, CAMDA 2000, Dec. 18-19 2000, Durham, NC), implemented in ProPeak (3Z Informatics, SC).
  • CART Classification And Regression Tree
  • BPS Biomarker Pattern Software V4.0
  • UMSA Unified Maximum Separability Analysis
  • the analytical methods are used individually and in cross-comparison to screen for peaks that are most contributory towards the discrimination between ovarian cancer patients and the non-cancer controls.
  • the biomarkers are purified (at least in part) and identified. The selected biomarkers, together with the tumor marker CA125, were evaluated individually and in combination through multivariate logistic regression.
  • identified biomarkers are used individually, in combinations thereof, and with or without C125.
  • the identified biomarkers include, the proteins at peaks 9.2kD, 54kD and 79kD.
  • the 79 kD protein was found to correspond to transferrin, while the 9.2 kD protein was determined to be a fragment of the haptoglobin precursor protein.
  • the third, 54kD protein was identified as immunoglobulin heavy chain.
  • a plurality of the identified biomarkers are detected, preferably at least two of the biomarkers are detected, most preferably at least three of the biomarkers are detected.
  • the most preferred markers are the 79 kD (Marker VII ) protein corresponding to transferrin the 54kD (Marker V) protein corresponding to immunoglobulin heavy chain the 9.2 kD (Marker II) protein corresponding to a fragment of the haptoglobin precursor protein, and; correlating the detection of one or more protein biomarkers with a diagnosis of ovarian cancer, wherein the correlation takes into account the detection of one or more protein biomarkers in each diagnosis, as compared to normal subjects.
  • one or more protein biomarkers are used to diagnose ovarian cancer. See Example 1 which follows.
  • the identified biomarker is substantially homologous to the 79 kD (Marker VII ) protein corresponding to transferrin.
  • the identified biomarker is about 80% homologous to transferrin, more preferably the identified biomarker is about 90% homologous to transferrin; most preferably the identified biomarker is about 95%, 97%, 98% and 99% homologous to transferrin.
  • the identified biomarker is substantially homologous to the 54kD (Marker V) protein corresponding to immunoglobulin heavy chain.
  • the identified biomarker is about 80% homologous to immunoglobulin heavy chain, more preferably the identified biomarker is about 90% homologous to immunoglobulin heavy chain; most preferably the identified biomarker is about 95%, 97%, 98% and 99% homologous to immunoglobulin heavy chain.
  • the identified biomarker is substantially homologous to the 9.2 kD (Marker II) protein corresponding to a fragment of the haptoglobin precursor protein.
  • the identified biomarker is about 80% homologous to the haptoglobin precursor protein, more preferably the identified biomarker is about 90% homologous to the haptoglobin precursor protein; most preferably the identified biomarker is about 95%, 97%, 98% and 99% homologous to the haptoglobin precursor protein.
  • Preferred methods for detection and diagnosis of cancer comprise detecting at least one or more protein biomarkers in a subject sample, and; correlating the detection of one or more protein biomarkers with a diagnosis of cancer, wherein the correlation takes into account the detection of one or more biomarker in each diagnosis, as compared to normal subjects, wherein the one or more protein markers are selected from: Marker I: having a molecular weight of about 8.6 kD Marker II: having a molecular weight of about 9.2 kD
  • Marker III having a molecular weight of about 19.8 kD Marker IV: having a molecular weight of about 39.8 kD Marker V: having a molecular weight of about 54 kD Marker VI: having a molecular weight of about 60 kD Marker VII: having a molecular weight of about 79 kD. wherein one or more protein biomarkers are used to diagnose cancer.
  • a preferred method for detection and diagnosis of ovarian cancer comprises detecting at least one or more protein biomarkers in a subject sample, wherein the protein markers are selected from: Marker I: having a molecular weight of about 8.6 kD
  • Marker II having a molecular weight of about 9.2 kD Marker III: having a molecular weight of about 19.8 kD Marker IV: having a molecular weight of about 39.8 kD Marker V: having a molecular weight of about 54 kD Marker VI: having a molecular weight of about 60 kD
  • Marker VII having a molecular weight of about 79 kD and; correlating the detection of one or more protein biomarkers with a diagnosis of ovarian cancer, wherein the correlation takes into account the detection of one or more protein biomarkers in each diagnosis, as compared to normal subjects.
  • one or more protein biomarkers are used to diagnose ovarian cancer.
  • a plurality of the biomarkers are detected, preferably at least two of the biomarkers are detected, more preferably at least three of the biomarkers are detected, most preferably at least four of the biomarkers are detected.
  • the most preferred markers are
  • Marker II having a molecular weight of about 9.2 kD Marker III: having a molecular weight of about 19.8 kD Marker VI: having a molecular weight of 60 kD Marker VII: having a molecular weight of about 79 kD and; correlating the detection of one or more protein biomarkers with a diagnosis of ovarian cancer, wherein the correlation takes into account the detection of one or more protein biomarkers in each diagnosis, as compared to normal subjects.
  • one or more protein biomarkers are used to diagnose ovarian cancer.
  • the amount of each biomarker is measured in the subject sample and the ratio of the amounts between the markers is determined.
  • the amount of each biomarker in the subject sample and the ratio of the amounts between the biomarkers and known ovarian cancer markers is also determined to assess the stage of ovarian cancer.
  • the most preferred markers are the 79 kD (Marker VII ) protein corresponding to transferrin; the 54kD (Marker V) protein corresponding to immunoglobulin heavy chain; the 9.2 kD (Marker II) protein corresponding to a fragment of the haptoglobin precursor protein. Any one or combination of these markers can be used to differentiate between different stages of ovarian cancer. These markers can be used together with a known ovarian cancer biomarker such as C 125. See the examples which follow and Table 2.
  • a single biomarker is used in combination with one or more known cancer biomarkers for diagnosing cancer, more preferably a plurality of the markers are used in combination with one or more known cancer markers for diagnosing cancer.
  • Preferred known cancer markers are ovarian cancer markers for diagnosing ovarian cancer, such as CA 125. It is preferred that one or more protein biomarkers are used in comparing protein profiles from patients susceptible to, or suffering from cancer, such as ovarian cancer, with normal subjects.
  • Biochip arrays useful in the invention include protein and nucleic acid arrays.
  • One or more markers are immobilized on the biochip array and subjected to laser ionization to detect the molecular weight of the markers. Analysis of the markers is, for example, by molecular weight of the one or more markers 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 markers detected.
  • 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 markers present in ovarian cancer patients and are lacking in non-cancer subject controls.
  • the biochip surfaces are, for example, ionic, anionic, comprised of immobilized nickel ions, comprised of a mixture of positive and negative ions, comprises one or more antibodies, single or double stranded nucleic acids, comprises proteins, peptides or fragments thereof, amino acid probes, comprises phage display libraries.
  • one or more of the markers are detected 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 marker or markers from the probe and detecting the deionized/ionized markers 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 marker or markers from the probe and detecting the desorbed/ionized marker or markers 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.
  • a process for purification of a biomarker comprising fractioning a sample comprising one or more protein biomarkers by size-exclusion chromatography and collecting a fraction that includes the one or more biomarker; and/or fractionating a sample comprising the one or more biomarkers by anion exchange chromatography and collecting a fraction that includes the one or more biomarkers. Fractionation is monitored for purity on normal phase and immobilized nickel arrays.
  • Generating data on immobilized marker fractions on an array is accomplished by subjecting said 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 markers present in cancer patients and are lacking in non-cancer subject controls.
  • Preferably fractions are subjected to gel electrophoresis and correlated with data generated by mass spectrometry.
  • gel bands representative of potential markers are excised and subjected to enzymatic treatment and are applied to biochip arrays for peptide mapping.
  • biomarkers are selected from: gel bands representing
  • Marker I having a molecular weight of about 8.6 kD
  • Marker II having a molecular weight of about 9.2 kD
  • Marker III having a molecular weight of about 19.8 kD
  • Marker IV having a molecular weight of about 39.8 kD
  • Marker V having a molecular weight of about 54 kD
  • Marker VI having a molecular weight of about 60 kD
  • Marker VII having a molecular weight of about 79 kD
  • Purified proteins for detection of ovarian cancer and/or generation of antibodies for further diagnostic assays are provided for.
  • Purified proteins are selected from:
  • Marker I having a molecular weight of about 8.6 kD;
  • Marker II having a molecular weight of about 9.2 kD;
  • Marker III having a molecular weight of about 19.8 kD
  • Marker IV having a molecular weight of about 39.8 kD
  • Marker V having a molecular weight of about 54 kD
  • Marker VI having a molecular weight of about 60 kD
  • Marker VII having a molecular weight of about 79 kD.
  • kits for aiding the diagnosis of cancer comprising: an adsorbent attached to a substrate, wherein the adsorbent retains one or more biomarker selected from:
  • Marker I having a molecular weight of about 8.6 kD
  • Marker II having a molecular weight of about 9.2 kD
  • Marker III having a molecular weight of about 19.8 kD
  • Marker IV having a molecular weight of about 39.8 kD;
  • Marker V having a molecular weight of about 54 kD
  • Marker VI having a molecular weight of about 60 kD
  • Marker VII having a molecular weight of about 79 kD.
  • the kit comprises written instructions for use of the kit for detection of cancer and the instructions provide for contacting a test sample with the absorbent and detecting one or more biomarkers retained by the adsorbent.
  • the kit provides for a substrate which allows for adsorption of said adsorbent.
  • the substrate can be hydrophobic, hydrophilic, charged, polar, metal ions.
  • the kit also suitably provides for an adsorbent wherein the adsorbent is an antibody, single or double stranded oligonucleotide, amino acid, protein, peptide or fragments thereof.
  • Detection of one or more protein biomarkers using the kit suitably may be by mass spectrometry or immunoassays such as an ELISA.
  • compositions are provided to further aid in the diagnosis of ovarian cancer: A composition comprising Marker I and one more biomarkers selected from Markers II, III, IV, V, VI, and VII.
  • a composition comprising Marker II and one more biomarkers selected from Markers I, III, IV, V, VI, and VII.
  • a composition comprising Marker III and at least one more biomarkers selected from Markers I, II, IV, V, VI, and VII.
  • a composition comprising Marker IV and at least one more biomarkers selected from Markers I, II, III, V, VI, and VII.
  • a composition comprising Marker V and at least one more biomarkers selected from Markers I, II, III, IV, VI, and VII.
  • a composition comprising Marker VI and one more biomarkers selected from Markers I, II, III, IV, V, and VII.
  • a composition comprising Marker VII and one more biomarkers selected from Markers I, II, III, IV, V, and VI.
  • each of the markers in the compositions is purified.
  • Figure 1 Representative spectrum obtained from SELDI analysis. Plasma sample was run on IMAC-Ni ProteinChip array. Upper panel shows a portion of the protein profile in spectrum view. Lower panel is same profile shown in pseudo-gel view.
  • Figures 2 A - 2B ProPeak analysis of 67 samples.
  • the UMSA component analysis module of ProPeak was used to project 67 samples on to a 3D space (non- cancer: green, cancer: red).
  • Figure 3 A-3C Biomarker Patterns Software analysis of 67 samples.
  • A Tree diagram shows that two peaks can be used to separate the patient data into non-cancer and cancer groups. Green squares indicate decision nodes, while terminal nodes are in shades of blue (non-cancer) and red (cancer), indicating classification into the two groups.
  • B Sample composition of terminal nodes (blue: non-cancer, green: cancer), nodes are left to right, as numbered in the tree-diagram.
  • C A graph depicting the cost value in relation to the number of terminal nodes.
  • Figure 4 Pseudo-gel view of SELDI analysis of 67 plasma samples showing relative abundance of all markers in three panels: 6-10kD, 15-45kD, and 50-90kD. Asterisks indicate markers of interest.
  • Non-cancer samples (38) are shown above blue line, cancer samples (29) shown below.
  • FIG. 5 Schematic diagram of protein purification protocol.
  • Protein Identification Molecular weights of peptide fragments were measured by tandem mass spectrometry using Q-TOF. Data from the 9.2kD candidate marker is shown above. Selected peaks were further analyzed by MS/MS fragmentation, as shown in the inset.
  • FIG. 7 ROC analysis based on all 80 patients to compare diagnostic performance of four biomarkers (9.2kD, 54kD, 60kD, and 79kD) individually and in combinations through logistic regression.
  • FIG. 9 ROC analysis based on 68 patients with available CA125 values to compare diagnostic performance of a combination of biomarkers 60kD and 79kD, CA125, and a diagnostic index combining the two biomarkers and CA125.
  • the present invention is based in part upon, the discovery of protein markers that are differentially present in samples of human cancer patients and control subjects, and the application of this discovery in methods and kits for aiding a human cancer diagnosis.
  • Some of these protein markers are found at an elevated level and or more frequently in samples from human cancer patients compared to a control (e.g., women in whom human cancer is undetectable). Accordingly, the amount of one or more markers found in a test sample compared to a control, or the mere detection of one or more markers in the test sample provides useful information regarding probability of whether a subject being tested has human cancer or not.
  • the protein markers of the present invention have a number of other uses.
  • the markers can be used to screen for compounds that modulate the expression of the markers in vitro or in vivo, which compounds in turn may be useful in treating or preventing human cancer in patients.
  • markers can be used to monitor responses to certain treatments of human cancer.
  • the markers can be used in the heredity studies. For instance, certain markers may be genetically linked. This can be determined by, e.g. , analyzing samples from a population of human cancer patients whose families have a history of human cancer. The results can then be compared with data obtained from, e.g., human cancer patients whose families do not have a history of human cancer. The markers that are genetically linked may be used as a tool to determine if a subject whose family has a history of human cancer is predisposed to having human cancer.
  • the invention provides methods for detecting markers which are differentially present in the samples of a human cancer patient and a control (e.g., women in whom human cancer is undetectable).
  • the markers can be detected in a number of biological samples.
  • the sample is preferably a biological fluid sample.
  • a biological fluid sample useful in this invention include blood, blood serum, plasma, nipple aspirate, urine, tears, saliva, etc. Because all of the markers are found in blood serum, blood serum is a preferred sample source for embodiments of the invention.
  • methods for qualifying ovarian cancer status in a subject comprising: measuring at least one biomarker in a sample from the subject, wherein the biomarker is selected from the group consisting of:
  • Marker I having a molecular weight of about 8.6 kD
  • Marker II having a molecular weight of about 9.2 kD
  • Marker III having a molecular weight of about 19.8 kD Marker IV: having a molecular weight of about 39.8 kD Marker V: having a molecular weight of about 54 kD Marker VI : having a molecular weight of about 60 kD Marker VII: having a molecular weight of about 79 kD, and combinations of such Markers I through VII; and correlating the measurement with ovarian cancer status.
  • any suitable methods can be used to detect or measure one or more of the markers described herein. These methods include, without limitation, mass spectrometry (e.g., laser desorption/ionization mass spectrometry), fluorescence (e.g. sandwich immunoassay), surface plasmon resonance, ellipsometry and atomic force microscopy. Additionally, the terms “detect”, “detecting, “measure”, “measuring” include any of a wide range of analyses including quantifying, qualifying and the like.
  • comparative protein profiles can be generated from patients diagnosed with ovarian serous carcinoma and from patients without known neoplastic diseases.
  • a subset of biomarkers was selected based on collaborative results from two supervised analytical methods.
  • high- throughput protein profiling combined with effective use of bioinformatics tools offers a viable approach to screening for tumor markers.
  • a preferred system utilizes chromatographic arrays (e.g. ProteinChip Arrays) to assay the samples e.g. using SELDI (Surface Enhanced Laser Desorption/ionization). Proteins bound to the arrays can be read e.g. in a ProteinChip Reader, a time-of-flight mass spectrometer.
  • SELDI Surface Enhanced Laser Desorption/ionization
  • Proteins bound to the arrays can be read e.g. in a ProteinChip Reader, a time-of-flight mass spectrometer
  • Marker in the context of the present invention refers to a polypeptide (of a particular apparent molecular weight) which is differentially present in a sample taken from patients having human cancer as compared to a comparable sample taken from control subjects (e.g., a person with a negative diagnosis or undetectable cancer, normal or healthy subject).
  • substantially homologous refers to a polypeptide with, at least about 70%, at least about 75%, at least about 80%, and at least about 85%, at least about 90%, or at least about 95% identity or greater to a known biomarker such as the 79 kD (Marker VII ) protein corresponding to transferrin; the 54kD (Marker V) protein corresponding to immunoglobulin heavy chain; the 9.2 kD (Marker II) protein corresponding to a fragment of the haptoglobin precursor protein. Percent identity and similarity between two sequences can be determined using a mathematical algorithm (see, e.g., Computational Molecular Biology, Lesk, A.
  • the sequences are aligned for optimal comparison purposes (e.g., gaps are introduced in one or both of a first and a second amino acid or nucleic acid sequence for optimal alignment and non- homologous sequences can be disregarded for comparison purposes).
  • the percent identity between the two sequences is a function of the number of identical positions shared by the sequences, taking into account the number of gaps, and the length of each gap which need to be introduced for optimal alignment of the two sequences.
  • the amino acid residues at corresponding amino acid positions are then compared. When a position in the first sequence is occupied by the same amino acid residue as the corresponding position in the second sequence, then the molecules are identical at that position (as used herein amino acid or "identity" is equivalent to amino acid or "homology").
  • a “comparison window” refers to a segment of any one of the number of contiguous positions selected from the group consisting of from 25 to 600, usually about 50 to about 200, more usually about 100 to about 150 in which a sequence may be compared to a reference sequence of the same number of contiguous positions after the two sequences are optimally aligned. Methods of alignment of sequences for comparison are well-known in the art.
  • the percent identity between two amino acid sequences can be determined using the Needleman and Wunsch algorithm (J. Mol. Biol. (48): 444-453, 1970) which is part of the GAP program in the GCG software package (available at http://www.gcg.com), by the local homology algorithm of Smith & Waterman (Adv. Appl Math. 2: 482, 1981), by the search for similarity methods of Pearson & Lipman (Proc. Natl Acad. Sci. USA 85: 2444, 1988) and Altschul, et al. (Nucleic Acids Res.
  • Gap parameters can be modified to suit a user's needs. For example, when employing the GCG software package, a NWSgapdna.CMP matrix and a gap weight of 40, 50, 60, 70, or 80 and a length weight of 1 , 2, 3, 4, 5, or 6 can be used.
  • Examplary gap weights using a Blossom 62 matrix or a PAM250 matrix are 16, 14, 12, 10, 8, 6, or 4, while exemplary length weights are 1, 2, 3, 4, 5, or 6.
  • the GCG software package can be used to determine percent identity between nucleic acid sequences. The percent identity between two amino acid or nucleotide sequences also can be determined using the algorithm of E. Myers and W. Miller (CABIOS 4: 11-17, 1989) which has been incorporated into the ALIGN program (version 2.0), using a PAM120 weight residue table, a gap length penalty of 12 and a gap penalty of 4.
  • a marker can be a polypeptide which is present at an elevated level or at a decreased level in samples of human cancer patients compared to samples of control subjects.
  • a marker can be a polypeptide which is detected at a higher frequency or at a lower frequency in samples of human cancer patients compared to samples of control subjects.
  • a marker can be differentially present in terms of quantity, frequency or both.
  • a polypeptide is differentially present between the two samples if the amount of the polypeptide in one sample is statistically significantly different from the amount of the 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 the two sets of samples if the frequency of detecting the polypeptide in the human cancer patients' samples is statistically significantly higher or lower than in the control samples.
  • a polypeptide is differentially present 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. 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 marker refers to an amount of a marker 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 marker refers to an amount of a marker in a subject's sample that is consistent with a diagnosis of human cancer.
  • 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 marker can be any amount or a range of amount which is to be compared against a test amount of a marker.
  • a control amount of a marker can be the amount of a marker in a person without human cancer.
  • a control amount can be either in absolute amount (e.g., ⁇ g/ml) or a relative amount (e.g., relative intensity of signals).
  • Probe refers to a device that is removably insertable into a gas phase ion spectrometer and comprises a substrate having a surface for presenting a marker for detection.
  • a probe can comprise a single substrate or a plurality of substrates. Terms such as ProteinChip ® , ProteinChip array, or chip are also used herein to refer to specific kinds of probes.
  • Substrate or “probe substrate” refers to a solid phase onto which an adsorbent can be provided (e.g., by attachment, deposition, etc.).
  • Adsorbent refers to any material capable of adsorbing a marker.
  • the term “adsorbent” is used herein to refer both to a single material ("monoplex adsorbent") (e.g., a compound or functional group) to which the marker is exposed, and to a plurality of different materials (“multiplex adsorbent”) to which the marker is exposed.
  • the adsorbent materials in a multiplex adsorbent are referred to as "adsorbent species.”
  • an addressable location on a probe substrate can comprise a multiplex adsorbent characterized by many different adsorbent species (e.g., anion exchange materials, metal chelators, or antibodies), having different binding characteristics.
  • Substrate material itself can also contribute to adsorbing a marker and may be considered part of an "adsorbent.”
  • Adsorption or “retention” refers to the detectable binding between an absorbent and a marker either before or after washing with an eluant (selectivity threshold modifier) or a washing solution.
  • Eluant or “washing solution” refers to an agent that can be used to mediate adsorption of a marker to an adsorbent. Eluants and washing solutions are also referred to as “selectivity threshold modifiers.” Eluants and washing solutions can be used to wash and remove unbound materials from the probe substrate surface.
  • Resolution refers to the detection of at least one marker in a sample. Resolution includes the detection of a plurality of markers in a sample by separation and subsequent differential detection. Resolution does not require the complete separation of one or more markers from all other biomolecules in a mixture. Rather, any separation that allows the distinction between at least one marker and other biomolecules suffices.
  • Gas phase ion spectrometer refers to an apparatus that measures a parameter which can be translated into mass-to-charge ratios of ions formed when a sample is volatilized and ionized. Generally ions of interest bear a single charge, and mass-to- charge ratios are often simply referred to as mass. Gas phase ion spectrometers include, for example, mass spectrometers, ion mobility spectrometers, and total ion current measuring devices.
  • Mass spectrometer refers to a gas phase ion spectrometer that includes an inlet system, an ionization source, an ion optic assembly, a mass analyzer, and a detector.
  • Laser desorption mass spectrometer refers to a mass spectrometer which uses laser as means to desorb, volatilize, and ionize an analyte.
  • Detect refers to identifying the presence, absence or amount of the object to be detected.
  • polypeptide refers to a polymer of amino acid residues.
  • the terms apply to amino acid polymers in which one or more amino acid residue is an analog or mimetic of a corresponding naturally occurring amino acid, as well as to naturally occurring amino acid polymers.
  • Polypeptides can be modified, e.g., by the addition of carbohydrate residues to form glycoproteins.
  • polypeptide include glycoproteins, as well as non-glycoproteins.
  • Detectable moiety refers to a composition detectable by spectroscopic, photochemical, biochemical, immunochemical, or chemical means.
  • useful labels include 32 P, 35 S, fluorescent dyes, electron-dense reagents, enzymes (e.g., as commonly used in an ELISA), biotin-streptavidin, dioxigenin, haptens and proteins for which antisera or monoclonal antibodies are available, or nucleic acid molecules with a sequence complementary to a target.
  • the detectable moiety often generates a measurable signal, such as a radioactive, chromogenic, or fluorescent signal, that can be used to quantify the amount of bound detectable moiety in a sample. Quantitation of the signal is achieved by, e.g., scintillation counting, densitometry, or flow cytometry.
  • 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.
  • antibody 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 3 , but does not include the heavy chain variable region. "Immunoassay” is an assay that uses an antibody to specifically bind an antigen (e.g., a marker). The immunoassay is characterized by the use of specific binding properties of a particular antibody to isolate, target, and/or quantify the antigen.
  • an antigen e.g., a marker
  • 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 marker Br 1 from specific species such as rat, mouse, or human can be selected to obtain only those polyclonal antibodies that are specifically immunoreactive with marker Br 1 and not with other proteins, except for polymorphic variants and alleles of marker Br 1. This selection may be achieved by subtracting out antibodies that cross-react with marker Br 1 molecules from other species.
  • a variety of immunoassay formats may be used to select antibodies specifically immunoreactive with a particular protein.
  • solid-phase ELISA immunoassays are routinely used to select antibodies specifically immunoreactive with a protein (see, e.g., Harlow & Lane, Antibodies, A Laboratory Manual (1988), for a description of immunoassay formats and conditions that can be used to determine specific immunoreactivity).
  • a specific or selective reaction will be at least twice background signal or noise and more typically more than 10 to 100 times background.
  • Energy absorbing molecule refers to a molecule that absorbs energy from an ionization source in a mass spectrometer thereby aiding desorption of analyte, such as a marker, from a probe surface. Depending on the size and nature of the analyte, the energy absorbing molecule can be optionally used. Energy absorbing molecules used in MALDI are frequently referred to as “matrix.” Cinnamic acid derivatives, sinapinic acid (“SPA”), cyano hydroxy cinnamic acid (“CHCA”) and dihydroxybenzoic acid are frequently used as energy absorbing molecules in laser desorption of bioorganic molecules.
  • SPA sinapinic acid
  • CHCA cyano hydroxy cinnamic acid
  • dihydroxybenzoic acid are frequently used as energy absorbing molecules in laser desorption of bioorganic molecules.
  • the sample is prepared prior to detection of biomarkers.
  • preparation involves fractionation of the 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.
  • a sample can be pre-fractionated according to size of proteins in a sample using size exclusion chromatography.
  • a size selection spin column is used for a biological sample wherein the amount of sample available is small.
  • a K30 spin column available from Princeton Separation, Ciphergen Biosystems, Inc., etc.
  • the first fraction that is eluted from the column (“fraction 1") has the highest percentage of high molecular weight proteins; fraction 2 has a lower percentage of high molecular weight proteins; fraction 3 has even a lower percentage of high molecular weight proteins; fraction 4 has the lowest amount of large proteins; and so on.
  • Each fraction can then be analyzed by gas phase ion spectrometry for the detection of markers.
  • a sample can be pre-fractionated by anion exchange chromatography.
  • Anion exchange chromatography allows pre-fractionation of the 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 (see Figure 2 and Example section below).
  • 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.
  • a sample can be pre-fractionated by heparin chromatography.
  • Heparin chromatography allows pre-fractionation of the markers in a sample also on the basis of affinity interaction with heparin and charge characteristics. Heparin, a sulfated mucopolysaccharide, will bind markers with positively charged moieties and a sample can be sequentially eluted with eluants having different pH's or salt concentrations. Markers eluted with an eluant having a low pH are more likely to be weakly positively charged. Markers eluted with an eluant having a high pH are more likely to be strongly positively charged.
  • heparin chromatography also reduces the complexity of a sample and separates markers according to their binding characteristics.
  • a sample can be pre-fractionated by removing proteins that are present in a high quantity or that may interfere with the detection of markers in a sample.
  • serum albumin is present in a high quantity and may obscure the analysis of markers.
  • a blood serum sample can be pre-fractionated by removing serum albumin.
  • Serum albumin can be removed using a substrate that comprises adsorbents that specifically bind serum albumin.
  • a column which comprises, e.g., Cibacron blue agarose (which has a high affinity for serum albumin) or anti-serum albumin antibodies can be used (see, e.g., Figures 1 and 3).
  • a sample can be pre-fractionated by isolating proteins that have a specific characteristic, e.g. are glycosylated.
  • a blood serum sample can be fractionated by passing the sample over a lectin chromatography column (which has a high affinity for sugars). Glycosylated proteins will bind to the lectin column and non-glycosylated proteins will pass through the flow through. Glycosylated proteins are then eluted from the lectin column with an eluant containing a sugar, e.g., N- acetyl-glucosamine and are available for further analysis.
  • affinity adsorbents exist which are suitable for pre-fractionating blood serum samples.
  • An example of one other type of affinity chromatography available to pre-fractionate a sample is a single stranded DNA spin column. These columns bind proteins which are basic or positively charged. Bound proteins are then eluted from the column using eluants containing denaturants or high pH.
  • a sample can be fractionated using a sequential extraction protocol.
  • sequential extraction a sample is exposed to a series of adsorbents to extract different types of biomolecules from a sample. For example, a sample is applied to a first adsorbent to extract certain proteins, and an eluant containing non-adsorbent proteins (i.e., proteins that did not bind to the first adsorbent) is collected. Then, the fraction is exposed to a second adsorbent. This further extracts various proteins from the fraction. This second fraction is then exposed to a third adsorbent, and so on.
  • any suitable materials and methods can be used to perform sequential extraction of a sample.
  • a series of spin columns comprising different adsorbents can be used.
  • a multi-well comprising different adsorbents at its bottom can be used.
  • sequential extraction can be performed on a probe adapted for use in a gas phase ion spectrometer, wherein the probe surface comprises adsorbents for binding biomolecules.
  • the sample is applied to a first adsorbent on the probe, which is subsequently washed with an eluant. Markers that do not bind to the first adsorbent is removed with an eluant.
  • the markers that are in the fraction can be applied to a second adsorbent on the probe, and so forth.
  • the advantage of performing sequential extraction on a gas phase ion spectrometer probe is that markers that bind to various adsorbents at every stage of the sequential extraction protocol can be analyzed directly using a gas phase ion spectrometer.
  • biomolecules in a sample can be separated by high- resolution electrophoresis, e.g., one or two-dimensional gel electrophoresis.
  • a fraction containing a marker 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 markers. 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.
  • 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 marker 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 in detail below.
  • cleaving reagents such as proteases (e.g., trypsin).
  • the digestion of biomolecules into small fragments provides a mass fingerprint of the biomolecules in the spot, which can be used to determine the identity of markers if desired.
  • HPLC high performance liquid chromatography
  • 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 of the 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 markers can be collected. The fraction can then be analyzed by gas phase ion spectrometry to detect markers. For example, the spots can be analyzed using either MALDI or SELDI (e.g., using ProteinChip ® array) as described in detail below.
  • a marker can be modified before analysis to improve its resolution or to determine its identity.
  • the markers may be subject to proteolytic digestion before analysis. Any protease can be used. Proteases, such as trypsin, that are likely to cleave the markers into a discrete number of fragments are particularly useful. The fragments that result from digestion function as a fingerprint for the markers, thereby enabling their detection indirectly. This is particularly useful where there are markers with similar molecular masses that might be confused for the marker in question. Also, proteolytic fragmentation is useful for high molecular weight markers because smaller markers 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 markers can be modified by the attachment of a tag of particular molecular weight that specifically bind to molecular markers, further distinguishing them.
  • the identity of the markers can be further determined by matching the physical and chemical characteristics of the modified markers in a protein database (e.g., SwissProt).
  • biomarkers in a sample are typically captured on a substrate for detection.
  • Traditional substrates include antibody -coated 96-well plates or nitrocellulose membranes that are subsequently probed for the presence of proteins.
  • investigators are making use of protein biochips to capture and detect proteins.
  • Many protein biochips are described in the art. These include, for example, protein biochips produced by Ciphergen Biosystems (Fremont, CA), Packard BioScience Company
  • protein biochips comprise a substrate having a surface.
  • a capture reagent or adsorbent is attached to the surface of the substrate.
  • the surface comprises a plurality of addressable locations, each of which location has the capture reagent bound there.
  • the capture reagent can be a biological molecule, such as a polypeptide or a nucleic acid, which captures other biomolecules in a specific manner.
  • the capture reagent can be a chromatographic material, such as an anion exchange material or a hydrophilic material. Examples of such protein biochips are described in the following patents or patent applications: U.S.
  • Ciphergen ProteinChip ® arrays include NP20, H4, SAX-2, WCX-2, IMAC-3, LSAX-30, LWCX-30, IMAC-40, PS- 10 and PS-20.
  • Ciphergen' s protein biochips comprise an aluminum substrate in the form of a strip. The surface of the strip is coated with silicon dioxide.
  • silicon oxide functions as a hydrophilic adsorbent to capture hydrophilic proteins.
  • H4 biochip has isopropyl functionalities for hydrophobic binding.
  • the SAX-2 biochip has quartemary ammonium functionalities for anion exchange.
  • the WCX-2 biochip has carboxylate functionalities for cation exchange.
  • the IMAC-3 biochip has copper ions immobilized through nitrilotriacetic acid for coordinate covalent 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 proteins.
  • the PS-series biochips are useful for binding biospecific adsorbents, such as antibodies, receptors, lectins, heparin, Protein A, biotin/streptavadin and the like, to chip surfaces where they function to specifically capture analytes from a sample.
  • biospecific adsorbents such as antibodies, receptors, lectins, heparin, Protein A, biotin/streptavadin and the like.
  • the LSAX-30 (anion exchange), LWCX-30 (cation exchange) and IMAC-40 (metal chelate) biochips have functionalized latex beads on their surfaces. Such biochips are further described in: WO 00/66265 (Rich et al.
  • Analytes captured on the surface of a protein biochip can be detected by any method known in the art. This includes, for example, mass spectrometry, fluorescence, surface plasmon resonance, ellipsometry and atomic force microscopy. Mass spectrometry, and particularly SELDI mass spectrometry, is a particularly useful method for detection of the biomarkers of this invention.
  • a laser desorption time-of-flight mass spectrometer is used in embodiments of the invention.
  • a substrate or a probe comprising markers is introduced into an inlet system.
  • the markers are desorbed and ionized into the gas phase by laser from the ionization source.
  • the ions generated are collected by an ion optic assembly, and then in a time-of-flight mass analyzer, ions are accelerated through a short high voltage field and let drift into a high vacuum chamber. At the far end of the high vacuum chamber, the accelerated ions strike a sensitive detector surface at a different time. Since the time-of-flight is a function of the mass of the ions, the elapsed time between ion formation and ion detector impact can be used to identify the presence or absence of markers of specific mass to charge ratio.
  • MALDI-MS Matrix-assisted laser desorption/ionization mass spectrometry
  • MALDI-MS is a method of mass spectrometry that involves the use of an energy absorbing molecule, frequently called a matrix, for desorbing proteins intact from a probe surface.
  • MALDI is described, for example, in U.S. patent 5,118,937 (Hillenkamp et al.) and U.S. patent 5,045,694 (Beavis and Chait).
  • the sample is typically mixed with a matrix material and placed on the surface of an inert probe.
  • Exemplary energy absorbing molecules include cinnamic acid derivatives, sinapinic acid (“SPA”), cyano hydroxy cinnamic acid (“CHCA”) and dihydroxybenzoic acid. Other suitable energy absorbing molecules are known to those skilled in this art.
  • the matrix dries, forming crystals that encapsulate the analyte molecules. Then the analyte molecules are detected by laser desorption/ionization mass spectrometry.
  • MALDI-MS is useful for detecting the biomarkers of this invention if the complexity of a sample has been substantially reduced using the preparation methods described above.
  • SELDI-MS Surface-enhanced laser desorption/ionization mass spectrometry, or SELDI-MS represents an improvement over MALDI for the fractionation and detection of biomolecules, such as proteins, in complex mixtures.
  • SELDI is a method of mass spectrometry in which biomolecules, such as proteins, are captured on the surface of a protein biochip using capture reagents that are bound there. Typically, non-bound molecules are washed from the probe surface before interrogation.
  • SELDI technology is available from Ciphergen Biosystems, Inc., Fremont CA as part of the ProteinChip® System. ProteinChip® arrays are particularly adapted for use in SELDI.
  • SELDI is described, for example, in: United States Patent 5,719,060 ("Method and Apparatus for Desorption and Ionization of Analytes," Hutchens and Yip, February 17, 1998,) United States Patent 6,225,047 ("Use of Retentate Chromatography to Generate Difference Maps," Hutchens and Yip, May 1, 2001) and Weinberger et al., "Time-of-flight mass spectrometry," in Encyclopedia of Analytical Chemistry, R.A. Meyers, ed., pp 11915- 11918 John Wiley & Sons Chichesher, 2000.
  • Markers on the substrate surface can be desorbed and ionized using gas phase ion spectrometry.
  • Any suitable gas phase ion spectrometers can be used as long as it allows markers on the substrate to be resolved.
  • gas phase ion spectrometers allow quantitation of markers.
  • a gas phase ion spectrometer is a mass spectrometer.
  • a substrate or a probe comprising markers on its surface is introduced into an inlet system of the mass spectrometer.
  • the markers are then desorbed by a desorption source such as a laser, fast atom bombardment, high energy plasma, electrospray ionization, thermospray ionization, liquid secondary ion MS, field desorption, etc.
  • the generated desorbed, volatilized species consist of preformed ions or neutrals which are ionized as a direct consequence of the desorption event.
  • Generated ions are collected by an ion optic assembly, and then a mass analyzer disperses and analyzes the passing ions.
  • the ions exiting the mass analyzer are detected by a detector.
  • the detector then translates information of the detected ions into mass-to-charge ratios. Detection of the presence of markers or other substances will typically involve detection of signal intensity. This, in turn, can reflect the quantity and character of markers bound to the substrate.
  • a mass spectrometer e.g., a desorption source, a mass analyzer, a detector, etc.
  • suitable components described herein or others known in the art in embodiments of the invention can be combined with other suitable components described herein or others known in the art in embodiments of the invention.
  • a laser desorption time-of-flight mass spectrometer is used in embodiments of the invention.
  • a substrate or a probe comprising markers is introduced into an inlet system.
  • the markers are desorbed and ionized into the gas phase by laser from the ionization source.
  • the ions generated are collected by an ion optic assembly, and then in a time-of-flight mass analyzer, ions are accelerated through a short high voltage field and let drift into a high vacuum chamber. At the far end of the high vacuum chamber, the accelerated ions strike a sensitive detector surface at a different time. Since the time-of-flight is a function of the mass of the ions, the elapsed time between ion formation and ion detector impact can be used to identify the presence or absence of markers of specific mass to charge ratio.
  • an ion mobility spectrometer can be used to detect markers.
  • the principle of ion mobility spectrometry is based on different mobility of ions. Specifically, ions of a sample produced by ionization move at different rates, due to their difference in, e.g., mass, charge, or shape, through a tube under the influence of an electric field. The ions (typically in the form of a current) are registered at the detector which can then be used to identify a marker or other substances in a sample.
  • One advantage of ion mobility spectrometry is that it can operate at atmospheric pressure.
  • a total ion current measuring device can be used to detect and characterize markers. This device can be used when the substrate has a only a single type of marker.
  • the total current generated from the ionized marker reflects the quantity and other characteristics of the marker.
  • the total ion current produced by the marker can then be compared to a control (e.g., a total ion current of a known compound).
  • the quantity or other characteristics of the marker can then be determined.
  • an immunoassay can be used to detect and analyze markers in a sample. This method comprises: (a) providing an antibody that specifically binds to a marker; (b) contacting a sample with the antibody; and (c) detecting the presence of a complex of the antibody bound to the marker in the sample.
  • nucleic acid and amino acid sequences for markers can be obtained by further characterization of these markers.
  • each marker can be peptide mapped with a number of enzymes (e.g., trypsin, V8 protease, etc.).
  • the molecular weights of digestion fragments from each marker can be used to search the databases, such as SwissProt database, for sequences that will match the molecular weights of digestion fragments generated by various enzymes. Using this method, the nucleic acid and amino acid sequences of other markers can be identified if these markers are known proteins in the databases.
  • the proteins can be sequenced using protein ladder sequencing.
  • Protein ladders can be generated by, for example, fragmenting the molecules and subjecting fragments to enzymatic digestion or other methods that sequentially remove a single amino acid from the end of the fragment. Methods of preparing protein ladders are described, for example, in International Publication WO 93/24834 (Chait et al.) and United States Patent 5,792,664 (Chait et al). The ladder is then analyzed by mass spectrometry. The difference in the masses of the ladder fragments identify the amino acid removed from the end of the molecule. If the markers are not known proteins in the databases, nucleic acid and amino acid sequences can be determined with knowledge of even a portion of the amino acid sequence of the marker.
  • degenerate probes can be made based on the N- terminal amino acid sequence of the marker. These probes can then be used to screen a genomic or cDNA library created from a sample from which a marker was initially detected. The positive clones can be identified, amplified, and their recombinant DNA sequences can be subcloned using techniques which are well known. See, e.g., Current Protocols for Molecular Biology (Ausubel et al, Green Publishing Assoc. and Wiley- Interscience 1989) and Molecular Cloning: A Laboratory Manual, 3rd Ed. (Sambrook et al, Cold Spring Harbor Laboratory, NY 2001).
  • antibodies that specifically bind to a marker 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, Nature 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 marker can be detected and/or quantified using any of suitable immunological binding assays known in the art (see, e.g., U.S. Patent Nos. 4,366,241; 4,376,110; 4,517,288; and 4,837,168).
  • 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 e.g., Western blot assay, or a slot blot assay.
  • a sample obtained from a subject can be contacted with the antibody that specifically binds the marker.
  • the antibody can be fixed to a solid support to facilitate washing and subsequent isolation of the 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 preferably 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-marker 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 marker in the sample can be detected using an indirect assay, wherein, for example, a second, labeled antibody is used to detect bound marker-specific antibody, and/or in a competition or inhibition assay wherein, for example, a monoclonal antibody which binds to a distinct epitope of the marker is incubated simultaneously with the mixture.
  • an indirect assay wherein, for example, a second, labeled antibody is used to detect bound marker-specific antibody, and/or in a competition or inhibition assay wherein, for example, a monoclonal antibody which binds to a distinct epitope of the marker is incubated simultaneously with the mixture.
  • incubation and/or washing steps may be required after each combination of reagents. 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, marker, 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 marker in a sample as well as the quantity of a marker in a sample. First, a test amount of a marker in a sample can be detected using the immunoassay methods described above.
  • a marker If a marker is present in the sample, it will form an antibody-marker complex with an antibody that specifically binds the marker under suitable incubation conditions described above.
  • the amount of an antibody-marker 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 marker need not be measured in absolute units, as long as the unit of measurement can be compared to a control.
  • the methods for detecting these markers in a sample have many applications. For example, one or more markers can be measured to aid human cancer diagnosis or prognosis. In another example, the methods for detection of the markers can be used to monitor responses in a subject to cancer treatment. In another example, the methods for detecting markers can be used to assay for and to identify compounds that modulate expression of these markers in vivo or in vitro.
  • Data generated by desorption and detection of markers can be analyzed using any suitable means.
  • data is analyzed with the use of a programmable digital computer.
  • the computer program generally contains a readable medium that stores codes. Certain code can be devoted to memory that includes the location of each feature on a probe, the identity of the adsorbent at that feature and the elution conditions used to wash the adsorbent.
  • the computer also contains code that receives as input, data on the strength of the signal at various molecular masses received from a particular addressable location on the probe. This data can indicate the number of markers detected, including the strength of the signal generated by each marker.
  • Data analysis can include the steps of determining signal strength (e.g., height of peaks) of a marker detected and removing "outliers" (data deviating from a predetermined statistical distribution).
  • the observed peaks can be normalized, a process whereby the height of each peak relative to some reference is calculated.
  • a reference can be background noise generated by instrument and chemicals (e.g., energy absorbing molecule) which is set as zero in the scale.
  • the signal strength detected for each marker or other biomolecules can be displayed in the form of relative intensities in the scale desired (e.g. , 100).
  • a standard e.g. , a serum protein
  • a standard may be admitted with the sample so that a peak from the standard can be used as a reference to calculate relative intensities of the signals observed for each marker or other markers detected.
  • the computer can transform the resulting data into various formats for displaying.
  • spectrum view or retentate map a standard spectral view can be displayed, wherein the view depicts the quantity of marker reaching the detector at each particular molecular weight.
  • peak map only the peak height and mass information are retained from the spectrum view, yielding a cleaner image and enabling markers with nearly identical molecular weights to be more easily seen.
  • gel view each mass from the peak view can be converted into a grayscale image based on the height of each peak, resulting in an appearance similar to bands on electrophoretic gels.
  • 3-D overlays several spectra can be overlaid to study subtle changes in relative peak heights.
  • two or more spectra can be compared, conveniently highlighting unique markers and markers which are up- or down-regulated between samples. Marker profiles (spectra) from any two samples may be compared visually.
  • Spotfire Scatter Plot can be used, wherein markers that are detected are plotted as a dot in a plot, wherein one axis of the plot represents the apparent molecular of the markers detected and another axis represents the signal intensity of markers detected. For each sample, markers that are detected and the amount of markers present in the sample can be saved in a computer readable medium.
  • the invention provides methods for aiding a human cancer diagnosis using one or more markers, for example Markers I through VII.
  • markers can be used alone, in combination with other markers in any set, or with entirely different markers (e.g., CA 125 oncogene product) in aiding human cancer diagnosis.
  • the markers are differentially present in samples of a human cancer patient, for example ovarian cancer patient, and a normal subject in whom human cancer is undetectable. For example, some of the markers are expressed at an elevated level and/or are present at a higher frequency in human cancer patients than in normal subjects. Therefore, detection of one or more of these markers in a person would provide useful information regarding the probability that the person may have human cancer.
  • embodiments of the invention include methods for aiding a human cancer diagnosis, wherein the method comprises: (a) detecting at least one marker in a sample, wherein the marker is selected from Marker I- VII; and (b) correlating the detection of the marker or markers with a probable diagnosis of human cancer.
  • the correlation may take into account the amount of the marker or markers in the sample compared to a control amount of the marker or markers (up or down regulation of the marker or markers) (e.g., in normal subjects in whom human cancer is undetectable).
  • the correlation may take into account the presence or absence of the markers in a test sample and the frequency of detection of the same markers in a control.
  • the correlation may take into account both of such factors to facilitate determination of whether a subject has a human cancer or not.
  • a sample is a blood serum sample from the subject.
  • the sample can be prepared as described above to enhance detectability of the markers.
  • a blood serum sample from the subject can be preferably fractionated by, e.g., Cibacron blue agarose chromatography and single stranded DNA affinity chromatography, anion exchange chromatography and the like.
  • Sample preparations, such as pre-fractionation protocols, is optional and may not be necessary to enhance detectability of markers depending on the methods of detection used. For example, sample preparation may be unnecessary if antibodies that specifically bind markers are used to detect the presence of markers in a sample.
  • Any suitable method can be used to detect a marker or markers in a sample.
  • gas phase ion spectrometry or an immunoassay can be used as described above. Using these methods, one or more markers can be detected.
  • a sample is tested for the presence of a plurality of markers. Detecting the presence of a plurality of markers, rather than a single marker alone, would provide more information for the diagnostician. Specifically, the detection of a plurality of markers in a sample would increase the percentage of true positive and true negative diagnoses and would decrease the percentage of false positive or false negative diagnoses.
  • the detection of the marker or markers is then correlated with a probable diagnosis of human cancer.
  • the detection of the mere presence or absence of a marker, without quantifying the amount of marker is useful and can be correlated with a probable diagnosis of human cancer.
  • markers II, III, VI can be more frequently detected in human ovarian cancer patients than in normal subjects.
  • a mere detection of one or more of these markers in a subject being tested indicates that the subject has a higher probability of having a human cancer.
  • the detection of markers can involve quantifying the markers to correlate the detection of markers with a probable diagnosis of human cancer. Thus, if the amount of the markers detected in a subject being tested is higher compared to a control amount, then the subject being tested has a higher probability of having a human cancer.
  • the detection of markers can further involve quantifying the markers to correlate the detection of markers with a probable diagnosis of human cancer wherein the markers are present in lower quantities in blood serum samples from human cancer patients than in blood serum samples of normal subjects.
  • the subject being tested has a higher probability of having a human cancer.
  • a control can be, e.g., the average or median amount of marker present in comparable samples of normal subjects in whom human cancer is undetectable.
  • the control amount is measured under the same or substantially similar experimental conditions as in measuring the test amount. For example, if a test sample is obtained from a subject's blood serum sample and a marker is detected using a particular probe, then a control amount of the marker is preferably determined from a serum sample of a patient using the same probe. It is preferred that the control amount of marker is determined based upon a significant number of samples from normal subjects who do not have human cancer so that it reflects variations of the marker amounts in that population.
  • Data generated by mass spectrometry can then be analyzed by a computer software.
  • 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 of the signal to determine whether the signal represents a "peak" in the signal corresponding to a marker of this invention, or other useful markers.
  • 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.
  • kits for aiding a diagnosis of human cancer wherein the kits can be used to detect the markers of the present invention.
  • the kits can be used to detect any one or more of the markers described herein, which markers are differentially present in samples of a human cancer patient and normal subjects.
  • the kits of the invention have many applications.
  • the kits can be used to differentiate if a subject has human ovarian cancer or has a negative diagnosis, thus aiding a human cancer diagnosis.
  • the kits can be used to identify compounds that modulate expression of one or more of the markers in in vitro or in vivo animal models for human cancer.
  • a kit comprises: (a) a substrate comprising an adsorbent thereon, wherein the adsorbent is suitable for binding a marker, and (b) instructions to detect the marker or markers by contacting a sample with the adsorbent and detecting the marker or markers retained by the adsorbent.
  • the kit may comprise an eluant (as an alternative or in combination with instructions) or instructions for making an eluant, wherein the combination of the adsorbent and the eluant allows detection of the markers using gas phase ion spectrometry.
  • Such kits can be prepared from the materials described above, and the previous discussion of these materials (e.g., probe substrates, 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.
  • the kit can further comprise instructions for suitable operational parameters in the form of a label or a separate insert.
  • the kit may have standard instructions informing a consumer how to wash the probe after a sample of blood serum is contacted on the probe.
  • 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 comprises (a) an antibody that specifically binds to a marker; 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 marker detected in a sample is a diagnostic amount consistent with a diagnosis of human ovarian cancer.
  • FIGO-stage I 3 FIGO-stage II and 28 FIGO-stage III patients.
  • the median age of these patients was 53 years (range: 36 to 84).
  • Specimens from 38 women without known neoplastic diseases were used as controls.
  • the median age of the controls was 57 years (range: 45 to 75).
  • Specimens, collected in EDTA, Vacutainer tubes, were centrifuged at 2,000 rpm for 20 min and plasma samples were harvested to avoid leukocyte contamination.
  • Specimens obtained prior to 2000 were analyzed for CA 125II using Centocor CA125II assays (Fujirebio Diagnostics, Malvern, PA). For the remaining specimens, CA125 levels were measured in either serum or EDTA plasma using the Tosoh AIA-PACK CA125 assay on the 600 II analyzer (Tosoh Medics, South San Francisco, CA).
  • the Centocor CA125II assay is equivalent to the Tosoh CA125 assay (unpublished data).
  • the Tosoh CA125 assay is approved for use in serum, however the assay was validated for plasma in house and results for serum and plasma were determined to be equivalent. Results were available in 68 out of the 80 total specimens.
  • each array was then pre-washed in the appropriate wash buffer: PBS pH 7.4, 50% ACN in dH2O, and 100 mM Na2HPO4 pH 6.0 for IMAC, H4 and SAX2 respectively. Fifty ⁇ L of each sample was applied to each array type and incubated on a shaker for 40 minutes at room temperature. Samples were washed using 100 ⁇ l PBS pH 7.4, 100 ⁇ l 50% ACN in dH2O, and 100 ⁇ l 50 mM Na2HPO4 for IMAC, H4, and SAX2 respectively, repeated twice, followed by two quick rinses in dH2O. After air-drying, sinapinic acid (SPA), prepared as per manufacturer's instructions, was applied to each spot.
  • SPA sinapinic acid
  • the arrays were analyzed on a PBS-II mass reader (Ciphergen Biosystems, Fremont, CA) using SELDI 2.1b software (Ciphergen Biosystems, Fremont, CA). Data was collected by averaging 60 laser shots with an intensity of 240 and a detector sensitivity of 8.
  • Ciphergen ProteinChip software system was used to identify qualified peaks from the raw spectrum data by applying a threshold to peak intensities that had been normalized against total ion current. Since more sophisticated procedures were used for the final peak selection, the initial threshold was set to capture the largest number of candidate peaks. Logarithmic transformation was applied to the data when needed to reduce peak intensity ranges. The final result is an m (peaks) by n (specimens) matrix, where an entry at row i column j presents the normalized relative abundance of proteins at mass weight corresponding to peak i in specimen j.
  • CART Classification And Regression Tree
  • the UMSA algorithm as implemented in ProPeak is a linear classifier while the CART algorithm in BPS is a binary decision tree-type nonlinear classifier.
  • the ranking and selection of peaks based on linear classification tend to be more robust, especially with the inherent variances and noise in the raw spectrum data.
  • a non-linear classifier might give a better classification result even though extra caution needs to be exercised to avoid over-fitting data with superfluous biomarkers.
  • the apparent consistency between results from these two approaches on our data provides additional confidence that the selected peaks reflect pathophysiological changes rather than artifactual differences.
  • the Classification and Regression Tree (CART) procedure constructs a binary decision tree that recursively partitions a given dataset into blocks of predicted positive and negative samples. The procedure minimizes a cost function that balances prediction errors and the total number of markers used. The relative importance of a peak is measured by the order in which it was selected in the decision tree and the number of correct predictions it is credited for.
  • Support vector machine (SVM) (Vapnik VN. Statistical Learning Theory. John Wiley & Sons, New York, 1998 ) has been applied to a number of biological expression data processing applications (Brown M, Grundy, WN, Lin, D, Cristianini, N, Sugnet, CW, Furey, TS, et al. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc Natl Acad Sci U S A 2000;97:262-67). The UMSA procedure modifies the SVM learning algorithm to allow for the incorporation of data distribution information.
  • UMSA tends to be less sensitive than the typical SVM to possible labeling errors in data, such as those resulting from specimen contamination or misdiagnosed cases.
  • ProPeak offers two analytical modules. The first is a UMSA component analysis module, which projects the original specimen as individual points into a three- dimensional component space. The components (axes) are linear combinations of the original spectrum peaks determined such that two pre-specified groups of data achieve maximum separability. The results can then be viewed in an interactive 3D display.
  • the second module in ProPeak uses a backward stepwise process to compute a significance score to rank individual markers according to their collective contribution towards the separation of two groups of specimens under UMSA.
  • peaks selected by BPS and UMSA analysis were evaluated individually, and in combinations of multiple peaks for their diagnostic performance using multivariate logistic regression. Diagnostic performance was assessed by estimating sensitivity and specificity, and using the area under the curve from receiver operating characteristic (ROC) curve analysis. For specimens with available CA125 values, results were compared to the diagnostic performance of CA125. Biomarker Identification.
  • Plasma samples 27 ⁇ L each, were first buffer-exchanged into 20 mM Tris- HC1, pH 9.0 buffer using K-30 size-selection spin columns (Ciphergen Biosystems, Fremont, CA) equilibrated with the same buffer. Proteins were then fractionated on anion-exchange spin columns based on their isoelectric point (pi). Each sample was applied to a spin microcolumn containing 100 ⁇ L of Q HyperD anion-exchanger resin (BioSepra), equilibrated in 20 mM Tris-HCl, pH 9.0 buffer.
  • a 16% acrylamide Tris- Glycine gel (Invitrogen/Novex) was used to isolate the 7 tol2 kD proteins, a 4-20% acrylamide Tris-Glycine gel was used for the 15 to 50 kD proteins and a 6% acrylamide Tris-Glycine gel was used for the 52 to 80 kD proteins.
  • Gels were stained with Colloidal Blue (Invitrogen/Novex) and destained with deionized water. By correlating the mass spectra and Coomassie stained protein bands for high and low abundance proteins, we were able to identify the particular protein bands of interest. These were subsequently punched out using a disposable Pasteur pipette.
  • the gel slices were destained and then the purified proteins in the gel slices were digested with 10 ⁇ L of 0.02 ⁇ g/ ⁇ L modified trypsin in 25 mM ammonium bicarbonate, pH 8.0 buffer.
  • Peptides generated by in-gel tryptic digestion were profiled using NP and H4 (hydrophobic) arrays. 1 -2 ⁇ L of each digest was applied to each spot on the array, proteins were allowed to concentrate to dryness before 0.5 ⁇ L of 20% saturated cyano-4-hydroxycinnamic acid (CHCA) in 50% acetonitrile, 0.5%) TFA solution was applied to each spot. After the arrays were completely dry, the ProteinChip reader (PBS II) was used for peptide mapping.
  • CHCA saturated cyano-4-hydroxycinnamic acid
  • Peptide standards were used to internally calibrate the MS spectra for accurate peptide mass determination, and those obtained from control samples (trypsin incubated with blank gel plugs) were subtracted from the peptide maps. Subsequently, peptide masses were used for database searching and protein identification using ProFound (Rockefeller University) and MASCOT (MatrixScience). Protein identity was further confirmed by sequencing selected peptides from the tryptic digest using a ProteinChip interface PCI- 1000 (Ciphergen, Fremont, CA) coupled to a Q-TOF II MS/MS (MicroMass, UK).
  • Mass spectra of the initial group of 67 patients were obtained from SELDI analysis using IMAC-Ni ProteinChips.
  • Figure 1 shows a representative view of the spectra showing proteins retained on the chip, in both spectrum and pseudo-gel view.
  • Spectra of the 67 samples were analyzed using two bioinformatics software packages, Biomarker Pattern Software V4.0 (BPS) (Ciphergen, CA), and ProPeak (3Z Informatics, SC).
  • Results were cross-compared in order to select a subset of peaks that possessed the maximum discriminatory power.
  • We then reapplied the UMSA component analysis using only these seven peaks to test whether they retained most of the discriminatory power of the original full spectrum (Figure 2B).
  • peaks (9.2kD, 54kD, 60kD, and 79kD) were actually used in the final statistical evaluation of diagnostic performance. They were selected for their relative high scores in UMSA analysis. The performance of individual peaks was compared to that from the logistic regression functions of all four peaks and two of the peaks (60kD and 79kD) using ROC analysis ( Figure 7).
  • the y-axis represents the combination of 60kD and 79kD through a logistic regression function.
  • the x-axis is the CA125 value in logarithmic scale with the recommended cutoff value at 35U/mL marked as a vertical line.
  • the dashed line shows that by combining the two biomarkers with CA125, a much improved separation between the two groups of patients can be achieved than using CA125 alone. Based on this observation, ROC analysis was performed using 68 patients with available CA125 values to compare the diagnostic performance of the combination of 60kD and 79kD, CA125, and the combination of all three markers (Figure 9). The addition of the two biomarkers improves the overall performance of CA125.
  • Table 1 compares the estimated sensitivities and specificities of (1) CA125 alone at two different cutoff values; (2) logistic regression function of 60kD and 79kD, and (3) an diagnostic index which is the linear combination of (1) and (2).
  • the first cutoff value of CA125 was the recommended value of 35U/mL.
  • the second value at 18.5U/mL was selected such that CA125 achieves maximum efficiency based on ROC analysis. This resulted in a specificity of 94.4%.
  • the remaining comparison, performed using this set specificity indicates that the diagnostic index from the combination of the two biomarkers and CA125 improves the sensitivity from 81.3% to 93.8%.
  • test sensitivities were calculated separately according to early and late disease stages.
  • the result shows that the diagnostic index from combining the two biomarkers and CA125 retains a high level of sensitivity for the early stage cancer patients.
  • the mean and standard deviation of the diagnostic index in the cancer group were 0.400 and 0.037, respectively, and in the non-cancer group were 0.285 and 0.620, respectively. The difference was highly significant (pO.OOOOOl).

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Abstract

L'invention concerne des nouveaux biomarqueurs destinés à la détection d'un cancer dans un prélèvement, en particulier du cancer de l'ovaire. Un aspect préféré de l'invention concerne des méthodes permettant d'évaluer le stade d'un cancer de l'ovaire chez un patient, consistant à mesurer au moins un des marqueurs I à VII dans un prélèvement, puis à comparer les mesures au stade du cancer de l'ovaire.
PCT/US2003/000531 2002-01-07 2003-01-07 Biomarqueurs destines a la detection du cancer de l'ovaire WO2003057014A2 (fr)

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EP1668149A1 (fr) * 2003-09-05 2006-06-14 Royal Women's Hospital Marqueur pour le diagnostic du cancer de l'ovaire
JP2007501380A (ja) * 2003-08-01 2007-01-25 コレロジック システムズ，インコーポレイティド 卵巣癌検出のための多重高解像度血清プロテオミック特性
WO2007030949A2 (fr) 2005-09-15 2007-03-22 Val-Chum, S.E.C. Procedes de diagnostic de cancer des ovaires et necessaires a cet effet
EP1952143A2 (fr) * 2005-11-22 2008-08-06 Frantz Biomarkers, LLC Methode de depistage d'un cancer ovarien
EP1952138A2 (fr) * 2005-11-22 2008-08-06 Frantz Biomarkers, LLC Methode de depistage d'une maladie inflammatoire ou d'un cancer
JP2008533471A (ja) * 2005-03-11 2008-08-21 ヴァーミリオン インコーポレイテッド 卵巣癌及び子宮内膜癌のバイオマーカー：ヘプシジン
WO2009058331A2 (fr) 2007-10-29 2009-05-07 Vermilllion, Inc. Biomarqueurs permettant la détection du cancer des ovaires à un stade précoce
US7605003B2 (en) 2002-08-06 2009-10-20 The Johns Hopkins University Use of biomarkers for detecting ovarian cancer
US7906758B2 (en) 2003-05-22 2011-03-15 Vern Norviel Systems and method for discovery and analysis of markers
WO2012123419A1 (fr) 2011-03-11 2012-09-20 Vib Vzw Molécules et procédés d'inhibition et de détection de protéines
WO2014152773A1 (fr) 2013-03-15 2014-09-25 The Trustees Of The University Of Pennsylvania Biomarqueurs sanguins prédisant la persistance d'un dysfonctionnement cognitif après commotion
US8962257B2 (en) 2010-03-08 2015-02-24 The Trustees Of The University Of Pennsylvania Methods for diagnosing heart failure and other cardiac diseases
JP2015083981A (ja) * 2005-06-24 2015-04-30 ヴァーミリオン インコーポレイテッド 卵巣癌用のバイオマーカー
US9057107B2 (en) 2011-03-08 2015-06-16 King Abdullah University Of Science And Technology Molecular biomarker set for early detection of ovarian cancer
AU2012216473B2 (en) * 2005-06-24 2015-08-20 Aspira Women’s Health Inc. Biomarkers for ovarian cancer: beta-2 microglobulin
AU2015210343B2 (en) * 2005-06-24 2017-09-28 Aspira Women’s Health Inc. BIOMARKERS FOR OVARIAN CANCER: ß-2 MICROGLOBULIN
US11906526B2 (en) 2019-08-05 2024-02-20 Seer, Inc. Systems and methods for sample preparation, data generation, and protein corona analysis

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US6511806B1 (en) * 1999-11-03 2003-01-28 Oncotech, Inc. Methods for cancer prognosis and diagnosis

Cited By (25)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7605003B2 (en) 2002-08-06 2009-10-20 The Johns Hopkins University Use of biomarkers for detecting ovarian cancer
US10466230B2 (en) 2003-05-22 2019-11-05 Seer, Inc. Systems and methods for discovery and analysis of markers
US7906758B2 (en) 2003-05-22 2011-03-15 Vern Norviel Systems and method for discovery and analysis of markers
JP2007501380A (ja) * 2003-08-01 2007-01-25 コレロジック システムズ，インコーポレイティド 卵巣癌検出のための多重高解像度血清プロテオミック特性
EP1668149A4 (fr) * 2003-09-05 2007-01-03 Royal Women S Hospital Marqueur pour le diagnostic du cancer de l'ovaire
EP1668149A1 (fr) * 2003-09-05 2006-06-14 Royal Women's Hospital Marqueur pour le diagnostic du cancer de l'ovaire
JP2008533471A (ja) * 2005-03-11 2008-08-21 ヴァーミリオン インコーポレイテッド 卵巣癌及び子宮内膜癌のバイオマーカー：ヘプシジン
AU2015210343B2 (en) * 2005-06-24 2017-09-28 Aspira Women’s Health Inc. BIOMARKERS FOR OVARIAN CANCER: ß-2 MICROGLOBULIN
JP2015083981A (ja) * 2005-06-24 2015-04-30 ヴァーミリオン インコーポレイテッド 卵巣癌用のバイオマーカー
AU2012216473B2 (en) * 2005-06-24 2015-08-20 Aspira Women’s Health Inc. Biomarkers for ovarian cancer: beta-2 microglobulin
WO2007030949A2 (fr) 2005-09-15 2007-03-22 Val-Chum, S.E.C. Procedes de diagnostic de cancer des ovaires et necessaires a cet effet
EP1952143A4 (fr) * 2005-11-22 2009-02-11 Frantz Biomarkers Llc Methode de depistage d'un cancer ovarien
EP1952138A2 (fr) * 2005-11-22 2008-08-06 Frantz Biomarkers, LLC Methode de depistage d'une maladie inflammatoire ou d'un cancer
EP1952138A4 (fr) * 2005-11-22 2009-02-18 Frantz Biomarkers Llc Methode de depistage d'une maladie inflammatoire ou d'un cancer
EP1952143A2 (fr) * 2005-11-22 2008-08-06 Frantz Biomarkers, LLC Methode de depistage d'un cancer ovarien
WO2009058331A2 (fr) 2007-10-29 2009-05-07 Vermilllion, Inc. Biomarqueurs permettant la détection du cancer des ovaires à un stade précoce
US8962257B2 (en) 2010-03-08 2015-02-24 The Trustees Of The University Of Pennsylvania Methods for diagnosing heart failure and other cardiac diseases
US9914974B2 (en) 2011-03-08 2018-03-13 King Abdullah University Of Science And Technology Molecular biomarker set for early detection of ovarian cancer
US9057107B2 (en) 2011-03-08 2015-06-16 King Abdullah University Of Science And Technology Molecular biomarker set for early detection of ovarian cancer
WO2012123419A1 (fr) 2011-03-11 2012-09-20 Vib Vzw Molécules et procédés d'inhibition et de détection de protéines
EP3384939A1 (fr) 2011-03-11 2018-10-10 Vib Vzw Molécules et procédés pour l'inhibition et la détection de protéines
WO2014152773A1 (fr) 2013-03-15 2014-09-25 The Trustees Of The University Of Pennsylvania Biomarqueurs sanguins prédisant la persistance d'un dysfonctionnement cognitif après commotion
US11906526B2 (en) 2019-08-05 2024-02-20 Seer, Inc. Systems and methods for sample preparation, data generation, and protein corona analysis
US12050222B2 (en) 2019-08-05 2024-07-30 Seer, Inc. Systems and methods for sample preparation, data generation, and protein corona analysis
US12241899B2 (en) 2019-08-05 2025-03-04 Seer, Inc. Systems and methods for sample preparation, data generation, and protein corona analysis

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