Methods and systems of detecting plasma protein biomarkers for diagnosing acute exacerbation of COPD

Abstract

Described are compositions and methods for diagnosing acute exacerbations of chronic obstructive pulmonary disease (AECOPD). Multiple reaction monitoring mass spectrometry (MRM-MS) was used to quantify the amount of protein biomarkers in plasma samples from human subjects. The amount of the biomarkers in the sample can distinguish AECOPD from a stable or convalescent state of COPD, or from a subject without COPD.

Claims

1. A method for diagnosing acute exacerbations of chronic obstructive pulmonary disease (AECOPD) in a subject, comprising: obtaining a protein expression dataset associated with a blood sample obtained from the subject, wherein the protein expression dataset comprises the expression levels of biomarkers apolipoprotein C-II (SEQ ID NO: 22), complement component C9 (SEQ ID NO: 34), apolipoprotein A-IV (SEQ ID NO: 275), fibronectin (SEQ ID NO: 276), lipopolysaccharide binding protein (SEQ ID NO: 277), inter-alpha-trypsin inhibitor heavy chain H2 (SEQ ID NO: 35), heparin cofactor 2 (SEQ ID NO: 33), apolipoprotein A-I (SEQ ID NO: 24), pigment epithelium-derived factor (SEQ ID NO: 29), hemopexin (SEQ ID NO: 42), beta-2-microglobulin (SEQ ID NO: 38), gelsolin (SEQ ID NO: 39), beta-2-glycoprotein 1 (SEQ ID NO: 27), afamin (SEQ ID NO: 23), histidine-rich glycoprotein (SEQ ID NO: 37), transthyretin (SEQ ID NO: 30), apolipoprotein A-II (SEQ ID NO: 26), protein AMBP (SEQ ID NO: 28), and complement component C6 (SEQ ID NO: 32); analyzing the protein expression dataset to determine a biomarker score for the subject, wherein the biomarker score is calculated based on weighted contributions of the biomarkers; comparing the biomarker score to a biomarker score of a control subject without AECOPD, wherein the biomarker score is greater in a subject with AECOPD than in a control subject without AECOPD; and administering a treatment comprising short-acting beta2-agonists, anticholinergic bronchodilators, methylxanthines, long-acting bronchodilators, expectorants, oxygen therapy, and/or antibiotics to the subject.

2. The method of claim 1, wherein the method provides a sensitivity of at least 90% in diagnosing AECOPD.

3. The method of claim 1, wherein the protein expression dataset is obtained using mass spectrometry, multiple reaction monitoring-mass spectrometry (MRM-MS), or an antibody.

4. The method of claim 1, wherein the biomarker score is determined based on the formula: Biomarker score=w.sub.0+w.sub.1*protein.sub.1+w.sub.2*protein.sub.2+ . . . +w.sub.N*protein.sub.N, where N is the number of proteins in the biomarker panel.

5. The method of claim 1, wherein the subject presents to a physician with dyspnea, cough, and sputum production.

6. The method of claim 1, wherein the method provides a specificity of at least 86% in diagnosing AECOPD.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) FIG. 1. Biomarker Discovery and Replication Strategy. Biomarker discovery steps, applied to the LEUKO cohort, are outlined in the blue box. After pre-processing, univariate analysis identifies candidate proteins based on statistically significant fold change at a false discovery rate <0.2. An elastic net model is applied to these candidate proteins with a final classifier model generated. This is subsequently followed by replication in the TNF- and RTP cohorts (yellow box).

(2) FIG. 2. Receiver Operating Characteristic (ROC) Curves for the LEUKO Discovery Cohort. The ROC curves are shown for CRP only (black line) and the 18-protein panel (orange line) when applied to the LEUKO discovery cohort. The 18-protein panel had improved performance metrics over CRP only.

(3) FIG. 3. Biomarker Scores Comparing AECOPD to Non-AECOPD States. Biomarker scores for the LEUKO, TNF-, and RIP cohorts are shown with red denoting patients during AECOPD and blue denoting patients in the convalescent phase. Biomarker scores were significantly elevated during the time of AECOPD but fell during the convalescent phase (Wilcoxon rank sum p-value=0.001 for LEUKO, <0.0001 for TNF-, and <0.0001 for RTP). The convalescent phase scores for the LEUKO, TNF-, and RTP cohorts were not statistically significantly different.

(4) FIG. 4. Stepwise AUC Analysis for Pooled Data. Incremental increases in AUC are graphed with each stepwise addition of a protein from the 18-protein panel for all three cohorts combined. A plateau AUC of 0.795 is achieved with the first 11 proteins.

(5) FIG. 5. Pathways Enriched By Statistically Significant Proteins. Significant process networks associated with the 21-protein biomarker panel are shown. The most significant pathway enriched by the biomarker proteins was HDL cholesterol transport (p=3.8210.sup.8), followed by blood coagulation (p=8.9010.sup.5), classical and alternative complement pathways (p=2.1110.sup.4 and p=2.6710.sup.3, respectively) and cell adhesion pathways (p=3.3210.sup.3).

(6) FIG. 6. Biomarker Scores in Stable Congestive Heart Failure Patients and Normal Controls. Biomarker scores for the 18-protein panel are shown for a cohort of stable chronic heart failure patients and for normal controls to compare with the three AECOPD cohorts. Scores for the chronic heart failure patients and for the normal controls were not statistically different from the convalescent scores of the three AECOPD cohorts.

(7) FIG. 7. Biomarker Discovery and Replication Strategy. Biomarker discovery steps, applied to the TNF- cohort, are outlined in the pink box. After pre-processing, univariate analysis identifies candidate proteins based on statistically significant differences between AECOPD and convalescent at a false discovery rate <0.01 with a fold change >1.2. An elastic net model is applied to these candidate proteins to generate a final classifier model. This is subsequently followed by replication in the LEUKO and RTP cohorts (blue box).

(8) FIGS. 8A-8C. Receiver Operating Characteristics (ROC) Curves for the AECOPD Cohorts. The ROC curves are shown for CRP only and the 5-protein panel in FIG. 8A; the TNF- discovery cohort in FIG. 8B; and the LEUKO cohort and the RTP cohort in FIG. 8C. The 5-protein panel showed improved performance metrics compared to CRP only.

(9) FIG. 9. Biomarker Scores Comparing AECOPD to Non-AECOPD States. Biomarker scores for the TNF-, RIP, and LEUKO cohorts are shown. Biomarker scores were significantly elevated during the time of AECOPD but fell during the convalescent phase (Wilcoxon rank sum p-value <0.001 for LEUKO, <0.001 for TNF-, and <0.001 for RTP). The convalescent phase scores for the LEUKO, TNF-, and RTP cohorts showed no statistically significant differences.

(10) FIG. 10. Biomarker Scores in Chronic Heart Failure Patients and Normal Controls. Biomarker scores for the 5-protein panel are shown for a cohort of chronic heart failure patients (n=218) and for healthy controls (n=49) to compare with the three AECOPD cohorts. Scores for the chronic heart failure patients and for the healthy controls were not statistically different from the convalescent scores of the TNF- cohort (p=0.07 and p=0.13, respectively). Included in this figure are also Day 3 and non-exacerbating COPD control patients in the RTP cohort, demonstrating that biomarker scores remain high in the immediate AECOPD period and that non-exacerbating COPD controls have similar biomarker scores to convalescent patients.

DEFINITIONS

(11) Most of the words used in this specification have the meaning that would be attributed to those words by one skilled in the art. Words specifically defined in the specification have the meaning provided in the context of the present teachings as a whole, and as are typically understood by those skilled in the art. In the event that a conflict arises between an art-understood definition of a word or phrase and a definition of the word or phrase as specifically taught in this specification, the specification shall control.

(12) As used in the specification and the appended claims, the singular forms a, an, and the include plural referents unless the context clearly dictates otherwise.

(13) Terms used in the claims and specification are defined as set forth below unless otherwise specified.

(14) Marker, markers, biomarker, or biomarkers, refers generally to a molecule (e.g. a peptide, protein, carbohydrate, or lipid) that is expressed in a cell or tissue, which is useful for the prediction or diagnosis of AECOPD. A marker in the context of the present disclosure encompasses, for example, cytokines, chemokines, growth factors, proteins, peptides, and metabolites, together with their related metabolites, mutations, variants, modifications, fragments, subunits, degradation products, elements, and other analytes or sample-derived measures. Markers also encompass non-blood borne factors and non analyte physiological markers of health status, and/or other factors or markers not measured from samples (e.g., biological samples such as bodily fluids), such as clinical parameters and traditional factors for clinical assessments. Markers can also include any indices that are calculated and/or created mathematically.

(15) Markers can also include combinations of any one or more of the foregoing measurements, including temporal trends and differences.

(16) To analyze includes measurement and/or detection of data associated with a marker (such as, e.g., presence or absence of a peptide or protein, or constituent expression or abundance levels) in the sample (or, e.g., by obtaining a dataset reporting such measurements, as described below). In some aspects, an analysis can include comparing the measurement and/or detection of at least one marker in samples from a subject pre- and post-treatment or other control subject(s). The markers of the present teachings can be analyzed by any of various conventional methods known in the art.

(17) A subject in the context of the present teachings is generally a mammal. The subject is generally a patient. The term mammal as used herein includes but is not limited to a human, non-human primate, dog, cat, mouse, rat, cow, horse, and pig. Mammals other than humans can be advantageously used as subjects that represent animal models of AECOPD. A subject can be male or female.

(18) A sample in the context of the present teachings refers to any biological sample that is isolated from a subject. A sample can include, without limitation, a single cell or multiple cells, fragments of cells, an aliquot of body fluid, whole blood, platelets, serum, plasma, red blood cells, white blood cells or leucocytes, endothelial cells, tissue biopsies, synovial fluid, lymphatic fluid, ascites fluid, and interstitial or extracellular fluid. The term sample also encompasses the fluid in spaces between cells, including gingival crevicular fluid, bone marrow, cerebrospinal fluid (CSF), saliva, mucous, sputum, semen, sweat, urine, or any other bodily fluids. Blood sample can refer to whole blood or any fraction thereof, including blood cells, red blood cells, white blood cells or leucocytes, platelets, serum and plasma. Samples can be obtained from a subject by means including but not limited to venipuncture, excretion, ejaculation, massage, biopsy, needle aspirate, lavage, scraping, surgical incision, or intervention or other means known in the art.

(19) In particular aspects, the sample s a blood sample from the subject.

(20) A dataset is a set of data (e.g., numerical values) resulting from evaluation of a sample. The values of the dataset can be obtained, for example, by experimentally obtaining measures from a sample and constructing a dataset from these measurements; or alternatively, by obtaining a dataset from a service provider such as a laboratory, or from a database or a server on which the dataset has been stored. Similarly, the term obtaining a dataset associated with a sample encompasses obtaining a set of data determined from at least one sample.

(21) In some embodiments, obtaining a dataset encompasses obtaining a sample, and processing the sample to experimentally determine the data, e.g., via measuring, microarray, one or more probes, antibody binding, ELISA, or mass spectometry. The phrase also encompasses receiving a set of data, e.g., from a third party that has processed the sample to experimentally determine the dataset. Additionally, the phrase encompasses mining data from at least one database or at least one publication or a combination of databases and publications.

(22) Measuring or measurement in the context of the present teachings refers to determining the presence, absence, quantity, amount, or effective amount of a marker or other substance (e.g., peptide or protein) in a clinical or subject-derived sample, including the presence, absence, or concentration levels of such markers or substances, and/or evaluating the values or categorization of a subject's clinical parameters.

(23) The term expression level data refers to a value that represents a direct, indirect, or comparative measurement of the level of expression or abundance of a peptide, polypeptide, or protein. For example, expression data can refer to a value that represents a direct, indirect, or comparative measurement of the protein (or peptide fragment thereof) expression level of a proteomic marker of interest. The term expression level can also include the relative or absolute amount, quantity or abundance of a proteomic marker (e.g. a peptide, polypeptide or protein) in a sample.

(24) The term receiver operating characteristic (ROC) refers to the performance of a classifier system as its discrimination threshold is varied.

(25) A biomarker is positively correlated with AECOPD if the expression level or abundance of the biomarker is increased in subjects suffering from or diagnosed with AECOPD. A biomarker is negatively correlated with AECOPD if the expression level or abundance of the biomarker is decreased in subjects suffering from or diagnosed with AECOPD.

DETAILED DESCRIPTION OF THE INVENTION

(26) Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) result in considerable morbidity and mortality. While early diagnosis of AECOPD could potentially prevent long-standing complications, a blood-based biomarker for AECOPD has yet to be developed for clinical practice. Described herein are compositions and methods useful for diagnosing AECOPD, and distinguishing AECOPD from stable or convalescent clinical states of COPD. In some embodiments, the biomarkers are proteins or peptides, for example, proteins or peptides found in blood plasma or serum.

(27) The compositions described herein include biomarkers that provide greater predictive value or diagnostic accuracy in diagnosing a COPD exacerbation compared to current biomarkers, such as C-reactive protein. In some embodiments, a biomarker score is calculated based on the weighted contributions of the marker proteins shown in Table 3, Table 7, or Table 10 or peptide fragments thereof. In some embodiments, the biomarker score is significantly greater in a subject with AECOPD than in a control subject without AECOPD. In some embodiments, the biomarker score is optimized to detect AECOPD with a sensitivity of at least 90% and/or a specificity of at least 86%. In some embodiments, the sensitivity of the biomarkers described herein for diagnosing AECOPD is at least 60%, 65%, 70%, 75%, 80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, or 98%. In some embodiments, the specificity of the biomarkers described herein for diagnosing AECOPD is at least 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100%. In some embodiments, the decision threshold for the biomarker score is optimized to detect AECOPD with a sensitivity of at least 90%, and the resulting sensitivity is at least 90% and the resulting specificity is at least 30%. In some embodiments, the predictive value or diagnostic accuracy (e.g., the sensitivity and/or specificity for diagnosing AECOPD, the ROC curve, or the area under the curve (AUC) estimate) of assays that use the biomarkers described herein is greater than using the marker C-reactive protein (CRP) alone.

(28) In some embodiments, the biomarkers provide an area under the curve (AUC) plateau of greater than 0.79,

(29) Markers and Clinical Factors

(30) In an embodiment, the methods described herein include obtaining a first dataset associated with a sample obtained from the subject (e.g., a blood sample), wherein the first dataset comprises quantitative expression data for one or more peptide or protein markers (e.g., expression data for two or more, three or more, four or more, or five or more markers) In some embodiments, the peptide or protein markers are selected from Table 2, Table 3, Table 4, Table 6, Table 7, or Table 10. In some embodiments, the peptide marker is a fragment of a protein selected from Table 2, Table 3, Table 4, Table 6, Table 7, or Table 10. This first sample can be taken, for example, during the exacerbation state of COPD or before treatment for AECOPD. In some embodiments, the method further includes analyzing the first dataset to determine the expression level or abundance of the one or more peptide or protein markers, wherein the expression level or abundance of the markers positively or negatively correlates with AECOPD in a subject.

(31) In another embodiment, the methods described herein include obtaining a second dataset associated with a sample obtained from the subject (e.g., another blood sample), wherein the second dataset comprises quantitative expression data for one or more peptide or protein markers. In some embodiments, the peptide or protein markers are selected from Table 2, Table 3, Table 4, Table 6, Table 7, or Table 10. In some embodiments, the peptide marker is a fragment of a protein selected from Table 2, Table 3, Table 4, Table 6, Table 7, or Table 10. This second sample can be taken, for example, during the stable or convalescent state of COPD, or after treatment for AECOPD. In some embodiments, the method further includes analyzing the second dataset to determine the expression level of the one or more peptide or protein markers, wherein the expression level or abundance of the markers positively or negatively correlates with AECOPD in a subject.

(32) In additional embodiments, the analysis includes both the first dataset and second dataset, wherein the aggregate analysis of marker expression levels positively or negatively correlates with AECOPD in a subject.

(33) The quantity of one or more markers described herein can be indicated as a value. A value can be one or more numerical values resulting from evaluation of a sample. The values can be obtained, for example, by experimentally obtaining measures from a sample by an assay performed in a laboratory, or alternatively, obtaining a dataset from a service provider such as a laboratory, or from a database or a server on which the dataset has been stored, e.g., on a storage memory.

(34) In an embodiment, the quantity of one or more markers can be one or more numerical values associated with the expression levels of peptides and/or proteins shown in Table 2, Table 3, Table 4, Table 6, Table 7, or Table 10 below, e.g., resulting from evaluation of a patient derived sample.

(35) A marker's associated value can be included in a dataset associated with a sample obtained from a subject. A dataset can include the marker expression value of two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, sixteen or more, seventeen or more, eighteen or more, nineteen or more, twenty or more, twenty-one or more, twenty-two or more, twenty-three or more, twenty-four or more, twenty-five or more, twenty-six or more, twenty-seven or more, twenty-eight or more, twenty-nine or more, or thirty or more marker(s). The value of the one or more markers can be evaluated by the same party that performed the assay using the methods described herein or sent to a third party for evaluation using the methods described herein.

(36) In some embodiments, one or more clinical factors in a subject can be assessed. In some embodiments, assessment of one or more clinical factors or variables in a subject can be combined with a marker analysis in the subject to determine AECOPD in a subject. Examples of relevant clinical factors or variables include, but are not limited to, forced expiratory volume in 1 second (FEV1) <60% predicted, FEV1/forced vital capacity (FVC) <or equal to 70%, acute increase in dyspnea, sputum volume, and/or sputum purulence without an alternative explanation.

(37) Assays

(38) Examples of assays for one or more markers include sequencing assays, microarrays (e.g. proteome arrays), antibody-binding assays, enzyme-linked immunosorbent assays (ELISAs), flow cytometry, protein assays, western blots, nephelometry, turbidimetry, chromatography, mass spectrometry (e.g., MRM-MS), immunoassays, including, by way of example, but not limitation, RIA, immunofluorescence, immunochemiluminescence, immunoelectrochemiluminescence, or competitive immunoassays, immunoprecipitation, and the assays described in the Examples section below. The information from the assay can be quantitative and sent to a computer system described herein. The information can also be qualitative, such as observing patterns or fluorescence, which can be translated into a quantitative measure by a user or automatically by a reader or computer system. In an embodiment, the subject can also provide information other than assay information to a computer system, such as race, height, weight, age, sex, eye color, hair color, family medical history and any other information that may be useful to a user, such as a clinical factor or variable described herein.

(39) Antibodies

(40) In some embodiments, the markers described herein are detected with antibodies that specifically bind to peptides and proteins described herein. The term antibody as used herein refers to immunoglobulin molecules and immunologically active portions of immunoglobulin molecules, i.e., molecules that contain antigen-binding sites that specifically bind an antigen. A molecule that specifically binds to a polypeptide described herein is a molecule that binds to that polypeptide or a fragment thereof, but does not substantially bind other molecules in a sample, e.g., a biological sample, which naturally contains the polypeptide. Examples of immunologically active portions of immunoglobulin molecules include F(ab) and F(ab)2 fragments which can be generated by treating the antibody with an enzyme such as pepsin. Described herein are polyclonal and monoclonal antibodies that bind to a polypeptide or peptide disclosed herein. The term monoclonal antibody or monoclonal antibody composition, as used herein, refers to a population of antibody molecules that contain only one species of an antigen binding site capable of immunoreacting with a particular epitope of a polypeptide or peptide disclosed herein. A monoclonal antibody composition thus typically displays a single binding affinity for a particular polypeptide or peptide disclosed herein with which it immunoreacts.

(41) Polyclonal antibodies can be prepared by immunizing a suitable subject with a desired immunogen, e.g., a polypeptide disclosed herein or a fragment thereof. The antibody titer in the immunized subject can be monitored over time by standard techniques, such as with an enzyme linked immunosorbent assay (ELISA) using immobilized polypeptide. If desired, the antibody molecules directed against the polypeptide can be isolated from the mammal (e.g., from the blood) and further purified by well-known techniques, such as protein A chromatography to obtain the IgG fraction. At an appropriate time after immunization, e.g., when the antibody titers are highest, antibody-producing cells can be obtained from the subject and used to prepare monoclonal antibodies by standard techniques, such as the hybridoma technique originally described by Kohler and Milstein, Nature 256:495-497 (1975), the human B cell hybridoma technique (Kozbor et al., Immunol. Today 4: 72 (1983)), the EBV-hybridoma technique (Cole et al., Monoclonal Antibodies and Cancer Therapy, Alan R. Liss, 1985, Inc., pp. 77-96) or trioma techniques. The technology for producing hybridomas is well known (see generally Current Protocols in Immunology (1994) Coligan et al., (eds.) John Wiley & Sons, Inc., New York, N.Y.). Briefly, an immortal cell line (typically a myeloma) is fused to lymphocytes (typically splenocytes) from a mammal immunized with an immunogen as described above, and the culture supernatants of the resulting hybridoma cells are screened to identify a hybridoma producing a monoclonal antibody that binds a polypeptide described herein.

(42) Any of the many well-known protocols used for fusing lymphocytes and immortalized cell lines can be applied for the purpose of generating a monoclonal antibody to a polypeptide described herein (see, e.g., Current Protocols in Immunology, supra; Golfre et al., Nature 266:55052 (1977); R. H. Kenneth, in Monoclonal Antibodies: A New Dimension In Biological Analyses, Plenum Publishing Corp., New York, N.Y. (1980); and Lerner, Yale J. Biol. Med. 54:387-402 (1981)). Moreover, the ordinarily skilled worker will appreciate that there are many variations of such methods that also would be useful.

(43) Alternative to preparing monoclonal antibody-secreting hybridomas, a monoclonal antibody to a polypeptide or peptide disclosed herein can be identified and isolated by screening a recombinant combinatorial immunoglobulin library (e.g., an antibody phage display library) with the polypeptide to thereby isolate immunoglobulin library members that bind the polypeptide. Kits for generating and screening phage display libraries are commercially available (e.g., the Pharmacia Recombinant Phage Antibody System, Catalog No. 27-9400-01; and the Stratagene SurfZAP Phage Display Kit, Catalog No. 240612). Additionally, examples of methods and reagents particularly amenable for use in generating and screening antibody display library can be found in, for example, U.S. Pat. No. 5,223,409; PCT Publication No. WO 92/18619; PCT Publication No. WO 91/17271; PCT Publication No. WO 92/20791; PCT Publication No. WO 92/15679; PCT Publication No. WO 93/01288; PCT Publication No. WO 92/01047; PCT Publication No. WO 92/09690; PCT Publication No. WO 90/02809; Fuchs et al., Bio/Technology 9: 1370-1372 (1991); Hay et al., Hum. Antibod. Hybridomas 3:81-85 (1992); Huse et al., Science 246: 1275-1281 (1989); and Griffiths et al., EMBO J. 12:725-734 (1993).

(44) Additionally, recombinant antibodies, such as chimeric and humanized monoclonal antibodies, comprising both human and non-human portions, which can be made using standard recombinant DNA techniques, are within the scope of the instant disclosure. Such chimeric and humanized monoclonal antibodies can be produced by recombinant DNA techniques known in the art.

(45) Single-chain antibodies are Fv molecules in which the heavy and light chain variable regions have been connected by a flexible linker to form a single polypeptide chain, which forms an antigen binding region. Single chain antibodies are discussed in detail in International Patent Application Publication No. WO 88/01649 and U.S. Pat. Nos. 4,946,778 and 5,260,203, the disclosures of which are incorporated by reference.

(46) In general, antibodies (e.g., polyclonal or monoclonal antibodies) can be used to detect a polypeptide marker (e.g., in a blood sample) in order to evaluate the abundance and expression of the polypeptide. The antibody can be coupled to a detectable substance to facilitate its detection. Examples of detectable substances include various enzymes, prosthetic groups, fluorescent materials, luminescent materials, bioluminescent materials; and radioactive materials. Examples of suitable enzymes include horseradish peroxidase, alkaline phosphatase, beta-galactosidase, or acetylcholinesterase; examples of suitable prosthetic group complexes include streptavidin/biotin and avidin/biotin; examples of suitable fluorescent materials include umbelliferone, fluorescein, fluorescein isothiocyanate, rhodamine, dichlorotriazinylamine fluorescein, dansyl chloride or phycoerythrin; an example of a luminescent material includes luminol; examples of bioluminescent materials include luciferase, luciferin, and aequorin, and examples of suitable radioactive material include .sup.125I, .sup.131I, .sup.35S or .sup.3H.

(47) Detection Assays

(48) Antibodies such as those described herein can be used in a variety of methods to determine the expression levels or abundance of the markers disclosed herein, and thus, determine AECOPD. In one aspect, kits can be made which comprise antibodies or reagents that can be used to quantify the markers of interest.

(49) In another aspect, expression levels or abundance of polypeptide markers can be measured using a variety of methods, including enzyme linked immunosorbent assays (ELISAs), western blots, immunoprecipitations immunofluorescence, and mass spectrometry. For example, a test sample from a subject is subjected to a measurement of protein expression levels using marker-specific antibodies. Variants of the protein markers described herein can be detected using polyclonal antibodies that bind the canononical or reference amino acid sequence.

(50) Various means of examining expression, composition, or abundance of the peptides or polypeptides described herein can be used, including: spectroscopy, colorimetry, electrophoresis, isoelectric focusing, and immunoassays (e.g., David et al., U.S. Pat. No. 4,376,110) such as immunoblotting (see also Current Protocols in Molecular Biology, particularly Chapter 10). For example, in one aspect, an antibody capable of binding to the polypeptide (e.g., as described above), preferably an antibody with a detectable label, can be used. Antibodies can be polyclonal, or monoclonal. An intact antibody, or a fragment thereof (e.g., Fab or F(ab)2) can be used. The term labeled, with regard to the probe or antibody, is intended to encompass direct labeling of the probe or antibody by coupling (i.e., physically linking) a detectable substance to the probe or antibody, as well as indirect labeling of the probe or antibody by reactivity with another reagent that is directly labeled. Examples of indirect labeling include detection of a primary antibody using a fluorescently labeled secondary antibody and end-labeling a DNA probe with biotin such that it can be detected with fluorescently labeled streptavidin.

(51) Computer Implementation

(52) In one embodiment, a computer comprises at least one processor coupled to a chipset. A memory, a storage device, a keyboard, a graphics adapter, a pointing device, and a network adapter can be coupled to the chipset. In some embodiments, a display is coupled to the graphics adapter. In one embodiment, the functionality of the chipset is provided by a memory controller hub and an I/O controller hub. In another embodiment, the memory is coupled directly to the processor instead of the chipset.

(53) The storage device is any device capable of holding data, like a hard drive, compact disk read-only memory (CD-ROM), MID, or a solid-state memory device. The memory holds instructions and data used by the processor. The pointing device may be a mouse, track ball, or other type of pointing device, and is used in combination with the keyboard to input data into the computer system. The graphics adapter displays images and other information on the display. The network adapter couples the computer system to a local or wide area network.

(54) As is known in the art, a computer can have different and/or other components than those described previously. In addition, the computer can lack certain components. Moreover, the storage device can be local and/or remote from the computer (such as embodied within a storage area network (SAN)).

(55) As is known in the art, the computer is adapted to execute computer program modules for providing functionality described herein. As used herein, the term module refers to computer program logic utilized to provide the specified functionality. Thus, a module can be implemented in hardware, firmware, and/or software. In one embodiment, program modules are stored on the storage device, loaded into the memory, and executed by the processor.

(56) Embodiments of the entities described herein can include other and/or different modules than the ones described here. In addition, the functionality attributed to the modules can be performed by other or different modules in other embodiments. Moreover, this description occasionally omits the term module for purposes of clarity and convenience.

TREATMENTS

(57) In some embodiments, the above methods further comprise providing a course of treatment based on the results of the assay using the markers described herein. In some embodiments, the course of treatment comprises short-acting beta2-agonists, such as albuterol; anticholinergic bronchodilators, such as ipratropium bromide; methylxanthines such as aminophylline and theophylline; long-acting bronchodilators; oral steroids such as prednisone and methylprednisone, expectorants, oxygen therapy, and/or antibiotics if indicated for a lung infection.

(58) Examples of antibiotics include, for mild to moderate exacerbations: Doxycycline (Vibramycin), 100 mg twice daily Trimethoprim-sulfamethoxazole (Bactrim DS, Septra DS), one tablet twice daily Amoxicillin-clavulanate potassium (Augmentin), one 500 mg/125 mg tablet three times daily or one 875 mg/125 mg tablet twice daily

(59) Macrolides: Clarithromycin (Biaxin), 500 mg twice daily Azithromycin (Zithromax), 500 mg initially, then 250 mg daily.

(60) Fluoroquinolones: Levofloxacin (Levaquin), 500 mg daily Gatifloxacin (Tequin), 400 mg daily Moxifloxacin (Avelox), 400 mg daily.

(61) For moderate to severe exacerbations:

(62) Cephalosporins: Ceftriaxone (Rocephin), 1 to 2 g IV daily Cefotaxime (Claforan), 1 g IV every 8 to 12 hours Ceftazidime (Fortaz), 1 to 2 g IV every 8 to 12 hours

(63) Antipseudomonal Penicillins: Piperacillin-tazobactam (Zosyn), 3.375 g IV every 6 hours Ticarcillin-clavulanate potassium (Timentin), 3.1 g IV every 4 to 6 hours

(64) Fluoroquinolones: Levofloxacin, 500 mg IV daily Gatifloxacin, 400 mg IV daily

(65) Aminoglycoside: Tobramycin (Tobrex), 1 mg per kg IV every 8 to 12 hours, or 5 mg per kg IV daily

EXAMPLES

Example 1

(66) This Example describes the development of a panel of biomarkers that can distinguish AECOPD from a convalescent state.

(67) Methods

(68) Study Populations.

(69) Biomarker discovery took place in 37 patients from the previously described and studied cohort evaluating the use of zileuton in the treatment of AECOPD (LEUKO) (15). Briefly, inclusion criteria were age >45 years, admission to the hospital for AECOPD, 10 pack-years smoking history, and a forced expiratory volume in 1 second (FEV1) <60% predicted. AECOPD was defined as an acute increase in dyspnea, sputum volume, and/or sputum purulence without an alternative explanation. Plasma samples used in this analysis were collected at the beginning of the hospitalization period and at day 30. We considered the initial sample collection at hospitalization to indicate an AECOPD whereas the day 30 sample was used to indicate a convalescent state.

(70) Biomarker replication occurred in patients from two other COPD cohorts. The first cohort studied the use of etanercept or prednisone in the treatment of AECOPD (TNF-; n=81) (16); the second cohort (The Rapid Transition Program or RTP, n=109) prospectively enrolled patients hospitalized for AECOPD for the primary purpose of biomarker discovery to diagnose and track AECOPD. Inclusion criteria for the TNF-, cohort were age >35 years, AECOPD presenting to a physician or emergency department, FEV1 70% predicted, FEV1/forced vital capacity (FVC) 70%, and 10 pack-years smoking history. AECOPD was diagnosed when two of the following three criteria were met: increased dyspnea, sputum volume, and sputum purulence. Plasma samples used in this analysis were obtained at baseline and at 14 days. The baseline sample was considered to indicate an AECOPD whereas the 14 day sample was used to indicate a convalescent state. For the RTP cohort, subjects had to be 19 years of age and admitted to the hospital with an AECOPD as determined by general internists or pulmonologists. Blood samples were collected at the time of admission to the hospital (indicating an AECOPD state) and at either day 30 or day 90 following admission (indicating the convalescent state)

(71) Sample Collection.

(72) Blood samples were collected in P100 plasma tubes (BD, Franklin Lake, N.J.) and stored on ice until processing. Blood was spun down within two hours of collection and plasma was stored at 80 until selected for proteomic analysis. Patient plasma samples were analyzed using MRM-MS at the UVic Genome BC Proteomics Centre (Victoria, BC, Canada) according to methods described previously (17). There were 230 peptides measured corresponding to 129 proteins, chosen on the basis of both a literature search and from a previous untargeted iTRAQ mass spectrometry analysis on COPD patients. Further details regarding the MRM-MS process, the iTRAQ mass spectrometry analysis, and the peptides measured in this disclosure are provided in Example 2.

(73) Statistical Analyses.

(74) Pre-processing of the MRM-MS data involved several steps. All peptides that had more than 25% missing values across all samples or did not pass quality control metrics were removed. Missing values were imputed with a value half of the minimum peptide expression, for each peptide separately. Relative response of peptide abundance to stable isotopically-labeled peptide abundance were log-base 2 transformed and summarized at the protein level to create protein expression data.

(75) Biomarker discovery was performed on the protein expression data using R (www.r-project.org) and Bioconductor (www.bioconductor.org). Proteins that passed all quality control metrics were analyzed for differential expression between the patients' exacerbation and convalescent samples, using limina (limina Bioconductor package). A false discovery rate (FDR) 0.2 was used as the criterion for selecting candidate proteins. An elastic net logistic regression model (18) (glmnet R package) was applied to the list of candidate proteins to build a classifier or biomarker score, which is the aggregation of the weighted contributions (linear predictors) of each protein in the model to the presence of AECOPD:
Biomarker score=w.sub.0+w.sub.1*protein.sub.1+w.sub.2*protein.sub.2+ . . . +w.sub.N*protein.sub.N
The performance characteristics of this biomarker score were estimated using leave-pair-out cross-validation (LPOCV). The LPOCV-based biomarker scores were also used to select decision thresholds, chosen such that convalescence or exacerbation would be detected with at least 90% success and Youden's index would be optimized. The classification model and decision thresholds obtained from LEUKO were applied to TNF- and RTP data for external replication. A summary of the overall workflow is shown in FIG. 1. To determine the minimum number of proteins required to reach an AUC plateau, a stepwise AUC analysis was performed with the incremental addition of each protein to the model using data pooled from all three cohorts. Finally, enrichment analysis was performed using MetaCore (Thomson Reuters) on the biomarker proteins to determine relevant biological networks in plasma associated with AECOPD.
Results

(76) Cohort Demographics.

(77) The demographic characteristics comparing the LEUKO, TNF-, and RTP cohorts are shown in Table 1. Patients from the LEUKO and RTP cohorts were more likely to be male than patients in the TNF- cohort, while patients from the TNF- cohort were more likely to be white. On average, patients enrolled in the three cohorts had moderate-to-severe COPD by spirometry. The majority of patients were being treated with bronchodilators.

(78) Biomarker Panel Performance.

(79) After quality check and pre-processing, the protein expression data consisted of 55 proteins. Of these, 21 had differential levels between exacerbation and convalescent time points at a FDR <0.2 (Table 2). The final elastic net model consisted of 18 of these proteins (plasma serine protease inhibitor, plasma kallikrein, and insulin-like growth factor-binding protein 3 were removed to create the final model). Compared to CRP alone, the 18-protein panel demonstrated a superior receiver operating characteristic (ROC) curve for diagnosing AECOPD in the LEUKO discovery cohort (FIG. 2). The area under the curve (AUC) estimate in the LEUKO cohort was 0.70 compared to 0.60 for CRP. The AUC estimates for the replication cohorts were 0.72 and 0.72 for the TNF- and RTP cohorts, respectively.

(80) A biomarker score based on the weighted contributions of the 18 proteins to the presence of an AECOPD state was calculated for each of the cohorts. The intercept and specific protein weights contributing to the biomarker score for the 18-protein panel are listed in Table 3. Biomarker scores at each time point for the three cohorts are shown in FIG. 3. In all three cohorts, the biomarker scores at exacerbation time points were significantly greater than the biomarker scores at convalescent time points (Wilcoxon rank sum p-value=0.001 for LEUKO, <0.0001 for TNF-, and <0.0001 for RTP). As well, the biomarker scores during convalescence in the two replication cohorts were not statistically different from the convalescence biomarker scores in the LEUKO discovery cohort.

(81) A biomarker score decision threshold optimized to detect AECOPD with 90% sensitivity in the LEUKO cohort yielded sensitivities of 92%, 81%, and 98% in the LEUKO, TNF-, and RTP cohorts, respectively. Conversely, a biomarker score decision threshold optimized to detect AECOPD with 90% specificity in the LEUKO cohort yielded specificities of 92%, 100%, and 86% in the LEUKO, TNF-, and RTP cohorts, respectively

(82) Stepwise AUC Selection.

(83) Using the pooled data from all three cohorts, the 18 proteins in the biomarker panel were assembled using a stepwise AIX selection to determine incremental predictive ability with each additional protein (FIG. 4). An AUC plateau of 0.795 was achieved with 11 of the 18 proteins, suggesting that a smaller subset of the panel could potentially be used with minimal loss to the overall AUC.

(84) Process Network Analysis.

(85) Results from the process network analysis are shown in FIG. 5. The 21 differentially expressed proteins were most significantly enriched for the high density lipoprotein (HDL) cholesterol transport pathway (p-value=3.8210.sup.8). Other significant pathways included blood coagulation (p-value=8.9010.sup.5), classical and alternative complement pathways (p-value=2.1110.sup.4 and p-value=2.6710.sup.3, respectively) and cell adhesion pathways (p-value=3.3210.sup.3).

(86) Discussion

(87) In this first-ever study employing MRM-MS for biomarker verification in AECOPD, we have generated a panel of 18 proteins significantly associated with an AECOPD state with the results replicated in two separate AECOPD cohorts. The performance of this panel was a marked improvement over more commonly used measures like CRP. Biomarker scores derived from this panel were significantly elevated in AECOPD, subsequently falling during convalescent periods. For a condition with a current dearth of available biomarkers at its disposal, this panel may represent a significant step forward not only in AECOPD diagnosis but also in the recognition of AECOPD resolution at which point therapy could potentially be tapered. While the AUC estimates for this protein panel remain modest, this may simply be due to the fact that COPD exacerbations are fundamentally heterogeneous in etiology and that we currently lack a gold standard for diagnosis outside of our own clinical acumen.

(88) Whether this particular biomarker panel can also predict AECOPD severity or AECOPD-related mortality, fluctuate in accordance with disease progression during an AECOPD, or identify patients at risk for an imminent AECOPD remains to be determined, but is grounds for further prospective study. As well, transitioning this biomarker panel to a multiplexed, clinical assay for prospective study in a real-world setting is a necessary next step. While an 18-protein panel may indeed prove difficult to transition to a clinically practical platform, our pooled analysis of incremental AUC gain suggests that simplification of the 18-protein panel to a smaller number of proteins is feasible without significant loss of predictive power.

(89) The MRM-MS approach, although previously applied to numerous other disease states such as lung cancer, psoriatic arthritis, and Parkinson's disease (14, 19, 20), marks a departure from traditional methods of biomarker discovery and verification in AECOPD. Previous attempts to identify biomarkers have interrogated known proteins with already available commercial immunoassay platforms, for instance CRP, angiopoietin-2, adrenomedullin, and troponin (6, 21-24), Unfortunately, proteins without such assays available may be entirely overlooked by this strategy. The cost and time required for immunoassay development, however, can be prohibitive (25). MRM-MS can fill this gap between biomarker discovery and verification by providing a cost-effective platform that can quantify proteins with greater sensitivity and specificity than that provided by immunoassays. Moreover, the multiplexing capacity of MRM-MS confers another distinct advantage over antibody-based tests.

(90) As a result, we identified through our protein panel key biological pathways not previously associated with AECOPD pathophysiology. While inflammatory proteins like CRP were indeed differentially expressed in AECOPD, inflammatory pathways were not in fact the most significant biological networks involved, a surprising finding given the extensive attention recently focused on inflammation in the etiology of AECOPD. Instead, AECOPD were most significantly associated with the HDL cholesterol pathway, with decreases in both apolipoprotein A-I (APOA1) and apolipoprotein A-II (APOA2) observed. While the associations between AECOPD and cardiovascular comorbidities have long been recognized (8, 26, 27), the specific role that HDL plays in the development of AECOPD has not yet been established. APOA1 is the major protein structure found in HDL, making up 70% of its weight, while APOA2 accounts for approximately 20% of the HDL protein (28). Deficiencies in APOA1 can lead to low HDL levels, accelerated coronary artery disease, early onset myocardial infarctions and elevated inflammatory markers such as CRP (29). Similarly, while the function of APOA2 remains largely unknown and deficient states have yet to be fully clinically characterized, lower APOA2 levels are nonetheless observed in patients with myocardial infarctions compared to normal controls (30). That AECOPD could be associated with low HDL states or triggered by small myocardial infarctions might suggest a particular cardiac phenotype of AECOPD distinct from infectious or inflammatory etiologies that can be identified by our protein panel.

(91) Another plausible mechanism by which low APOA1 and APOA2 could lead to AECOPD might relate to their antioxidant properties. Both APOA1 and APOA2 carry paraoxonase 1 (PON1), an antioxidant and antiatherogenic enzyme that furthermore can localize to key lung compartments such as club cells and type 1 pneumocytes (31). PON1 activity is decreased in the presence of cigarette smoke (32) and patients with COPD have lower serum levels of PON1 compared to healthy subjects (33). Low APOA1 and APOA2 levels could potentially aggravate an already PON1-deficient state, rendering the lung acutely vulnerable to further oxidative stresses. Although purely speculative at this time, this could hypothetically be the trigger for an AECOPD. Nonetheless, evidence in the literature is still conflicting regarding HDL and COPD. For instance, one study has found that higher, not lower, HDL levels are associated with worse airflow obstruction and greater emphysema (34). On the other hand, a recent investigation of serum from atopic asthmatic subjects revealed that both HDL and APOA1 levels are positively correlated with FEV1 (35). Future studies clarifying the role of HDL and HDL-related proteins in the pathogenesis of AECOPD and other diseases of the airways are clearly warranted.

(92) There were several limitations to our study. First, the three cohorts utilized for biomarker discovery and verification were fundamentally different in terms of baseline demographic markers like age, sex, and lung function. Therefore, the protein panel discovered in the LEUKO cohort may have actually performed better had the subjects in the verification cohorts aligned more similarly with the discovery cohort. However, this study demonstrates that the biomarker panel can likely be applied across a wide variety of COPD phenotypes with consistent results. Secondly, the MRM-MS approach is limited by the list of peptides initially chosen for analysis. In this sense, it relies completely on an a priori assessment and cannot as such be considered a truly comprehensive evaluation of all possible biomarkers. In the present study, we conducted a hypothesis-free, unbiased proteomics experiment using iTRAQ which informed the choice of peptides that were interrogated with MRM-MS. Nevertheless, given the limitations of iTRAQ and other unbiased proteomics platforms currently available, almost certainly there are as yet undiscovered proteins that are likely to play a significant role in AECOPD. Finally, the performance of the protein panel in clinical states that can often be confused with AECOPD, such as congestive heart failure exacerbations, pneumonia, and pulmonary embolus, is unknown but would be critical in determining its ultimate use in undifferentiated patients presenting with non-specific symptoms such as dyspnea. It should be noted that we applied the 18-protein biomarker panel to a cohort of stable congestive heart failure patients and to a cohort of healthy controls, the resulting biomarker scores were equivalent to those of non-exacerbating COPD patients (see FIG. 6).

(93) In summary, we demonstrate for the first time the application of the MRM-MS platform to biomarker discovery in the diagnosis of AECOPD. Not only could this panel distinguish AECOPD from the convalescent COPD state in multiple, independent cohorts, but it also revealed potential novel mechanisms for AECOPD by implicating HDL cholesterol pathways previously unreported in the AECOPD literature.

REFERENCES

(94) 1. Donaldson G C, Seemungal T A, Bhowmik A, Wedzicha J A. Relationship between exacerbation frequency and lung function decline in chronic obstructive pulmonary disease. Thorax 2002; 57: 847-852. 2. Seemungal T A, Donaldson G C, Paul E A, Bestall J C, Jeffries D J, Wedzicha J A. Effect of exacerbation on quality of life in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med 1998; 157: 1418-1422. 3. Soler-Cataluna J J, Martinez-Garcia M A, Roman Sanchez P, Salcedo E, Navarro M, Ochando R. Severe acute exacerbations and mortality in patients with chronic obstructive pulmonary disease. Thorax 2005; 60: 925-931. 4. Chronic obstructive pulmonary disease (COPD) fact sheet. Chicago, Ill.: American Lung Association, 2014. (Accessed Oct. 4, 2014) 5. Celli B R, MacNee W. Standards for the diagnosis and treatment of patients with COPD: a summary of the ATS/ERS position paper. Eur Respir J 2004; 23: 932-946. 6. Hurst J R, Donaldson G C, Perera W R, Wilkinson T M, Bilello J A, Hagan G W, Vessey R S, Wedzicha J A. Use of plasma biomarkers at exacerbation of chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2006; 174: 867-874. 7. Bozinovski 5, Hutchinson A, Thompson M, Macgregor L, Black J, Giannakis E, Karlsson A S, Silvestrini R, Sinallwood D, Vlahos R, Irving L B, Anderson G P. Serum amyloid a is a biomarker of acute exacerbations of chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2008; 177: 269-278. 8. Patel A R, Kowlessar B S, Donaldson G C, Mackay A J, Singh R, George S N, Garcha D S, Wedzicha J A, Hurst J R. Cardiovascular risk, myocardial injury, and exacerbations of chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2013; 188: 1091-1099. 9. Seemungal T, Harper-Owen R, Bhowmik A, Moric I, Sanderson G, Message S, Maccallum P, Meade T W, Jeffries D J, Johnston S L, Wedzicha J A. Respiratory viruses, symptoms, and inflammatory markers in acute exacerbations and stable chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2001; 164: 1618-1623, 10. Peacock J L, Anderson H R, Bremner S A, Marston L, Seemungal T A, Strachan D P, Wedzicha J A. Outdoor air pollution and respiratory health in patients with COPD. Thorax 2011; 66: 591-596. 11. De Serres G, Lampron N, La Forge J, Rouleau I, Bourbeau J, Weiss K, Barret B, Boivin G. Importance of viral and bacterial infections in chronic obstructive pulmonary disease exacerbations. J Clin Virol 2009; 46: 129-133. 12. Meng Z, Veenstra T D. Targeted mass spectrometry approaches for protein biomarker verification. J Proteomics 2011; 74: 2650-2659. 13. Method of the Year 2012. Nat Methods 2012; 10: 1. 14. Chen C D, Wang C L, Yu C J, Chien K Y, Chen Y T, Chen M C, Chang Y S, Wu C C, Yu J S. Targeted proteomics pipeline reveals potential biomarkers for the diagnosis of metastatic lung cancer in pleural effusion. J Proteome Res 2014; 13: 2818-2829. 15. Woodruff P G, Albert R K, Bailey W C, Casaburi R, Connett J E, Cooper J A, Jr., Criner G J, Curtis J L, Dransfield M T, Han M K, Hamden S M, Kim V, Marchetti N, Martinez F J, McEvoy C E, Niewoehner D E, Reilly J J, Rice K, Scanlon P D, Scharf S M, Sciurba F C, Washko G R, Lazarus S C. Randomized trial of zileuton for treatment of COPD exacerbations requiring hospitalization. COPD 2011; 8: 21-29. 16. Aaron S D, Vandemheen K L, Maltais F, Field S K, Sin D D, Bourbeau J, Marciniuk D D, FitzGerald J M, Nair P, Mallick R. TNFalpha antagonists for acute exacerbations of COPD: a randomised double-blind controlled trial. Thorax 2013; 68: 142-148. 17. Percy A J, Chambers A G, Yang J, Jackson A M, Domanski D, Burkhart J, Sickmann A, Borchers C H. Method and platform standardization in MRM-based quantitative plasma proteomics. J Proteomics 2013; 95: 66-76. 18. Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Series B 2005; 67: 301-320. 19. Ademowo O S, Hernandez B, Collins E, Rooney C, Fearon U, van Kuijk A W, Tak P P, Gerlag D M, FitzGerald O, Pennington S R. Discovery and confirmation of a protein biomarker panel with potential to predict response to biological therapy in psoriatic arthritis. Ann Rheum Dis 2014 Sep. 3 [Epub ahead of print]. 20. Alberio T, McMahon K, Cuccurullo M, Gethings L A, Lawless C, Zibetti M, Lopiano L, Vissers J P, Fasano M. Verification of a Parkinson's disease protein signature in T-lymphocytes by multiple reaction monitoring. J Proteome Res 2014; 13: 3554-3561. 21. Meng D Q, Li X J, Song X Y, Xin J B, Yang W B. Diagnostic and Prognostic Value of Plasma Adrenomedullin in COPD Exacerbation. Respir Care 2014; 59:1542-9. 22. Wedzicha J A, Seemungal T A, MacCallum P K, Paul E A, Donaldson G C, Bhowmik A, Jeffries D J, Meade T W. Acute exacerbations of chronic obstructive pulmonary disease are accompanied by elevations of plasma fibrinogen and serum IL-6 levels. Thromb Haemost 2000; 84: 210-215. 23. Nikolakopoulou S, Hillas G, Perrea D, Tentolouris N, Loukides S, Kostikas K, Simoes D, Georgakopoulou E, Vogiatzakis E, Veldekis D, Bakakos P. Serum angiopoietin-2 and CRP levels during COPD exacerbations. COPD 2014; 11: 46-51. 24. Soyseth V, Bhatnagar R, Holmedahl N H, Neukamm A, Hoiseth A D, Hagve T A, Einvik G, Omland T. Acute exacerbation of COPD is associated with fourfold elevation of cardiac troponin T. Heart 2013; 99: 122-126. 25. Issaq H J, Veenstra T D. Would you prefer multiple reaction monitoring or antibodies with your biomarker validation? Expert Rev Proteomics 2008; 5: 761-763. 26. Donaldson G C, Hurst J R, Smith C J, Hubbard R B, Wedzicha J A. Increased risk of myocardial infarction and stroke following exacerbation of COPD. Chest 2010; 137: 1091-1097. 27. McAllister D A, Maclay J D, Mills N L, Leitch A, Reid P, Carruthers R, O'Connor J, McAlpine L, Chalmers G, Newby D E, Clark E, Macfarlane P W, Macnee W. Diagnosis of myocardial infarction following hospitalisation for exacerbation of COPD. Eur Respir J 2012; 39: 1097-1103. 28. Maiga S F, Kalopissis A D, Chabert M. Apolipoprotein A-II is a key regulatory factor of HDL metabolism as appears from studies with transgenic animals and clinical outcomes. Biochimie 2014; 96: 56-66. 29. Al-Sarraf A, Al-Ghofaili K, Sullivan D R, Wasan K M, Hegele R, Frohlich J. Complete Apo AI deficiency in an Iraqi Mandaean family: case studies and review of the literature. J Clin Lipidol 2010; 4: 420-426. 30. Buring J E, O'Connor G T, Goldhaber S Z, Rosner B, Herbert P N, Blum C B, Breslow J L, Hennekens C H. Decreased HDL2 and HDL3 cholesterol, Apo A-I and Apo A-II, and increased risk of myocardial infarction. Circulation 1992; 85: 22-29. 31. Rodrigo L, Hernandez A F, Lopez-Caballero J J, Gil F, Pla A. Immunohistochemical evidence for the expression and induction of paraoxonase in rat liver, kidney, lung and brain tissue. Implications for its physiological role. Chem Biol Interact 2001; 137: 123-137. 32. Isik B, Ceylan A, Isik R. Oxidative stress in smokers and non-smokers. Inhal Toxicol 2007; 19: 767-769. 33. Rumora L, Rajkovic M G, Kopcinovic L M, Pancirov D, Cepelak I, Grubisic T Z. Paraoxonase 1 activity in patients with chronic obstructive pulmonary disease. COPD 2014; 11: 539-545. 34. Burkart K M, Manichaikul A, Wilk J B, Ahmed F S, Burke G L, Enright P, Hansel N N, Haynes D, Heckbert S R, Hoffman E A, Kaufman J D, Kurai J, Loehr L, London S J, Meng Y, O'Connor G T, Oelsner E, Petrini M, Pottinger T D, Powell C A, Redline S, Rotter J I, Smith L J, Soler Artigas M, Tobin M D, Tsai M Y, Watson K, White W, Young T R, Rich S S, Barr R G. APOM and high-density lipoprotein cholesterol are associated with lung function and percent emphysema. Eur Respir J 2014; 43: 1003-1017. 35. Barochia A V, Kaler M, Cuento R A, Gordon E M, Weir N A, Sampson M, Fontana J R, MacDonald S, Moss J, Manganiello V, Remaley A T, Levine S J. Serum Apolipoprotein A-I and Large HDL Particles are Positively Correlated with FEV in Atopic Asthma. Am J Respir Crit Care Med 2015 Feb. 18 [Epub ahead of print].

(95) TABLE-US-00001 TABLE 1 Demographic Data for LEUKO, TNF-, and RTP Cohorts LEUKO TNF- RTP p- Characteristic (n = 37) (n = 81) (n = 109) value* Age (years) 62.11 8.19 67.06 9.28 67.79 10.54 0.009 Male (%) 56.76 37.04 63.30 0.001 BMI (kg/m.sup.2) 27.04 5.65 26.56 7.14 27.37 6.88 0.852 White Race (%) 59.46 98.77 82.41 <0.001 Smoking Status <0.001 Current (%) 29.73 23.46 52.29 Former (%) 70.27 70.37 33.94 Smoking 47.86 28.02 47.85 28.23 53.39 36.05 0.476 pack-years FEV1 (L) 1.00 0.62 0.94 0.47 1.66 0.85 <0.001 (Exacerbation) FEV1 31.92 15.27 34.41 13.87 57.19 20.11 <0.001 (% Predicted) (Exacerbation) FVC (L) 2.35 0.93 2.33 1.00 2.98 1.16 0.007 (Exacerbation) FVC N/A 66.65 20.88 81.28 19.28 0.001 (% Predicted) (Exacerbation) FEV1/FVC (%) 41.92 11.61 40.78 13.14 55.52 13.82 <0.001 (Exacerbation) FEV1 (L) 1.00 0.58 N/A 1.34 0.60 0.074 (Convalescence) FEV1 34.79 17.39 N/A 49.64 16.19 0.009 (% Predicted) (Convalescence) FVC (L) 2.36 0.86 N/A 2.67 0.79 0.252 (Convalescence) FVC 63.28 20.07 N/A 76.21 18.49 0.044 (% Predicted) (Convalescence) FEV1/FVC (%) 41.83 15.83 N/A 51.47 16.47 0.064 (Convalescence) Bronchodilator 94.59 100 95.42 0.134 Use (%) Inhaled 67.57 95.00 44.95 <0.001 Corticosteroid Use (%) Abbreviations: BMI body mass index; FEV1 forced expiratory volume in 1 second; FVC forced capacity; N/A: not available *P-values were generated using an ANOVA test for continuous variables and chi-square tests for categorical variables.

(96) TABLE-US-00002 TABLE2 SignificantProteinsDifferentlyExpressedinAECOPDComparedtotheConvalescentState Direction AECOPD UniProt Gene Fold Relativeto Peptide ProteinName ID Symbol P-value FDR Change Convalescence TAAQNLYEK Apolipoprotein P02655 APOC2 0.0001 0.0068 1.20 down (SEQIDNO:1) C-II (SEQID NO:22) IAPQLSTEELVSLGEK Afamin P43652 AFM 0.0004 0.0100 1.24 down (SEQIDNO:2) (SEQID NO:23) ATEHLSTLSEK Apolipoprotein P02647 APOA1 0.0012 0.0181 1.12 down (SEQIDNO:3) A-I (SEQID NO:24) YWGVASFLQK Retinol-binding P02753 RBP4 0.0015 0.0181 1.33 down (SEQIDNO:4) protein4 (SEQID NO:25) SPELQAEAK Apolipoprotein P02652 APOA2 0.0016 0.0181 1.21 down (SEQIDNO:5) A-II (SEQID NO:26) ATVVYQGER Beta-2- P02749 APOH 0.0032 0.0292 1.21 down (SEQIDNO:6) glycoprotein1 (SEQID NO:27) AFIQLWAFDAVK ProteinAMBP P02760 AMBP 0.0040 0.0313 1.18 down (SEQIDNO:7) (SEQID NO:28) TVQAVLTVPK Pigmentepithelium- P36955 SERPINF1 0.0046 0.0313 1.16 down (SEQIDNO:8) derivedfactor (SEQID NO:29) GSPAINVAVHVFR Transthyretin P02766 TTR 0.0070 0.0345 1.31 down (SEQIDNO:9) (SEQID NO:30) AVVEVDESGTR Plasmaserine P05154 SERPINA5 0.0070 0.0345 1.26 down (SEQIDNO:10) proteaseinhibitor (SEQID NO:31) GFVVAGPSR Complement P13671 C6 0.0074 0.0345 1.01 up (SEQIDNO:11) componentC6 (SEQID NO:32) SVNDLYIQK Heparincofactor2 P05546 SERPIND1 0.0075 0.0345 1.21 down (SEQIDNO:12) (SEQID NO:33) VVEESELAR Complement P02748 C9 0.0087 0.0367 1.12 up (SEQIDNO:13) componentC9 (SEQID NO:34) FLHVPDTFEGHFDGVPVISK Inter-alphatrypsin P19823 ITIH2 0.0113 0.0444 1.11 down (SEQIDNO:14) inhibitorheavy (SEQID chainH2 NO:35) AFVFPK C-reactiveprotein P02741 CRP 0.0124 0.0454 1.29 up (SEQIDNO:15) (SEQID NO:36) DGYLFQLLR Histindine-rich P04196 HRG 0.0140 0.0475 1.08 down (SEQIDNO:16) glycoprotein (SEQID NO:37) IQVYSR Beta-2- P61769 B2M 0.0147 0.0475 1.19 down (SEQIDNO:17) microglobulin (SEQID NO:38) TGAQELLR Gelsolin P06396 GSN 0.0169 0.0517 1.04 down (SEQIDNO:18) (SEQID NO:39) VSEGNHDIALIK Plasmakallirein P03952 KLKB1 0.0264 0.0732 1.18 down (SEQIDNO:19) (SEQID NO:40) FLNVLSPR Insulin-likegrowth P19736 IGFBP3 0.0266 0.0732 1.21 down (SEQIDNO:20) factor-binding (SEQID protein3 NO:41) NFPSPVDAAFR Hemopexin P02790 HPX 0.0583 0.1526 1.19 down (SEQIDNO:21) (SEQID NO:42)

(97) TABLE-US-00003 TABLE 3 Biomarker Score Intercept and Specific Protein Weights Biomarker score = w.sub.0 + w.sub.1 .Math. protein.sub.1 + w.sub.2 .Math. protein.sub.2 + . . . + w.sub.N .Math. protein.sub.N Intercept/Protein w Intercept (w.sub.0) 0.805 Inter-alpha-trypsin inhibitor heavy chain H2 1.349 Heparin cofactor 2 1.313 Apolipoprotein A-I 1.311 Pigment epithelium-derived factor 1.274 Apolipoprotein C-II 0.693 Hemopexin 0.407 Beta-2-microglobulin 0.297 C-reactive protein 0.143 Gelsolin 0.046 Beta-2-glycoprotein 1 0.033 Afamin 0.021 Histidine-rich glycoprotein 0.001 Retinol-binding protein 4 0.057 Transthyretin 0.261 Apolipoprotein A-II 0.494 Complement component C9 0.872 Protein AMBP 0.947 Complement component C6 2.591

Example 2

(98) This example provides additional details regarding the methods used in Example 1.

(99) Multiple Reaction Monitoring (MRM)-Mass Spectrometry (MS) Methods

(100) In analytical chemistry, MS is able to identify the chemical composition of a sample by determining the mass-to-charge ratio of analyte ions. Further fragmentation of analyte ions by collision-induced dissociation (tandem MS) allows for protein identification and quantification. Stable isotopes standards (SIS) such as .sup.13C, .sup.15N, and .sup.18O are used as internal standards for the quantification step, in which the relative peak height or peak area of the analyte is compared to the stable isotope-labeled standard. MRM-MS achieves additional specificity, however, by monitoring a precursor ion and one of its collision-induced dissociation-generated product ions while still retaining the precursor and product ions of the stable isotope standard for quantification.

(101) MRM Assay Development

(102) Methods for MRM assay development have been previously described (1). First, to identify peptide sequences corresponding to the target protein, a BLAST (Basic Local Assignment Search Tool) search is performed with the goal peptide length between 5 and 25 amino acids. Up to 8 candidate peptides per protein are generated with the list further narrowed based on solubility and liquid chromatography (LC) retention time. SIS versions of the peptides selected are then made. SIS peptides are purified using high-performance LC. The concentration of the synthetic peptide is determined by acid hydrolysis and amino acid analysis. A final SIS mixture is generated by ensuring that the concentration of the SIS peptide is equivalent to the concentration in normal plasma.

(103) Target Protein Candidates

(104) 230 peptides corresponding to 129 proteins were chosen for this study (see Table 4 for the full list). These were chosen based on a literature search and from a previous mass spectrometry analysis on COPD patients enrolled in the Evaluation of COPD Longitudinally to Identify Predict Surrogate Endpoints (ECLIPSE) cohort (GSK Study No. SCO104960, ClinicalTrials.gov NCT00292552) (2).

(105) In the latter analysis, untargeted proteomics with 8-plex isobaric tags for relative and absolute quantification (iTRAQ) was performed on plasma from 300 subjects. iTRAQ analysis was performed in five phases: plasma depletion, trypsin digestion and iTRAQ labeling, high pH reversed phase fractionation, liquid chromatography (LC)-mass spectrometry (MS), and MS data analysis. The 14 most abundant plasma proteins were depleted using a custom-made 5 mL avian immunoaffinity column (Genway Biotech, San Diego, Calif., USA). Samples were digested with sequencing grade modified trypsin (Promega, Madison, Wis., USA) and labeled with iTRAQ reagents 113, 114, 115, 116, 117, 118, 119, and 121 according to the manufacturer's protocol (Applied Biosystems, Foster City, Calif., USA). Each iTRAQ set consisted of seven patient samples and one pool of the patient samples. The reference was randomly assigned to one of the iTRAQ labels. The study samples were randomized to the remaining seven iTRAQ labels by balancing phenotypes between the 43 iTRAQ sets.

(106) High pH reversed phase fractionation was performed with an Agilent 1260 (Agilent, Calif., USA) equipped with an XBridge C18 BEH300 (Waters, Mass., USA) 250 mm4.6 mm, 5 um, 300A HPLC column. The peptide solution was separated by on-line reversed phase liquid chromatography using a Thermo Scientific EASY-nanoLC II system with a reversed-phase pre-column Magic C-18AQ (Michrom BioResources Inc, Auburn, Calif.) and a reversed-phase nano-analytical column packed with Magic C-18AQ (Michrom BioResources Inc, Auburn, Calif.), at a flow rate of 300 nl/min. The chromatography system was coupled on-line to an LTQ Orbitrap Velos mass spectrometer equipped with a Nanospray Flex source (Thermo Fisher Scientific, Bremen, Germany). All data was analyzed using ProteinPilot Software 3.0 (AB SCIEX, Framingham, Mass.) and were searched against the Uniprot, version 072010, human database.

(107) A total of 981 proteins were detected in at least one sample. Of these, 84 passed our pre-filtering rule, i.e. to be present in at least 75% of samples. We then compared subjects who had frequent exacerbation (at least 2 exacerbations per year for two years) with those who did not (no exacerbation for two years after blood collection), by means of limma, which identified 43 statistically significant proteins (see Table 4).

(108) MRM-MS Assay

(109) Solution and Sample Preparation

(110) The plasma proteolytic digests were prepared manually as previously described (3). In brief, this involved denaturing, reducing, alkylating, and quenching 10-fold diluted plasma (30 l) with 1% sodium deoxycholate (30 L at 10%), 5 mM tris(2-carboxyethyl) phosphine (26.1 L at 50 mM), 10 mM iodoacetamide (29 L at 100 mM), and 10 mM dithiothreitol (29 L at 100 mM; all prepared in 25 mM ammonium bicarbonate), respectively. The protein denaturation and Cys-Cys reduction steps occurred simultaneously for 30 min at 60 C., while Cys alkylation and iodoacetamide quenching followed sequentially for 30 min at 37 C. Thereafter, proteolysis was initiated with the addition of TPCK-treated trypsin (10.5 L at 0.8 mg/mL; Worthington) at a 25:1 substrate:enzyme ratio. After overnight incubation at 37 C., proteolysis was arrested by the sequential addition of a chilled SIS peptide mixture (30 L, fmol/L for the samples) and a chilled FA solution (52.5 L of 1.9%) to a digest aliquot (117.50 L). The acid insoluble surfactant was then pelleted by centrifugation and 133.3 L of each peptide supernatant was removed for solid phase extraction (Oasis HLB pElution Plate 30 m). Following concentration, the eluates were lyophilized to dryness and rehydrated in 50 L of 0.1% FA (final concentration: 1 g/L) for LC-MRM/MS analysis.

(111) LC-MRM/MS Equipment and Conditions

(112) Ten L injections of the plasma digests were separated with a Zorbax Eclipse Plus RP-UHPLC column (2.1150 mm, 1.8 m particle diameter; Agilent) that was contained within a 1290 Infinity system (Agilent). Peptide separations were achieved at 0.4 mL/min over a 43 min run, via a multi-step LC gradient (1.5-81% mobile phase B; mobile phase compositions: A was 0.1% FA in H.sub.2O while B was 0.1% FA in ACN). The exact gradient was as follows (time in min, B): 0, 1.5%; 1.5, 6.3%; 16, 13.5%; 18, 13.77%; 33, 22.5%; 38, 40.5%; 39, 81%; 42.9, 81%; 43, 1.5%. The column and autosampler were maintained at 50 C. and 4 C., respectively. A post-column equilibration of 4 min was used after each sample analysis. Each individual sample was run in singleton.

(113) The LC system was interfaced to a triple quadrupole mass spectrometer (Agilent 6490) via a standard-flow ESI source, operated in the positive ion mode. The general MRM acquisition parameters employed were as follows: 3.5 kV capillary voltage, 300 V nozzle voltage, 11 L/min sheath gas flow at a temperature of 250 C., 15 L/min drying gas flow at a temperature of 150 C., 30 psi nebulizer gas pressure, 380 V fragmentor voltage, 5 V cell accelerator potential, and unit mass resolution in the quadrupole mass analyzers. Specific LC-MS acquisition parameters were employed for optimal peptide ionization/fragmentation and scheduled MRM. Note that the peptide optimizations were empirically optimized previously by direct infusion of the purified SIS peptides.

(114) Protein Quantitation

(115) The MRM data was processed with MassHunter Quantitative Analysis software (Agilent), for verification of peak selection and integration.

REFERENCES

(116) 1. Cohen Freue G V, Borchers C H. Multiple reaction monitoring (MRM): principles and application to coronary artery disease. Circ-Cardiovasc Gene 2012; 5: 378. 2. Vestbo J, Anderson W, Coxson H O, Crim C, Dawber F, Edwards L, Hagan G, Knobil K, Lomas D A, MacNee W, Silverman E K, Tal-Singer R. Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE). Eur Respir J 2008; 31: 869-873. 3. Percy A J, Chambers A G, Yang J, Hardie D B, Borchers C H. Advances in multiplexed MRM-based protein biomarker quantitation toward clinical utility. Biochim Biophys Acta 2014; 1844: 917-926.

(117) TABLE-US-00004 TABLE4 PeptidesandCorrespondingProteins *DenotespeptidesdiscoveredfromapreviousuntargetediTRAQmassspectrometry analysisperformedontheECLIPSEcohort. Accession Gene Peptide ProteinName Number Symbol IDAVYEAPQEEK(SEQIDNO: 72kDatypeIVcollagenase P08253 MMP2 43) IIGYTPDLDPETVDDAFAR(SEQ 72kDatypeIVcollagenase P08253 MMP2 IDNO:44) DSYVGDEAQSK(SEQIDNO: Actin,alphacardiacmuscle1 P68032 ACTC 45) SYELPDGQVITIGNER(SEQID Actin,alphacardiacmuscle1 P68032 ACTC NO:46) TSLGSDSSTQAK(SEQIDNO: Adenylatecyclasetype9 O60503 ADCY9 47) GDIGETGVPGAEGPR(SEQID Adiponectin Q15848 ADIPO NO:48) IFYNQQNHYDGSTGK(SEQID Adiponectin Q15848 ADIPO NO:49) IAPQLSTEELVSLGEK(SEQID Afamin P43652 AFAM NO:50) LPNNVLQEK(SEQIDNO:51) Afamin P43652 AFAM AVLDVFEEGTEASAATAVK Alpha-1-antichymotrypsin P01011 AACT (SEQIDNO:52) NLAVSQVVHK(SEQIDNO:53) Alpha-1-antichymotrypsin P01011 AACT ITPNLAEFAFSLYR(SEQIDNO: Alpha-1-antitrypsin P01009 A1AT 54) LSITGTYDLK(SEQIDNO:55) Alpha-1-antitrypsin P01009 A1AT ATWSGAVLAGR(SEQIDNO: Alpha-1B-glycoprotein P04217 A1BG 56) LETPDFQLFK(SEQIDNO:7) Alpha-1B-glycoprotein P04217 A1BG DFLQSLK*(SEQIDNO:58) Alpha-2-antiplasmin P08697 A2AP LGNQEPGGQTALK*(SEQID Alpha-2-antiplasmin P08697 A2AP NO:59) APHGPGLIYR*(SEQIDNO:60) Alpha-2-HS-glycoprotein P02765 FETUA HTLNQIDEVK*(SEQIDNO:61) Alpha-2-HS-glycoprotein P02765 FETUA LLIYAVLPTGDVIGDSAK(SEQ Alpha-2-macroglobulin P01023 A2MG IDNO:62) TEHPFTVEEFVLPK(SEQIDNO: Alpha-2-macroglobulin P01023 A2MG 63) ALQDQLVLVAAK*(SEQIDNO: Angiotensinogen P01019 ANGT 64) DDLYVSDAFHK*(SEQIDNO: Antithrombin-III P01008 ANT3 65) FATTFYQHLADSK*(SEQIDNO: Antithrombin-III P01008 ANT3 66) ATEHLSTLSEK(SEQIDNO:67) ApolipoproteinA-I P02647 APOA1 SPELQAEAK(SEQIDNO:68) ApolipoproteinA-II P02652 APOA2 SLAPYAQDTQEK(SEQIDNO: ApolipoproteinA-IV P06727 APOA4 69) FPEVDVLTK(SEQIDNO:70) ApolipoproteinB-100 P04114 APOB ILGEELGFASLHDLQLLGK(SEQ ApolipoproteinB-100 P04114 APOB IDNO:71) TAAQNLYEK*(SEQIDNO:72) ApolipoproteinC-II P02655 APOC2 TYLPAVDEK*(SEQIDNO:73) ApolipoproteinC-II P02655 APOC2 FSEFWDLDPEVR(SEQIDNO: ApolipoproteinC-III P02656 APOC3 74) GWVTDGFSSLK(SEQIDNO: ApolipoproteinC-III P02656 APOC3 75) VTEPISAESGEQVER(SEQID ApolipoproteinL1 O14791 APOL1 NO:76) WWTQAQAHDLVIK(SEQIDNO: ApolipoproteinL1 O14791 APOL1 77) FVTVQTISGTGALR(SEQIDNO: Aspartateaminotransferase, P00505 AATM 78) mitochondria! ATVVYQGER(SEQIDNO:79) Beta-2-glycoprotein1 P02749 APOH PDNGFVNYPAKPTLYYK(SEQ Beta-2-glycoprotein1 P02749 APOH IDNO:80) IQVYSR(SEQIDNO:81) Beta-2-microglobulin P61769 B2MG VNHVTLSQPK(SEQIDNO:82) Beta-2-microglobulin P61769 B2MG ALEQDLPVNIK*(SEQIDNO:83) Beta-Ala-Hisdipeptidase Q96KN2 CNDP1 SVVLIPLGAVDDGEHSQNEK* Beta-Ala-Hisdipeptidase Q96KN2 CNDP1 (SEQIDNO:84) AFVFPK(SEQIDNO:85) C-reactiveprotein P02741 CRP ESDTSYVSLK(SEQIDNO:86) C-reactiveprotein P02741 CRP EDVYVVGTVLR*(SEQIDNO: C4b-bindingproteinalphachain P04003 C4BPA 87) LSLEIEQLELQR*(SEQIDNO: C4b-bindingproteinalphachain P04003 C4BPA 88) YEIVVEAR(SEQIDNO:89) Cadherin-5 P33151 CADH5 YTFVVPEDTR(SEQIDNO:90) Cadherin-5 P33151 CADH5 ESISVSSEQLAQFR(SEQIDNO: Carbonicanhydrase1 P00915 CAH1 91) VLDALQAIK(SEQIDNO:92) Carbonicanhydrase1 P00915 CAH1 EALIQFLEQVHQGIK*(SEQID CarboxypeptidaseNcatalytic P15169 CBPN NO:93) chain IVQLIQDTR*(SEQIDNO:94) CarboxypeptidaseNcatalytic P15169 CBPN chain LVGGLHR(SEQIDNO:95) CD5antigen-like O43866 CD5L GAYPLSIEPIGVR(SEQIDNO: Ceruloplasmin P00450 CERU 96) IYHSHIDAPK(SEQIDNO:97) Ceruloplasmin P00450 CERU ELDESLQVAER*(SEQIDNO: Clusterin P10909 CLUS 98) EPQDTYHYLPFSLPHR*(SEQID Clusterin P10909 CLUS NO:99) LANLTQGEDQYYLR*(SEQID Clusterin P10909 CLUS NO:100) SALVLQYLR*(SEQIDNO:101) CoagulationfactorIX P00740 FA9 VSVSQTSK*(SEQIDNO:102) CoagulationfactorIX P00740 FA9 AEVDDVIQVR(SEQIDNO:103) CoagulationfactorV P12259 FAS VAQVIIPSTYVPGTTNHDIALLR CoagulationfactorVII P08709 FA7 (SEQIDNO:104) VSQYIEWLQK(SEQIDNO:105) CoagulationfactorVII P08709 FA7 NLFLTNLDNLHENNTHNQEK CoagulationfactorVIII P00451 FA8 (SEQIDNO:106) ETYDFDIAVLR*(SEQIDNO: CoagulationfactorX P00742 FA10 107) TGIVSGFGR*(SEQIDNO:108) CoagulationfactorX P00742 FA10 LHEAFSPVSYQHDLALLR(SEQ CoagulationfactorXII P00748 FA12 IDNO:109) VVGGLVALR(SEQIDNO:110) CoagulationfactorXII P00748 FA12 AVPPNNSNAAEDDPTVEQGV CoagulationfactorXIIIAchain P00488 F13A VPR(SEQIDNO:111) SIVLTIPEIIIK(SEQID CoagulationfactorXIIIAchain P00488 F13A NO:112) IQTHSTTYR(SEQIDNO:113) CoagulationfactorXIIIBchain P05160 F13B LIENGYFHPVK(SEQIDNO: CoagulationfactorXIIIBchain P05160 F13B 114) PAFSAIR*(SEQIDNO:115) ComplementC1qsubcomponent P02745 C1QA subunitA SLGFCDTTNK*(SEQIDNO: ComplementC1qsubcomponent P02745 C1QA 116) subunitA FQSVFTVTR*(SEQIDNO:117) ComplementC1qsubcomponent P02747 C1QC subunitC TNQVNSGGVLLR*(SEQIDNO: ComplementC1qsubcomponent P02747 C1QC 118) subunitC GLTLHLK*(SEQIDNO:119) ComplementC1rsubcomponent P00736 C1R GYGFYTK*(SEQIDNO:120) ComplementC1rsubcomponent P00736 C1R SYPPDLR*(SEQIDNO:121) ComplementC1rsubcomponent P00736 C1R TLDEFTIIQNLQPQYQFR*(SEQ ComplementC1rsubcomponent P00736 C1R IDNO:122) VSVHPDYR*(SEQIDNO:123) ComplementC1rsubcomponent P00736 C1R SDFSNEER*(SEQIDNO:124) ComplementC1ssubcomponent P09871 C1S TNFDNDIALVR*(SEQIDNO: ComplementC1ssubcomponent P09871 C1S 125) DHENELLNK(SEQIDNO:126) ComplementC2 P06681 CO2 HAFILQDTK(SEQIDNO:127) ComplementC2 P06681 CO2 TGLQEVEVK(SEQIDNO:128) ComplementC3 P01024 CO3 DHAVDLIQK(SEQIDNO:129) ComplementC4-A P0C0L4 CO4A VGDTLNLNLR(SEQIDNO:130) ComplementC4-A P0C0L4 CO4A VLSLAQEQVGGSPEK(SEQID ComplementC4-A P0C0L4 CO4A NO:131) GFVVAGPSR(SEQIDNO:132) ComplementcomponentC6 P13671 CO6 ELSHLPSLYDYSAYR(SEQID ComplementcomponentC7 P10643 CO7 NO:133) LIDQYGTHYLQSGSLGGEYR ComplementcomponentC7 P10643 CO7 (SEQIDNO:134) SYTSHTNEIHK(SEQIDNO:135) ComplementcomponentC7 P10643 CO7 SLPVSDSVLSGFEQR(SEQID ComplementcomponentC8gamma P07360 CO8G NO:136) chain VQEAHLTEDQIFYFPK(SEQID ComplementcomponentC8gamma P07360 CO8G NO:137) chain LSPIYNLVPVK(SEQIDNO:138) ComplementcomponentC9 P02748 CO9 VVEESELAR(SEQIDNO:139) ComplementcomponentC9 P02748 CO9 EELLPAQDIK(SEQIDNO:140) ComplementfactorB P00751 CFAB THHDGAITER(SEQIDNO:141) ComplementfactorD P00746 CFAD SSNLIILEEHLK*(SEQIDNO: ComplementfactorH P08603 CFAH 142) SSQESYAHGTK*(SEQIDNO: ComplementfactorH P08603 CFAH 143) HGNTDSEGIVEVK*(SEQIDNO: ComplementfactorI P05156 CFAI 144) IVIEYVDR*(SEQIDNO:145) ComplementfactorI P05156 CFAI AQLLQGLGFNLTER(SEQID Corticosteroid-bindingglobulin P08185 CBG NO:146) HLVALSPK(SEQIDNO:147) Corticosteroid-bindingglobulin P08185 CBG TLDEILQEK(SEQIDNO:148) Cyclin-dependentkinase11A Q9UQ88 CD11A TSNLLLSHAGILK(SEQIDNO: 149) Cyclin-dependentkinase11A Q9UQ88 CD11A ALDFAVGEYNK(SEQIDNO: Cystatin-C P01034 CYTC 150) ALQVVR(SEQIDNO:151) Cystatin-C P01034 CYTC ELPSLQHPNEQK(SEQIDNO: Extracellularmatrixprotein1 Q16610 ECM1 152) NVALVSGDTENAK(SEQIDNO: Extracellularmatrixprotein1 Q16610 ECM1 153) LVVLPFPK*(SEQIDNO:154) Fetuin-B Q9UGM5 FETUB VNDAQEYR*(SEQIDNO:155) Fetuin-B Q9UGM5 FETUB AHYGGFTVQNEANK(SEQID Fibrinogenbetachain P02675 FIBB NO:156) QGFGNVATNTDGK(SEQIDNO: Fibrinogenbetachain P02675 FIBB 157) YEASILTHDSSIR(SEQIDNO: Fibrinogengammachain P02679 FIBG 158) HTSVQTTSSGSGPFTDVR*(SEQ Fibronectin P02751 FINC IDNO:159) SSPVVIDASTAIDAPSNLR*(SEQ Fibronectin P02751 FINC IDNO:160) GYHLNEEGTR(SEQIDNO:161) Fibulin-1 P23142 FBLN1 SQETGDLDVGGLQETDK(SEQ Fibulin-1 P23142 FBLN1 IDNO:162) TGYYFDGISR(SEQIDNO:163) Fibulin-1 P23142 FBLN1 TGAQELLR*(SEQIDNO:164) Gelsolin P06396 GELS PGGGFVPNFQLFEK*(SEQID Glutathioneperoxidase3 P22352 GPX3 NO:165) VGYVSGWGR(SEQIDNO:166) Haptoglobin P00738 HPT TYFPHFDLSHGSAQVK(SEQID Hemoglobinsubunitalpha P69905 HBA NO:167) VGAHAGEYGAEALER(SEQID Hemoglobinsubunitalpha P69905 HBA NO:168) LYLVQGTQVYVFLTK*(SEQID Hemopexin P02790 HEMO NO:169) NFPSPVDAAFR*(SEQIDNO: Hemopexin P02790 HEMO 170) GETHEQVHSILHFK*(SEQID Heparincofactor2 P05546 HEP2 NO:171) NYNLVESLK*(SEQIDNO:172) Heparincofactor2 P05546 HEP2 SVNDLYIQK*(SEQIDNO:173) Heparincofactor2 P05546 HEP2 SPLNDFQVLR(SEQIDNO:174) Hepatocytegrowthfactor-like P26927 HGFL protein DGYLFQLLR*(SEQIDNO:175) Histidine-richglycoprotein P04196 HRG FLNVLSPR(SEQIDNO:176) Insulin-likegrowthfactor- P17936 IBP3 bindingprotein3 YGQPLPGYTTK(SEQIDNO: Insulin-likegrowthactor- P17936 IBP3 177) bindingprotein3 NLIAAVAPGAFLGLK*(SEQID Insulin-likegrowthfactor- P35858 ALS NO:178) bindingproteincomplex acidlabilesubunit VAGLLEDTFPGLLGLR*(SEQID Insulin-likegrowthfactor- P35858 ALS NO:179) bindingproteincomplex acidlabilesubunit ETAVDGELVVLYDVK*(SEQID Inter-alpha-trypsininhibitor P19823 ITIH2 NO:180) heavychainH2 FLHVPDTFEGHFDGVPVISK* Inter-alpha-trypsininhibitor P19823 ITIH2 (SEQIDNO:181) heavychainH2 SPEQQETVLDGNLIIR(SEQID Inter-alpha-trypsininhibitor Q14624 ITIH4 NO:182) heavychainH4 ISTLSCENK(SEQIDNO:183) Interleukin-18 Q14116 IL18 IITGLLEFEVYLEYLQNR(SEQID Interleukin-6 P05231 IL6 NO:184) VGSALFLSHNLK*(SEQIDNO: Kallistatin P29622 KAIN 185) DIPTNSPELEETLTHTITK(SEQ Kininogen-1 P01042 KNG1 IDNO:186) TVGSDTFYSFK(SEQIDNO: Kininogen-1 P01042 KNG1 187) DLLHVLAFSK(SEQIDNO:188) Leptin P41159 LEP YSENSTTVIR(SEQIDNO:189) Leptinreceptor P48357 LEPR GLQYAAQEGLLALQSELLR Lipopolysaccharide-binding P18428 LBP (SEQIDNO:190) protein ITLPDFTGDLR(SEQIDNO:191) Lipopolysaccharide-binding P18428 LBP protein LGSFEGLVNLTFIHLQHNR* Lumican P51884 LUM (SEQIDNO:192) (SEQIDNO:!) LPSGLPVSLLTLYLDNNK*(SEQ Lumican P51884 LUM IDNO:193) SLEDLQLTHNK*(SEQIDNO: Lumican P51884 LUM 194) SLEYLDLSFNQIAR*(SEQID Lumican P51884 LUM NO:195) FNSVPLTDTGHER(SEQIDNO: Macrophagecolony-stimulating P09603 CSF1 196) factor1 APGELEHGLITFSTR(SEQID Mannan-bindinglectinserine P48740 MASP1 NO:197) protease1 TEGQFVDLTGNRSEQIDNO: Mannose-bindingproteinC P11226 MBL2 198) LGLGADVAQVTGALR(SEQID Matrixmetalloproteinase-9 P14780 MMP9 NO:199) SLGPALLLLQK(SEQIDNO: Matrixmetalloproteinase-9 P14780 MMP9 200) FVGTPEVNQTTLYQR(SEQID Metalloproteinaseinhibitor1 P01033 TIMP1 NO:201) GFQALGDAADIR(SEQIDNO: Metalloproteinaseinhibitor1 P01033 TIMP1 202) ELTLEDLK(SEQIDNO:203) Monocytedifferentiationantigen P08571 CD14 CD14 FPAIQNLALR(SEQIDNO:204) Monocytedifferentiationantigen P08571 CD14 CD14 STLSVGVSGTLVLLQGAR(SEQ Monocytedifferentiationantigen P08571 CD14 IDNO:205) CD14 IANVFTNAFR(SEQIDNO:206) Myeloperoxidase P05164 PERM VVLEGGIDPILR(SEQIDNO: Myeloperoxidase P05164 PERM 207) AAPAPAPPPEPERPK(SEQID Myosinlightchain3 P08590 MYL3 NO:208) PSLSHLLSQYYGAGVAR*(SEQ N-acetylmuramoyl-Lalanine Q96PD5 PGRP2 IDNO:209) amidase TDCPGDALFDLLR*(SEQIDNO: N-acetylmuramoyl-L-alanine Q96PD5 PGRP2 210) amidase ALPAVETQAPTSLATK(SEQID Peptidaseinhibitor16 Q6UXB8 PI16 NO:211) ATAVVDGAFK(SEQIDNO:212) Peroxiredoxin-2 P32119 PRDX2 GLFIIDGK(SEQIDNO:213) Peroxiredoxin-2 P32119 PRDX2 SSGLVSNAPGVQIR(SEQID Phosphatidylcholine-sterol NO:214) acyltransferase P04180 LCAT AVEPQLQEEER(SEQIDNO: Phospholipidtransferprotein P55058 PLTP 215) FLEQELETITIPDLR(SEQIDNO: Phospholipidtransferprotein P55058 PLTP 216) TSLEDFYLDEER*(SEQIDNO: Pigmentepithelium-derived P36955 PEDF 217) factor IVQAVLTVPK*(SEQIDNO: Pigmentepithelium-derived P36955 PEDF 218) factor VSEGNHDIALIK(SEQIDNO: Plasmakallikrein P03952 KLKB1 219) LLDSLPSDTR(SEQIDNO:220) PlasmaproteaseC1inhibitor P05155 IC1 AVVEVDESGTR(SEQIDNO: Plasmaserineproteaseinhibitor P05154 IPSP 221) FSIEGSYQLEK(SEQIDNO: 222) Plasmaserineproteaseinhibitor P05154 IPSP VILGAHQEVNLEPHVQEIEVSR* Plasminogen P00747 PLMN (SEQIDNO:223) DEISTTDAIFVQR*(SEQIDNO: Plasminogenactivatorinhibitor P05121 PAM 224) 1 FSLETEVDLR*(SEQIDNO:225) Plasminogenactivatorinhibitor P05121 PAI1 1 AFIQLWAFDAVK*(SEQIDNO: ProteinAMBP P02760 AMBP 226) ETLLQDFR*(SEQIDNO:227) ProteinAMBP P02760 AMBP HHGPTITAK*(SEQIDNO:228) ProteinAMBP P02760 AMBP ETSNFGFSLLR(SEQIDNO: ProteinZ-dependentprotease Q9UK55 ZPI 229) inhibitor LFDEINPETK(SEQIDNO:230) ProteinZ-dependentprotease Q9UK55 ZPI inhibitor DQYYNIDVPSR(SEQIDNO: Proteoglycan4 Q92954 PRG4 231) GFGGLTGQIVAALSTAK(SEQID Proteoglycan4 Q92954 PRG4 NO:232) ELLESYIDGR*(SEQIDNO:233) Prothrombin P00734 THRB ETAASLLQAGYK*(SEQIDNO: Prothrombin P00734 THRB 234) YWGVASFLQK*(SEQIDNO: Retinol-bindingprotein4 P02753 RET4 235) AEFAEVSK(SEQIDNO:236) Serumalbumin P02768 ALBU LVNEVTEFAK(SEQIDNO:237) Serumalbumin P02768 ALBU FRPDGLPK(SEQIDNO:238) SerumamyloidA-4protein P35542 SAA4 GPGGVWAAK(SEQIDNO:239) SerumamyloidA-4protein P35542 SAA4 AYSLFSYNTQGR(SEQIDNO: SerumamyloidP-component P02743 SAMP 240) IQNILTEEPK*(SEQIDNO:241) Serumparaoxonase/arylesterase1 P27169 PON1 SFNPNSPGK*(SEQIDNO:242) Serumparaoxonase/arylesterase1 P27169 PON1 IALGGLLFPASNLR(SEQIDNO: Sexhormone-bindingglobulin P04278 SHBG 243) VVLSQGSK(SEQIDNO:244) Sexhormone-bindingglobulin P04278 SHBG GGTLGTPQTGSENDALYEYLR* Tetranectin P05452 TETN (SEQIDNO:245) AVLHIGEK*(SEQIDNO:246) Thyroxine-bindingglobulin P05543 THBG FSISATYDLGATLLK*(SEQID Thyroxine-bindingglobulin P05543 THBG NO:247) TLYETEVFSTDFSNISAAK*(SEQ Thyroxine-bindingglobulin P05543 THBG IDNO:248) IPVVLPEDEGIYTAFASNIK(SEQ Titin Q8WZ42 TITIN IDNO:249) VAGESAEPEPEPEADYYAK Transforminggrowthfactor P01137 TGFB1 (SEQIDNO:250) beta-1 VEQHVELYQK(SEQIDNO:251) Transforminggrowthfactor P01137 TGFB1 beta-1 AADDTVVEPFASGK*(SEQID Transthyretin P02766 TTHY NO:252) GSPAINVAVHVFR*(SEQIDNO: Transthyretin P02766 TTHY 253) LHIDEMDSVPTVR(SEQIDNO: Vascularcelladhesionprotein1 P19320 VCAM1 254) LAGLGLQQLDEGLFSR(SEQID Vasorin Q6EMK4 VASN NO:255) SLTLGIEPVSPTSLR(SEQID Vasorin Q6EMK4 VASN NO:256) YLQGSSVQLR(SEQIDNO:257) Vasorin Q6EMK4 VASN ELPEHTVK*(SEQIDNO:258) VitaminD-bindingprotein P02774 VTDB THLPEVFLSK*(SEQIDNO:259) VitaminD-bindingprotein P02774 VTDB LGEYDLR(SEQIDNO:260) VitaminK-dependentproteinC P04070 PROC TFVLNFIK(SEQIDNO:261) VitaminK-dependentproteinC P04070 PROC YLDWIHGHIR(SEQIDNO:262) VitaminK-dependentproteinC P04070 PROC SFQTGLFTAAR*(SEQIDNO: 263) VitaminK-dependentproteinS P07225 PROS VYFAGFPR*(SEQIDNO:264) VitaminK-dependentproteinS P07225 PROS DFAEHLLIPR(SEQIDNO:265) VitaminK-dependentproteinZ P22891 PROZ ENFVLTTAK(SEQIDNO:266) VitaminK-dependentproteinZ P22891 PROZ DVVVGIEGPIDAAFTR*(SEQID Vitronectin P04004 VTNC NO:267) FEDGVLDPDYPR*(SEQIDNO: Vitronectin P04004 VTNC 268) IGWPNAPILIQDFETLPR(SEQID vonWillebrandfactor P04275 VWF NO:269) ILAGPAGDSNVVK(SEQIDNO: vonWillebrandfactor P04275 VWF 270) AGEVQEPELR*(SEQIDNO: Zinc-alpha-2-glycoprotein P25311 ZA2G 271) YSLTYIYTGLSK*(SEQIDNO: Zinc-alpha-2-glycoprotein P25311 ZA2G 272)

Example 3

(118) This example describes the further development of a panel of protein biomarkers that can distinguish AECOPD from a convalescent state.

(119) Introduction

(120) In patients with chronic obstructive pulmonary disease (COPD), fixed airflow limitation often results in symptoms such as dyspnea, cough, and sputum production. The periodic worsening of these symptoms is known as an acute exacerbation (AECOPD), an event that can have lasting detrimental effects on lung function (when experienced repeatedly),[1] respiratory-related quality of life,[2] and mortality.[3] Economically, the impact of AECOPD is profound, as annual AECOPD-related costs in the United States alone amount to $30 billion.[4] The diagnosis of an AECOPD, largely made on the basis of clinical gestalt, is fraught with uncertainty.[5] In recent years, the search for a blood-based biomarker to distinguish AECOPD from states of relative clinical stability has focused on common inflammatory markers such as plasma C-reactive protein (CRP) [6] and serum amyloid protein.[7] Such a restrictive strategy, however, overlooks the fundamental heterogeneity of AECOPD in which respiratory viruses, bacterial infection, air pollution, and cardiac dysfunction can all interact through distinct pathways to initiate an event. [8-11]

(121) A comprehensive approach to biomarkers could potentially revolutionize the diagnosis and management of AECOPD, ideally revealing a panel of biomarkers that could accurately identify AECOPD early in the clinical course to enable intervention. Shotgun proteomics, requiring no a priori hypothesis, offers an unbiased platform to detect biomarker candidates, yet is limited by low-throughput, poor accuracy, and suboptimal quantitation. On the other hand, multiple reaction monitoring-mass spectrometry (MRM-MS) offers an inexpensive, high-throughput platform with the ability to quantify hundreds of targeted proteins based on precursor-product ion pairs,[12] and in 2012 was selected by Nature as Method of the Year.[13] It has since been employed to verify and validate biomarker panels in cystic fibrosis and lung cancer among many other diseases.[14 15] To date, MRM-MS has not been applied to the problem of COPD and AECOPD, but may provide an exceptional opportunity to discover new clinically applicable biomarkers. This study is the first of its kind to employ MRM-MS to identify biomarkers distinguishing AECOPD from periods of clinical stability.

(122) Methods

(123) Study Populations. Biomarker discovery involved 72 patients from the previously described and studied cohort evaluating the use of etanercept or prednisone in the treatment of AECOPD (TNF-, Clinicaltrials.gov identifier: NCT00789997).[16] Inclusion criteria for the TNF- cohort were age >35 years, an AECOPD presenting to a physician or emergency department, FEV170% predicted, FEV1/forced vital capacity (FVC)70%, and 10 pack-years smoking history. AECOPD was diagnosed when two of the following three criteria were met: increased dyspnea, sputum volume, and sputum purulence. Plasma samples used in this analysis were obtained at baseline and at day 14. The baseline sample was considered to indicate an AECOPD whereas the day 14 sample was used to indicate a convalescent state.

(124) Biomarkers were confirmed in patients from two other AECOPD cohorts. The first replication cohort was a randomized controlled trial evaluating the use of zileuton in the treatment of AECOPD (LEUKO, n=37, Clinicaltrials.gov identifier: NCT00493974).[17] Briefly, inclusion criteria were age >45 years, admission to the hospital for AECOPD, >10 pack-years smoking history, and forced expiratory volume in 1 second (FEV1)<60% predicted. An AECOPD was defined as an acute increase in dyspnea, sputum volume, and/or sputum purulence without an alternative explanation. Plasma samples used in this analysis were collected at the beginning of the hospitalisation period and at day 30. We considered the initial sample collection at hospitalisation to indicate an AECOPD whereas the day 30 samples were used to indicate a convalescent state.

(125) The second replication cohort (Rapid Transition Program or RTP, n=109) included prospectively enrolled patients admitted to two large teaching hospitals for AECOPD for the primary purpose of biomarker discovery to diagnose and track AECOPD. For the RTP cohort, subjects had to be admitted to the hospital with an AECOPD as determined by general internists or pulmonologists. Blood samples were collected at the time of admission to the hospital and at either day 30 or 90 following admission (both time points indicating the convalescent state).

(126) Sample Collection. LEUKO and RTP blood samples were collected in lavender-top EDTA tubes with the plasma layer isolated following centrifugation and stored at 80 C. Blood samples from the TNF- cohort were collected in P100 tubes (BD, Franklin Lake, N.J.) and stored on ice until processing. Blood was spun down within two hours of collection and plasma was stored at 80 C. until selected for proteomic analysis. Patient plasma samples were analysed using MRM-MS at the UVic Genome BC Proteomics Centre (Victoria, BC, Canada) according to methods described previously.[18] There were 230 peptides measured corresponding to 129 proteins, selected on the basis of both a literature search and from a previous untargeted iTRAQ mass spectrometry analysis on COPD patients. These proteins broadly represented inflammatory cytokines, cell homeostasis, coagulation, lipid metabolism, and immune response.

(127) Statistical Analyses. Statistical analysis was performed using R (www.r-project.org) and Bioconductor (www.bioconductor.org). Pre-processing of the MRM-MS data involved several steps. All peptides that had more than 25% missing values (signifying the peptide was below the limit of detection) across all samples or that did not pass quality control metrics were removed. Remaining missing values were imputed with a value equal to half of the minimum peptide level, for each peptide separately. Relative response of peptide abundance to stable isotopically-labeled peptide abundance was log-base 2 transformed and summarised at the protein level to create protein level data.

(128) Proteins were analysed for differential expression between the patients' exacerbation and convalescent samples, using limma (limma Bioconductor package). A false discovery rate (FDR)<0.01 and fold change >1.2 were used as the criteria for selecting candidate proteins. An elastic net logistic regression model [19] (glmnet R package) was applied to the list of candidate proteins to build a classifier or biomarker score, which is the aggregation of the weighted contributions (linear predictors, denoted here as w.sub.N w.sub.i) of each protein in the model to the presence of AECOPD:
Biomarker score=w.sub.0+w.sub.1*protein.sub.1+w.sub.2*protein.sub.2+ . . . +w.sub.N*protein.sub.N

(129) The performance characteristics of this biomarker panel were estimated using leave-pair-out cross-validation (LPOCV) in the discovery cohort. The LPOCV-based biomarker scores were also used to select decision thresholds, chosen to detect convalescence or exacerbation with at least 90%, and to optimize Youden's index given this requirement. The classification model and decision thresholds obtained from TNF- were applied to the LEUKO and RTP cohorts for external replication. A summary of the overall workflow is shown in FIG. 1.

(130) Results

(131) Cohort Demographics.

(132) The demographic characteristics comparing the TNF-, LEUKO, and RTP cohorts are those shown in Table 5.

(133) TABLE-US-00005 TABLE 5 Demographic Data for TNF-, LEUKO, and RTP Cohorts TNF- LEUKO RTP p- Characteristic (n = 72) (n = 37) (n = 109) value* Age (years) 67.06 9.28 62.11 8.19 67.79 10.54 0.009 Male (%) 37.04 56.76 63.30 0.001 BMI (kg/m.sup.2) 26.56 7.14 27.04 5.65 27.37 6.88 0.852 Caucasian (%) 98.77 59.46 82.41 <0.001 Smoking Status <0.001 Current (%) 23.46 29.73 52.29 Former (%) 70.37 70.27 33.94 Smoking 47.85 28.23 47.86 28.02 53.39 36.05 0.476 pack-years FEV1 (L) 0.94 0.47 1.00 0.62 1.66 0.85 <0.001 FEV1 34.41 13.87 31.92 15.27 57.19 20.11 <0.001 (% Predicted) FVC (L) 2.33 1.00 2.35 0.93 2.98 1.16 0.007 FVC 66.65 20.88 56.9 15.7 81.28 19.28 0.001 (% Predicted) FEV1/FVC 40.78 13.14 41.92 11.61 55.52 13.82 <0.001 (%) Bronchodilator 100 94.59 95.42 0.134 Use (%) Inhaled 95.00 67.57 44.95 <0.001 Corticosteroid Use (%) Values are reported as mean standard deviation or percentages. Abbreviations: BMI body mass index; FEV1 forced expiratory volume in 1 second; FVC forced vital capacity *P-values were generated using an ANOVA test for continuous variables and chi-quare tests for categorical variables.

(134) Patients in the RTP cohort had better lung function than patients in the LEUKO and TNF- cohorts, but were also more likely to be current rather than former smokers. Fewer patients in the RTP cohort were also being treated with inhaled corticosteroids. The majority of patients in all three cohorts were being treated with bronchodilators.

(135) Biomarker Panel Performance.

(136) After the removal of peptides with more than 250% missing values across all samples and those that failed quality control metrics, the MRM-MS data consisted of 55 proteins. Of these, seven showed differential levels between exacerbation and convalescent time points at a FDR 0.01, with a fold change >1.2 (Table 6).

(137) TABLE-US-00006 TABLE6 SignificantProteinsDifferentiallyExpressedinAECOPDComparedtothe ConvalescentState Direction AECOPD UniProt Gene p- Fold Relativeto Peptide ProteinName ID Symbol value FDR Change Convalescence SLAPYAQDTQEK Apolipoprotein P06727 APOA4 0.000 0.000 1.33 Down (SEQIDNO:69) A-IV VVEESELAR Complement P02748 C9 0.000 0.000 1.23 Up (SEQIDNO:13) componentC9 SSPVVIDASTAIDAPSNL Fibronectin P02751 FN1 0.000 0.000 1.23 Down R(SEQIDNO:160) TAAQNLYEK(SEQID Apolipoprotein P02655 APOC2 0.000 0.000 1.27 Down NO:1) C-II AFVFPK C-reactiveprotein P02741 CRP 0.000 0.001 1.64 Up (SEQIDNO:15) GSPAINVAVHVFR Transthyretin P02766 TTR 0.000 0.001 1.20 Down (SEQIDNO:9) ITLPDFTGDLR Lipopolysaccharide- P18428 LBP 0.002 0.005 1.20 Up (SEQIDNO:191) bindingprotein Abbreviations: FDR-false discovery rate; AECOPD-acute exacerbations of COPD

(138) The final elastic net model consisted of five of these proteins (CRP and transthyretin were removed to create the final model). Compared to CRP alone, the 5-protein panel demonstrated a superior receiver operating characteristics (ROC) curve for diagnosing AECOPD in all three cohorts (FIG. 8). The area under the curve (AUC) for the 5-protein was panel was 0.73, 0.77, and 0.79 in the TNF-, LEUKO, and RTP cohorts, respectively. In comparison, the AUC for CRP was 0.63, 0.61, and 0.76 in the TNF-, LEUKO, and RTP cohorts, respectively.

(139) A biomarker score based on the weighted contributions of the 5 proteins to the presence of an AECOPD state was calculated for each of the cohorts. The intercept and specific protein weights contributing to the biomarker score for the 5-protein panel are listed in Table 7.

(140) TABLE-US-00007 TABLE 7 Biomarker Score Intercept and Specific Protein Weights Biomarker score = w.sub.0 + w.sub.1 .Math. protein.sub.1 + w.sub.2 .Math. protein.sub.2 + . . . + w.sub.N .Math. protein.sub.N Intercept/Protein w Intercept (w.sub.0) 0.272 Apolipoprotein A-IV 1.016 Complement component C9 0.643 Fibronectin 0.321 Apolipoprotein C-II 0.225 Lipopolysaccharide-binding protein 0.289

(141) Biomarker scores at each time point for the three cohorts are shown in FIG. 9. In all three cohorts, the biomarker scores at exacerbation time points were significantly greater than the biomarker scores at convalescent time points (Wilcoxon rank sum p-value <0.001 for LEUKO, <0.001 for TNF-, and <0.001 for RTP). In addition, the biomarker scores during convalescence in the two replication cohorts were not statistically different from the convalescence biomarker scores in the TNF- discovery cohort. For the RTP cohort, for which additional time points were available, Day 30 and Day 90 biomarker scores (signifying convalescence) were significantly different from Baseline and Day 3 scores (p<0.001). Control subjects (COPD subjects who were not exacerbating) had biomarker scores equivalent to convalescent scores (p=0.35).

(142) A biomarker score decision threshold optimised to detect AECOPD with at least 90% sensitivity in the TNF- cohort yielded sensitivities of 90%, 91%, and 93% in the TNF-, LEUKO, and RTP cohorts, respectively. Conversely, a biomarker score decision threshold optimized to detect AECOPD with 90% specificity in the TNF- cohort yielded specificities of 90%, 92%, and 94% in the TNF-, LEUKO, and RTP cohorts, respectively.

(143) Discussion

(144) In this first-ever study employing MRM-MS for biomarker verification in AECOPD, we have generated a promising panel of five proteins significantly associated with an AECOPD state with the results replicated in two separate AECOPD cohorts. Biomarker scores derived from this panel were significantly elevated in AECOPD compared to convalescent periods and the performance of this panel provided a significant increase in the AUC estimate over CRP. In a real life setting (i.e. the RTP cohort), the biomarker classifier based on these five proteins generated an AUC of 0.79. Now that we have identified the most promising five proteins in the classifier, in the future, we can build more precise MS assays to interrogate these proteins, which will further improve AUC values to values >0.8. This will make clinical translation possible. [20] For a medical condition with a current shortage of available biomarkers, this panel may represent a significant step forward not only in AECOPD diagnosis but also in the recognition of AECOPD resolution at which point therapy could potentially be tapered. Additionally, this panel could be used to identify patients who may need greater intensity or duration of therapy.

(145) Further, when tested in non-exacerbating COPD patients who were also enrolled in the RTP cohort and presenting to outpatient follow-up clinics, biomarker scores were no different from AECOPD patients in the convalescent state (see FIG. 10), reinforcing the panel's ability to distinguish between AECOPD and non-AECOPD states.

(146) The MRM-MS approach, although previously applied to numerous other disease states such as lung cancer, psoriatic arthritis, and Parkinson's disease,[14 23 24] marks a departure from traditional methods of biomarker discovery and verification in AECOPD. Previous attempts at identifying biomarkers have relied on known proteins with already available commercial immunoassay platforms, for instance CRP, IL-6, angiopoietin-2, adrenomedullin, and troponin. [6 25-28] Unfortunately, proteins lacking available commercial immunoassays may be entirely overlooked by this strategy. The cost and time required for immunoassay development, however, can be prohibitive.[29] MRM-MS can fill the gap between biomarker discovery and verification by providing a cost-effective platform for quantifying proteins with greater specificity than that provided by immunoassays. Moreover, the multiplexing capacity of MRM-MS confers another distinct advantage over antibody-based tests.

(147) Using MRM-MS, we identified through our protein panel key biological pathways not previously associated with AECOPD pathophysiology. While inflammatory proteins like CRP were indeed differentially expressed in AECOPD, our final biomarker model was not comprised of these proteins, a surprising finding given the extensive attention recently focused on inflammation in the etiology of AECOPD. Instead, our panel was particularly notable for the inclusion of two proteins relating to the cholesterol pathway, apolipoprotein A-IV (APOA4) and apolipoprotein C-II (APOC2) (both decreased in the setting of AECOPD compared to convalescence). While the associations between AECOPD and cardiovascular comorbidities have long been recognized,[8 30 31] the specific role that these proteins play in the development of AECOPD has not yet been established. APOA4, a 46-kDa glycoprotein secreted in the small intestine, is an important constituent of chylomicrons and circulates in plasma either bound to high-density lipoproteins (HDL) or in a free state.[32 33] While it is primarily associated with lipid metabolism and transport,[34 35] it importantly plays a role in anti-oxidant,[36] anti-inflammatory,[37 38] and anti-atherogenic [39 40] responses. The protein's relative decrease during AECOPD might suggest that it plays a protective role in the lung as well, although further studies are needed to establish a precise mechanism. APOC2, an 8.8-kDa protein, circulates in plasma bound to chylomicrons, very low-density lipoproteins (VLDL) and HDL where it serves as an activator of lipoprotein lipase. Deficiencies in APOC2, often inherited as rare autosomal recessive mutations, result in excessive triglyceride levels. Connections between APOC2 to COPD pathogenesis, however, have not been established in the literature.

(148) The three cohorts utilized for biomarker discovery and verification were fundamentally different in terms of baseline demographic markers like age, sex, and lung function. Therefore, the protein panel discovered in the TNF- cohort may have actually performed better had the subjects in the verification cohorts aligned more similarly with the discovery cohort. This study demonstrates that the biomarker panel can likely be applied across a wide variety of COPD phenotypes with consistent results. We applied the 5-protein biomarker panel to a cohort of chronic heart failure patients and a cohort of healthy controls and the resulting biomarker scores were equivalent to those of convalescent AECOPD patients (see FIG. 10 and Table 8 for cohort demographics and results).

(149) TABLE-US-00008 TABLE 8 Demographic Characteristics of Heart Failure and Healthy Control Patients Heart Failure Cohort Healthy Control Characteristic (n = 218) (n = 49) Age (mean standard 64.9 11.0 45.9 14.4 deviation) Male (%) 75.7 24.5 White Race (%) 76.9 79.5 Smoking Status Never (%) 37.2 N/A Current (%) 11.0 Former (%) 51.8

(150) In summary, we demonstrate here for the first time the application of an MRM-MS platform to biomarker discovery in the diagnosis of AECOPD. Not only was this panel able to distinguish AECOPD from the convalescent COPD state in multiple, independent cohorts, but it also revealed potential novel mechanisms for AECOPD by implicating cholesterol pathways previously unreported in the AECOPD literature. For a clinical problem with no current diagnostic test available, our panel may be a significant addition to the management algorithm of COPD patients.

(151) Funding Sources:

(152) Funding was provided by Genome Canada, Genome British Columbia, Genome Quebec, the Canadian Institutes of Health Research, PROOF Centre, St. Paul's Hospital Foundation, the Canadian Respiratory Research Network, and the National Heart, Lung, and Blood Institute's COPD Clinical Research Network (Grants U10 HL074441, U10 HL074418, U10 HL074428, U10 HL074409, U10 HL074407, U10 HL074422, U10 HL074416, U10 HL074408, U10 HL074439, U10 HL0744231, and U10 HL074424).

REFERENCES

(153) 1 Donaldson G C, Seemungal T A, Bhowmik A, et al. Relationship between exacerbation frequency and lung function decline in chronic obstructive pulmonary disease. Thorax 2002; 57(10):847-52. 2 Seemungal T A, Donaldson G C, Paul E A, et al. Effect of exacerbation on quality of life in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med 1998; 157(5 Pt 1): 1418-22. 3 Soler-Cataluna J J, Martinez-Garcia M A, Roman Sanchez P, et al. Severe acute exacerbations and mortality in patients with chronic obstructive pulmonary disease. Thorax 2005; 60(11):925-31. 4 Chronic obstructive pulmonary disease (COPD) fact sheet. Chicago, Ill.: American Lung Association, 2014. (Accessed Oct. 4, 2014) 5 Celli B R, MacNee W. Standards for the diagnosis and treatment of patients with COPD: a summary of the ATS/ERS position paper. Eur Respir J 2004; 23(6):932-46. 6 Hurst J R, Donaldson G C, Perera W R, et al. Use of plasma biomarkers at exacerbation of chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2006; 174(8):867-74. 7 Bozinovski S, Hutchinson A, Thompson M, et al. Serum amyloid a is a biomarker of acute exacerbations of chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2008; 177(3):269-78. 8 Patel A R, Kowlessar B S, Donaldson G C, et al. Cardiovascular risk, myocardial injury, and exacerbations of chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2013; 188(9):1091-9. 9 Seemungal T, Harper-Owen R, Bhowmik A, et al. Respiratory viruses, symptoms, and inflammatory markers in acute exacerbations and stable chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2001; 164(9): 1618-23. 10 Peacock J L, Anderson H R, Bremner S A, et al. Outdoor air pollution and respiratory health in patients with COPD. Thorax 2011; 66(7):591-6. 11 De Serres G, Lampron N, La Forge J, et al. Importance of viral and bacterial infections in chronic obstructive pulmonary disease exacerbations. J Clin Virol 2009; 46(2):129-33. 12 Meng Z, Veenstra T D. Targeted mass spectrometry approaches for protein biomarker verification. J Proteomics 2011; 74(12):2650-9. 13 Method of the Year 2012. Nat Methods 2012; 10(1):1. 14 Chen C D, Wang C L, Yu C J, et al. Targeted proteomics pipeline reveals potential biomarkers for the diagnosis of metastatic lung cancer in pleural effusion. J Proteome Res 2014; 13(6):2818-29. 15 Quon B S, Dai D L, Hollander Z, et al. Discovery of novel plasma protein biomarkers to predict imminent cystic fibrosis pulmonary exacerbations using multiple reaction monitoring mass spectrometry. Thorax 2015 Mar. 16 [Epub ahead of print]. 16 Aaron S D, Vandemheen K L, Maltais F, et al. TNFalpha antagonists for acute exacerbations of COPD: a randomised double-blind controlled trial. Thorax 2013; 68(2):142-8. 17 Woodruff P G, Albert R K, Bailey W C, et al. Randomized trial of zileuton for treatment of COPD exacerbations requiring hospitalization. COPD 2011; 8(1):21-9. 18 Percy A J, Chambers A G, Yang J, et al. Method and platform standardization in MRM-based quantitative plasma proteomics. J Proteomics 2013; 95:66-76. 19 Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Series B 2005; 67(2):301-20. 20 Sin D D, Hollander Z, DeMarco M L, et al. Biomarker Development for COPD: From Discovery to Clinical Implementation. Am J Respir Crit Care Med 2015 Jul. 15 [Epub ahead of print]. 21 Leidy N K, Wilcox T K, Jones P W, et al. Standardizing measurement of chronic obstructive pulmonary disease exacerbations. Reliability and validity of a patient-reported diary. Am J Respir Crit Care Med 2011; 183(3):323-9. 22 Leidy N K, Murray L T, Jones P, et al. Performance of the EXAcerbations of chronic pulmonary disease tool patient-reported outcome measure in three clinical trials of chronic obstructive pulmonary disease. Ann Am Thorac Soc 2014; 11(3):316-25. 23 Ademowo O S, Hernandez B, Collins E, et al. Discovery and confirmation of a protein biomarker panel with potential to predict response to biological therapy in psoriatic arthritis. Ann Rheum Dis 2014 Sep. 3 [Epub ahead of print]. 24 Alberio T, McMahon K, Cuccurullo M, et al. Verification of a Parkinson's disease protein signature in T-lymphocytes by multiple reaction monitoring. J Proteome Res 2014; 13(8):3554-61. 25 Meng D Q, Li X J, Song X Y, et al. Diagnostic and Prognostic Value of Plasma Adrenomedullin in COPD Exacerbation. Respir Care 2014; 59(10):1542-9. 26 Wedzicha J A, Seemungal T A, MacCallum P K, et al. Acute exacerbations of chronic obstructive pulmonary disease are accompanied by elevations of plasma fibrinogen and serum IL-6 levels. Thromb Haemost 2000; 84(2):210-5. 27 Nikolakopoulou S, Hillas G, Perrea D, et al. Serum angiopoietin-2 and CRP levels during COPD exacerbations. COPD 2014; 11(1):46-51. 28 Soyseth V, Bhatnagar R, Holmedahl N H, et al. Acute exacerbation of COPD is associated with fourfold elevation of cardiac troponin T. Heart 2013; 99(2): 122-6. 29 Issaq H J, Veenstra T D. Would you prefer multiple reaction monitoring or antibodies with your biomarker validation? Expert Rev Proteomics 2008; 5(6):761-3. 30 Donaldson G C, Hurst J R, Smith C J, et al. Increased risk of myocardial infarction and stroke following exacerbation of COPD. Chest 2010; 137(5):1091-7. 31 McAllister D A, Maclay J D, Mills N L, et al. Diagnosis of myocardial infarction following hospitalisation for exacerbation of COPD. Eur Respir J 2012; 39(5):1097-103. 32 Utermann G, Beisiegel U. Apolipoprotein A-IV: a protein occurring in human mesenteric lymph chylomicrons and free in plasma. Isolation and quantification. Eur J Biochem 1979; 99(2):333-43. 33 Green P H, Glickman R M, Riley J W, et al. Human apolipoprotein A-IV. Intestinal origin and distribution in plasma. J Clin Invest 1980; 65(4):911-9. 34 Dvorin E, Gorder N L, Benson D M, et al. Apolipoprotein A-IV. A determinant for binding and uptake of high density lipoproteins by rat hepatocytes. J Biol Chem 1986; 261(33):15714-8. 35 Goldberg I J, Scheraldi C A, Yacoub L K, et al. Lipoprotein ApoC-II activation of lipoprotein lipase. Modulation by apolipoprotein A-IV. J Biol Chem 1990; 265(8):4266-72. 36 Qin X, Swertfeger D K, Zheng S, et al. Apolipoprotein AIV: a potent endogenous inhibitor of lipid oxidation. Am J Physiol 1998; 274(5 Pt 2):H1836-40. 37 Vowinkel T, Mori M, Krieglstein C F, et al. Apolipoprotein A-IV inhibits experimental colitis. J Clin Invest 2004; 114(2):260-9. 38. Recalde D, Ostos M A, Badell E, et al. Human apolipoprotein A-IV reduces secretion of proinflammatory cytokines and atherosclerotic effects of a chronic infection mimicked by lipopolysaccharide. Arterioscler Thromb Vasc Biol 2004; 24(4):756-61. 39 Cohen R D, Castellani L W, Qiao J H, et al. Reduced aortic lesions and elevated high density lipoprotein levels in transgenic mice overexpressing mouse apolipoprotein A-IV. J Clin Invest 1997; 99(8):1906-16. 40 Duverger N, Tremp G, Caillaud J M, et al. Protection against atherogenesis in mice mediated by human apolipoprotein A-IV. Science 1996; 273(5277):966-8.

Example 4

(154) CRP and NT-proBNP Biomarker Panel

(155) This example describes another panel of protein biomarkers that can distinguish AECOPD from convalescent state.

(156) For a larger cohort of RTP patients, the level of CRP and N-terminal pro B-type Natriuretic Peptide (NT-proBNP) was measured on clinical assays. The demographics of this larger RTP cohort are shown in Table 9 below. A biomarker (see Table 10) was created based on a weighted combination of these two proteins:
Biomarker score=1.244+0.0289*CRP+0.000597*NTproBNP

(157) The AUC estimate for the above biomarker was 0.79. When the decision threshold was optimized for 900/sensitivity in this RTP cohort, the resulting sensitivity and specificity estimates were 91% and 31%, respectively.

(158) TABLE-US-00009 TABLE 9 Demographics of larger RTP cohort. Age years 68.16 11.39 Male (%) 62.82 BMI (kg/m.sup.2) 27.27 7.61 Caucasian (%) 83.64 Smoking Status Current (%) Former (%) Smoking pack-years 42.84 20.29 FEV1 (L) 1.53 0.74 FEV1 (% Predicted) 53.96 22.14 FVC (L) 2.78 1.03 FVC (% Predicted) 76.7 22.36 FEV1/FVC (%) 55.11 15.11 Bronchodilator Use (%) 97.92 Inhaled Corticosteroid Use (%) 36.46

(159) Table 10 provides the biomarkers used in this example.

(160) TABLE-US-00010 TABLE10 ProteinswithdifferentiallevelsbetweenAECOPDand convalescent/stableCOPD DirectionAECOPD Relativeto Biomarker Convalescent/ SEQID ProteinName StableCOPD sequence NO: CRP Up MEKLLCFLVLTSLSHAFGQT 273 DMSRKAFVFPKESDTSYVSL KAPLTKPLKAFTVCLHFYTE LSSTRGYSIFSYATKRQDNE ILIFWSKDIGYSFTVGGSEI LFEVPEVTVAPVHICTSWES ASGIVEFWVDGKPRVRKSLK KGYTVGAEASIILGQEQDSF GGNFEGSQSLVGDIGNVNMW DFVLSPDEINTIYLGGPFSP NVLNWRALKYEVQGEVFTKP QLWP NT-proBNP Up HPLGSPGSASDLETSGLQEQ 274 RNHLQGKLSELQVEQTSLEP LQESPRTPGVWKSREVATEG IRGHRKMVLYTLRAPR

(161) It is understood that the examples and embodiments described herein are for illustrative purposes only and that various modifications or changes in light thereof will be suggested to persons skilled in the art and are to be included within the spirit and purview of this application and scope of the appended claims. All publications, sequence accession numbers, patents, and patent applications cited herein are hereby incorporated by reference in their entirety for all purposes.