WO2006071843A2 - Biomarqueurs destines au cancer du sein - Google Patents

Biomarqueurs destines au cancer du sein Download PDF

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WO2006071843A2
WO2006071843A2 PCT/US2005/047005 US2005047005W WO2006071843A2 WO 2006071843 A2 WO2006071843 A2 WO 2006071843A2 US 2005047005 W US2005047005 W US 2005047005W WO 2006071843 A2 WO2006071843 A2 WO 2006071843A2
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breast cancer
biomarker
biomarkers
status
subject
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PCT/US2005/047005
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WO2006071843A3 (fr
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Vladimir Podust
Jacob Hendrik Beijnen
Marie-Christine Willemine Gast
Nathan Harris
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Vereniging Het Nederlands Kanker Instituut
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Publication of WO2006071843A2 publication Critical patent/WO2006071843A2/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
    • G01N33/57415—Specifically defined cancers of breast

Definitions

  • the invention relates generally to clinical diagnostics.
  • Breast cancer is the most common malignancy among women and has one of the highest fatality rates of all cancers affecting females. Most breast cancers appear as a slowly growing, painless mass. There are a number of physical signs which might suggest the presence of breast cancer, and these may be discovered through a breast examination. Breast cancer metastasizes by direct extension, and via the lymphatics and the blood stream. Distant spread of the disease may be confirmed by lymph node biopsy, by x-ray surveys of skeleton and chest, and, when appropriate, by liver and bone scans using radioisotopes.
  • breast cancer therapy depends mainly on the extent of the disease and the patient's age.
  • methods currently used to treat breast cancer including surgery, radiotherapy, hormone therapy, and chemotherapy.
  • Successful cancer therapy is directed to the primary tumor and to any metastases, whether clinically apparent or microscopic.
  • breast tumors may be cured with combined modality therapy, each of the above methods may be used alone, or in conjunction with one or more other therapies.
  • systemic therapy such as chemotherapy.
  • Adjuvant chemotherapy in particular, has a definite role in the treatment of patients with breast cancer and axillary lymph node involvement.
  • Stage I and Stage II breast carcinomas can be conservatively managed by partial mastectomy (lumpectomy) plus a standard axillary node dissection, followed by irradiation of the remaining breast tissue.
  • Chemotherapy is sometimes used as an adjuvant to surgery.
  • Radiotherapy also may be used as an adjuvant to surgery, particularly in conjunction with a partial mastectomy and a standard axillary node dissection.
  • the present invention provides a biomarker or a combination of biomarkers, capable of non-invasively detecting breast cancer status.
  • a method for detecting breast cancer status in a subject, involving (a) measuring at least one biomarker in a biological sample from the subject, where the biomarker is selected from among those listed in Table 1 , infra, and (b) correlating the measurement with breast cancer.
  • the inventive methodology involves correlating, with the status of breast cancer, the measurement of at least one biomarker from Table 1 in combination with the status of one or more other prognosis indicators.
  • the biomarker or biomarkers are selected from the group consisting of M8939, M3089, M3820, M2756, M3967, M5907, and M3105.
  • the inventive method involves measuring M3105 and M5907.
  • the method involves measuring M3089 and M8939.
  • the method involves measuring M3820 (tree I and VI).
  • the method involves measuring M8939 (tree II).
  • the method involves measuring M9198 and the breast cancer status is breast cancer recurrence following adjuvant therapy.
  • At least one biomarker is measured by mass spectrometry.
  • the biomarker is measured by capturing the biomarker with a capture reagent on a surface of a SELDI probe and detecting the captured biomarkers by laser desorption-ionization mass spectrometry.
  • the biomarker is measured by immunoassay.
  • the capture reagent comprises an antibody.
  • the capture reagent comprises an IMAC sorbent.
  • the sample can be serum, for example.
  • the correlating is performed by a software classification algorithm.
  • the breast cancer status is breast cancer versus non-breast cancer.
  • the breast cancer status is breast cancer recurrence.
  • the invention is directed to a method for determining the course of breast cancer, involving (a) measuring, at a first time, at least one biomarker in a biological sample from the subject, wherein the at least one biomarker is selected from the group consisting of the biomarkers of Table 1, (b) measuring, at a second time, the at least one biomarker in a biological sample from the subject and (c) comparing the first measurement and the second measurement, wherein the comparative measurements determine the course of the breast cancer.
  • the invention is directed to a kit containing (a) a solid support containing at least one capture reagent attached thereto, wherein the capture reagent binds at least one biomarker from the group consisting of the biomarkers of Table 1 and (b) instructions for using the solid support to detect a biomarker of Table 1.
  • a kit of the invention also can include a receptacle containing at least one of the biomarkers of Table 1.
  • the kit also contains an immobilized, metal affinity capture chromatography reagent.
  • the kit also contains a strong cation exchange chromatography sorbent.
  • the solid support containing a capture reagent is a SELDI probe.
  • the capture reagent is an antibody.
  • the invention is directed to a kit containing (a) a solid support containing at least one capture reagent attached thereto, wherein the at least one capture reagent binds at least one biomarker selected from the group consisting of the biomarkers of Table 1 and (b) a container containing at least one of the biomarkers.
  • the kit additionally contains an immobilized metal affinity capture chromatography sorbent.
  • the kit additionally contains a strong ion exchange chromatography sorbent.
  • the solid support comprising a capture reagent is a SELDI probe.
  • the capture reagent is an antibody.
  • the invention is directed to a software product containing: (a) code that accesses data attributed to a sample, the data comprising measurement of at least one biomarker in the sample, the biomarker selected from the group consisting of the biomarkers of Table 1; and (b) code that executes a classification algorithm that detects breast cancer or determines the breast cancer recurrence status of the sample as a function of the measurement.
  • the invention is directed to a purified biomolecule selected from the biomarkers of Table 1.
  • the invention is directed to a method involving communicating to a subject a diagnosis relating to breast cancer or breast cancer recurrence status determined from the correlation of biomarkers in a sample from the subject, wherein the biomarkers are selected from biomarkers of Table 1.
  • the diagnosis is communicated to the subject via a computer-generated medium.
  • the invention is directed to a method for identifying a compound that interacts with a biomarker of Table 1 , involving (a) contacting the biomarker with a test compound and (b) determining whether the test compound interacts with the biomarker.
  • the invention is directed to a method for modulating the concentration of a biomarker of Table 1 in a cell, wherein the method comprises contacting the cell with a compound that modulates the expression of the biomarker.
  • the invention is directed to a method of treating breast cancer in a subject, involving administering to the subject a therapeutically effective amount of a compound that inhibits expression of an up-regulated biomarker of Table 1.
  • the invention is directed to a method of treating breast cancer in a subject, involving administering to the subject a therapeutically effective amount of a compound that increases expression of a down-regulated biomarker of Table 1.
  • a method for detecting breast cancer status in a subject involving determining and correlating the phenotype of haptoglobin with breast cancer status, in combination with the status of at least one other prognosis indicator.
  • the other prognosis indicators are the grade of ductal carcinoma in situ (DCIS), the extent of tumor angiogenesis, the size of tumor, the number of positive axillary lymph nodes, the presence or absence of her2/Neu, estrogen receptor, progesterone receptor, and p53 protein, respectively, and the mitotic activity index (MAI).
  • FIG. 1 shows mass spectra obtained for four patients with stage IIIA breast cancer prior to surgery and four healthy women. The mass-to-charge ratio of some of the biomarkers of this invention are shown.
  • FIG. 2 shows six classification trees for classifying a sample as breast cancer or non-breast cancer using certain biomarkers of this invention.
  • FIG. 3 shows mass spectra obtained for four breast cancer patients with a relatively long recurrence-free survival and four breast cancer patients with a relatively short recurrence-free survival following adjuvant chemotherapy.
  • FIG. 4 shows two Kaplan-Meier curves.
  • the upper curve is composed of the recurrence-free survival data for patients that exhibited a high abundant peak at m/z 9198 (e.g., relative intensity > 20).
  • the lower curve is composed of the recurrence-free survival data for patients exhibiting a low abundant peak at m/z 9198 (e.g., relative intensity of m/z9198 ⁇ 20).
  • FIG. 5 shows Kaplan-Meier analysis of the recurrence-free survival data for a group of 114 patients, when they are split into three haptoglobin phenotype subgroups.
  • the upper, middle and lower curves correspond to the patients carrying phenotypes HpI-I, Hp2-1 and Hp2-2, respectively.
  • FIG. 6 A and FIG. 6B show Kaplan-Meier analysis of the recurrence-free survival data for a group of 432 patients, when they are split into three haptoglobin phenotype subgroups (FIG. 6A), or two haptoglobin phenotype subgroups (FIG. 6B).
  • FIG. 6A the upper, middle and lower curves correspond to the patients carrying phenotypes HpI-I, Hp2-1 and Hp2-2, respectively.
  • the upper curve corresponds to the patients carrying phenotypes HpI-I or Hp2-1 and the lower curve corresponds to the patients carrying phenotype Hp2-2 only.
  • FIG. 6A the upper curve corresponds to the patients carrying phenotypes HpI-I or Hp2-1
  • the lower curve corresponds to the patients carrying phenotype Hp2-2 only.
  • FIG. 8 A and FIG. 8B show Kaplan-Meier analysis of the recurrence-free survival data for a group of 177 patients who have developed focal angiogenesis, when they are split into three haptoglobin phenotype subgroups (FIG. 8A), or two haptoglobin phenotype subgroups (FIG. 8B).
  • FIG. 8A the upper, middle and lower curves correspond to the patients carrying phenotypes HpI-I, Hp2-1 and Hp2-2, respectively.
  • the upper curve corresponds to the patients carrying phenotypes HpI-I or Hp2-1 and the lower curve corresponds to the patients carrying phenotype Hp2-2 only.
  • FIG. 9 A and FIG. 9B show Kaplan-Meier analysis of the recurrence-free survival data for a group of 280 patients with 4-9 positive axillary lymph nodes, when they are split into three haptoglobin phenotype subgroups (FIG. 9A), or two haptoglobin phenotype subgroups (FIG. 9B).
  • FIG. 9A the upper, middle and lower curves correspond to the patients carrying phenotypes HpI-I, Hp2-1 and Hp2-2, respectively.
  • the upper curve corresponds to the patients carrying phenotypes HpI-I or Hp2-1 and the lower curve corresponds to the patients carrying phenotype Hp2-2 only.
  • FIG. 1OA and FIG. 1OB show Kaplan-Meier analysis of the recurrence-free survival data for a group of 314 patients who have been characterized as her2/Neu negative, when they are split into three haptoglobin phenotype subgroups (FIG. 10A), or two haptoglobin phenotype subgroups (FIG. 10B).
  • FIG. 10A the upper, middle and lower curves correspond to the patients carrying phenotypes HpI-I, Hp2-1 and Hp2-2, respectively.
  • the upper curve corresponds to the patients carrying phenotypes HpI-I or Hp2-1 and the lower curve corresponds to the patients carrying phenotype Hp2-2 only.
  • FIG. 1 IA and FIG. 1 IB show Kaplan-Meier analysis of the recurrence-free survival data for a group of 292 patients who have been characterized as ER positive, when they are split into three haptoglobin phenotype subgroups (FIG. 1 IA), or two haptoglobin phenotype subgroups (FIG. 1 IB).
  • FIG. 1 IA the upper, middle and lower curves correspond to the patients carrying phenotypes HpI-I, Hp2-1 and Hp2-2, respectively.
  • the upper curve corresponds to the patients carrying phenotypes HpI-I or Hp2-1 and the lower curve corresponds to the patients carrying phenotype Hp2-2 only.
  • FIG. 12A and FIG. 12B show Kaplan-Meier analysis of the recurrence-free survival data for a group of 233 patients who have been characterized as PR positive, when they are split into three haptoglobin phenotype subgroups (FIG. 12A), or two haptoglobin phenotype subgroups (FIG. 12B).
  • FIG. 12A the upper, middle and lower curves correspond to the patients carrying phenotypes HpI-I, Hp2-1 and Hp2-2, respectively.
  • the upper curve corresponds to the patients carrying phenotypes HpI-I or Hp2-1 and the lower curve corresponds to the patients carrying phenotype Hp2-2 only.
  • FIG. 13A and FIG. 13B show Kaplan-Meier analysis of the recurrence-free survival data for a group of 201 patients who have been characterized as p53 protein negative, when they are split into three haptoglobin phenotype subgroups (FIG. 13A), or two haptoglobin phenotype subgroups (FIG. 13B).
  • FIG. 13 A the upper, middle and lower curves correspond to the patients carrying phenotypes Hp2-1, HpI-I and Hp2-2, respectively.
  • the upper curve corresponds to the patients carrying phenotypes HpI-I or Hp2-1 and the lower curve corresponds to the patients carrying phenotype Hp2-2 only.
  • FIG. 14A and FIG. 14B show Kaplan-Meier analysis of the recurrence-free survival data for a group of 299 patients who have been characterized as either ER positive or PR 5 positive, when they are split into three haptoglobin phenotype subgroups (FIG. 14A), or two haptoglobin phenotype subgroups (FIG. 14B).
  • FIG. 14A the upper, middle and lower curves correspond to the patients carrying phenotypes HpI-I, Hp2-1 and Hp2-2, respectively.
  • the upper curve corresponds to the patients carrying phenotypes HpI-I or Hp2-1 and the lower curve corresponds to the patients carrying phenotype Hp2-2 only.
  • FIG. 15 shows Kaplan-Meier analysis of the recurrence-free survival data for a group of 170 patients whose MAI is between 0-9, when they are split into two haptoglobin phenotype subgroups.
  • the upper curve corresponds to the patients carrying phenotypes HpI- 1 or Hp2-1 and the lower curve corresponds to the patients carrying phenotype Hp2-2 only.
  • a biomarker is an organic biomolecule that is differentially present in a sample taken from a subject of one phenotypic status (e.g., having a disease), as compared with another phenotypic status (e.g., not having the disease).
  • a biomarker is differentially present between different phenotypic statuses if the mean or median expression level of the 0 biomarker in the different groups is calculated to be statistically significant. Common tests for statistical significance include, among others, t-test, ANOVA, Kruskal-Wallis, Wilcoxon, Mann- Whitney and odds ratio.
  • Biomarkers, alone or in combination provide measures of relative risk that a subject belongs to one phenotypic status or another. Therefore, they are useful as markers for disease (diagnostics), prognostics, therapeutic effectiveness of a drug 5 (theranostics), and drug toxicity.
  • BIOMARKERS FOR BREAST CANCER 2.1. Biomarkers
  • this invention provides polypeptide-based biomarkers that are differentially present in female subjects who have breast cancer, versus healthy controls (non- 0 breast cancer).
  • an approach is provided that employs biomarkers to characterize female subjects in terms of the probability of longer versus shorter recurrence-free survival, following adjuvant therapy.
  • the biomarkers are characterized by mass-to-charge ratio as determined by mass spectrometry, by the shape of their spectral peak in time-of-flight mass spectrometry, and by their binding characteristics to adsorbent surfaces. These characteristics provide one method to determine whether a particular detected biomolecule is a biomarker of this invention. These characteristics represent inherent characteristics of the biomolecules and not process limitations in the manner in which the biomolecules are discriminated. In one aspect, this invention provides these biomarkers in isolated form. [0041] Biomarkers for detecting the existence of breast cancer were discovered using SELDI technology employing ProteinChip® arrays from Ciphergen Biosystems, Inc.
  • Ciphergen Serum samples were collected from female subjects diagnosed with breast cancer and subjects diagnosed as normal. Unfractionated samples were applied to SELDI biochips and spectra of polypeptides in the samples were generated by time-of-flight mass spectrometry on a Ciphergen PBSIIc mass spectrometer. The spectra thus obtained were analyzed by Biomarkers Patterns Software, which yielded six classification trees, and a custom made clustering and classification algorithm, that yielded a Bayesian classifier, which used an average of 15 mass-to-charge ratio's (m/z's). This technique is described in more detail in Example 1.
  • the biomarkers of this invention are characterized by their mass-to-charge ratio, as determined by mass spectrometry.
  • the mass-to-charge ratio of each biomarker is provided in Table 1 after the "M.”
  • M3089 has a measured mass-to-charge ratio of 3089.
  • the mass-to-charge ratios were determined from mass spectra generated on a Ciphergen PBSIIc mass spectrometer. This instrument has a mass accuracy of about +/- 0.1%. Additionally, the instrument has a mass resolution of about 400 to 1000 m/dm, where m is mass and dm is the mass spectral peak width at 0.5 peak height. The mass-to- charge ratio of the biomarkers was determined using Ciphergen' s Biomarker WizardTM software.
  • Biomarker WizardTM assigns a mass-to-charge ratio to a biomarker by clustering the mass-to-charge ratios of the same peaks from all the spectra analyzed, as determined by the PBSIIc, taking the average mass-to-charge-ratio of the cluster in all spectra. Accordingly, the masses provided reflect these specifications.
  • the biomarkers of this invention are further characterized by the shape of their spectral peak in time-of-flight mass spectrometry. Mass spectra showing peaks representing the biomarkers are presented in FIG. 1.
  • the biomarkers of this invention are further characterized by their binding properties on chromatographic surfaces. Most of the biomarkers bind to immobilized metal affinity capture adsorbents (e.g., IMAC30 array) after washing four times - twice with binding buffer (PBS, pH 7.4, containing 0.5 M NaCl and 0.1% w/v TritonX-100), and twice with PBS, 0.5M NaCl.
  • binding buffer pH 7.4, containing 0.5 M NaCl and 0.1% w/v TritonX-100
  • the M9198 biomarker binds to a strong anion exchange adsorbent (e.g., QlO) after washing four times - twice with binding buffer (10OmM sodium phosphate buffer, pH 8.0, containing 0.1% w/v TritonX-100) and twice with 10OmM sodium phosphate buffer, pH 8.0.
  • a strong anion exchange adsorbent e.g., QlO
  • biomarkers of this invention have been determined and is indicated in Table 1. The method by which this determination was made is described in the Example Section. For biomarkers whose identity has been determined, the presence of the biomarker can be determined by other methods known in the art.
  • biomarkers of this invention are characterized by mass-to-charge ratio, binding properties and spectral shape, they can be detected by mass spectrometry without knowing their specific identity. If desired, however, any biomarker can be identified by determining, for example, the amino acid sequence of the polypeptides.
  • a biomarker can be peptide-mapped with a number of enzymes, such as trypsin or V8 protease, and the molecular weights of the digestion fragments can be used to search databases for sequences that match the molecular weights of the digestion fragments generated by the various enzymes.
  • protein biomarkers can be sequenced using tandem MS technology, pursuant to which methodology the protein is isolated, for example, by gel electrophoresis.
  • a band containing the biomarker is cut out and the protein is subject to protease digestion.
  • Individual protein fragments are separated by a first mass spectrometer.
  • the fragment is then subjected to collision-induced cooling, which fragments the peptide and produces a polypeptide ladder.
  • a polypeptide ladder then is analyzed by the second mass spectrometer of the tandem MS.
  • the difference in masses of the members of the polypeptide ladder identifies the amino acids in the sequence.
  • An entire protein can be sequenced this way, or a sequence fragment can be subjected to database mining to find identity candidates.
  • the preferred biological source for detection of the biomarkers is serum.
  • the biomarkers can be detected in other bodily fluids, however, such as urine.
  • the biomarkers of this invention are biomolecules. In one aspect, therefore, the present invention provides these biomolecules in isolated form.
  • the biomarkers can be isolated from biological fluids, such as urine or serum. They can be isolated by any method known in the art, based on both their mass and their binding characteristics. For example, a sample comprising the biomolecules can be subject to chromatographic fractionation, as described herein, and subject to further separation by, e.g., acrylamide gel electrophoresis. Knowledge of the identity of the biomarker also allows their isolation by immunoaffinity chromatography.
  • Pre-translational modified forms include allelic variants, slice variants and RNA editing forms.
  • Post-translationally modified forms include forms resulting from proteolytic cleavage (e.g., fragments of a parent protein), glycosylation, phosphorylation, lipidation, oxidation, methylation, cystinylation, sulphonation and acetylation.
  • modified protein cluster The collection of proteins including a specific protein and all modified forms of it is referred to herein as a "protein cluster.”
  • Modified forms of any biomarker of this invention (including any of Markers M3089 through M9198 of Table 1) also may be used, themselves, as biomarkers. In certain cases the modified forms may exhibit better discriminatory power in diagnosis than the specific forms set forth herein.
  • Modified forms of a biomarker including any of Markers M3089 through M9198 of Table 1 can be initially detected by any methodology that can detect and distinguish the modified from the biomarker.
  • a preferred method for initial detection involves first capturing the biomarker and modified forms of it, e.g., with biospecific capture reagents, and then detecting the captured proteins by mass spectrometry. More specifically, the proteins are captured using biospecific capture reagents, such as antibodies, aptamers or Affibodies that recognize the biomarker and modified forms of it. This method also will also result in the capture of protein interactors that are bound to the proteins or that are otherwise recognized by antibodies and that, themselves, can be biomarkers. Preferably, the biospecific capture reagents are bound to a solid phase.
  • the captured proteins can be detected by SELDI mass spectrometry or by eluting the proteins from the capture reagent and detecting the eluted proteins by traditional MALDI or by SELDI.
  • SELDI mass spectrometry is especially attractive because it can distinguish and quantify modified forms of a protein based on mass and without the need for labeling.
  • the biospecific capture reagent is bound to a solid phase, such as a bead, a plate, a membrane or a chip.
  • a solid phase such as a bead, a plate, a membrane or a chip.
  • Methods of coupling biomolecules, such as antibodies, to a solid phase are well known in the art. They can employ, for example, bifunctional linking agents, or the solid phase can be derivatized with a reactive group, such as an epoxide or an imidizole, that will bind the molecule on contact.
  • Biospecific capture reagents against different target proteins can be mixed in the same place, or they can be attached to solid phases in different physical or addressable locations. For example, one can load multiple columns with derivatized beads, each column able to capture a single protein cluster.
  • antibody-derivatized bead-based technologies such as xMAP technology of Luminex (Austin, TX) can be used to detect the protein clusters.
  • the biospecific capture reagents must be specifically directed toward the members of a cluster in order to differentiate them.
  • the surfaces of biochips can be derivatized with the capture reagents directed against protein clusters either in the same location or in physically different addressable locations.
  • One advantage of capturing different clusters in different addressable locations is that the analysis becomes simpler.
  • the modified form can be used as a biomarker in any of the methods of this invention.
  • detection of the modified form can be accomplished by any specific detection methodology including affinity capture followed by mass spectrometry, or traditional immunoassay directed specifically the modified form.
  • Immunoassay requires biospecific capture reagents, such as antibodies, to capture the analytes.
  • the assay must be designed to specifically distinguish protein and modified forms of protein. This can be done, for example, by employing a sandwich assay in which one antibody captures more than one form and second, distinctly labeled antibodies, specifically bind, and provide distinct detection of, the various forms. Antibodies can be produced by immunizing animals with the biomolecules.
  • This invention contemplates traditional immunoassays including, for example, sandwich immunoassays including ELISA or fluorescence-based immunoassays, as well as other enzyme immunoassays. 3. DETECTION OF BIOMARKERS FOR BREAST CANCER
  • the biomarkers of this invention can be detected by any suitable method.
  • Detection paradigms that can be employed to this end include optical methods, electrochemical methods (voltammetry and amperometry techniques), atomic force microscopy, and radio frequency methods, e.g., multipolar resonance spectroscopy.
  • Biochips generally comprise solid substrates and have a generally planar surface, to which a capture reagent
  • the surface of a biochip comprises a plurality of addressable locations, each of which has the capture reagent bound there.
  • Protein biochips are biochips adapted for the capture of polypeptides. Many protein biochips are described in the art. These include, for example, protein biochips produced by Ciphergen, Packard BioScience Company (Meriden CT), Zyomyx (Hayward, CA), Phylos (Lexington, MA), and Biacore (Uppsala, Sweden). Examples of such protein biochips are described in the following patents or published patent applications: U.S. Patent No. 6,225,047; PCT International Publication No. WO 99/51773; U.S. Patent No. 6,329,209, PCT International Publication No. WO 00/56934 and U.S. Patent No. 5,242,828.
  • the biomarkers of this invention are detected by mass spectrometry, a method that employs a mass spectrometer to detect gas phase ions.
  • mass spectrometers are time-of-flight, magnetic sector, quadrupole filter, ion trap, ion cyclotron resonance, electrostatic sector analyzer and hybrids of these.
  • the mass spectrometer is a laser desorption/ionization mass spectrometer.
  • the analytes are placed on the surface of a mass spectrometry probe, a device adapted to engage a probe interface of the mass spectrometer and to present an analyte to ionizing energy for ionization and introduction into a mass spectrometer.
  • a laser desorption mass spectrometer employs laser energy, typically from an ultraviolet laser, but also from an infrared laser, to desorb analytes from a surface, to volatilize and ionize them and make them available to the ion optics of the mass spectrometer.
  • a preferred mass spectrometric technique for use in the invention is "Surface Enhanced Laser Desorption and Ionization" or "SELDI," as described, for example, in U.S. Patents No. 5,719,060 and No. 6,225,047, both to Hutchens and Yip.
  • This refers to a method of desorption/ionization gas phase ion spectrometry (e.g., mass spectrometry) in which an analyte (here, one or more of the biomarkers) is captured on the surface of a SELDI mass spectrometry probe.
  • SELDI Surface Enhanced Laser Desorption and Ionization
  • SELDI affinity capture mass spectrometry
  • SEAC Surface-Enhanced Affinity Capture
  • This version involves the use of probes that have a material on the probe surface that captures analytes through a non-covalent affinity interaction (adsorption) between the material and the analyte.
  • the material is variously called an “adsorbent,” a “capture reagent,” an “affinity reagent” or a “binding moiety.”
  • Such probes can be referred to as “affinity capture probes” and as having an “adsorbent surface.”
  • the capture reagent can be any material capable of binding an analyte.
  • the capture reagent is attached to the probe surface by physisorption or chemisorption.
  • the probes have the capture reagent already attached to the surface.
  • the probes are pre-activated and include a reactive moiety that is capable of binding the capture reagent, e.g., through a reaction forming a covalent or coordinate covalent bond.
  • Epoxide and acyl-imidazole are useful reactive moieties to covalently bind polypeptide capture reagents such as antibodies or cellular receptors.
  • Nitrilotriacetic acid and iminodiacetic acid are useful reactive moieties that function as chelating agents to bind metal ions that interact non-covalently with histidine containing peptides.
  • Adsorbents are generally classified as chromatographic adsorbents and biospecific adsorbents.
  • Chromatographic adsorbent refers to an adsorbent material typically used in chromatography.
  • Chromatographic adsorbents include, for example, ion exchange materials, metal chelators (e.g., nitrilotriacetic acid or iminodiacetic acid), immobilized metal chelates, hydrophobic interaction adsorbents, hydrophilic interaction adsorbents, dyes, simple biomolecules (e.g., nucleotides, amino acids, simple sugars and fatty acids) and mixed mode adsorbents (e.g., hydrophobic attraction/electrostatic repulsion adsorbents).
  • Biospecific adsorbent refers to an adsorbent comprising a biomolecule, e.g., a nucleic acid molecule (e.g., an aptamer), a polypeptide, a polysaccharide, a lipid, a steroid or a conjugate of these (e.g., a glycoprotein, a lipoprotein, a glycolipid, a nucleic acid (e.g., DNA)-protein conjugate).
  • the biospecific adsorbent can be a macromolecular structure such as a multiprotein complex, a biological membrane or a virus. Examples of biospecific adsorbents are antibodies, receptor proteins and nucleic acids.
  • Biospecific adsorbents typically have higher specificity for a target analyte than chromatographic adsorbents. Further examples of adsorbents for use in SELDI can be found in U.S. Patent No. 6,225,047.
  • a "bioselective adsorbent” refers to an adsorbent that binds to an analyte with an affinity of at least 10 "8 M.
  • Protein biochips produced by Ciphergen Biosystems, Inc. comprise surfaces having chromatographic or biospecific adsorbents attached thereto at addressable locations.
  • Ciphergen ProteinChip ® arrays include NP20 (hydrophilic); H4 and H50 (hydrophobic); SAX-2, Q-10 and LSAX-30 (anion exchange); WCX-2, CM-IO and LWCX-30 (cation exchange); IMAC-3, IMAC-30 and IMAC-40 (metal chelate); and PS-IO, PS-20 (reactive surface with acyl imidizole, epoxide) and PG-20 (protein G coupled through acyl imidizole). Hydrophobic ProteinChip arrays have isopropyl or nonylphenoxy-poly(ethylene glycol)methacrylate functionalities. Anion exchange ProteinChip arrays have quaternary ammonium functionalities.
  • Cation exchange ProteinChip arrays have carboxylate functionalities.
  • Immobilized metal chelate ProteinChip arrays have nitrilotriacetic acid functionalities that adsorb transition metal ions, such as copper, nickel, zinc, and gallium, by chelation.
  • Preactivated ProteinChip arrays have acyl imidizole or epoxide functional groups that can react with groups on proteins for covalent binding.
  • WO 03/040700 Um et al, "Hydrophobic Surface Chip,” May 15, 2003
  • U.S. Patent Publication No. US 2003/0218130 Al Boschetti et al., "Biochips With Surfaces Coated With Polysaccharide-Based Hydrogels," April 14, 2003
  • PCT International Publication No. WO2004/076511 Huang et al, "Photocrosslinked Hydrogel Surface Coatings," September 10, 2004.
  • a probe with an adsorbent surface is contacted with the sample for a period of time sufficient to allow the biomarker or biomarkers that may be present in the sample to bind to the adsorbent.
  • the substrate is washed to remove unbound material.
  • Any suitable washing solutions can be used; preferably, aqueous solutions are employed.
  • the extent to which molecules remain bound can be manipulated by adjusting the stringency of the wash.
  • the elution characteristics of a wash solution can depend, for example, on pH, ionic strength, hydrophobicity, degree of chaotropism, detergent strength, and temperature.
  • an energy absorbing molecule then is applied to the substrate with the bound biomarkers.
  • the biomarkers bound to the substrates are detected in a gas phase ion spectrometer such as a time-of-flight mass spectrometer.
  • the biomarkers are ionized by an ionization source such as a laser, the generated ions are collected by an ion optic assembly, and then a mass analyzer disperses and analyzes the passing ions. The detector then translates information of the detected ions into mass-to-charge ratios. Detection of a biomarker typically will involve detection of signal intensity. Thus, both the quantity and mass of the biomarker can be determined.
  • an ionization source such as a laser
  • a mass analyzer disperses and analyzes the passing ions.
  • the detector then translates information of the detected ions into mass-to-charge ratios. Detection of a biomarker typically will involve detection of signal intensity. Thus, both the quantity and mass of the biomarker can be determined.
  • SELDI Surface-Enhanced Neat Desorption
  • SEND probe Another version of SELDI is Surface-Enhanced Neat Desorption (SEND), which involves the use of probes comprising energy absorbing molecules that are chemically bound to the probe surface ("SEND probe").
  • EAM energy absorbing molecules
  • EAM denotes molecules that are capable of absorbing energy from a laser desorption/ionization source and, thereafter, contribute to desorption and ionization of analyte molecules in contact therewith.
  • the EAM category includes molecules used in MALDI, frequently referred to as "matrix,” and is exemplified by cinnamic acid derivatives, sinapinic acid (SPA), cyano- hydroxy-cinnamic acid (CHCA) and dihydroxybenzoic acid, ferulic acid, and hydroxyaceto- phenone derivatives.
  • the energy absorbing molecule is incorporated into a linear or cross-linked polymer, e.g., a polymethacrylate.
  • the composition can be a co-polymer of ⁇ -cyano-4-methacryloyloxycinnamic acid and acrylate.
  • the composition is a co-polymer of ⁇ -cyano-4-methacryloyloxycinnamic acid, acrylate and 3-(tri-ethoxy)silyl propyl methacrylate.
  • the composition is a co-polymer of ⁇ -cyano-4-methacryloyloxycinnamic acid and octadecylmethacrylate ("Cl 8 SEND"). SEND is further described in U.S. Patent No. 6,124,137 and PCT International Publication No. WO 03/64594 (Kitagawa, "Monomers And Polymers Having Energy Absorbing Moieties Of Use In Desorption/Ionization Of Analytes," August 7, 2003).
  • SEAC/SEND is a version of SELDI in which both a capture reagent and an energy absorbing molecule are attached to the sample presenting surface. SEAC/SEND probes therefore allow the capture of analytes through affinity capture and ionization/desorption without the need to apply external matrix.
  • the Cl 8 SEND biochip is a version of SEAC/SEND, comprising a Cl 8 moiety which functions as a capture reagent, and a CHCA moiety which functions as an energy absorbing moiety.
  • SELDI Surface-Enhanced Photolabile Attachment and Release
  • SEPAR and other forms of SELDI are readily adapted to detecting a biomarker or biomarker profile, pursuant to the present invention.
  • the biomarkers can be first captured on a chromatographic resin having chromatographic properties that bind the biomarkers.
  • this could include a variety of methods. For example, one could capture the biomarkers on a cation exchange resin, such as CM Ceramic HyperD F resin, wash the resin, elute the biomarkers and detect by MALDI.
  • this method could be preceded by fractionating the sample on an anion exchange resin before application to the cation exchange resin.
  • one could fractionate on an anion exchange resin and detect by MALDI directly.
  • Time-of-flight mass spectrometry generates a time-of-flight spectrum.
  • the time-of-flight spectrum ultimately analyzed typically does not represent the signal from a single pulse of ionizing energy against a sample, but rather the sum of signals from a number of pulses. This reduces noise and increases dynamic range.
  • This time-of- flight data is then subject to data processing.
  • data processing typically includes TOF-to-m/z transformation to generate a mass spectrum, 5 baseline subtraction to eliminate instrument offsets and high frequency noise filtering to reduce high frequency noise.
  • Data generated by desorption and detection of biomarkers can be analyzed with the use of a programmable digital computer.
  • the computer program analyzes the data to indicate the number of biomarkers detected, and optionally the strength of the signal and the
  • Data analysis can include steps of determining signal strength of a biomarker and removing data deviating from a predetermined statistical distribution. For example, the observed peaks are normalized, to compensate for varying levels of total protein in the samples.
  • the reference can be background noise generated by the instrument and chemicals such as the energy absorbing
  • the computer can transform the resulting data into various formats for display.
  • the standard spectrum can be displayed, but in one useful format only the peak height and mass information are retained from the spectrum view, yielding a cleaner image and enabling biomarkers with nearly identical molecular weights to be more easily seen.
  • two or more spectra are compared, conveniently highlighting unique biomarkers and biomarkers that are up- or down-regulated between samples. Using any of these formats, one can readily determine whether a particular biomarker is present in a sample.
  • Peak selection generally involves the identification of peaks in the spectrum that represent signal from an analyte. Peak selection can be done visually, but software is available, as part
  • Ciphergen's ProteinChip® software package that can automate the detection of peaks.
  • this software functions by identifying signals having a signal-to-noise ratio above a selected threshold and labeling the mass of the peak at the centroid of the peak signal.
  • many spectra are compared to identify identical peaks present in some selected percentage of the mass spectra.
  • One version of this software clusters all peaks
  • Software used to analyze the data can include code that applies an algorithm to the analysis of the signal to determine whether the signal represents a peak in a signal that corresponds to a biomarker according to the present invention.
  • the software also can subject the data regarding observed biomarker peaks to classification tree or ANN analysis, to determine whether a biomarker peak or combination of biomarker peaks is present that indicates the status of the particular clinical parameter under examination. Analysis of the data may be "keyed" to a variety of parameters that are obtained, either directly or indirectly, from the mass spectrometric analysis of the sample.
  • These parameters include, but are not limited to, the presence or absence of one or more peaks, the shape of a peak or group of peaks, the height of one or more peaks, the log of the height of one or more peaks, and other arithmetic manipulations of peak height data.
  • a preferred protocol for the detection of the biomarkers of this invention is as follows.
  • the biological sample to be tested e.g., serum
  • the sample may be pre-fractionated. This simplifies the sample and improves sensitivity.
  • a preferred method of pre-fractionation involves contacting the sample with an anion exchange chromatographic material, such as Q HyperD (BioSepra, SA).
  • Q HyperD BioSepra, SA
  • the bound materials are then subject to stepwise pH elution, using buffers at pH 9, pH 7, pH 5, pH4, and pH 3. See Example 1 - Buffer list. (The fractions in which the biomarkers are eluted also is indicated in Table 1.)
  • Various fractions are collected, containing the biomarker.
  • the sample to be tested then is contacted with an affinity capture probe comprising a strong anion exchange adsorbent, preferably QlO, or an IMAC adsorbent, preferably an IMAC30 ProteinChip® array (Ciphergen), again as indicated in Table 1.
  • the probe is washed with a buffer that will retain the biomarker while washing away unbound molecules.
  • a suitable wash for each biomarker is the buffer identified in Table 1.
  • the biomarkers are detected by laser desorption/ionization mass spectrometry.
  • antibodies that recognize the biomarker are available, for example in the case of ⁇ 2-microglobulin, cystatin, transferrin, transthyretin or albumin, these can be attached to the surface of a probe, such as a pre-activated PS 10 or PS20 ProteinChip® array (Ciphergen). These antibodies can capture the biomarkers from a sample onto the probe surface. Then the biomarkers can be detected by, e.g., laser desorption/ionization mass spectrometry.
  • the biomarkers of this invention can be measured by immunoassay.
  • Immunoassay requires biospecific capture reagents, such as antibodies, to capture the biomarkers.
  • Antibodies can be produced by methods well known in the art, e.g., by immunizing animals with the biomarkers. Biomarkers can be isolated from samples based on their binding characteristics. Alternatively, if the amino acid sequence of a polypeptide biomarker is known, the polypeptide can be synthesized and used to generate antibodies by methods well known in the art.
  • This invention contemplates traditional immunoassays including, for example, sandwich immunoassays including ELISA or fluorescence-based immunoassays, as well as other enzyme immunoassays.
  • sandwich immunoassays including ELISA or fluorescence-based immunoassays, as well as other enzyme immunoassays.
  • SELDI-based immunoassay a biospecific capture reagent for the biomarker is attached to the surface of an MS probe, such as a pre-activated ProteinChip array. The biomarker then is specifically captured on the biochip through this reagent, and the captured biomarker is detected by mass spectrometry.
  • the biomarkers of the invention can be used in diagnostic tests to assess breast cancer status in a subject, e.g., to diagnose breast cancer.
  • breast cancer status includes any distinguishable manifestation of the disease, including non-disease.
  • Exemplary of such disease status are the presence or absence of the disease (e.g., breast cancer vs. non-breast cancer), the risk of developing the disease, the stage of the disease, the progress of disease (e.g., progress of disease or remission of disease over time), the recurrence-free survival following treatment with adjuvant therapy, and the effectiveness or response to treatment of disease.
  • the power of a diagnostic test to predict status correctly is commonly measured as the sensitivity of the assay, the specificity of the assay, or the area under a receiver operated characteristic ("ROC") curve.
  • Sensitivity is the percentage of true positives that are predicted by a test to be positive, while specificity is the percentage of true negatives that are predicted by a test to be negative.
  • An ROC curve provides the sensitivity of a test as a function of 1- specificity. The greater the area under the ROC curve, the more powerful the predictive value of the test. Other useful measures of the utility of a test are positive predictive value and negative predictive value. Positive predictive value is the percentage of actual positives that test as positive. Negative predictive value is the percentage of actual negatives that test as negative.
  • Preferred biomarkers and biomarker combinations of this invention show a statistical difference, in relation to different breast cancer statuses, of at least p ⁇ 0.05, p ⁇ 10 '2 , p ⁇ 10 "3 , p ⁇ 10 "4 , p ⁇ 10 "5 , p ⁇ 10 "6 , p ⁇ 10 "7 , p ⁇ 10 "8 , p ⁇ 10 '9 or p ⁇ 10 "10 .
  • Diagnostic tests that use these biomarkers or biomarker combinations show a sensitivity and specificity of at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 98%, and about 100%.
  • Each biomarker listed in Table 1 is differentially present in breast cancer. Accordingly, each is a candidate for use individually in aiding the determination of breast cancer status.
  • An illustrative method to this end involves, first, measuring the selected biomarker in a subject sample, using the methods described herein (for example, capture on a SELDI biochip, followed by detection by mass spectrometry), and then comparing the measurement with a diagnostic amount or cut-off, which distinguishes a positive breast cancer status from a negative breast cancer status.
  • the diagnostic amount represents a measured amount of a biomarker above which or below which a subject is classified as having a particular breast cancer status. For example, if the biomarker is up-regulated compared to normal during breast cancer, then a measured amount above the diagnostic cut-off provides a diagnosis of breast cancer. Alternatively, if the biomarker is down-regulated during breast cancer, then a measured amount below the diagnostic cut-off provides a diagnosis of breast cancer. Additionally, if the biomarker is up-regulated in breast cancer patients with long recurrence-free survival, then a measured amount above the diagnostic cut-off provides a diagnosis of long recurrence- free survival.
  • the particular diagnostic cut-off used in an assay by adjusting the particular diagnostic cut-off used in an assay, one can increase sensitivity or specificity of the diagnostic assay depending on the preference of the diagnostician.
  • the particular diagnostic cut-off can be determined, for instance, by measuring the amount of the biomarker in a statistically significant number of samples from subjects with the different breast cancer statuses, as was done here, and drawing the cut-off to suit the diagnostician's desired levels of specificity and sensitivity.
  • biomarkers While individual biomarkers may be useful diagnostic biomarkers, it has been found that a combination of biomarkers can provide greater predictive value of a particular status than single biomarkers alone. Specifically, the detection of a plurality of biomarkers in a sample can increase the sensitivity and/or specificity of the test. Any permutation of biomarker combinations of the biomarkers recited in Table 1 is useful for breast cancer diagnosis. For example, any combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 50 biomarkers are useful for breast cancer diagnosis.
  • Example 1 The protocols described in Example 1 below were used to generate mass spectra from 250 patient samples, 140 of which were diagnosed with breast cancer and 110 of which did not exhibit breast cancer.
  • the peak masses and heights were abstracted into a discovery data set.
  • This data set was used to train a learning algorithm, employing classification and regression tree analysis (CART) (Ciphergen's Biomarker PatternsTM software).
  • CART chooses many subsets of the peaks at random. For each subset, CART generated a best or near best classification tree, to classify a sample as breast cancer or non-breast cancer.
  • CART classification and regression tree analysis
  • Tree I uses m/z3820
  • tree II uses m/z8939
  • tree III uses m/z8939 and m/z3090
  • tree IV uses m/z3105 and m/z5907
  • tree V uses m/z3089 and m/z8939
  • tree VI uses m/z3820.
  • these biomarkers are recognized as powerful classifiers for breast cancer when used in combination with each other and, optionally, with other biomarkers.
  • the markers of trees I - VI can distinguish breast cancer from non-breast cancer with sensitivities and specificities of at least those set out in Table 2.
  • Table 2 presents the combinations of markers used in the particular classification trees generated by Biomarker PatternTM software, along with the sensitivity and specificity of the particular tree. The latter are determined by prospective validation, using the two datasets that were not used in the construction of the classification tree.
  • the mass-to-charge ratio of the biomarkers identified in Table 2 may be slightly different from the masses of the biomarkers in Table 1. Some of this difference is due to rounding of the masses; the expected mass accuracy is about +/- 0.1%. Other differences apparently are due to the Biomarker WizardTM algorithm that assigns mass-to- charge ratio based on the average of the peaks in the cluster.
  • classification trees in particular the cut-off values used in making branching decisions, depends on the details of the assay used to generate the discovery data set.
  • the data acquisition parameters of the assay that produced the data used in the present analysis are provided in the Example.
  • the operator In developing a classification algorithm from, for example, a new sample set or a different assay protocol, the operator uses a protocol that detects these biomarkers and keys the learning algorithm to include them. 4.2.1. Classification trees of Figure 2
  • biomarkers M3105 and M5907 are particularly useful in combination to classify breast cancer v. non-breast cancer. These combinations are particularly useful in a recursive partitioning process as shown in FIG. 2. The measure of 5 each cut-off depends on the particulars of the assay protocol.
  • M3105 is the root decision node of the classification tree. Subjects having an amount of this biomarker above the cut-off (i.e., M3105 ⁇ 1.970) are sent to terminal node 3 and are classified as having breast cancer. Subjects below the cut-off are sent to a decision node 2 based on M5907.
  • Subjects having an amount of M5907 above the cut-off are sent to terminal node 2 and are classified as healthy.
  • Subjects having an amount of M5907 below the cut-off are sent to terminal node 1 and are classified as having breast cancer.
  • biomarkers M3089 and M8939 are particularly useful in combination to classify breast cancer v. non-breast cancer. These combinations are 15 particularly useful in a recursive partitioning process as shown in FIG. 2. The measure of each cut-off depends on the particulars of the assay protocol.
  • M3089 is the root decision node of the classification tree. Subjects having an amount of this biomarker above the cut-off (i.e., M3089 ⁇ 4.895) are sent to terminal node 3 and are classified as having breast cancer. Subjects below the cut-off are 0 sent to a decision node 2 based on M8939.
  • Subjects having an amount of M8939 above the cut-off are sent to terminal node 2 and are classified as having breast cancer.
  • Subjects having an amount of M5907 below the cut-off are sent to terminal node 1 and are classified as healthy.
  • biomarkers M3089 and M8939 are particularly useful in 5 combination to classify breast cancer v. non-breast cancer. These combinations are particularly useful in a recursive partitioning process as shown in FIG. 2. The measure of i each cut-off depends on the particulars of the assay protocol.
  • M8939 is the root decision node of the classification tree. Subjects having an amount of this biomarker above the cut-off (i.e., M8939 ⁇ 25.593) are sent to 0 terminal node 3 and are classified as having breast cancer. Subjects below the cut-off are sent to a decision node 2 based on M3089. [0106] Subjects having an amount of M3089 above the cut-off (i.e., M3089 ⁇ 3.426) are sent to terminal node 2 and are classified as having breast cancer. Subjects having an amount of M5907 below the cut-off are sent to terminal node 1 and are classified as healthy.
  • DCIS grade of ductal carcinoma in situ
  • Table 3 presents a ranking of prognostic variables for breast cancer
  • Table 4 from Coradini and Daidone, Curr. Opin. Obstet. Gynecol 16: 49-55 (2004), lists promising biomolecular prognostic factors in breast cancer.
  • each of the biomarkers listed in Table 1 is a candidate for individual use, to aid the determination of breast cancer status. With the incidence of more patient data accrued and, hence, more extensive statistical analysis, the candidate status of a given maker may transition to confirmed individual use, as described above. Alternatively, it may prove expedient to seek a correlation between breast cancer status and the combination of a biomarker or group of biomarkers from Table 1 with at least one other breast cancer prognostic factor. Thus, the present invention contemplates such combinations including but not limited to those between one or more Table 1 markers and any of the factors listed in Tables 3 and 4.
  • this invention provides methods for determining the risk of developing disease in a subject.
  • Biomarker amounts or patterns are characteristic of various risk states, e.g., high, medium, or low.
  • the risk of developing a disease is determined by measuring the relevant biomarker or biomarkers and then either submitting them to a classification algorithm or comparing them with a reference amount and/or pattern of biomarkers that is associated with the particular risk level.
  • this invention provides methods for determining the stage of disease in a subject.
  • Each stage of the disease has a characteristic amount of a biomarker or relative amounts of a set of biomarkers (a "pattern").
  • the stage of a disease is determined by measuring the relevant biomarker or biomarkers and then either submitting them to a classification algorithm or comparing them with a reference amount and/or pattern of biomarkers that is associated with the particular stage.
  • this invention provides methods for determining the course of disease in a subject.
  • Disease course refers to changes in disease status over time, including disease progression (worsening) and disease regression (improvement). Over time, the amounts or relative amounts (e.g., the pattern) of the biomarkers changes. Therefore, the trend of these markers, either increased or decreased over time toward diseased or non- diseased indicates the course of the disease.
  • this method involves measuring one or more biomarkers in a subject at least two different time points, e.g., a. first time and a second time, and comparing the change in amounts, if any. The course of disease is determined based on these comparisons. Similarly, this method is useful for determining the response to treatment. If a treatment is effective, then the biomarkers will trend toward normal, if treatment is ineffective, the biomarkers will trend toward disease indications. This method is also useful to monitor disease status of breast cancer patients following surgery or other therapy.
  • this invention provides methods for determining breast cancer recurrence-free survival.
  • a long recurrence-free survival is defined as a recurrence-free survival of on average 1817 days, as determined by analysis of the patient data in Example 2.
  • a short recurrence-free survival is defined as a recurrence-free survival of on average 1250 days, as determined by analysis of the patient data in Example 2.
  • Example 2 investigates whether the protein profiles of sera, collected after surgery, but before adjuvant therapy, are related or predict the occurrence of relapses.
  • the subject methodology may further comprise managing subject treatment, based on the status.
  • Such management includes the actions of the physician or clinician subsequent to determining breast cancer status. For example, if a physician makes a diagnosis of breast cancer, then a certain regime of treatment, such as surgery, followed by adjuvant therapy ⁇ e.g., radiotherapy, chemotherapy, anti-hormonal therapy, or a combination thereof) might follow. Alternatively, a diagnosis of non-breast cancer might be followed with further testing to determine a specific disease that the patient might be suffering from. Also, if the diagnostic test gives an inconclusive result on breast cancer status, further tests may be called for.
  • adjuvant therapy e.g., radiotherapy, chemotherapy, anti-hormonal therapy, or a combination thereof
  • Additional embodiments of the invention relate to the communication of assay results or diagnoses or both to technicians, physicians or patients, for example.
  • computers will be used to communicate assay results or diagnoses or both to interested parties, e.g., physicians and their patients.
  • the assays will be performed or the assay results analyzed in a country or jurisdiction which differs from the country or jurisdiction to which the results or diagnoses are communicated.
  • a diagnosis based on the presence or absence in a test subject of any the biomarkers of Table 1 is communicated to the subject as soon as possible after the diagnosis is obtained.
  • the diagnosis may be communicated to the subject by the subject's treating physician.
  • the diagnosis may be sent to a test subject by email or communicated to the subject by phone.
  • a computer may be used to communicate the diagnosis by email or phone.
  • the message containing results of a diagnostic test may be generated and delivered automatically to the subject using a combination of computer hardware and software which will be familiar to artisans skilled in telecommunications.
  • One example of a healthcare-oriented communications system is described in U.S. Patent No.
  • data derived from the spectra e.g., mass spectra or time-of- flight spectra
  • samples such as "known samples”
  • a "known sample” is a sample that has been pre-classified.
  • the data that are derived from the spectra and are used to form the classification model can be referred to as a "training data set.”
  • the classification model can recognize patterns in data derived from spectra generated using unknown samples. The classification model can then be used to classify the unknown samples into classes. This can be useful, for example, in predicting whether or not a particular biological sample is associated with a certain biological condition (e.g., diseased versus non-diseased).
  • the training data set that is used to form the classification model may comprise raw data or pre-processed data.
  • raw data can be obtained directly from time-of-flight spectra or mass spectra, and then may be optionally "pre-processed" as described above.
  • Classification models can be formed using any suitable statistical classification (or "learning") method that attempts to segregate bodies of data into classes based on objective parameters present in the data.
  • Classification methods may be either supervised or unsupervised. Examples of supervised and unsupervised classification processes are described in Jain, "Statistical Pattern Recognition: A Review," IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (1), pp. 4-37, January 2000.
  • supervised classification training data containing examples of known categories are presented to a learning mechanism, which learns one or more sets of relationships that define each of the known classes. New data then may be applied to the learning mechanism, which then classifies the new data using the learned relationships.
  • supervised classification processes include linear regression processes (e.g., multiple linear regression (MLR), partial least squares (PLS) regression and principal components regression (PCR)), binary classification trees (e.g., recursive partitioning processes such as CART - classification and regression trees), artificial neural networks such as back propagation networks, discriminant analyses (e.g., Bayesian classifier or Fischer analysis), logistic classifiers, and support vector classifiers (support vector machines).
  • linear regression processes e.g., multiple linear regression (MLR), partial least squares (PLS) regression and principal components regression (PCR)
  • binary classification trees e.g., recursive partitioning processes such as CART - classification and regression trees
  • artificial neural networks such as back propagation networks
  • discriminant analyses e.g.,
  • a preferred supervised classification method is a recursive partitioning process.
  • Recursive partitioning processes use recursive partitioning trees to classify spectra derived from unknown samples. Further details about recursive partitioning processes are provided in published U.S. Patent Application No. 2002 0138208 Al to Paulse et al, "Method for analyzing mass spectra.”
  • the classification models that are created can be formed using unsupervised learning methods.
  • Unsupervised classification attempts to learn classifications based on similarities in the training data set, without pre-classifying the spectra from which the training data set was derived.
  • Unsupervised learning methods include cluster analyses. A cluster analysis attempts to divide the data into "clusters" or groups that ideally should have members that are very similar to each other, and very dissimilar to members of other clusters. Similarity is then measured using some distance metric, which measures the distance between data items, and clusters together data items that are closer to each other.
  • Clustering techniques include the MacQueen's K-means algorithm and the Kohonen's Self-Organizing Map algorithm.
  • the classification models can be formed on and used on any suitable digital computer. Suitable digital computers include micro, mini, or large computers using any standard or specialized operating system, such as a Unix, WindowsTM or LinuxTM based operating system. The digital computer that is used may be physically separate from the mass spectrometer that is used to create the spectra of interest, or it may be coupled to the mass spectrometer. [0124]
  • the training data set and the classification models according to embodiments of the invention can be embodied by computer code that is executed or used by a digital computer.
  • the computer code can be stored on any suitable computer readable media including optical or magnetic disks, sticks, tapes, etc., and can be written in any suitable computer programming language including C, C++, visual basic, etc.
  • the learning algorithms described above are useful both for developing classification algorithms for the biomarkers already discovered, or for finding new biomarkers for breast cancer.
  • the classification algorithms form the base for diagnostic tests by providing diagnostic values ⁇ e.g., cut-off points) for biomarkers used singly or in combination. 6.
  • KITS FOR DETECTION OF BIOMARKERS FOR BREAST CANCER are useful both for developing classification algorithms for the biomarkers already discovered, or for finding new biomarkers for breast cancer.
  • the classification algorithms form the base for diagnostic tests by providing diagnostic values ⁇ e.g., cut-off points
  • kits for qualifying breast cancer status which kits are used to detect biomarkers according to the invention.
  • the kit comprises a solid support, such as a chip, a microtiter plate or a bead or resin having a capture reagent attached thereon, wherein the capture reagent binds a biomarker of the invention.
  • the kits of the present invention can comprise mass spectrometry probes for SELDI, such as ProteinChip ® arrays.
  • the kit can comprise a solid support with a reactive surface, and a container comprising the biospecific capture reagent.
  • the kit can also comprise a washing solution or instructions for making a washing solution, in which the combination of the capture reagent and the washing solution allows capture of the biomarker or biomarkers on the solid support for subsequent detection by, e.g., mass spectrometry.
  • the kit may include more than type of adsorbent, each present on a different solid support.
  • such a kit can comprise instructions for suitable operational parameters in the form of a label or separate insert.
  • the instructions may inform a consumer about how to collect the sample, how to wash the probe or the particular biomarkers to be detected.
  • the kit can comprise one or more containers with biomarker samples, to be used as standard(s) for calibration.
  • the biomarkers can be used to screen for compounds that modulate the expression of the biomarkers in vitro or in vivo, which compounds in turn may be useful in treating or preventing breast cancer in patients.
  • Such compounds include, but are not limited to small molecules, antibodies, peptides and RNAi.
  • the biomarkers can be used to monitor the response to treatments for breast cancer.
  • the biomarkers can be used in heredity studies to determine if the subject is at risk for developing breast cancer.
  • kits of this invention could include a solid substrate having a hydrophobic function, such as a protein biochip (e.g., a Ciphergen H50 ProteinChip array, e.g., ProteinChip array) and a sodium acetate buffer for washing the substrate, as well as instructions providing a protocol to measure the biomarkers of this invention on the chip and to use these measurements to diagnose breast cancer.
  • a protein biochip e.g., a Ciphergen H50 ProteinChip array, e.g., ProteinChip array
  • a sodium acetate buffer for washing the substrate
  • Compounds suitable for therapeutic testing may be screened initially by identifying compounds which interact with one or more biomarkers listed in Table 1.
  • screening might include recombinantly expressing a biomarker listed in Table 1 , purifying the biomarker, and affixing the biomarker to a substrate.
  • Test compounds would then be contacted with the substrate, typically in aqueous conditions, and interactions between the test compound and the biomarker are measured, for example, by measuring elution rates as a function of salt concentration.
  • Certain proteins may recognize and cleave one or more biomarkers of Table 1, in which case the proteins may be detected by monitoring the digestion of one or more biomarkers in a standard assay, e.g., by gel electrophoresis of the proteins.
  • the ability of a test compound to inhibit the activity of one or more of the biomarkers of Table 1 may be measured.
  • One of skill in the art will recognize that the techniques used to measure the activity of a particular biomarker will vary depending on the function and properties of the biomarker. For example, an enzymatic activity of a biomarker may be assayed provided that an appropriate substrate is available and provided that the concentration of the substrate or the appearance of the reaction product is readily measurable.
  • the ability of potentially therapeutic test compounds to inhibit or enhance the activity of a given biomarker may be determined by measuring the rates of catalysis in the presence or absence of the test compounds.
  • test compounds to interfere with a non-enzymatic (e.g., structural) function or activity of one of the biomarkers of Table 1 may also be measured.
  • a non-enzymatic function or activity of one of the biomarkers of Table 1 may also be measured.
  • the self-assembly of a multi-protein complex which includes one of the biomarkers of Table 1 may be monitored by spectroscopy in the presence or absence of a test compound.
  • test compounds which interfere with the ability of the biomarker to enhance transcription may be identified by measuring the levels of biomarker-dependent transcription in vivo or in vitro in the presence and absence of the test compound.
  • Test compounds capable of modulating the activity of any of the biomarkers of Table 1 may be administered to patients who are suffering from or are at risk of developing breast cancer or other cancer.
  • the administration of a test compound which increases the activity of a particular biomarker may decrease the risk of breast cancer in a patient if the activity of the particular biomarker in vivo prevents the accumulation of proteins for breast cancer.
  • the administration of a test compound which decreases the activity of a particular biomarker may decrease the risk of breast cancer in a patient if the increased activity of the biomarker is responsible, at least in part, for the onset of breast cancer.
  • the invention provides a method for identifying compounds useful for the treatment of disorders such as breast cancer which are associated with increased levels of modified forms of the biomarkers of Table 1.
  • cell extracts or expression libraries may be screened for compounds which catalyze the cleavage of the full-length biomarkers of Table 1 to form truncated forms of the biomarkers of Table 1.
  • cleavage of the biomarkers of Table 1 may be detected by attaching a fluorophore to the biomarkers of Table 1 which remains quenched when the biomarkers of Table 1 are uncleaved but which fluoresces when the protein is cleaved.
  • a version of the full-length biomarkers of Table 1 may be modified so as to render the amide bond between certain amino acids uncleavable, which may be used to selectively bind or "trap" the cellular protesase which cleaves the full-length biomarkers of Table 1 at that site in vivo.
  • Methods for screening and identifying proteases and their targets are well-documented in the scientific literature, e.g., in Lopez-Ottin et ah, Nature Reviews, 3:509-519 (2002).
  • the invention provides a method for treating or reducing the progression or likelihood of a disease, e.g., breast cancer, which is associated with the increased levels of truncated forms of the biomarkers of Table 1.
  • a disease e.g., breast cancer
  • combinatorial libraries may be screened for compounds which inhibit the cleavage activity of the identified proteins. Methods of screening chemical libraries for such compounds are well-known in art. See, e.g., Lopez-Otin et al. (2002).
  • inhibitory compounds may be intelligently designed based on the structure of the biomarkers of Table 1.
  • screening a test compound includes obtaining samples from test subjects before and after the subjects have been exposed to a test compound.
  • the levels in the samples of one or more of the biomarkers listed in Table 1 may be measured and analyzed to determine whether the levels of the biomarkers change after exposure to a test compound.
  • the samples may be analyzed by mass spectrometry, as described herein, or the samples may be analyzed by any appropriate means known to one of skill in the art.
  • the levels of one or more of the biomarkers listed in Table 1 may be measured directly by Western blot using radio- or fluorescently-labeled antibodies which specifically bind to the biomarkers.
  • changes in the levels of mRNA encoding the one or more biomarkers may be measured and correlated with the administration of a given test compound to a subject.
  • the changes in the level of expression of one or more of the biomarkers may be measured using in vitro methods and materials.
  • human tissue cultured cells which express, or are capable of expressing, one or more of the biomarkers of Table 1 may be contacted with test compounds.
  • Subjects who have been treated with test compounds will be routinely examined for any physiological effects which may result from the treatment.
  • the test compounds will be evaluated for their ability to decrease disease likelihood in a subject.
  • test compounds will be screened for their ability to slow or stop the progression of the disease.
  • Example 1 Discovery of biomarkers for distinguishing breast cancer patients from healthy controls [0138] This study aimed to develop and validate an assay that identifies new, discriminatory serum protein profiles in breast cancer patients. Serum samples of female breast cancer patients and healthy controls were analyzed using the ProteinChip® SELDI- TOF MS system. Patients were eligible if they were firstly diagnosed with primary breast cancer stage I - III, if the diagnosis was pathologically confirmed, and if the serum sample was obtained prior to any treatment. Healthy controls were recruited among people accompanying patients on hospital visits.
  • Serum samples were obtained from the Netherlands Cancer Institute serum bank. A total of 250 serum samples were included in this study.
  • C-reactive protein (CRP), transferrin and CAl 5.3 levels were measured, using standard, validated clinical methods (CRP: Near Infrared Particle Immmuno Assay rate methode, using LX20 (Beckman-Coulter), Transferrin: turbimetric immunoassay, using LX20 (Beckman-Coulter), CA 15.3: Electrochemiluminescence immunoassay "ECLIA", using MODULAR ANALYTICS El 70 analyzer (Roche).) [0140]
  • chip chemistries hydrophobic, hydrophilic, anion exchange, cation exchange, and immobilized metal affinity capture (IMAC)
  • binding- and washing- procedures and sample pretreatments were initially evaluated to determine which affinity chemistry and sample pretreatment procedure provided the best serum profiles in terms of number and resolution of proteins.
  • the IMAC3 Array type combined with Ni as the immobilized metal ion, was initially observed to give the best results. This Array type was used for the analysis of all serum samples.
  • Data were collected, after optimization, by averaging 65 laser shots with an intensity of 200 and a detector sensitivity of 9. Data were calibrated externally using a peptide molecular mass standard, All-in-one peptide molecular mass standard (Ciphergen).
  • the IMAC3 array In addition to the similar functionality of the IMAC3 array, it has a hydrophobic barrier coating surrounding the spots. Repetition of the analysis of group 1 and 2 using the IMAC30 array type gave an improved reproducibility, high protein levels and distinctive protein profiles. Therefore, this array type and corresponding assay conditions were used in the subsequent analysis of the serum samples in group 3.
  • the performance of all trees was determined by a) offering the blinded data that were used in the construction of the classification tree, and b) offering the blinded datasets that were not used in the construction of the tree (e.g. blinded prospective validation).
  • the results of this (prospective) validation are presented in Table 5.
  • the software did not select any demographic co-variable, patient characteristic or serum levels of CRP, transferrin and CA15.3 to be a classifier.
  • the performance on the test set served as a quality measure of the cluster set.
  • the performance was measured as the average of the false positive (N classified as B) and false negative (B classified as N) rates of the test samples.
  • N classified as B false positive
  • B classified as N false negative
  • the steps of ranking the clusters and training and testing the classifier are performed in a 10- fold cross-validation procedure.
  • the output of this procedure is an optimal number of top- ranked clusters and a trained classifier. To ensure independent validation, this process of optimizing the set of clusters and training the classifier is wrapped in a second 3-fold cross- validation loop.
  • the best classifier was the simple Bayesian classifier, which used an average of 15 m/z's to result in an optimum validation performance of 93.1%. Four of these 15 m/z's were also found using the Biomarker Patterns Software: m/z 8939 (rank no. 1 of 15), m/z 3089 (no. 3), m/z 3105 (no. 4), m/z 3820 (no. 7).
  • amino acid sequence of the M8939 biomarker was determined to be:
  • the M2756, M3089, M3820 and M3967 biomarkers were detected in F4 (pH 4.0 fraction) and F 5 (pH 3.0 fraction) of the anion exchange fractionation that was performed during the purification of the M8939 biomarker (described above). They were subsequently detected in the retentate obtained by the size fractionation using an YM-50 MW spin concentrator. Following treatment with 50% ACN, they eluted from the column, as confirmed by NP20 profiling.
  • the markers present in the 50% ACN eluate were directly sequenced on the PCI- 1000 interface/Tandem MS. All markers were found to be C-terminal truncations of albumin, with albumin being cleaved at different amino-acids (cysteine, leucine, phenylalanine and glutamate) within the sequence. [0160] The amino acid sequences of the M2756, M3089, M3820 and M3967 biomarkers were determined to be as follows:
  • CRP Near Infrared Particle Immmuno Assay rate method, using LX20 (Beckman-Coulter)
  • Transferrin Immunoturbimetric assay, using LX20 (Beckman-Coulter)
  • CA 15.3 Electrochemiluminescence immunoassay "ECLIA”, using the MODULAR ANALYTICS E 170 analyzer (Roche)
  • Haptoglobin immunoturbidimetric assay, using the COBAS INTEGRA Tina-quant® Haptoglobin ver.2 cassette on the INTEGRA 400 analyzer (Roche).
  • FIG. 3 A representative example of the spectra obtained is presented in Figure 3.
  • Figure 4 contains two Kaplan-Meier curves.
  • the upper curve is composed of the recurrence-free survival data of the patients exhibiting a high abundant peak at m/z 9198, and the lower curve is composed of the recurrence-free survival data of the patients exhibiting a low abundant peak at m/z 9198.
  • the M9198 biomarker thus is a candidate marker for identification of breast cancer patients, after surgery but prior to adjuvant chemotherapy, who are at risk for rapid disease progression. This creates an opportunity for treatment therapy strategy optimization and follow-up.
  • Anion exchange fractionation [0170] Anion exchange resin (Q HyperD F, BioSepra) was equilibrated with Ul buffer (IM urea, 0.2% CHAPS, 50 mM Tris-HCl, pH 9). The serum samples were thawed on ice and denatured by dilution with U9 buffer (9M urea, 2% CHAPS, 50 mM Tris-HCl, pH 9). After incubating for 30 minutes, each sample was diluted with Ul buffer. This sample mixture was applied to the anion exchange column, and incubated for 30 minutes.
  • Ul buffer IM urea, 0.2% CHAPS, 50 mM Tris-HCl, pH 9
  • the flow-through fraction containing unbound material, was eluted from the column and the column was sequentially washed with 50 mM Tris-HCl pH 9 (Fl), 50 mM HEPES pH 7 (F2), and 100 mM NaAcetate pH 5 (F3). Each fraction was profiled on both NP20 and IMAC30 Ni Arrays. The 9198 Da marker was observed in F3 (pH 5.0 elution). As expected, the marker was not observed in the same fraction from the "low" serum sample. Size fractionation
  • F3 from Q fractionation was spun through a 5OkDa MW spin concentrator and the membrane was washed with water. The marker was found in both filtrates and completely absent in the retentate. The flow through and wash fractions were desalted using reversed phase beads.
  • the desalted wash fraction was run on an 4-12% Bis-Tris NuPage gel.
  • the band presumably containing the marker was subsequently excised and the marker was eluted from the gel slice using a combination of formic acid, ACN, 2-propanol.
  • Profiling of the eluate on NP20 confirmed the presence of the marker.
  • the passive elution fraction was submitted to protease digestion using trypsin. After incubation, the tryptic digest sample was spotted on an NP20 Array, and mass spectra of the digest were acquired on the ProteinChip Reader. Identification by peptide mapping and peptide sequencing
  • Haptoglobin consists of two types of polypeptide chains, the alpha chain and the beta chain.
  • the polymorphism of haptoglobin is due to the existence of two forms of the alpha chains, alphal and alpha2 chains, which are genetically coded by Hpt 1 and Hpf alleles, respectively. Both alleles also code for the invariant beta chain.
  • haptoglobin occurs as three major phenotypes: two homozygous types, HpI-I and Hp2-2, and one heterozygous type, Hp2-1.
  • Alpha-1 chain presents exclusively in HpI-I and Hp2-l phenotypes.
  • Example 2 demonstrates that M9198, the alphal chain of haptoglobin (see Table 1), is a candidate biomarker for patients with a longer recurrence-free survival.
  • the following study illuminates the correlation between the phenotypes of haptoglobin, in combination with other prognosis indicators, and the recurrence-free survival of breast cancer patients.
  • haptoglobin phenotype of all 63 patient sera was assessed by native one- dimensional gel electrophoresis (PAGE), followed by peroxidase staining. Sera were thawed and 1 ⁇ l of serum subsequently was mixed with 19 ⁇ l of a 1 : 100 dilution (in phosphate buffered saline pH 7.4) of hemolysate. Following incubation for 5 minutes at room temperature, 10 ⁇ l of 3x native PAGE sample buffer (composed of 30 ml glycerol, 18.8 ml IM Tris-HCl (pH 6.8) and 1.5 ml 1% (w/v) bromophenol blue, made to 100 ml in water) was added and mixed.
  • PAGE native one- dimensional gel electrophoresis
  • haptoglobin phenotype can be considered a direct derivative of the haptoglobin genome.
  • haptoglobin phenotype of chemotherapy for example, is to be expected.
  • haptoglobin phenotype could be determined in plasma samples. Therefore, study population could be expanded further with additional plasma samples, leading to a total of 114 patients.
  • FIG. 5 contains three Kaplan-Meier curves for a population of 114 patients.
  • the upper curve is composed of the recurrence-free survival data of the patients carrying phenotype HpI-I
  • the patients in the middle curve all carry phenotype Hp2-1
  • the lower curve is composed of the recurrence-free survival data of the patients carrying phenotype Hp2-2.
  • Figures 6A and 6B present the Kaplan-Meier curves for a population of 432 patients.
  • the upper two (superimposed) curves in Figure 6 A originate from patients carrying phenotypes HpI-I or Hp2-1, while the lower curve is composed of the recurrence-free survival data of the patients carrying phenotype Hp2-2.
  • haptoglobin alpha- 1 chain which is M9198 biomarker, is an effective marker in this regard when combined with each of several prognosis indicators: relatively low DCIS value (grade II), focal angiogenesis, a relatively low number of positive axillary lymph nodes (4-9), her2/Neu negative, ER or PR positive, p53 protein negative, and relatively low MAI (0-9).

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Abstract

L'invention concerne des biomarqueurs à base de protéines et des combinaisons de biomarqueurs utiles dans la qualification de l'état du cancer du sein chez une patiente. Par exemple, les biomarqueurs selon l'invention sont utiles pour classifier un échantillon d'un sujet selon qu'il est atteint ou non du cancer du sein. Les biomarqueurs peuvent être détectés par la spectrométrie de masse SELDI.
PCT/US2005/047005 2004-12-27 2005-12-27 Biomarqueurs destines au cancer du sein WO2006071843A2 (fr)

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WO2007141595A3 (fr) * 2005-11-10 2008-04-17 Aurelium Biopharma Inc Diagnostic tissulaire du cancer du sein
US20090325212A1 (en) * 2008-06-27 2009-12-31 Microsoft Corporation Data standard for biomaterials

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WO2002006316A2 (fr) * 2000-07-14 2002-01-24 Zycos, Inc. Composes en rapport avec $g(a)-msh et methodes d'utilisation
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WO2004090550A2 (fr) * 2003-04-08 2004-10-21 Colotech A/S Procede de detection d'un cancer colorectal dans des echantillons humains

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WO2007141595A3 (fr) * 2005-11-10 2008-04-17 Aurelium Biopharma Inc Diagnostic tissulaire du cancer du sein
US7662580B2 (en) 2005-11-10 2010-02-16 Aurelium Biopharma Inc. Tissue diagnostics for breast cancer
US20090325212A1 (en) * 2008-06-27 2009-12-31 Microsoft Corporation Data standard for biomaterials

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