WO2005043111A2 - Serum biomarkers for sars - Google Patents

Abstract

The present invention provides biomarkers and biomarker combinations that are useful in qualifying SARS status in a patient. In addition, the present invention provides diagnostic methodology employing these biomarkers and combinations thereof that can distinguish between SARS and normal.

Description

SERUM BIOMARKERS FOR SARS

CROSS-REFERENCES TO RELATED APPLICATIONS [0001] This application claims benefit of U.S. Provisional Application No. 60/487,469, filed July 14, 2003, and U.S. Provisional Application No. 60/510,341, filed October 10, 2003, which applications are incorporated herein by reference for all purposes.

BACKGROUND OF THE INVENTION [0002] Severe acute respiratory syndrome (SARS) is a disease caused by an apparently new infective agent, SARS-associated coronavirus. Although the first case of SARS was reported in the Guangdong province of southern China in November, 2002, it was not recognized at this time that the disease was in fact an infectious disease. Consequently, the severity of the public health issues surrounding the need for proper and early diagnosis of the disease were not recognized. It was not until February, 2003 that public health officials realized that a cluster of 305 patients in Southern China, 5 of whom died, had had symptoms of SARS during the period beginning the prior November. During the subsequent two months, outbreaks of the disease appeared in Hong Kong, and on March 12, the World Health Organization (WHO) issued a global alert and an international travel alert. Although multiple laboratories globally engaged in efforts to identify a diagnostic method for the causative agent of SARS, no such diagnostic was found, in part because the causative agent had not yet been found. [0003] It was not until the third week of March, 2003 when laboratories in Hong Kong, the United States, and Germany identified coronavirus in patients with SARS. Sequencing of this SARS-associated coronavirus revealed novel sequences indicating that it was a novel strain, and evidence suggests that this novel strain has a primary animal host that is not human but has recently evolved to have the ability to infect humans. (See, e.g., Science. 2003 May 30;300(5624): 1399-404. Epub 2003 May 01. Marra et at.; and http://ybweb.bcgsc.ca/sars/TOR2_finished_genome_assembly_290403.fasta.) [0004] The discovery and sequencing of the SARS-associated coronavirus gave hope that a diagnostic test might be easily constructed against SARS. However, this did not occur. Polymerase chain reaction (PCR) tests have an apparent clinical sensitivity of 50%, and the large fraction of false negatives limits its clinical utility. Antibody tests are generally not positive until 21 days post-infection, which is too late to be of general clinical use. [0005] The difficulty in diagnosing SARS has major clinical and public health implications. Underdiagnosis may lead to increased rates of transmission and, consequently, mortality; overdiagnosis may lead to increased health cost due to the overtreatment and quarantine of individuals suspected of having SARS. Because the number of cases cannot be accurately counted, the case-specific mortality cannot be calculated; the numbers generally quoted range from 5-10%. In addition, the impact on the global economy is staggering. The travel advisories by the WHO led to a shutdown of the local economies in Hong Kong, China, Canada, and Taiwan, and, by extension, slowed down the ability of the interdependent global economy. Consequently, a diagnostic tool with high sensitivity and specificity for acute SARS would dramatically improve the care of patients with SARS as well as the ability for public health officials to deal with future outbreaks. BRIEF SUMMARY OF THE INVENTION [0006] In accordance with the present invention, biomarkers and combinations of biomarkers are provided that are used to identify SARS. [0007] In one aspect, this invention provides a method comprising measuring at least one biomarker in a sample from a subject, wherein the at least one biomarker is selected from the group consisting of: M3939.08/M3941.56, M4137.71, M4823.48,

M4097.99, M8136.64, M8902.83, M7625.91, M6633.58 and M11514.20. In one preferred embodiment, the at least one biomarker is selected from the group consisting of: M3941.56, M4823.48, M4097.99, M8902.83, M7625.91 and M6633.58. In another preferred embodiment, the method comprises measuring each of the following biomarkers: M3939.08, M8136.64, Ml 1514.2 and M4137.71. It is noted that the measured mass of biomarker

M3939.08 is within the mass margin of error of the measured mass of biomarker M3941.56 and, thus, M3939.08 and M3941.56 are believed to be the same biomarker. The difference in mass is believed to result from assignment of mass by software. [0008] In another aspect, this invention provides a method for qualifying SARS status in a subject comprising: measuring at least one biomarker in a biological sample from the subject, wherein at least one biomarker is selected from the group consisting of: M3939.08/M3941.56, M4137.71, M4823.48, M4097.99, M8136.64, M8902.83, M7625.91, M6633.58 and Ml 1514.20; and correlating the measurement with SARS status. In one preferred embodiment, the at least one biomarker is selected from the group consisting of: M3939.08/M3941.56, M4823.48, M4097.99, M8902.83, M7625.91 and M6633.58. In another preferred embodiment, the method comprises measuring each of the following biomarkers: M3939.08/M3941.56, M8136.64, M11514.2 and M4137.71. It has surprisingly been found that the combination of these four biomarkers are particularly effective for classifying SARS v. non-SARS status. In one embodiment of the foregoing methods, the biomarkers are measured by capturing the biomarkers on an adsorbent surface of a SELDI probe and detecting the captured biomarkers by laser desorption-ionization mass spectrometry. The adsorbent can be a cation exchange adsorbent or a biospecific adsorbent. [0009] In another embodiment of the foregoing methods, the biomarkers are measured by immunoassay. In another embodiment, the biomarkers are captured on an adsorbent surface of a protein biochip and the captured biomarkers are measured. In another embodiment, the method comprises measuring a plurality of the biomarkers. [0010] In another embodiment, the method comprises measuring M3941.56, wherein a measured up-regulation of M3941.56 correlates acute SARS v. non-SARS or v. non-SARS fever. Alternatively, the method comprises measuring M4823.48, wherein a measured down-regulation of M4823.48 correlates acute SARS v. non-SARS or v. non- SARS fever. Alternatively, the method comprises measuring M4823.48 and M4097.99, wherein a measured down-regulation of M4823.48 and a measured up regulation of M4097.99 correlates acute SARS v. non-SARS. Alternatively, the method comprises M6633.58, wherein a measured down-regulation of M6633.58 correlates acute SARS v. convalescent SARS. Alternatively, the method comprises measuring M7625.91 and M6633.58 and correlating the measurements with acute SARS v. convalescent SARS. [0011] In another embodiment, the method comprises measuring M3939.08, M8136.64, Ml 1514.2 and M4137.71, and correlating the measurements with SARS v. non- SARS (e.g., healthy, pneumonia, fever and lung cancer). [0012] In another embodiment, the sample is serum. [0013] In another embodiment, correlating comprises comparing the measured amount with a diagnostic amount and qualifying SARS status based on the comparison. Correlating can be performed by a software classification algorithm. [0014] In another embodiment, SARS status is selected from non-SARS fever, acute SARS and convalescent SARS. [0015] Another embodiment further comprises managing subject treatment based on the status. Managing subject treatment can be selected from ordering more tests, prescribing medication and taking no further action. [0016] Another embodiment further comprises measuring the at least one biomarker after subject management. [0017] In another aspect, this invention provides a kit comprising a solid support comprising at least one capture reagent attached thereto, wherein the capture reagent binds at least one biomarker from a first group consisting of M3939.08/M3941.56, M4137.71, M4823.48, M4097.99, M8136.64, M8902.83, M7625.91, M6633.58 and Ml 1514.20; and instructions for using the solid support to detect the biomarker or a container comprising at least one of the biomarkers. In one preferred embodiment, at least one capture reagent binds a biomarker from a first group consisting of M3939.08/M3941.56, M4823.48, M4097.99, M8902.83, M7625.91 and M6633.58. In another preferred embodiment, the capture reagents bind the following biomarkers: M3939.08/M3941.56, M8136.64, Ml 1514.2 and M4137.71. For instance, in this embodiment the kit can comprise four capture reagents, each of which binds the biomarkers of interest. The foregoing kits can further comprise a container comprising at least one of the biomarkers. The solid support can be a SELDI probe, and the adsorbent can be a cation exchange adsorbent. [0018] In another aspect, this invention provides a software product comprising: code that accesses data attributed to a sample, the data comprising measurement of at least one biomarker in the sample, the biomarker selected from the group consisting of: M3939.08/M3941.56, M4137.71, M4823.48, M4097.99, M8136.64, M8902.83, M7625.91, M6633.58 and Ml 1514.20; and code that executes a classification algorithm that classifies the SARS status of the sample as a function of the measurement. In one preferred embodiment, the biomarker is selected from the group consisting of M3939.08/M3941.56, M4823.48, M4097.99, M8902.83, M7625.91 and M6633.58. In another preferred embodiment, the biomarkers are each of the following four biomarkers: M3939.08/M3941.56, M8136.64, Ml 1514.2 and M4137.71.

BRIEF DESCRIPTION OF THE DRAWINGS [0019] Figure 1 shows spectra showing M3941.56 in an acute SARS and a non-SARS subject. [0020] Figure 2 shows spectra showing M4823.48 in an acute SARS and a non-SARS subject. [0021] Figure 3 shows spectra showing M4097.99 in an acute SARS and a non-SARS subject. [0022] Figure 4 shows spectra showing M3941.56 in an acute SARS and a non-SARS fever subject. [0023] Figure 5 shows spectra showing M4823.48 in an acute SARS and a non-SARS fever subject. [0024] Figure 6 shows spectra showing M8902.83 in an acute SARS and a convalescent SARS subject. [0025] Figure 7 shows spectra showing M6633.58 in an acute SARS and a convalescent SARS subject. [0026] Figure 8 shows a decision tree for classifying acute SARS infection and non-SARS. The tree has two decision nodes including biomarkers M4823.48 and

M4097.99. Samples falling into terminal nodes 1 and 3 are classified as non-SARS, while samples falling into terminal node 2 are classified as acute SARS. In the decision trees herein, leftward branches indicate the biomarker measures below the cutoff, and rightward branches indicate the biomarker measures above the cutoff. [0027] Figure 9 shows a decision tree for classifying acute SARS infection and convalescent SARS (more than 30 days after infection). The tree has three decision nodes including biomarkers M7625.91 and M6633.58. Samples falling into terminal nodes 1 and 3 are classified as acute SARS, while samples falling into terminal nodes 2 and 4 are classified as non-SARS. M6633 is down-regulated in SARS. [0028] Figure 10 shows spectra from non-SARS and SARS samples. A peak at 3941.56 is clearly visible in SARS. [0029] Figure 11 shows spectra from two acute SARS patients, "Ding" and "Yang" on three different days after infection. The amount of biomarker M3941.56 fluctuates. [0030] Figure 12 shows a decision tree for classifying SARS v. non-SARS.

The root node (top) and descendant nodes are shown as ovals, and the terminal nodes 1-5 are shown as rectangles. The numbers in each node represent the classes [S (SARS) and NS (non-SARS)]. The numbers under the root and descendant nodes are the mass value followed by the peak intensity value. For example, the mass value under the root node is 3939.08 kDa, and the intensity is <=1.7107. [0031] Figure 13 illustrates a representative sample of a SELDI spectrum showing the combination of four peak masses that can be used to correctly classify the same as SARS in the node three terminal. The arrows in the magnified panels identify the protein peaks used in the classifier. The first number under each panel is the mass, and the second number is the peak intensity. [0032] Figure 14 demonstrates the reproducibility of the SELDI assay. Serum samples were randomly selected one from each group of healthy donor number one (CI), SARS 4 days (S4), fever number 7 (F7) and pneumonia number 10 (P10) and applied on spots in duplicate. [0033] Figure 15 illustrates the detection of SARS patients in different periods by using the SARS early biomakers' pattern. By using the biomarker patterns to test 210 SARS patients in 1, 2, 3, 4 and > 4 weeks after onset, the results were 97.3% (36/37), 89.09% (49/55), 86.0% (43/50), 93.1% (27/29) and 79.49% (31/39), respectively.

DETAILED DESCRIPTION OF THE INVENTION^" I. BIOMARKERS FOR SARS [0034] This invention provides biomarkers that are differentially present in subjects depending on SARS status. The biomarkers of this invention include those presented in Table 1. The biomarkers of this invention are named according to their mass-to- charge ratio (i.e., M3941.56 has a mass-to-charge ratio of 3941.56). All the biomarkers bind to cation exchange adsorbents (e.g., the Ciphergen® WCX ProteinChip© array) after washing with sodium acetate, pH 4.0. The preferred biological source for detection of the biomarkers is serum. The biomarkers in the first seven rows were detected by washing with 50 mM sodium acetate, and the biomarkers in the bottom row of Table 1 were detected by washing with 100 mM sodium acetate. The preferred biological source of all of the biomarkers is serum.

TABLE 1

[0035] The biomarkers of this invention were discovered on a Ciphergen Biosystems, Inc. PBS II mass spectrometer. This instrument has a mass accuracy of about +/- 0.15 percent. Additionally, the instrument has a mass resolution of about 400 to 1000 m/dm, where m is mass and dm is the mass spectral peak width at 0.5 peak height. Accordingly, the masses provided reflect these specifications. [0036] As used herein, a "biomarker" is an organic biomolecule, particularly a polypeptide or protein, which is differentially present in a sample taken from a subject of one phenotypic status (e.g., having a disease) as compared with another phenotypic status (e.g., not having the disease). A biomarker is differentially present between different phenotypic statuses if the mean or median expression level of the biomarker in the different groups is calculated to be statistically significant. Common tests for statistical significance include, among others, t-test, ANOVA, Kruskal-Wallis, Wilcoxon, Mann- Whitney and odds ratio. Biomarkers, alone or in combination, provide measures of relative risk that a subject belongs to one phenotypic status or another. Therefore, they are useful in diagnostics and theranostics (the determination of the expected effectiveness of a drug). [0037] More particularly, the biomarkers of this invention are biomolecules that comprise a polypeptide. The biomarkers are characterized by an apparent molecular weight as determined by mass spectrometry, by the shape of their spectral peak in mass spectrometry and by their binding characteristics to adsorbent surfaces. [0038] Presenting the mass and affinity characteristics of a given biomarker within the invention, as in this description, characterizes that biomarker in a manner that allows one to determine whether a particular detected biomolecule is a biomarker of this invention. These characteristics represent inherent characteristics of the biomolecules and not process limitations in the manner in which the biomolecules are discriminated. [0039] The biomarkers of the present invention, which are present in serum, were identified by comparing mass spectra of samples derived from serum obtained from different groups of subjects. In one embodiment, these included subjects with acute SARS, subjects with fever but without SARS ("non-SARS fever"), subjects convalescing from SARS - about 30 days post infection, and control subjects without SARS - either healthy or with other disease. In another embodiment, these included subjects with SARS and control subjects without SARS - either healthy or with other disease such as subjects with fever, but without SARS ("fever"), subjects with pneumonia, but without SARS ("pneumonia"), and subjects with lung cancer, but without SARS ("lung cancer"). [0040] Detection of the biomarkers involved placing a serum sample on an adsorbent spot of a Ciphergen WCX ProteinChip array, followed by washing with 50 mM or 100 mM sodium acetate at pH 4.0. Sinnipinic acid was applied to the spot and allowed to dry. The chips were then analyzed by Surface-Enhanced Laser Desorption/Ionization Time- of-flight mass spectrometry (SELDI-TOF-MS, PBSU), and a retentate map was generated in which the individual proteins were displayed as separate peaks on the basis of their mass-to- charge ratio. [0041] Spectra data thus obtained were analyzed by Ciphergen Express*"¹ Data Manager Software with Biomarker Wizard and Biomarker Pattern Software from Ciphergen Biosystems, Inc. The mass spectra for each group were subjected to scatter plot analysis. A Mann- Whitney test analysis was employed to compare SARS and control groups for each protein cluster in the scatter plot, and protein clusters were selected that differed significantly (p<0.01) between the two groups. [0042] Because the biomarkers of this invention are characterized by mass-to- charge ratio and binding properties, they can be detected by mass spectrometry without knowing their specific identity. However, if desired, the biomarkers may be identified by, for example, determining the amino acid sequence of the polypeptides. For example, a biomarker can be peptide-mapped with a number of enzymes, such as trypsin or V8 protease, and the molecular weights of the digestion fragments can be used to search databases for sequences that match the molecular weights of the digestion fragments generated by the various enzymes. Alternatively, if the biomarkers are not proteins included in known databases, degenerate probes can be made based on the N-terminal amino acid sequence of the biomarker, which then are used to screen a genomic or cDNA library created from a sample from which the biomarker was initially detected. The positive clones can be identified, amplified, and their recombinant DNA sequences can be determined using techniques which are well known. Finally, protein biomarkers can be sequenced using protein ladder sequencing. Protein ladders can be generated by fragmenting the molecules and subjecting fragments to enzymatic digestion or other methods that sequentially remove a single amino acid from the end of the fragment. The ladder is then analyzed by MS. The difference in masses of the ladder fragments identifies the amino acid removed from the end of the molecule. [0043] Based on mass-to-charge ratio and binding properties, it is believed that Ml 1514.2 is Serum Amyloid Alpha (SAA). Again, however, because the biomarkers of this invention are characterized by mass-to-charge ratio and binding properties, they can be detected by mass spectrometry without knowing their specific identity.

II. DETERMINATION OF SUBJECT SARS STATUS [0044] The biomarkers of the invention can be used in diagnostic tests to assess SARS status in a subject, e.g., to diagnose SARS. The phrase "SARS status" includes distinguishing, inter alia, acute SARS v. non-SARS, acute SARS v. non-SARS fever and acute SARS v. convalescent SARS (more than 30 days post infection). Based on this status, further procedures may be indicated, including additional diagnostic tests or therapeutic procedures or regimens. [0045] The power of a diagnostic test to correctly predict status is commonly measured as the sensitivity of the assay, the specificity of the assay or the area under a receiver operated characteristic ("ROC") curve. Sensitivity is the percentage of true positives that are predicted by a test to be positive, while specificity is the percentage of true negatives that are predicted by a test to be negative. An ROC curve provides the sensitivity of a test as a function of 1 -specificity. The greater the area under the ROC curve, the more powerful the predictive value of the test. Other useful measures of the utility of a test are positive predictive value and negative predictive value. Positive predictive value is the percentage of actual positives who test as positive. Negative predictive value is the percentage of actual negatives that test as negative. [0046] In certain embodiments, the biomarkers of this invention show a statistical difference in different SARS statuses of at least p <0.05, p ≤l 0^"2, p <10^"3, p ≤IO^"4 or p ≤IO^"5. Diagnostic tests that use these biomarkers alone or in combination show a sensitivity and specificity of at least 75%, at least 80%, at least 85%, at least 90%, at least 95%o, at least 98% and about 100%. [0047] Any biomarker of this invention, individually, is useful in aiding in the determination of SARS status. First, the selected biomarker is measured in a subject sample using the methods described herein, e.g., capture on a SELDI biochip followed by detection by mass spectrometry. Then, the measurement is compared with a diagnostic amount or cutoff that distinguishes a positive SARS status from a negative SARS status. The diagnostic amount represents a measured amount of a biomarker above which or below which a subject is classified as having a particular SARS status. For example, if the biomarker is up- regulated compared to normal during SARS infection, then a measured amount above the diagnostic cutoff provides a diagnosis of SARS. Alternatively, if the biomarker is down- regulated during SARS infection, then a measured amount below the diagnostic cutoff provides a diagnosis of SARS. As is well understood in the art, by adjusting the particular diagnostic cutoff used in an assay one can increase sensitivity or specificity of the diagnostic assay depending on the preference of the diagnostician. [0048] In some embodiments, the mere presence or absence of a biomarker, without quantifying the amount of the biomarker, is useful and can be correlated with a probable diagnosis of SARS. Thus, a detected presence or absence, respectively, of these biomarkers in a subject being tested indicates that the subject has a higher probability of having SARS. [0049] While individual biomarkers are useful diagnostic biomarkers, it has been found that a combination of biomarkers can provide greater predictive value of a particular status than single biomarkers alone. Specifically, the detection of a plurality of biomarkers in a sample can increase the percentage of true positive and true negative diagnoses and decreases the percentage of false positive or false negative diagnoses. [0050] Biomarkers M4823.48 and M4097.99 are useful for classifying subjects as acute SARS v. non-SARS, where M4823.48 is down regulated and M4097.99 is upregulated in acute SARS compared with non-SARS. [0051] Biomarkers M3939.08, 8136.64, Ml 1514.2 and M4137.71 are particularly useful in combination to classify SAvRS v. non-SARS. This combination is particularly useful in a recursive partitioning process as shown in Figure 12. In this case, M3939.08 is the root node ofthe decision tree. Subjects having an amount of this biomarker above the cut-off are sent to terminal node 5 and classified as having SARS. Subjects below the cut-off are sent to a descendent node based on M8136.64. Subjects having an amount of this biomarker below the cut-off are sent to terminal node 1 and classified as non-SARS. Subjects having an amount of this biomarker above the cut-off are sent to a further decision node based on Ml 1514.2. Subjects having an amount of this biomarker below the cut-off are sent to terminal node 2 and are classified as non-SARS. Subjects having an amount of this biomarker above the cut-off are sent to a further descendent node based on M4137.71. Subjects having an amount of this biomarker below the cut-off are sent to terminal node 3 and classified as having SARS. Subjects having an amount of this biomarker above t ie cutoff are sent to terminal node 4 and are classified as non-SARS. The measure of each cut-off depends on the particulars ofthe assay protocol, of course. In this case, the cut-offs are based on the protocol set forth in Example π. [0052] More particularly, biomarkers M7625.91 and M6633.58 are useful in combination to qualify acute SARS v. convalescent SARS. [0053] Furthermore, the combination of biomarkers M3939.08, M8136.64, Ml 1514.2 and M4137.71 are particularly useful in diagnosing SARS v. non-SARS fever (see, Figure 12.) [0054] In certain embodiments off the methods of qualifying SARS status, the methods further comprise managing subject treatment based on the status. Such management describes the actions ofthe physician or clinician subsequent to determining SARS status. For example, if a physician makes a diagnosis of^" acute SARS, then a certain regime of treatment, such as a specific antiviral, or quarantine, might follow. Alternatively, a diagnosis of non-SARS or convalescent SARS might be followed with no treatment. If the diagnostic test gives an inconclusive result on SARS status, further tests may be called for. III. DETECTION OF BIOMARKERS FOR SARS [0055] The biomarkers of this invention can be detected by any sutiable method. Detection paradigms that can be employed to this end include optical methods, electrochemical methods (voltametry and amperometry techniques), atomic force microscopy, and radio frequency methods, e.g. , multipolar resonance spectroscopy.

Illustrative of optical methods, in addition to microscopy, both confocal and non-confocal, are detection of fluorescence, luminescence, chemiluminescence, absorbance, reflectance, transmittance, and birefringence or refractive index (e.g., surface plasmon resonance, ellipsometry, a resonant minor method, a grating coupler waveguide method or interferometry). [0056] Pursuant to one aspect ofthe present invention, a sample is analyzed by means of a "biochip," a term that denotes a solid substrate, having a generally planar surface, to which a capture reagent (adsorbent) is attached. Frequently, the surface of a biochip comprises a plurality of addressable locations, each of which has the capture reagent bound there. [0057] "Protein biochip" refers to a biochip adapted for the capture of polypeptides. Many protein biochips are described in the art. These include, for example, protein biochips produced by Ciphergen Biosystems (Fremont, CA), Packard BioScience Company (Meriden CT), Zyomyx (Hayward, CA), Phylos (Lexington, MA) and Biacore (Uppsala, Sweden). Examples of such protein biochips are described in the following patents or published patent applications: U.S. Patent No. 6,225,047; PCT International Publication No. WO 99/51773; U.S. Patent No. 6,329,209, PCT International Publication No. WO 00/56934 and U.S. Patent No. 5,242,828. A. Detection by mass spectrometry [0058] In one aspect, the biomarkers of this invention are detected by mass spectrometry. Examples of mass spectrometers are time-of-flight, magnetic sector, quadrupole filter, ion trap, ion cyclotron resonance, electrostatic sector analyzer and hybrids of these. "Mass spectrometry" refers to the use of a mass spectrometer to detect gas phase ions. "Laser desorption mass spectrometer" refers to a mass spectrometer which uses laser as a means to desorb, volatilize, and ionize an analyte. [0059] A prefereed mass spectrometric technique for use in the invention is "Surface Enhanced Laser Desorption and Ionization" or "SELDI," as described, for example, in U.S. Patents No. 5,719,060 and No. 6,225,047, both to Hutchens and Yip. This refers to a method of desorption ionization gas phase ion spectrometry (e.g., mass spectrometry) in which an analyte (here, one or more ofthe biomarkers) is captured on the surface of a SELDI probe that engages the probe interface ofthe mass spectrometer. [0060] One version of SELDI is called "Surface-Enhanced Affinity Capture" or "SEAC." This involves the use ofprobes comprised of an absorbent surface (a "SEAC probe"). In this context, "probe" refers to a device adapted to engage a probe interface and to present an analyte to ionizing energy for ionization and introduction into a mass spectrometer. A probe typically includes a solid substrate, either flexible or rigid, that has a sample-presenting surface, on which an analyte is presented to the source of ionizing energy. [0061] Another version of SELDI is Surface-Enhanced Neat Desorption

(SEND), which involves the use ofprobes comprising energy absorbing molecules that are chemically bound to the probe surface ("SEND probe"). The phrase "Energy absorbing molecules" (EAM) denotes molecules that are capable of absorbing energy from a laser desorption/ionization source and, thereafter, contributing to desorption and ionization of analyte molecules in contact therewith. The EAM category includes molecules used in

MALDI , frequently refened to as "matrix," and is exemplified by cinnamic acid derivatives, sinapinic acid (SPA), cyano-hydroxy-cinnamic acid (CHCA) and dihydroxybenzoic acid, ferulic acid, and hydroxyaceto-phenone derivatives. In certain embodiments, the energy absorbing molecule is incorporated into a linear or cross-linked polymer, e.g., a polymethacrylate. For example, the composition can be a co-polymer of α-cyano-4- methacryloyloxycinnamic acid and acrylate. In another embodiment, the composition is a co- polymer of α-cyano-4-methacryloyloxycinnamic acid, acrylate and 3-(tri-ethoxy)silyl propyl methacrylate. In another embodiment, the composition is a co-polymer of α-cyano-4- methacryloyloxycinnamic acid and octadecylmethacrylate ("C18 SEND"). SEND is further described in U.S. Patent No. 6,124,137. [0062] SEAC/SEND is a version of SELDI in which both a capture reagent and an energy absorbing molecule are attached to the sample presenting surface. SEAC/SEND probes therefore allow the capture of analytes through affinity capture and ionization/desorption without the need to apply external matrix. The C18 SEND biochip is a version of SEAC/SEND, comprising a C18 moiety which functions as a capture reagent, and a CHCA moiety which functions as an energy absorbing moiety. [0063] Another version of SELDI, called Surface-Enhanced Photolabile Attachment and Release (SEPAR), involves the use ofprobes having moieties attached to the surface that can covalently bind an analyte, and then release the analyte through breaking a photolabile bond in the moiety after exposure to light, e.g., to laser light. For instance, see U.S. Patent No. 5,719,060. SEPAR and other forms of SELDI are readily adapted to detecting a biomarker or biomarker profile, pursuant to the present invention. [0064] A "selective surface" can be used to capture the biomarkers for SELDI analysis. The selective surface has an "adsorbent," also called a "binding moiety" or "capture reagent" attached to the surface. An "adsorbent" or "capture reagent" or "binding moiety," can be any material capable of binding an analyte. The capture reagent may be attached directly to the substrate ofthe selective surface, or the substrate maybe a "reactive surface" that carries a "reactive moiety" that is capable of binding the capture reagent, e.g. , through a reaction forming a covalent or coordinate covalent bond. Epoxide and carbodiimidizole are useful reactive moieties to covalently bind polypeptide capture reagents such as antibodies or cellular receptors. Nitriloacetic acid and iminodiacetic acid are useful reactive moieties that function as chelating agents to bind metal ions that interact non-covalently with histidine containing peptides. [0065] "Chromatographic adsorbent" refers to an adsorbent material typically used in chromatography. Chromatographic adsorbents include, for example, ion exchange materials, metal chelators (e.g., nitriloacetic acid or iminodiacetic acid), immobilized metal chelates, hydrophobic interaction adsorbents, hydrophilic interaction adsorbents, dyes, simple biomolecules (e.g., nucleotides, amino acids, simple sugars and fatty acids) and mixed mode adsorbents (e.g., hydrophobic attraction/electrostatic repulsion adsorbents). "Biospecific adsorbent" refers to an adsorbent comprising a biomolecule, e.g., a nucleic acid molecule (e.g., an aptamer), a polypeptide, a polysaccharide, a lipid, a steroid or a conjugate of these (e.g., a glycoprotein, a lipoprotein, a glycolipid, a nucleic acid (e.g., DNA)-protein conjugate), hi certain instances, the biospecific adsorbent can be a macromolecular structure such as a multiprotein complex, a biological membrane or a virus. Examples of biospecific adsorbents are antibodies, receptor proteins and nucleic acids. Biospecific adsorbents typically have higher specificity for a target analyte than chromatographic adsorbents. Further examples of adsorbents for use in SELDI can be found in U.S. Patent No. 6,225,047. A "bioselective adsorbent" refers to an adsorbent that binds to an analyte with an affinity of at least 10^"8 M. "Adsorption" refers to detectable non-covalent binding of an analyte to an adsorbent or capture reagent. [0066] Protein biochips produced by Ciphergen Biosystems comprise surfaces having chromatographic or biospecific adsorbents attached thereto at addressable locations. Ciphergen ProteinChip^® arrays include NP20 (hydrophilic); H4 and H50 (hydrophobic); SAX-2, Q-10 and LSAX-30 (anion exchange); WCX-2, CM-10 and LWCX-30 (cation exchange); MAC-3, AC-30 and IMAC 40 (metal chelate); and PS-10, PS-20 (reactive surface with carboimidizole, expoxide) and PG-20 (protein G coupled through carboimidizole). [0067] The cation exchange biochips, WCX-2, CM-10 and LWCX-30, have carboxylate functionalities for cation exchange. [0068] Such biochips are further described in: U.S. Patent No. 6,579,719 (Hutchens and Yip, "Retentate Chromatography," June 17, 2003); PCT International Publication No . WO 00/66265 (Rich et al. , "Probes for a Gas Phase Ion Spectrometer," November 9, 2000); U.S. Patent No. 6,555,813 (Beecher et al, "Sample Holder with Hydrophobic Coating for Gas Phase Mass Spectrometer," April 29, 2003); U.S. Patent Application US 2003 0032043 Al (Pohl and Papanu, "Latex Based Adsorbent Chip," July 16, 2002); and PCT International Publication No. WO 03/040700 (Urn et al, "Hydrophobic Surface Chip," May 15, 2003); U.S. Provisional Patent Application No. 60/367,837,

(Boschetti et al, "Biochips With Surfaces Coated With Polysaccharide-Based Hydrogels," May 5, 2002) and U.S. Patent application entitled "Photocrosslinked Hydrogel Surface Coatings" (Huang et al, filed February 21, 2003). [0069] In keeping with the above-described principles, a substrate with an adsorbent is contacted with the sample, e.g., patient serum, for a period of time sufficient to allow biomarker or markers that may be present to bind to the adsorbent. After an incubation period, the substrate is washed to remove unbound material. Any suitable washing solutions can be used; preferably, aqueous solutions are employed. The extent to which molecules remain bound can be manipulated by adjusting the stringency ofthe wash. The emtion characteristics of a wash solution can depend, for example, on pH, ionic strength, hydrophobicity, degree of chaotropism, detergent strength, and temperature. Unless the probe has both SEAC and SEND properties, an energy absorbing molecule then is applied to the substrate with the bound biomarkers. [0070] The biomarkers bound to the substrates are detected in a gas phase ion spectiometer such as a time-of-flight mass spectiometer. The biomarkers are ionized by an ionization source such as a laser, the generated ions are collected by an ion optic assembly, and then a mass analyzer disperses and analyzes the passing ions. The detector then translates information ofthe detected ions into mass-to-charge ratios. Detection of a biomarker typically will involve detection of signal intensity. Thus, both the quantity and mass ofthe biomarker can be determined. [0071] In another mass spectrometry method, the biomarkers can be first captured on a chromatographic resin having chromatographic properties that bind the biomarkers. In the present example, this includes cation exchange resins or an immuno- chromatographic resin that comprises antibodies that bind the biomarkers. Unbound material can be washed from the resin. Then the biomarkers can be eluted from the resin. Finally, the eluted biomarkers can be detected by MALDI or by SELDI. [0072] Analysis of analytes by time-of-flight mass spectrometry generates a time-of-flight spectrum. The time-of-flight spectrum ultimately analyzed typically does not represent the signal from a single pulse of ionizing energy against a sample, but rather the sum of signals from a number of pulses. This reduces noise and increases dynamic range. This time-of-flight data is then subject to data processing, h Ciphergen's ProteinChip® software, data processing typically includes TOF-to-M/Z transformation to generate a mass spectrum, baseline subtraction to eliminate instrument offsets and high frequency noise filtering to reduce high frequency noise. [0073] Data generated by desorption and detection of biomarkers can be analyzed with the use of a programmable digital computer. The computer program analyzes the data to indicate the number of biomarkers detected, and optionally the strength ofthe signal and the determined molecular mass for each biomarker detected. Data analysis can include steps of determining signal strength of a biomarker and removing data deviating from a predetermined statistical distribution. For example, the observed peaks can be normalized, by calculating the height of each peak relative to some reference. The reference can be background noise generated by the instrument and chemicals such as the energy absorbing molecule which is set as zero in the scale. [0074] The computer can transform the resulting data into various formats for display. The standard spectrum can be displayed, but in one useful format only the peak height and mass information are retained from the spectrum view, yielding a cleaner image and enabling biomarkers with nearly identical molecular weights to be more easily seen. In another useful format, two or more spectia are compared, conveniently highlighting unique biomarkers and biomarkers that are up- or down-regulated between samples. Using any of these formats, one can readily determine whether a particular biomarker is present in a sample. [0075] Analysis generally involves the identification of peaks in the spectrum that represent signal from an analyte. Peak selection can be done visually, but software is available, as part of Ciphergen' s ProteinChip® software package, that can automate the detection of peaks. In general, this software functions by identifying signals having a signal- to-noise ratio above a selected threshold and labeling the mass ofthe peak at the centioid of the peak signal. In one useful application many spectra are compared to identify identical peaks present in some selected percentage ofthe mass spectia. One version of this software clusters all peaks appearing in the various spectia within a defined mass range, and assigns a mass (M/Z) to all the peaks that are near the mid-point ofthe mass (M/Z) cluster. [0076] Software used to analyze the data can include code that applies an algorithm to the analysis ofthe signal to determine whether the signal represents a peak in a signal that conesponds to a biomarker according to the present invention. The software also can subject the data regarding observed biomarker peaks to classification tree or ANN analysis, to determine whether a biomarker peak or combination of biomarker peaks is present that indicates SARS status. Analysis ofthe data may be "keyed" to a variety of parameters that are obtained, either directly or indirectly, from the mass spectrometric analysis ofthe sample. These parameters include, but are not limited to, the presence or absence of one or more peaks, the shape of a peak or group of peaks, the height of one or more peaks, the log ofthe height of one or more peaks, and other arithmetic manipulations of peak height data.

B. Detection by immunoassay [0077] In another embodiment, the biomarkers of this invention can be measured by immunoassay. Immunoassay requires biospecific capture reagents, such as antibodies, to capture the biomarkers. Antibodies can be produced by immunizing animals with the biomarkers. Biomarkers can be isolated from samples based on their binding characteristics. Alternatively, if the amino acid sequence of a polypeptide biomarker is known, the polypeptide can be synthesized and used to generate antibodies by methods well known in the art. [0078] This invention contemplates traditional immunoassays including, for example, sandwich immunoassays including ELISA or fluorescence-based immunoassays, as well as other enzyme immunoassays. Furthermore, if a biospecific capture reagent for the biomarker is attached to the surface of an MS probe, one can perform a SELDI-based immunoassay. IV. GENERATION OF CLASSIFICATION ALGORITHMS FOR QUALIFYING SARS STATUS [0079] In some embodiments, data derived from the spectra (e.g., mass spectra or time-of-flight spectia) that are generated using samples such as "known samples" can then be used to "train" a classification model. A "known sample" is a sample that has been pre- classified. The data that are derived from the spectra and are used to form the classification model can be refened to as a "training data set." Once trained, the classification model can recognize patterns in data derived from spectia generated using unknown samples. The classification model can then be used to classify the unknown samples into classes. This can be useful, for example, in predicting whether or not a particular biological sample is associated with a certain biological condition (e.g., diseased versus non-diseased). [0080] The training data set that is used to form the classification model may comprise raw data or pre-processed data. In some embodiments, raw data can be obtained directly from time-of-flight spectia or mass spectra, and then may be optionally "pre- processed" as described above. [0081] Classification models can be formed using any suitable statistical classification (or "learning") method that attempts to segregate bodies of data into classes based on objective parameters present in the data. Classification methods may be either supervised or unsupervised. Examples of supervised and unsupervised classification processes are described in Jain, "Statistical Pattern Recognition: A Review", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, January 2000, the teachings of which are incorporated by reference. [0082] In supervised classification, training data containing examples of known categories are presented to a learning mechanism, which learns one or more sets of relationships that define each ofthe known classes. New data may then be applied to the learning mechanism, which then classifies the new data using the learned relationships. Examples of supervised classification processes include linear regression processes (e.g., multiple linear regression (MLR), partial least squares (PLS) regression and principal components regression (PCR)), binary decision trees (e.g., recursive partitioning processes such as CART - classification and regression trees), artificial neural networks such as back propagation networks, discriminant analyses (e.g., Bayesian classifier or Fischer analysis), logistic classifiers, and support vector classifiers (support vector machines). [0083] A prefened supervised classification method is a recursive partitioning process. Recursive partitioning processes use recursive partitioning trees to classify spectia derived from unknown samples. Further details about recursive partitioning processes are provided in U.S. Patent Application 2002 0138208 Al to Paulse et al, "Method for analyzing mass spectra." [0084] In other embodiments, the classification models that are created can be formed using unsupervised learning methods. Unsupervised classification attempts to learn classifications based on similarities in the training data set, without pre classifying the spectia from which the training data set was derived. Unsupervised learning methods include cluster analyses. A cluster analysis attempts to divide the data into "clusters" or groups that ideally should have members that are very similar to each other, and very dissimilar to members of other clusters. Similarity is then measured using some distance metric, which measures the distance between data items, and clusters together data items that are closer to each other. Clustering techniques include the MacQueen's K-means algorithm and the Kohonen's Self- Organizing Map algorithm. [0085] Learning algorithms asserted for use in classifying biological information are described, for example, in PCT International Publication No. WO 01/31580 (Barnhill et al, "Methods and devices for identifying patterns in biological systems and methods of use thereof), U.S. Patent Application 2002 0193950 Al (Gavin et al, "Method or analyzing mass spectra"), U.S. Patent Application 2003 0004402 Al (Hitt et al, "Process for discriminating between biological states based on hidden patterns from biological data"), and U.S. Patent Application 2003 0055615 Al (Zhang and Zhang, "Systems and methods for processing biological expression data"). [0086] The classification models can be formed on and used on any suitable digital computer. Suitable digital computers include micro, mini, or large computers using any standard or specialized operating system such as a Unix, Windows™ or Linux™ based operating system. The digital computer that is used may be physically separate from the mass spectiometer that is used to create the spectra of interest, or it maybe coupled to the mass spectrometer. [0087] The training data set and the classification models according to embodiments ofthe invention can be embodied by computer code that is executed or used by a digital computer. The computer code can be stored on any suitable computer readable media including optical or magnetic disks, sticks, tapes, etc., and can be written in any suitable computer programming language including C, C++, visual basic, etc. [0088] The learning algorithms described above are useful both for developing classification algorithms for the biomarkers already discovered, or for finding new biomarkers for SARS. The classification algorithms, in turn, form the base for diagnostic tests by providing diagnostic values (e.g., cut-off points) for biomarkers used singly or in combination.

V. KITS FOR DETECTION OF BIOMARKERS FOR SA S [0089] In another aspect, the present invention provides kits for qualifying SARS status, which kits are used to detect biomarkers according to the invention. In one embodiment, the kit comprises a solid support, such as a chip, a microtiter plate or a bead or resin having a capture reagent attached thereon, wherein the capture reagent binds a biomarker ofthe invention. Thus, for example, the kits ofthe present invention can comprise mass spectrometry probes for SELDI, such as ProteinChip^® arrays. In the case of biospecfϊc capture reagents, the kit can comprise a solid support with a reactive surface, and a container comprising the biospecific capture reagent. [0090] The kit can also comprise a washing solution or instructions for making a washing solution, in which the combination ofthe capture reagent and the washing solution allows capture ofthe biomarker or biomarkers on the solid support for subsequent detection by, e.g., mass spectiometiy. The kit may include more than type of adsorbent, each present on a different solid support. [0091] In a further embodiment, such a kit can comprise instructions for suitable operational parameters in the form of a label or separate insert. For example, the instructions may inform a consumer about how to collect the sample or how to wash the probe. [0092] In yet another embodiment, the kit can comprise one or more containers with biomarker samples, to be used as standard(s) for calibration.

VI. USE OF BIOMARKERS FOR SARS IN SCREENING ASSAYS [0093] The methods ofthe present invention have other applications as well. For example, the biomarkers can be used to screen for compounds that modulate the expression ofthe biomarkers in vitro or in vivo, which compounds in turn may be useful in treating or preventing SARS in patients, hi another example, the biomarkers can he used to monitor the response to treatments for SARS. In yet another example, the biomarkers can be used in heredity studies to determine if the subject is at risk for developing SARS. [0094] Thus, for example, the kits of this invention could include a solid substrate having an cation exchange function, such as a protein biochip (e.g., a Ciphergen cation exchange ProteinChip array, e.g., WCX ProteinChip array) and a sodium acetate buffer for washing the substrate, as well as instructions providing a protocol to measure the biomarkers of this invention on the chip and to use these measurements to diagnose SARS.

[0095] The following examples are offered by way of illustration, and are not limiting. Example 1. Discovery of biomarkers for SARS [0096] Serum samples were collected from patients seen in Beijing City, China from January 1 through June 30, 2003. The samples were immediately aliquoted and stored at -80°C until assayed. Clinical diagnosis of SARS was assessed using the rules ofthe World Health Organization. [0097] Serum samples were evaluated using a Ciphergen ProteinChip®

System. Various chip chemistries (hydrophobic, ionic, cationic, and metal binding) were initially evaluated to determine which affinity chemistry provided the best serum profiles in terms of number and resolution of proteins. The WCX2 chip was observed to give the best results. [0098] Samples (3 μ) were diluted with U9 buffer (50 mM Tris-HCl pH 9, 9M urea, 2% CHAPS) (6 μ) in 4°C for 30 min and then into 108 μ binding buffer (50 mM NaAOc, pH 4.0) (total dilution: 39x ) and mixed well. 100 μ of diluted samples were loaded onto WCX biochips using a bioprocessor. 100 μ of diluted samples were loaded into each well covering a chip spot and incubated for 1 hr with gentle shaking at RT. Buffer was removed from the wells. 200 μ ofthe same binding buffer was added into each well and washed for 5 min with shaking. Washing was repeated. The washing buffer was removed and each well was washed with 200 μ HPLC-grade water to quick rinse the wells. The water was removed from the wells. Before SELDI analysis, 0.5 ml of a saturated solution of the EAM (sinapinic acid) in 50% (v/v) acetonitrile, 0.5% trifluoroacetic acid was applied onto each chip areay twice, letting the array surface air dry between each sinapinic acid application. [0099] Chips were placed in the Ciphergen PBSII mass spectrometer reader (Ciphergen Biosystems, Inc.), and time-of-flight spectra were generated. Mass accuracy was calibrated externally using the All-in- 1 peptide molecular mass standard (Ciphergen Biosystems, Inc.). [0100] The data analysis process used in this study involved three stages: (a) peak detection and alignment; (b) selection of peaks with the highest discriminatory power; and (c) data analysis using Biomarker Pattern Software (Ciphergen Biosystems, Inc.). [0101] Peak detection was performed using Ciphergen SELDI software version 3.0. [0102] The mass range from 2,000-50,000 Da was selected for analysis because this range contained the majority ofthe resolved protein/peptides. The molecular masses from 0-1,000 Da were eliminated from analysis because this area contains adducts and artifacts ofthe EAM and possibly other chemical contaminants. Peak detection involved (a) baseline subtraction, (b) mass accuracy calibration, and (c) automatic peak detection. [0103] The Biomarker Patterns Software was used to analyze the proteomic feature using a ttaining data set. Classification trees split up a data set into two nodes, using one rule at a time in the form of a question. The splitting decision is defined by presence or absence and the intensity levels of one peak. Classification of terminal nodes is determined by the class of samples representing the majority of samples in that node. A "cost" function is calculated that reflects the heterogeneity of each node. Peaks selected by this process to form the splitting rules are the ones that achieve the maximum reduction of cost in the two descendant nodes.

Example 2. Discovery of biomarkers for SARS 1. Patents and Samples [0104] Over 2000 serum specimens from the suspected/probable SARS patients admitted to 38 major hospitals in Beijing area between April 14 and June 5, 2003 were eligible for inclusion. The serum procurement, data management and blood collection protocols were approved by the Beijing SARS-contiol working group and according to WHO's biosafety guidelines. Among them, only 74 retrospective samples were selected from the probable patients whose blood samples were collected with onset of symptoms within 7 days at the time of admission. Probable cases were referred based on the eligibility criteria set forth by WHO. These referred probable cases also had radiographic evidence of infiltrates consistent with pneumonia or respiratory distress syndrome (RDS) on chest X-ray (CXR). The paired convalescent serum samples of this SARS cohort were tested positive in IgM seroconversion by IFA method (Beijing Genomics Institute), and 4 of them also tested positive in a DNA array test (Xiao et al, manuscript in preparation). The 147 non-SARS control serum samples were from recruited healthy donors (79), or patients with respiratory infections [pneumonia (16), high fever (33, 6 Flu among them)] or lung cancer (19). They were all negative in SARS-CoV seroconversion. [0105] These patients and samples were then divided into two parts, one part for the "training" set and the other for the "blinded" test set.

2. Proteomic analysis [0106] Three different chip chemistries (hydrophobic, anionic and cationic) were first evaluated to determine which affinity chemistry permitted the best serum profiles in terms ofthe number and resolution of proteins. The weak cationic exchange chip, i.e., the WCX chip, was observed to yield the best results with mass spectra from 0 to 200 kDa. The WCX chips in an 8-well bioprocessor format (Ciphergen) were chosen to allow a larger volume of serum for the chip array. The bioprocessor was pretreated with 150 μl of 100 mM sodium acetate (pH 4) on a platform shaker at a speed of 250 rpm for 5 min. The extra sodium acetate was discarded by inverting the bioprocessor on a paper towel. This process was repeated twice. The serum samples were thawed on ice in a Biosafety Level II cabinet, and 20 μl of each sample were mixed with 30 μl of U9 buffer (9 M urea, 1% 3-[(3- cholamiddopropyl) dimethylammonio]-l-propanesulfonic acid (CHAPS) in PBS) in a 1.5 ml eppendorf tube and vortexing at 4°C for 20 min, 100 μl of Ul buffer (U9 buffer diluted by 9- fold using 50 mM Tris-HCl (pH 7)) were then added to the serum/urea mixture, vortexing for 10 min and the reaction was stopped by addition of 600 μl of sodium acetate on ice. To each well, 50 μl ofthe serum/urea sample were applied, and the bioprocessor was sealed and shaken on a platform shaker at a speed of 250 rpm for 30 min. The extra serum/urea solution was discarded and washed three times with 100 mM sodium acetate as mentioned above.

The chips were removed from the bioprocessor, washed twice with deionized water, air dried, and added with 0.5 μl of EAM sinapinic acid saturated in 50% (v/v) acetonitrile, 0.5% trifluoroacetic acid. After air-drying, the sinapinic acid application was repeated. Chips were then placed in the Protein Biological System II (PBS II) mass spectrometer reader (Ciphergen), and time-of-flight spectra were generated by the average of 104 laser shots collected in the positive mode. The parameters for low energy readings were set with high mass to 50,000 Daltons, optimized from 3,000 to 15,000 Da at laser intensity 200, detector sensitivity 8, and a focus by optimization center. High energy readings were set with high mass up to 200,000 Da, optimized from 10,000 Da to 50,000 Da at laser intensity 230, detector sensitivity 9. Mass accuracy was calibrated externally using the All-in-One peptide molecular mass standard (Ciphergen).

3. Bioinformatics and biostatistics [0107] Peak detection was performed using Biomarker Wizard software 3.1.1

(Ciphergen). The mass-to-charge ratios (m/z) between 2,000 and 20,000 Da were selected for analysis because this range contained the majority ofthe resolved protein and peptides. The m/z between 0 and 2,000 Da was eliminated from analysis to avoid the interference from adducts, artifacts ofthe energy absorbing molecules, and possibly other chemical contaminants. Peak detection involved (1) baseline subtraction, (2) mass normalization to average using common calibrant peak 6635.1, and (3) normalization to the total ion cunent intensity with minimal M/Z=2,000 by using external normalization coefficient 0.2 (Normalization factor for individual spectrum^O^/Average ion current from each spectrum) for spectia obtained at different times or locations. The settings used for auto-detect peaks to cluster in the first pass were (1) signal to noise ratio = 5, p2) minimal peak threshold = 5% of all spectra. The peak clusters were completed by second-pass peak detection using (a) signal to noise ratio = 2, (b) 0.3% of mass for cluster window. An average of 99 peaks was detected in each spectrum. The mass range from 20,000-200,000 Da was analyzed in parallel. 4. Analytical procedure [0108] Data Analysis. The data analysis process used in this study involved three stages: (a) peak detection and alignment; (b) selection of peaks with the highest discriminatory power; and (c) data analysis using a decision tree algorithm. A random sampling [SARS acute, fever, pneumonia, lung cancer and health] with two strata [SARS acute and non-SARS] was used to separate the entire data set into training and test data sets. The tiaining data set consisted of SELDI spectia from 37 SARS acute, 74 non-SARS serum samples. The validity and accuracy ofthe classification algorithm were then challenged with a blinded test data set consisting of 37 SARS acute and 73 non-SARS samples. [0109] Decision Tree Classification. Construction ofthe decision tree classification algorithm was performed as described previously with modification based on the Biomarker Patters Software (BPS, Ciphergen). Classification trees were split into two branches or nodes, using one rule at a time of a question. Target variable levels were set at 2 and minimum value 0, and the decision was made based on the presence or absence and the intensity of one peak using Gini or Twoing method and favored even splits from 0.00 to 2.00 varied by 0.2 each time, with V-fold cross-validation from 6-12 changed by 2 for the growth of 88 trees. The lowest cost tree (value 0.068, Gini 2.0, V-fold 10) was selected for the final test. [0110] The classification algorithm used four masses between 3 and 12 kDa (m z=3939.08, 4137.71, 8136.64, and 11514.2 Da) and generated 5 terminal nodes (Figure 12). These discriminatory peaks efficiently split SARS specimens into terminal nodes 3 and 5, and non-SARS samples into terminal nodes 1, 2 and 4. Each mass peak showed the mean intensity ratio of SARS v. non-SARS greater than 3 and P value close to zero. Notably, the protein or peptide of masses at 3939.08, 8136.64, and 11514.2 Da was upregulated in acute SARS patients, while that ofthe mass at 4137.71 Da was down-regulated as compared to healthy or respiratory tract infection controls. A representative spectrum of SARS specimen aligned with that of a healthy control showed the combination ofthe four fingerprints in node 3 required for pattern recognition in the classifier (Figure 13). The unique presence ofthe "root" biomarker m/z = 3939.08 was demonstrated in the alignment of representative spectra of SARS acute (1, 3, 5 and 7 days post the onset of illness, from terminal node 5) and those of healthy control, fever and Flu, and pneumonia (Figure 10). This classification algorithm conectly predicted 37 of 37 (100%) ofthe SARS acute samples, and classified 72 of 74 (97.3%) ofthe controls as non-SARS (Table 1). [0111] The above classifier utilized only those masses in the low energy readings (m/z < 50 kDa). Alternatively, if masses from both the high (m z < 200 kDa) and low energy readings were pooled together, the classification algorithm would use five masses between 4 and 16 kDa (m/Z=4824.28, 8136.64, 11505.30, 14023.00 and 15369.20 Da (with masses m/z=8136.64 and 11505.30 overlapped with those in the previous paragraph) and generate 6 terminal nodes, which yielded the sensitivity and specificity of 94.6% (35 of 37) and 95.9% (71 of 74), respectively. However, because most ofthe SARS cases in this classifier (34 of 37) fell into the terminal node with the proteins/peptides down-regulated (M14023.0 < 0.611087 and M4824.28 < 0.746989 and M15369.2 < 3.27656), and because this alternative algorithm had to combine two energy settings for analysis, the decision tree generated with only low energy readings (Figure 12) might be more sensitive (sensitivity 100%) and more convenient for a clinical application. [0112] In summary, the algorithm based on WCX chip selectively identified a cluster pattern that completely delineated SARS probable patients from healthy and respiratory tract infection controls in the training set (sensitivity 100% and specificity 97.3%). This discriminatory profiling method precisely classified 97.3% SARS acute and 91.8% non-SARS in the completely blinded test set. More strinkingly, our classifier was able to discriminate SARS-CoV infection from other bacterial (mycoplasma, TB) and local (influenza) or systemic (measles) virus infections ofthe respiratory tract with specificity reaching 100% (95% CI 88-100). This was attributable to the inclusion of these inflammatory control samples in training and optimization ofthe classification algorithm. We have performed analogous assays utilizing the identical tiee classification upon samples from patients with onset of fever greater than 2, 3, 4 and > 5 weeks. The results indicated that algorithm we established in this work was applicable with sensitivity and specificity reaching (89.2%:91.8%), (86.0%:91.8%), (93.1%:91.8%), (79.5%:91.8%), respectively. [0113] The contents of each document mentioned in the application are incorporated herein in their entirety by reference.

Claims

WHAT IS CLAIMED IS: 1. A method for qualifying SARS status in a subject comprising: a. measuring at least one biomarker in a biological sample from the subject, wherein the at least one biomarker is selected from the group consisting of: M3939.08/M3941.56, M4137.71, M4823.48, M4097.99, M8136.64, M8902.83, M7625.91, M6633.58 and Ml 1514.20; and b. correlating the measurement with SARS status.

2. The method of claim 1 , wherein the at least one biomarker is selected from the group consisting of: M3939.08/M3941.56, M4823.48, M4097.99, M8902.83, M7625.91 and M6633.58.

3. The method of claim 1, comprising measuring each of M3939.08/M3941.56, M8136.64, M11514.20 and M4137.71.

4. The method of any of claims 1 , 2 or 3 , wherein the at least one biomarker is measured by capturing the biomarker on an adsorbent surface of a SELDI probe and detecting the captured biomarkers by laser deso tion-ionization mass spectiometiy.

5. The method of any of claims 1, 2 or 3, wherein the at least one biomarker is measured by immunoassay.

6. The method of any of claims 1, 2 or 3, wherein the at least one biomarker is captured on an adsorbent surface of a protein biochip and the captured biomarkers are measured.

7. The method of any of claims 1 or 2 comprising measuring a plurality ofthe biomarkers.

8. The method of claim 1 or claim 2 comprising measuring M3941.56, wherein a measured up-regulation of M3941.56 correlates acute SARS v. non-SARS or v. non-SARS fever.

9. The method of claim 1 or claim 2 comprising measuring M4823.48, wherein a measured down-regulation of M4823.48 correlates acute SARS v. non-SARS or v. non-SARS fever.

10. The method of claim 1 or claim 2 comprising measuring M4823.48 and M4097.99, wherein a measured down-regulation of M4823.48 and a measured up regulation of M4097.99 correlates acute SARS v. non-SARS.

11. The method of claim 1 or claim 2 comprising measuring M6633.58, wherein a measured down-regulation of M6633.58 correlates acute SARS v. convalescent SARS.

12. The method of any of claims 1 , 2 or 3, wherein the sample is serum.

13. The method of any of claims 1 , 2 or 3 , wherein correlating comprises comparing the measured amount with a diagnostic amount and qualifying SARS status based on the comparison.

14. The method of any of claims 1 , 2 or 3, wherein the conelating is performed by a software classification algorithm.

15. The method of claim 1 or claim 2 wherein SARS status is selected from non-SARS fever, acute SARS and convalescent SARS.

16. The method of claim 1 or claim 3 wherein SARS status is selected

17. The method of any of claim 1 , 2 or 3 further comprising (c) managing subject treatment based on the status.

18. The method of claim 4, wherein the adsorbent is a cation exchange adsorbent.

19. The method of claim 4, wherein the adsorbent is a biospecific adsorbent.

20. The method of claim 2 comprising measuring M7625.91 and M6633.58 and conelating the measurements with acute SARS v. convalescent SARS.

21. The method of claim 17, wherein managing subj ect treatment is selected from ordering more tests, prescribing medication and taking no further action.

22. The method of claim 17, further comprising: (d) measuring the at least one biomarker after subject management.

23. A method comprising measuring at least one biomarker in a sample from a subject, wherein the at least one biomarker is selected from the group consisting of: M3939.08/M3941.56, M4137.71, M4823.48, M4097.99, M8136.64, M8902.83, M7625.91, M6633.58 and Ml 1514.20.

24. The method of claim 23, wherein the at least one biomarker is selected from the group consisting of: M3939.08/M3941.56, M4823.48, M4097.99, M8902.83, M7625.91 and M6633.58.

25. The method of claim 23, further comprising measuring each ofthe following biomarkers: M3939.08/M3941.56, M8136.64, M11514.20 and M4137.71.

26. The method of any of claims 23 or 24, comprising measuring a plurality of the biomarkers .

27. The method of any of claim 23, 24 or 25, wherein the biomarker is measured by capturing the biomarker on an adsorbent surface of a SELDI probe and detecting the captured biomarkers by laser desorption-ionization mass spectrometry.

28. The method of any of claim 23, 24 or 25, wherein the sample is serum.

29. A kit comprising: (a) a solid support comprising at least one capture reagent attached thereto, wherein the capture reagents bind at least one a biomarker from a first group consisting of: M3939.08/M3941.56, M4137.71, M4823.48, M4097.99, M8136.64, M8902.83, M7625.91, M6633.58 and Ml 1514.20; and (b) instructions for using the solid support to detect the biomarker.

30. The kit of claim 29, wherein the capture reagents bind at least one biomarker from a first group consisting of: M3939.08/M3941.56, M4823.48, M4097.99, M8902.83, M7625.91 and M6633.58.

31. The kit of claim 29, wherein the capture reagents bind the biomarkers : M3939.08/M3941.56, M8136.64, M11514.20 and M4137.71.

32. The kit of any of claims 29, 30 or 31 , additionally comprising (c) a container comprising at least one ofthe biomarkers.

33. The kit of any of claims 29, 30 or 31 , wherem the solid support comprising a capture reagent is a SELDI probe.

34. The kit of any of claims 29, 30 or 31, wherein the solid support comprising a capture reagent is an cation exchange adsorbent.

35. A kit comprising: (a) a solid support comprising at least one capture reagent attached thereto, wherein the capture reagents bind at least one biomarker from a first group consisting of: M3939.08/M3941.56, M4137.71, M4823.48, M4097.99, M8136.64, M8902.83, M7625.91, M6633.58 and Ml 1514.20; and (b) a container comprising at least one ofthe biomarkers.

36. The kit of claim 28, wherein the capture reagents bind at least one biomarker from a first group consisting of: M3939.08/M3941.56, M4823.48, M4097.99, M8902.83, M7625.91 and M6633.58.

37. The kit of claim 35 , wherein the capture reagents bind the following biomarkers: M3939.08/M3941.56, M8136.64, M11514.20 and M4137.71.

38. The kit of any of claims 35, 36 or 37, wherein the solid support comprising a capture reagent is a SELDI probe.

39. The kit of any of claims 35 , 36 or 37, wherein the solid support comprising a capture reagent is a cation exchange adsorbent.

40. A software product comprising: a. code that accesses data attributed to a sample, the data comprising measurement of at least one biomarker in the sample, the biomarker selected from the group consisting of: M3939.08/M3941.56, M4137.71, M4823.48, M4097.99, M8136.64, M8902.83, M7625.91, M6633.58 and Ml 1514.20; and b. code that executes a classification algorithm that classifies the SARS status ofthe sample as a function ofthe measurement.

41. The software product of claim 40, wherein the biomarker is selected from the group consisting of: M3939.08/M3941.56, M4823.48, M4097.99, M8902.83, M7625.91 and M6633.58.

42. The software product of claim 40, wherein the biomarkers comprise each ofthe following biomarkers: M3939.08/M3941.56, M8136.64, Ml 1514.20 and M4137.71.