WO2007133957A1 - Biomarkers for mesothelioma: apoc1 and apoa2 - Google Patents

Abstract

The present invention provides protein-based biomarkers and biomarker combinations that are useful in qualifying mesothelioma status in a patient. In particular, it has been found that ApoC1 and ApoA2 are biomarkers for mesothelioma. The biomarkers can be detected by SELDI mass spectrometry.

Description

BIOMARKERS FOR MESOTHELIOMA: APOCl AND APOA2

CROSS REFERENCE TO RELATED APPLICATION

[0001] This application claims the benefit of U.S. Provisional Application No. 60/799,284, filed May 9, 2006, which is herein incorporated by reference in its entirety.

FIELD OF THE INVENTION

[0002] The invention relates generally to clinical diagnostics. BACKGROUND OF THE INVENTION

[0003] Malignant mesothelioma (MM) is a highly aggressive neoplasm, arising from the mesothelial surfaces of the pleural and peritoneal cavities, the pericardium, or the tunica vaginalis, and is commonly described as associated with a history of asbestos exposure. Simian Virus-40 infection has been discussed as a potential cause (Carbone et al., Oncogene 9: 1781-90, 1994). With median survival durations of 8-18 months from onset of symptoms, the prognosis is poor (Martino and Pass, CHn Lung Cancer 5: 290-8, 2004). Recent advances in both surgical and medical therapy have improved survival, but the treatments remain toxic and selection of appropriate patients for these therapies is difficult. Conclusive diagnosis has proven difficult in the past, since exclusive markers for MM have been lacking. Several imaging techniques such as chest x-ray, CT scan or MRI have proven useful when mesothelioma is suspected due to the presence of pleural effusion combined with a history of occupational or secondary asbestos exposure. When there is a suspicion for cancer; removal of pleural effusion or a biopsy is necessary to confirm the presence of cancerous cells. However, only in 33 to 84% of cases cytologic evidence is found in the pleural fluid (Whitaker, Cytopathology 11: 139-151, 2000). Also, the early symptoms of mesothelioma are generally non-specific, and may lead to a delay in diagnosis. Therefore, research into novel biomarkers for MM has been performed during the last years to yield information that may contain potential to guide therapeutic decisions in the near future. This research has been focusing on distinguishing malignant mesothelioma from other cancer types (mainly adenocarcinoma, which is particularly difficult to distinguish from mesothelioma when invaded into the pleura; Segal et al., Pathology of mesothelioma. In: Robinson BWS, Chahinian AP, eds. Mesothelioma). Consequently, research questions and sample collections considered have been depending on the interest fields of the reporting research groups. Several reports (Saqi et al., Diagn Cytopathol. 33(2): 65-70, 2005; Soomro et al, J Pak Med Assoc. 55(5):205-9, 2005; Dejmek and Hjerpe, Diagn Cytopathol. 32(3): 160-6, 2005; Ordonez, Hum Pathol. 36(4):372-80, 2005; Kimura and Kimura, Pathol Int. 55(2): 83-6, 2005; Barberis et al., Acta Cytol. 41(6):1757-61, 1997; Schwaller et al., Anticancer Res. 24(<5):4003-9, 2004; Malle et al., Acta Cytol. 49(7):ll-6, 2005; Pass et al., N Engl J Med, 15: 353; Robinson et al., Lung Cancer. 49(1): S109-11, 2005; Hassan et al., Clin Cancer Res 12: 447-453, 2006; Scherpereel et al., Am J Respir Crit Care Med 2006 (Epub ahead of print)) have been published on the results generated and are reviewed in Table 5 with their specific sensitivity and specificity performance for the discrimination of malignant mesotheliomas versus other cancer types.

[0004] The initially employed methods consisted of microscopic observations and immunohistochemistry (IHC) approaches on cells from either excised tumor tissue or cells in body cavity fluids (ascites, pleural effusions). Expression of epithelial membrane antigen on the luminal aspects of the tumor in IHC staining methods has proven essential in the diagnostic process. Reactions of cellular fractions with specific antibodies have characterized malignant mesothelioma by the presence of the proteins EMA (aka CA 15 -3 or mucin-1) (Wolanski et al., Cancer 82: 583-90, 1998; Saad et al., Diagn Cytopath 32(3): 156- 9, 2005), calretinin (Schwaller et al., Anticancer Res. 24(<5):4003-9, 2004), WTl (Wilms' tumor 1 antigen), cytokeratin 5/6, HBME-I (recognizes exclusively mesothelial cells), or mesothelin and a negative staining for the antigens carcinoembryonic antigen (CEA); thyroid transcription factor- 1; the tumor glycoproteins B72.3, MOC-31, and Ber-EP4; and the epithelial glycoprotein BG8 (Segal et al., Pathology of mesothelioma. In: Robinson BWS, Chahinian AP, eds. Mesothelioma). Studies with these same antibodies have also shown recognition of other tumor types, for example ovarian carcinoma do stain positive for mesothelin and WTl. However, sensitivity and specificity of tumor type determination success based on these luminal aspect proteins of the tumor may vary between tumor types.

[0005] In addition to IHC staining approaches on cells from body cavity fluids or tumor tissue biopsies, more readily accessible patient sample routes have been pursued for investigation of informative diagnostic content: correlation of specific protein expression levels with the malignant mesothelioma pathology were investigated in serum for osteopontin (Pass et al., N Engl J Med, 15: 353) and soluble mesothelin-related protein (SMRP) (Robinson et al., Lung Cancer. 49(1): S109-11, 2005; Hassan et al., Clin Cancer Res 12: 447- 453, 2006; Scherpereel et al., Am J Respir Crit Care Med 2006 (Epub ahead of print)).

[0006] A need exits for new methods of detecting mesothelioma in a subject. This invention is directed to this and other ends.

SUMMARY OF THE INVENTION

[0007] It has been found that ApoCl and ApoA2 are biomarkers for mesothelioma.

[0008] This invention provides, inter alia, a method for qualifying mesothelioma status in a subject comprising: (a) measuring one or more biomarkers in a biological sample from the subject, wherein at least one biomarker is ApoCl or ApoA2; and (b) correlating the measurement or measurements with a mesothelioma status selected from mesothelioma and non-mesothelioma. In certain embodiments, the method comprises measuring a plurality of biomarkers in the biological sample and the plurality of biomarkers comprises ApoCl and ApoA2. In certain aspects, ApoCl is mature ApoCl and ApoA2 is mature ApoA2. In certain aspects, the plurality of biomarkers further comprises at least one biomarker selected from the group consisting of: SMRP (soluble mesothelin-related protein), osteopontin and cytokeratin 8. In certain aspects, the one or more biomarkers is measured by mass spectrometry, e.g., SELDI-MS. In certain aspects, the at least one biomarker is measured by immunoassay. In certain aspects, the sample is pleural fluid. In certain aspects, the correlating is performed by executing a software classification algorithm. In certain aspects, non-mesothelioma is non-mesothelioma presenting with pleural effusion. In certain aspects, non-mesothelioma is a cancer. In certain aspects, the subject has been treated for mesothelioma and the mesothelioma is recurrence of cancer. In certain aspects, the method further comprises (c) reporting the status to the subject, (c) recording the status on a tangible medium, (c) managing subject treatment based on the status and/or (d) measuring the at least one biomarker after subject management and correlating the measurement with disease progression.

[0009] The invention also provides a method for determining the course of mesothelioma comprising: (a) measuring, at a first time, one or more biomarkers in a biological sample from the subject, wherein at least one biomarker is ApoCl or ApoA2; (b) measuring, at a second time, the at least one biomarker in a biological sample from the subject; and (c) comparing the first measurement and the second measurement; wherein the comparative measurements determine the course of the mesothelioma. In certain aspects, the method comprises measuring ApoCl and ApoA2 in a sample from a subject. In certain aspects, the method further comprises measuring at least one of SMRP (soluble mesothelin- related protein), osteopontin and cytokeratin 8 in the sample.

[0010] In another aspect this invention provides a kit comprising: (a) a solid support comprising at least one capture reagent attached thereto, wherein the capture reagent binds ApoCl or ApoA2; and (b) instructions for using the solid support to detect ApoCl or ApoA2. In certain aspects, the solid support comprising a capture reagent is a SELDI probe. In certain aspects, the method further comprises a standard reference of ApoCl or ApoA2.

[0011] In another aspect, this invention provides a kit comprising: (a) at least one solid support comprising at least one capture reagent attached thereto, wherein the capture reagent binds or reagents bind ApoCl and ApoA2; and (b) instructions for using the solid support or supports to detect ApoCl and ApoA2. In certain aspects, the solid support comprising a capture reagent is a SELDI probe. In certain aspects, the kit further comprises a standard reference of ApoCl and ApoA2.

[0012] Also provided by the present invention is 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, wherein at least one biomarker is ApoCl or ApoA2; and (b) code that executes a classification algorithm that classifies the mesothelioma status of the sample as a function of the measurement. In certain aspects, the at least one biomarker comprises ApoCl and ApoA2. In certain aspects, the at least one biomarker further comprises at least one biomarker selected from SMRP (soluble mesothelin-related protein), osteopontin and cytokeratin 8. In certain aspects, the at least one biomarker further comprises β2-microglobulin.

[0013] This invention further provides a method comprising communicating to a subject a diagnosis relating to mesothelioma status determined from the correlation of at least one biomarker in a sample from the subject, wherein at least one biomarker is ApoCl or ApoA2. In certain aspects, the at least one biomarker comprises ApoC2 and ApoA2. In certain aspects, the diagnosis is communicated to the subject via a computer-generated medium. [0014] Also provided is a method for identifying a compound that interacts with ApoCl or ApoA2, wherein said method comprises: (a) contacting ApoCl or ApoA2 with a test compound; and (b) determining whether the test compound interacts with ApoCl or ApoA2.

BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1. Apo C-I isoforms expression levels in selected MM ("malignant mesothelioma") and non-MM patient samples. (A) Negative control experiment in which lug Apo C-I presented to beads coupled with negative control antibody gives confidence in specificity of results with specific antibody. (B) Eluate (3 out of 30 μl) after lOμl of pleural effusion volumes for selected patient samples of MM (53, 52, 9) and non-MM (80, 78, 100) groups was incubated with (Apo C-I)-coupled ProtA beads. Replicate spectra represent independent technical duplicates of whole process of capture, elution and analysis on NP20 surface type ProteinChip array. (C) Calibrator protein Apo C-I (7.5 pmol) on NP20 ProteinChip array.

[0016] Figure 2. Captured Apo C-I isoform MWs correspond with decreased signal intensity after depleting incubations. In all spectra the same peaks as in the Apo C-I calibrator protein sample are present, confirming the hypothesized identity of the 6.6 and 6.4 kDa peaks as Apo C-I. (A) SELDI-TOF-MS spectrum for calibrator protein Apo C-I (7.5 pmol) applied on NP20 ProteinChip array. (B) Representative SELDI-TOF-MS spectra of eluates of MM patient (53) and non-MM patient (78) after bead based Apo C-I antibody capture. (C) SELDI-TOF-MS analysis of (Apo C-I)-depleted samples (only 5μl out of 50μl final capture volume analyzed, corresponding with 3μl out of 30μl eluate analyzed) on CMlO ProteinChip array surface at pH 4.0. (D) SELDI-TOF-MS analysis of non-depleted pleural effusion samples for MM and non-MM representative sample on CMlO ProteinChip array, pH 4.0.

[0017] Figure 3. Relative abundance of different Apo C-I isoforms in pleural effusions is dependent on sample being part of MM or non-MM groups. Relative ratio of the non-MM/MM average intensity values in the 6.4 kDa cluster peaks are smaller than in the 6.6 kDa cluster. (A) Representative images of the 6.4, 6.6 and barely detectable 6.8 kDa clusters in the spectra of a MM (52) and non-MM (100) patient pleural effusion sample. (B) Graph plotting the average intensity for the separate detectable peaks in the 6.4 and 6.6 kDa clusters of the selected three MM (52, 53, 9) and three non-MM (80, 78, 100) patient samples. (C) Table reflecting the selected MM and non-MM group samples' actual average values and the corresponding non-MM/MM ratios for the separate peaks.

[0018] Figure 4. Multivariate and bivariate model performance. (A) Principal Component Analysis with all discovered expression level differences as listed in Table 3. Two-dimensional projection of the dominant vectors. (B) Scatter plot of for samples' signal intensities Apo C-I versus Apo A-II.

[0019] Figure 5: Suggestion for sample workup flow scheme towards pleural mesothelioma malignancy diagnosis. Triage step, consisting of IHC analysis of pleural fluid cyto-analysis for CEA, Ber-EP4 and CD138 determinants on the cells' luminal aspects. Outcomes showing absence of these negative markers would initiate complementary processing of negative samples for presence of positive markers in pleural effusion fluid, serum or cells pelleted from pleural fluid. Target markers can be single markers or panels selected from Apo C-I, SMRP or osteopontin.

DETAILED DESCRIPTION OF THE INVENTION

1. INTRODUCTION

[0020] A biomarker is an organic biomolecule 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. As such, they are useful as markers for disease (diagnostics), therapeutic effectiveness of a drug (theranostics) and of drug toxicity.

[0021] Biomarkers of this invention were discovered using SELDI. Accordingly, they are characterized, in part, by their mass-to-charge ratio, the shape of the peak in a mass spectrum and their binding characteristics. These characteristics represent inherent characteristics of the biomolecule and not process limitations in the manner in which the biomolecule is discriminated. [0022] Biomarkers of this invention are characterized in part by their mass-to- charge ratio. The mass-to-charge ratio of each biomarker is provided herein. A particular molecular marker designated, for example, as "M6614" has a measured mass-to-charge ratio of 6614 D. The mass-to-charge ratios were determined from mass spectra generated on a Ciphergen PBS II mass spectrometer or a Ciphergen PCS 4000 mass spectrometer (Ciphergen Biosystems, Inc., Fremont, CA ("Ciphergen")). The PBS II is 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. The PCS 4000 instrument has a mass accuracy of about +/- 0.12 % raw data with an expected externally calibrated mass accuracy of 0.1% and internally calibrated mass accuracy of 0.01%. Additionally, the instrument has a mass resolution of about 1000 to 2000 m/dm, where m is mass and dm is the mass spectral peak width at 0.5 peak height. The mass- to-charge ratio of the biomarkers was determined using Biomarker Wizard™ software (Ciphergen). Biomarker Wizard software assigns a mass-to-charge ratio to a biomarker by clustering the mass-to-charge ratios of the same peaks from all the spectra analyzed, as determined by the PBS II or PCS 4000, taking the maximum and minimum mass-to-charge- ratio in the cluster, and dividing by two. Accordingly, the masses provided reflect these specifications.

[0023] Biomarkers of this invention are further characterized by the shape of their spectral peak in time-of-flight mass spectrometry. Mass spectra showing peaks representing the biomarkers are presented in the Figures.

[0024] Biomarkers of this invention also are characterized by their binding characteristics to adsorbent surfaces. The binding characteristics of each biomarker also are described herein.

2. BIOMARKERS FOR MESOTHELIOMA

[0025] It has been found that ApoCl and ApoA2 are biomarkers for mesothelioma. More particularly, it has been found that the ApoCl and ApoA2 levels in a biological sample are decreased in meosthelioma versus non-mesothelioma. Put another way, diminished ApoCl and/or ApoA2 levels are correlated with mesothelioma.

[0026] In certain embodiments, the disease statuses to be distinguished are: mesothelioma versus non-mesothelioma presenting with pleural effusion (e.g., malignancy versus non-malignancy such as infection or cardiovascular disease), and mesothelioma versus other malignancy (optionally presenting with pleural effusion) (e.g., lung cancer). Based on the status determined, further procedures may be indicated, including additional diagnostic tests or therapeutic procedures or regimens.

[0027] Specific biomarkers discovered are presented in Table 1 and Table 2. The "ProteinChip assay" column refers to chromatographic fraction in which the biomarker is found, the type of biochip to which the biomarker binds and the wash conditions, as per the Examples. In each case, the biomarkers each may be found using a variety of alternate ProteinChip assays. The "theoretical mass" provides the expected mass based on amino acid sequence and modifications such as disulfide bonds, etc.

[0028] ApoCl and ApoA2 were discovered to be biomarkers for mesothelioma using SELDI technology employing Ciphergen's ProteinChip arrays. More specifically, ApoCl levels can distinguish mesothelioma from non-mesothelioma, particularly when presented in a subject with pleural effusion. Pleural fluid samples were collected from subjects diagnosed with mesothelioma, non-malignant pleural effusion and malignancies with pleural effusion. The samples were applied to SELDI biochips and spectra of polypeptides in the samples were generated by time-of-flight mass spectrometry on a Ciphergen PBS Hc mass spectrometer. The spectra thus obtained were analyzed by CiphergenExpress™ Data Manager Software with Biomarker Wizard and Biomarker Pattern Software from Ciphergen. The mass spectra for each group were subjected to scatter plot analysis. A Mann- Whitney test analysis was employed to compare mesothelioma and control groups for each protein cluster in the scatter plot, and proteins were selected that differed significantly (p<0.01) between the two groups. This method is described in more detail in the Example section.

[0029] The preferred biological sources for detection of ApoCl and ApoA2 is pleural fluid. These biomarkers also may be detected in blood (e.g., serum or plasma).

2.1. ApoCl

[0030] One biomarker useful in this invention is apolipoprotein Cl, also referred to as ApoCl. The full-length ApoCl precursor is an 83 amino acid protein with a molecular weight of 9332 Da (SwissProt Accession No. P02654). In certain aspects, the ApoCl biomarkers of the present invention correspond to a mature version of the protein in which the signal sequence, representing the first 26 amino acids of the precursor, is removed, leaving a 57 amino acid protein. The amino acid sequence of mature ApoCl is: TPDV SSALDKLKEF GNTLEDKARE LISRIKQSEL SAKMREWFSE TFQKVKEKLK IDS (SEQ ID NO: 1).

[0031] References to "mature ApoCl" refer to a protein having this amino acid sequence, whether or not further modified. ApoCl is recognized by antibodies available from, e.g., Academy Bio-Medical Company, Inc. (Houston, TX). ApoCl elutes from an anion exchange resin at pH 9. It binds to a biochip having a cation exchange adsorbent surface and to a biochip having a hydrophobic adsorbent surface. Various non-limiting forms of the ApoCl biomarker are presented in Table 1.

TABLE 1

[0032] In certain aspects, the ApoCl biomarkers of the present invention correspond to truncated ApoCl. In certain embodiments, ApoCl is truncated at the amino terminal by 2 amino acids. The amino acid sequence is as follows:

DV SSALDKLKEF GNTLEDKARE LISRIKQSEL SAKMREWFSE TFQKVKEKLK IDS (SEQ ID NO:2). 2.2. ApoA2

[0033] Another biomarker that is useful in this invention is apolipoprotein A2, also referred to as ApoA2. The full-length ApoA2 precursor is a 100 amino acid protein (SwissProt Accession No. P02652). In certain aspects, the ApoA2 biomarker of the present invention corresponds to a mature version of the protein in which the first 23 amino acids of the precursor are removed, leaving a 77 amino acid protein. The amino acid sequence of mature ApoA2 is:

QAKEPCV ESLVSQYFQT VTDYGKDLME KVKSPELQAE AKSYFEKSKE QLTPLIKKAG TELVNFLSYF VELGTQPATQ (SEQ ID NO: 3).

[0034] References to "mature ApoA2" refer to a protein having this amino acid sequence, whether or not further modified. In certain aspects, as detected by mass spectrometry, the mature ApoA2 protein is further modified, containing pyro-Glu at position 1 and a cysteinylated Cys at position 6. This accounts for the mass being higher than predicted from the amino acid sequence alone. In certain aspects, the mature ApoA2 protein is dimerized. ApoA2 is recognized by antibodies available from, e.g., Academy Bio-Medical Company (cat. # 12A-RIa). ApoA2 elutes from an anion exchange resin in the organic wash. It binds to a biochip having a hydrophobic adsorbent surface. An Apo A2 biomarker is presented in Table 2. ApoA2 can be visualized on H50 arrays or IMAC30 or IMAC50 arrays, but is preferentially visualized on H50 arrays. In certain aspects, the Apo A2 biomarkers of the present invention correspond to truncated Apo A2.

TABLE 2

3. BIOMARKERS AND DIFFERENT FORMS OF A PROTEIN

[0035] Proteins frequently exist in a sample in a plurality of different forms. These forms can result from either or both of pre- and post-translational modification. Pre- translational modified forms include allelic variants, splice variants and RNA editing forms. Post-translationally modified forms include forms resulting from proteolytic cleavage (e.g., cleavage of a signal sequence or fragments of a parent protein), glycosylation, phosphorylation, lipidation, oxidation, methylation, cysteinylation, sulphonation and acetylation. When detecting or measuring a protein in a sample, the ability to differentiate between different forms of a protein depends upon the nature of the difference and the method used to detect or measure. For example, an immunoassay using a monoclonal antibody will detect all forms of a protein containing the eptiope and will not distinguish between them. However, a sandwich immunoassay that uses two antibodies directed against different epitopes on a protein will detect all forms of the protein that contain both epitopes and will not detect those forms that contain only one of the epitopes. In diagnostic assays, the inability to distinguish different forms of a protein has little impact when the forms detected by the particular method used are equally good biomarkers as any particular form. However, when a particular form (or a subset of particular forms) of a protein is a better biomarker than the collection of different forms detected together by a particular method, the power of the assay may suffer. In this case, it is useful to employ an assay method that distinguishes between forms of a protein and that specifically detects and measures a desired form or forms of the protein. Distinguishing different forms of an analyte or specifically detecting a particular form of an analyte is referred to as "resolving" the analyte.

[0036] Mass spectrometry is a particularly powerful methodology to resolve different forms of a protein because the different forms typically have different masses that can be resolved by mass spectrometry. Accordingly, if one form of a protein is a superior biomarker for a disease than another form of the biomarker, mass spectrometry may be able to specifically detect and measure the useful form where traditional immunoassay fails to distinguish the forms and fails to specifically detect to useful biomarker.

[0037] One useful methodology combines mass spectrometry with immunoassay. First, a biosepcific capture reagent (e.g., an antibody, aptamer or Affibody that recognizes the biomarker and other forms of it) is used to capture the biomarker of interest. Preferably, the biospecific capture reagent is bound to a solid phase, such as a bead, a plate, a membrane or an array. After unbound materials are washed away, the captured analytes are detected and/or measured by mass spectrometry. (This method also will also result in the capture of protein interactors that are bound to the proteins or that are otherwise recognized by antibodies and that, themselves, can be biomarkers.) Various forms of mass spectrometry are useful for dectecting the protein forms, including laser desorption approaches, such as traditional MALDI or SELDI, and electrospray ionization. [0038] Thus, when reference is made herein to detecting a particular protein or to measuring the amount of a particular protein, it means detecting and measuring the protein with or without resolving various forms of protein. For example, the step of "measuring ApoCl" includes measuring ApoCl by means that do not differentiate between various forms of the protein in a sample (e.g., certain immunoassays) as well as by means that differentiate some forms from other forms or that measure a specific form of the protein (e.g., any and/or all of ApoCl precursor, M6614, M6626, M6656 and M6656, individually or in combination). In contrast, when it is desired to measure a particular form or forms of a protein, the particular form or forms are specified. For example, "measuring M6614" means measuring M6614 in a way that distinguishes it from other forms of ApoCl, e.g., M6656 and M6821. Similarly, reference to "measuring ApoA2" includes measuring any and/or all forms of ApoA2, including, for example, ApoA2 precursor or M8799 found in a subject test sample, individually or in combination.

4. DETECTION OF BIOMARKERS FOR MESOTHELIOMA

[0039] The biomarkers of this invention can be detected by any suitable method. Detection paradigms 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 mirror method, a grating coupler waveguide method or interferometry).

[0040] In one embodiment, a sample is analyzed by means of a biochip. A biochip generally comprises a solid substrate having a substantially planar surface, to which a capture reagent (also called an adsorbent or affinity reagent) is attached. Frequently, the surface of a biochip comprises a plurality of addressable locations, each of which has the capture reagent bound there.

[0041] Protein biochips are biochips adapted for the capture of polypeptides. Many protein biochips are described in the art. These include, for example, protein biochips produced by Ciphergen Biosystems, Inc. (Fremont, CA), Zyomyx (Hayward, CA), Invitrogen (Carlsbad, CA), Biacore (Uppsala, Sweden) and Procognia (Berkshire, UK). Examples of such protein biochips are described in the following patents or published patent applications: U.S. Patent No. 6,225,047 (Hutchens & Yip); U.S. Patent No. 6,537,749 (Kuimelis and Wagner); U.S. Patent No. 6,329,209 (Wagner et al.); PCT International Publication No. WO 00/56934 (Englert et al.); PCT International Publication No. WO 03/048768 (Boutell et al.) and U.S. Patent No. 5,242,828 (Bergstrom et al.).

4.1. Detection by Mass Spectrometry

[0042] In a preferred embodiment, the biomarkers of this invention are detected by mass spectrometry, a method that employs a mass spectrometer to detect gas phase ions. Examples of mass spectrometers are time-of-flight, magnetic sector, quadrupole filter, ion trap, ion cyclotron resonance, electrostatic sector analyzer and hybrids of these.

[0043] In a further preferred method, the mass spectrometer is a laser desorption/ionization mass spectrometer. In laser desorption/ionization mass spectrometry, the analytes are placed on the surface of a mass spectrometry probe, a device adapted to engage a probe interface of the mass spectrometer and to present an analyte to ionizing energy for ionization and introduction into a mass spectrometer. A laser desorption mass spectrometer employs laser energy, typically from an ultraviolet laser, but also from an infrared laser, to desorb analytes from a surface, to volatilize and ionize them and make them available to the ion optics of the mass spectrometer. The analyis of proteins by LDI can take the form of MALDI or of SELDI. The analyis of proteins by LDI can take the form of MALDI or of SELDI.

[0044] Laser desorption/ionization in a single TOF instrument typically is performed in linear extraction mode. Tandem mass spectrometers can employ orthogonal extraction modes.

4.1.1. SELDI

[0045] A preferred mass spectrometric technique for use in the invention is "Surface Enhanced Laser Desorption and Ionization" or "SELDI," as described, for example, in U.S. Patents No. 5,719,060 and No. 6,225,047, both to Hutchens and Yip. This refers to a method of desorption/ionization gas phase ion spectrometry (e.g., mass spectrometry) in which an analyte (here, one or more of the biomarkers) is captured on the surface of a SELDI mass spectrometry probe.

[0046] SELDI also has been called is called "affinity capture mass spectrometry" or "Surface-Enhanced Affinity Capture" ("SEAC"). This version involves the use of probes that have a material on the probe surface that captures analytes through a non- covalent affinity interaction (adsorption) between the material and the analyte. The material is variously called an "adsorbent," a "capture reagent," an "affinity reagent" or a "binding moiety." Such probes can be referred to as "affinity capture probes" and as having an "adsorbent surface." The capture reagent can be any material capable of binding an analyte. The capture reagent is attached to the probe surface by physisorption or chemisorption. In certain embodiments the probes have the capture reagent already attached to the surface. In other embodiments, the probes are pre-activated and include a reactive moiety that is capable of binding the capture reagent, e.g., through a reaction forming a covalent or coordinate covalent bond. Epoxide and acyl-imidizole are useful reactive moieties to covalently bind polypeptide capture reagents such as antibodies or cellular receptors. Nitrilotriacetic acid and iminodiacetic acid are useful reactive moieties that function as chelating agents to bind metal ions that interact non-covalently with histidine containing peptides. Adsorbents are generally classified as chromatographic adsorbents and biospecific adsorbents.

[0047] "Chromatographic adsorbent" refers to an adsorbent material typically used in chromatography. Chromatographic adsorbents include, for example, ion exchange materials, metal chelators (e.g., nitrilotriacetic acid or iminodiacetic acid), immobilized metal chelates, hydrophobic interaction adsorbents, hydrophilic interaction adsorbents, dyes, simple biomolecules (e.g., nucleotides, amino acids, simple sugars and fatty acids) and mixed mode adsorbents (e.g., hydrophobic attraction/electrostatic repulsion adsorbents).

[0048] "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). In 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.

[0049] Protein biochips produced by Ciphergen comprise surfaces having chromatographic or biospecific adsorbents attached thereto at addressable locations. Ciphergen' s ProteinChip^® arrays include NP20 (hydrophilic); H4 and H50 (hydrophobic); SAX-2, Q-10 and (anion exchange); WCX-2 and CM-10 (cation exchange); IMAC-3, IMAC- 30 and IMAC-50 (metal chelate); and PS-IO, PS-20 (reactive surface with acyl-imidizole, epoxide) and PG-20 (protein G coupled through acyl-imidizole). Hydrophobic ProteinChip arrays have isopropyl or nonylphenoxy-poly(ethylene glycol)methacrylate functionalities. Anion exchange ProteinChip arrays have quaternary ammonium functionalities. Cation exchange ProteinChip arrays have carboxylate functionalities. Immobilized metal chelate ProteinChip arrays have nitrilotriacetic acid functionalities (IMAC 3 and IMAC 30) or O- methacryloyl-N,N-bis-carboxymethyl tyrosine funtionalities (IMAC 50) that adsorb transition metal ions, such as copper, nickel, zinc, and gallium, by chelation. Preactivated ProteinChip arrays have acyl-imidizole or epoxide functional groups that can react with groups on proteins for covalent binding.

[0050] Such biochips are further described in: U.S. Patent No. 6,579,719 (Hutchens and Yip, "Retentate Chromatography," June 17, 2003); U.S. Patent 6,897,072 (Rich et al, "Probes for a Gas Phase Ion Spectrometer," May 24, 2005); 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 Publication No. U.S. 2003-0032043 Al (Pohl and Papanu, "Latex Based Adsorbent Chip," July 16, 2002); and PCT International Publication No. WO 03/040700 (Um et al, "Hydrophobic Surface Chip," May 15, 2003); U.S. Patent Publication No. US 2003-0218130 Al (Boschetti et al, "Biochips With Surfaces Coated With Polysaccharide-Based Hydrogels," April 14, 2003) and U.S. Patent Publication No. U.S. 2005-059086 Al (Huang et al., "Photocrosslinked Hydrogel Blend Surface Coatings," March 17, 2005).

[0051] In general, a probe with an adsorbent surface is contacted with the sample for a period of time sufficient to allow the biomarker or biomarkers that may be present in the sample to bind to the adsorbent. 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 of the wash. The elution characteristics of a wash solution can depend, for example, on pH, ionic strength, hydrophobicity, degree of chaotropism, detergent strength, and temperature. Unless the probe has both SEAC and SEND properties (as described herein), an energy absorbing molecule then is applied to the substrate with the bound biomarkers.

[0052] In yet another method, one can capture the biomarkers with a solid-phase bound immuno-adsorbent that has antibodies that bind the biomarkers. After washing the adsorbent to remove unbound material, the biomarkers are eluted from the solid phase and detected by applying to a SELDI biochip that binds the biomarkers and analyzing by SELDI.

[0053] The biomarkers bound to the substrates are detected in a gas phase ion spectrometer such as a time-of-flight mass spectrometer. The biomarkers are ionized by an ionization source such as a laser, the generated ions are collected by an ion optic assembly, and then a mass analyzer disperses and analyzes the passing ions. The detector then translates information of the detected ions into mass-to-charge ratios. Detection of a biomarker typically will involve detection of signal intensity. Thus, both the quantity and mass of the biomarker can be determined.

4.1.2. SEND

[0054] Another method of laser desorption mass spectrometry is called Surface- Enhanced Neat Desorption ("SEND"). SEND involves the use of probes 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, contribute to desorption and ionization of analyte molecules in contact therewith. The EAM category includes molecules used in MALDI, frequently referred to as "matrix," and is exemplified by cinnamic acid derivatives, sinapinic acid (SPA), cyano-hydroxy-cinnamic acid (CHCA) and dihydroxybenzoic acid, ferulic acid, and hydroxyaceto-phenone derivatives. 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 OC- 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 ("C 18 SEND"). SEND is further described in U.S. Patent No. 6,124,137 and PCT International Publication No. WO 03/64594 (Kitagawa, "Monomers And Polymers Having Energy Absorbing Moieties Of Use In Desorption/ionization Of Analytes," August 7, 2003).

[0055] SEAC/SEND is a version of laser desorption mass spectrometry in which both a capture reagent and an energy absorbing molecule are attached to the sample presenting surface. SEAC/SEND probes therefore allow the capture of analytes through affinity capture and ionization/desorption without the need to apply external matrix. The Cl 8 SEND biochip is a version of SEAC/SEND, comprising a Cl 8 moiety which functions as a capture reagent, and a CHCA moiety which functions as an energy absorbing moiety.

4.1.3. SEPAR

[0056] Another version of LDI is called Surface-Enhanced Photolabile Attachment and Release ("SEPAR"). SEPAR involves the use of probes having moieties attached to the surface that can covalently bind an analyte, and then release the analyte through breaking a photolabile bond in the moiety after exposure to light, e.g., to laser light (see, U.S. Patent No. 5,719,060). SEPAR and other forms of SELDI are readily adapted to detecting a biomarker or biomarker profile, pursuant to the present invention.

4.1.4. MALDI

[0057] MALDI is a traditional method of laser desorption/ionization used to analyte biomolecules such as proteins and nucleic acids. In one MALDI method, the sample is mixed with matrix and deposited directly on a MALDI array. However, the complexity of biological samples such as serum and urine makes this method less than optimal without prior fractionation of the sample. Accordingly, in certain embodiments with biomarker s are preferably first captured with biospecific (e.g., an antibody) or chromatographic materials coupled to a solid support such as a resin (e.g., in a spin column). Specific affinity materials that bind the biomarkers of this invention are described above. After purification on the affinity material, the biomarkers are eluted and then detected by MALDI.

4.1.5. Other forms of ionization in mass spectrometry

[0058] In another method, the biomarkers are detected by LC-MS or LC-LC- MS. This involves resolving the proteins in a sample by one or two passes through liquid chromatography, followed by mass spectrometry analysis, typically electrospray ionization.

4.1.6. Data Analysis

[0059] 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. In 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.

[0060] Data generated by desorption and detection of biomarkers can be analyzed with the use of a programmable digital computer. The computer program analyzes the data to indicate the number of biomarkers detected, and optionally the strength of the signal and the 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.

[0061] 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 spectra are compared, conveniently highlighting unique biomarkers and biomarkers that are up- or down-regulated between samples. Using any of these formats, one can readily determine whether a particular biomarker is present in a sample.

[0062] 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 of the peak at the centroid of the peak signal. In one useful application, many spectra are compared to identify identical peaks present in some selected percentage of the mass spectra. One version of this software clusters all peaks appearing in the various spectra within a defined mass range, and assigns a mass (M/Z) to all the peaks that are near the mid-point of the mass (M/Z) cluster.

[0063] Software used to analyze the data can include code that applies an algorithm to the analysis of the signal to determine whether the signal represents a peak in a signal that corresponds to a biomarker according to the present invention. The software also can subject the data regarding observed biomarker peaks to classification tree or ANN analysis, to determine whether a biomarker peak or combination of biomarker peaks is present that indicates the status of the particular clinical parameter under examination. Analysis of the data may be "keyed" to a variety of parameters that are obtained, either directly or indirectly, from the mass spectrometric analysis of the sample. These parameters include, but are not limited to, the presence or absence of one or more peaks, the shape of a peak or group of peaks, the height of one or more peaks, the log of the height of one or more peaks, and other arithmetic manipulations of peak height data.

4.1.7. General protocol for SELDI detection of biomarkers for mesothelioma

[0064] A preferred protocol for the detection of the biomarkers of this invention is as follows. The biological sample to be tested, e.g., pleural effusion, preferably is subject to pre-fractionation before SELDI analysis. This simplifies the sample and improves sensitivity. A preferred method of pre-fractionation involves contacting the sample with an anion exchange chromatographic material, such as Q HyperD (BioSepra, SA). The bound materials are then subject to stepwise pH elution using buffers at pH 9, pH 7, pH 5, pH 4, pH3 and organic wash. Various fractions containing the biomarker are collected.

[0065] The sample to be tested (preferably pre-fractionated) is then contacted with an affinity capture probe comprising a cation exchange adsorbent (preferably a CMlO ProteinChip array) or a hydrophobic adsorbent (preferably an H50 ProteinChip array), again as indicated in Table 1. The biomarkers are detected by laser desorption/ionization mass spectrometry.

[0066] Alternatively, if antibodies that recognize the biomarker are available, they can be attached to a preactivated biochip, such as Ciphergen PS-10 or PS20 ProteinChip array, used to capture the analyte, and then the analyte can be examined by laser desorption/ionization mass spectroemtry.

[0067] Any robot that performs fluidics operations can be used in these assays, for example, those available from Hewlett Packard and Hamilton.

4.2. Detection by Immunoassay

[0068] In another embodiment of the invention, the biomarkers of the invention are measured by a method other than mass spectrometry or other than methods that rely on a measurement of the mass of the biomarker. In one such embodiment that does not rely on mass, the biomarkers of this invention are measured by immunoassay. Immunoassay requires biospecific capture reagents, such as antibodies, to capture the biomarkers. Antibodies can be produced by methods well known in the art, e.g., by immunizing animals with the biomarkers. Biomarkers can be isolated from samples based on their binding characteristics. Alternatively, if the amino acid sequence of a polypeptide biomarker is known, the polypeptide can be synthesized and used to generate antibodies by methods well known in the art.

[0069] This invention contemplates traditional immunoassays including, for example, sandwich immunoassays including ELISA or fluorescence-based immunoassays, as well as other enzyme immunoassays. Nephelometry is an assay done in liquid phase, in which antibodies are in solution. Binding of the antigen to the antibody results in changes in absorbance, which is measured. In the SELDI-based immunoassay, a biospecific capture reagent for the biomarker is attached to the surface of an MS probe, such as a pre-activated ProteinChip array. The biomarker is then specifically captured on the biochip through this reagent, and the captured biomarker is detected by mass spectrometry.

5. DETERMINATION OF SUBJECT MESOTHELIOMA STATUS

[0070] The biomarkers of the invention can be used in diagnostic tests to assess mesothelioma status in a subject, e.g., to diagnose mesothelioma. The phrase "mesothelioma status" includes any distinguishable manifestation of the disease, including non-disease. For example, mesothelioma status includes, without limitation, the presence or absence of disease (e.g., mesothelioma v. non-mesothelioma), the risk of developing disease, the stage of the disease, the progression of disease (e.g., progress of disease or remission of disease over time) and the effectiveness or response to treatment of disease.

[0071] The correlation of test results with mesothelioma status involves applying a classification algorithm of some kind to the results to generate the status. The classification algorithm may be as simple as determining whether or not the amount of ApoCl measured is above or below a particular cut-off number. When multiple biomarkers are used, the classification algorithm may be a linear regression formula. Alternatively, the classification algorithm may be the product of any of a number of learning algorithms described herein.

[0072] In the case of complex classification algorithms, it may be necessary to perform the algorithm on the data, thereby determining the classification, using a computer, e.g., a programmable ditigal computer. In either case, one can then record the status on tangible medium, for example, in computer-readable format such as a memory drive or disk or simply printed on paper. The result also could be reported on a computer screen. 5.1. Single Markers

[0073] The power of a diagnostic test to correctly predict status is commonly measured as the sensitivity of the assay, the specificity of the assay or the area under a receiver operated characteristic ("ROC") curve. Sensitivity is the percentage of true positives that are predicted by a test to be positive, while specificity is the percentage of true negatives that are predicted by a test to be negative. An ROC curve provides the sensitivity of a test as a function of 1 -specificity. The greater the area under the ROC curve, the more powerful the predictive value of the test. Other useful measures of the utility of a test are positive predictive value and negative predictive value. Positive predictive value is the percentage of people who test positive that are actually positive. Negative predictive value is the percentage of people who test negative that are actually negative.

[0074] The biomarkers of this invention show a statistical difference in different mesothelioma statuses. 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%, at least 98% and about 100%.

[0075] Each biomarker listed in Table 1 and Table 2 is differentially present in mesothelioma, and, therefore, each is individually useful in aiding in the determination of mesothelioma status. The method involves, first, measuring the selected biomarker in a subject sample using the methods described herein, e.g., capture on a SELDI biochip followed by detection by mass spectrometry and, second, comparing the measurement with a diagnostic amount or cut-off that distinguishes a positive mesothelioma status from a negative mesothelioma status. The diagnostic amount represents a measured amount of a biomarker above which or below which a subject is classified as having a particular mesothelioma status. For example, because ApoCl is down-regulated in mesothelioma compared to normal, a measured amount of ApoCl below the diagnostic cutoff provides a diagnosis of mesothelioma. As is well understood in the art, by adjusting the particular diagnostic cut-off used in an assay, one can increase sensitivity or specificity of the diagnostic assay depending on the preference of the diagnostician. The particular diagnostic cut-off can be determined, for example, by measuring the amount of the biomarker in a statistically significant number of samples from subjects with the different mesothelioma statuses, as was done here, and drawing the cut-off to suit the diagnostician's desired levels of specificity and sensitivity. 5.2. Combinations of Markers

[0076] 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 sensitivity and/or specificity of the test. A combination of at least two biomarkers is sometimes referred to as a "biomarker profile" or "biomarker fingerprint." Accordingly, ApoCl and Apo A2 can be combined with other biomarkers for mesothelioma to improve the sensitivity and/or specificity of the diagnostic test.

[0077] In particular, it has been found that a diagnostic test for mesothelioma status involving the measurement of both M6614 (ApoCl) and M8799 (ApoA2) has greater predictive power than the measurement of ApoCl alone. As indicated, ApoCl levels and ApoA2 levels are decreased in mesothelioma. It further has been found that a diagnostic test combining at least three biomarkers or, in certain instances, seven biomarkers, provides greater predictive power than the measurement of both ApoCl and ApoA2. More specifically, it is contemplated that a diagnostic test for mesothelioma status will include measuring ApoCl and/or ApoA2 and any, some or all of SMRP (soluble mesothelin-related protein), osteopontin, and cytokeratin 8, and correlating these measurements with mesothelioma status.

5.3. Mesothelioma status

[0078] Determining mesothelioma status typically involves classifying an individual into one of two or more groups (statuses) based on the results of the diagnostic test. The diagnostic tests described herein can be used to classify between a number of different states.

5.3.1. Presence of Disease

[0079] In one embodiment, this invention provides methods for determining the presence or absence of mesothelioma in a subject (status: mesothelioma v. non- mesothelioma). The presence or absence of mesothelioma is determined by measuring the relevant biomarker or biomarkers and then either submitting them to a classification algorithm or comparing them with a reference amount and/or pattern of biomarkers that is associated with the particular risk level. 5.3.2. Determining Risk of Developing Disease

[0080] In one embodiment, this invention provides methods for determining the risk of developing mesothelioma in a subject (status: low-risk v. high risk). Biomarker amounts or patterns are characteristic of various risk states, e.g., high, medium or low. The risk of developing a disease is determined by measuring the relevant biomarker or biomarkers and then either submitting them to a classification algorithm or comparing them with a reference amount and/or pattern of biomarkers that is associated with the particular risk level

5.3.3. Determining Stage of Disease

[0081] In one embodiment, this invention provides methods for determining the stage of disease in a subject. Each stage of the disease has a characteristic amount of a biomarker or relative amounts of a set of biomarkers (a pattern). The stage of a disease is determined by measuring the relevant biomarker or biomarkers and then either submitting them to a classification algorithm or comparing them with a reference amount and/or pattern of biomarkers that is associated with the particular stage. For example, one can classify between early stage mesothelioma and non-mesothelioma or among stage I mesothelioma, stage II mesothelioma and stage III mesothelioma.

5.3.4. Determining Course (Progression/Remission) of Disease

[0082] In one embodiment, this invention provides methods for determining the course of disease in a subject. Disease course refers to changes in disease status over time, including disease progression (worsening) and disease regression (improvement). Over time, the amounts or relative amounts (e.g., the pattern) of the biomarkers changes. For example, ApoCland transthryetin are decreased in disease. Therefore, the trend of these markers, either increased or decreased over time toward diseased or non-diseased indicates the course of the disease. Accordingly, this method involves measuring one or more biomarkers in a subject for at least two different time points, e.g., a first time and a second time, and comparing the change in amounts, if any. The course of disease is determined based on these comparisons.

5.4. Reporting the Status

[0083] Additional embodiments of the invention relate to the communication of assay results or diagnoses or both to technicians, physicians or patients, for example. In certain embodiments, computers will be used to communicate assay results or diagnoses or both to interested parties, e.g., physicians and their patients. In some embodiments, the assays will be performed or the assay results analyzed in a country or jurisdiction which differs from the country or jurisdiction to which the results or diagnoses are communicated.

[0084] In a preferred embodiment of the invention, a diagnosis based on the differential presence in a test subject of any the biomarkers of Table 1 or Table 2 is communicated to the subject as soon as possible after the diagnosis is obtained. The diagnosis may be communicated to the subject by the subject's treating physician. Alternatively, the diagnosis may be sent to a test subject by email or communicated to the subject by phone. A computer may be used to communicate the diagnosis by email or phone. In certain embodiments, the message containing results of a diagnostic test may be generated and delivered automatically to the subject using a combination of computer hardware and software which will be familiar to artisans skilled in telecommunications. One example of a healthcare-oriented communications system is described in U.S. Patent Number 6,283,761; however, the present invention is not limited to methods which utilize this particular communications system. In certain embodiments of the methods of the invention, all or some of the method steps, including the assaying of samples, diagnosing of diseases, and communicating of assay results or diagnoses, may be carried out in diverse (e.g., foreign) jurisdictions.

5.5. Subject Management

[0085] In certain embodiments of the methods of qualifying mesothelioma status, the methods further comprise managing subject treatment based on the status. Such management includes the actions of the physician or clinician subsequent to determining mesothelioma status. For example, if a physician makes a diagnosis of mesothelioma, then a certain regime of treatment, such as prescription or administration of chemotherapy might follow. Alternatively, a diagnosis of non-mesothelioma or non-mesothelioma might be followed with further testing to determine a specific disease that might the patient might be suffering from. Also, if the diagnostic test gives an inconclusive result on mesothelioma status, further tests may be called for.

6. GENERATION OF CLASSIFICATION ALGORITHMS FOR QUALIFYING MESOTHELIOMA STATUS

[0086] In some embodiments, data derived from the spectra (e.g., mass spectra or time-of-flight spectra) 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 referred to as a "training data set." Once trained, the classification model can recognize patterns in data derived from spectra generated using unknown samples. The classification model can then be used to classify the unknown samples into classes. This can be useful, for example, in predicting whether or not a particular biological sample is associated with a certain biological condition (e.g., diseased versus non-diseased).

[0087] 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 spectra or mass spectra, and then may be optionally "pre- processed" as described above.

[0088] 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.

[0089] 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 of the 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).

[0090] A preferred supervised classification method is a recursive partitioning process. Recursive partitioning processes use recursive partitioning trees to classify spectra derived from unknown samples. Further details about recursive partitioning processes are provided in U.S. Patent 6,675,104 (Paulse et al, "Method for analyzing mass spectra"). [0091] 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 spectra from which the training data set was derived. Unsupervised learning methods include cluster analyses. A cluster analysis attempts to divide the data into "clusters" or groups that ideally should have members that are very similar to each other, and very dissimilar to members of other clusters. Similarity is then measured using some distance metric, which measures the distance between data items, and clusters together data items that are closer to each other. Clustering techniques include the MacQueen's K-means algorithm and the Kohonen's Self- Organizing Map algorithm.

[0092] 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 No. 2002 0193950 Al (Gavin et al, "Method or analyzing mass spectra"), U.S. Patent Application No. 2003 0004402 Al (Hitt et al, "Process for discriminating between biological states based on hidden patterns from biological data"), and U.S. Patent Application No. 2003 0055615 Al (Zhang and Zhang, "Systems and methods for processing biological expression data").

[0093] 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 spectrometer that is used to create the spectra of interest, or it may be coupled to the mass spectrometer.

[0094] The training data set and the classification models according to embodiments of the invention can be embodied by computer code that is executed or used by a digital computer. The computer code can be stored on any suitable computer readable media including optical or magnetic disks, sticks, tapes, etc., and can be written in any suitable computer programming language including C, C++, visual basic, etc.

[0095] The learning algorithms described above are useful both for developing classification algorithms for the biomarkers already discovered, or for finding new biomarkers for mesothelioma. 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.

7. COMPOSITIONS OF MATTER

[0096] In another aspect, this invention provides compositions of matter based on the biomarkers of this invention.

[0097] In one embodiment, this invention provides biomarkers of this invention in purified form. Purified biomarkers have utility as antigens to raise antibodies. Purified biomarkers also have utility as standards in assay procedures. As used herein, a "purified biomarker" is a biomarker that has been isolated from other proteins and peptides, and/or other material from the biological sample in which the biomarker is found. The biomarkers can be isolated from biological fluids, such as urine or serum. Biomarkers may be purified using any method known in the art, including, but not limited to, mechanical separation (e.g., centrifugation), ammonium sulphate precipitation, dialysis (including size-exclusion dialysis), electrophoresis (e.g. acrylamide gel electrophoresis) size-exclusion chromatography, affinity chromatography, anion-exchange chromatography, cation-exchange chromatography, and methal-chelate chromatography. Such methods may be performed at any appropriate scale, for example, in a chromatography column, or on a biochip.

[0098] In another embodiment, this invention provides a biospecific capture reagent, optionally in purified form, that specifically binds a biomarker of this invention. In one embodiment, the biospecific capture reagent is an antibody. Such compositions are useful for detecting the biomarker in a detection assay, e.g., for diagnostics.

[0099] In another embodiment, this invention provides an article comprising a biospecific capture reagent that binds a biomarker of this invention, wherein the reagent is bound to a solid phase. For example, this invention contemplates a device comprising bead, array, membrane, monolith or microtiter plate derivatized with the biospecific capture reagent. Such articles are useful in biomarker detection assays.

[0100] In another aspect this invention provides a composition comprising a biospecific capture reagent, such as an antibody, bound to a biomarker of this invention, the composition optionally being in purified form. Such compositions are useful for purifying the biomarker or in assays for detecting the biomarker.

[0101] In another embodiment, this invention provides an article comprising a solid substrate to which is attached an adsorbent, e.g., a chromatographic adsorbent or a biospecific capture reagent, to which is further bound a biomarker of this invention. In one embodiment, the article is a biochip or a probe for mass spectrometry, e.g., a SELDI probe. Such articles are useful for purifying the biomarker or detecting the biomarker.

8. KITS FOR DETECTION OF BIOMARKERS FOR MESOTHELIOMA

[0102] In another aspect, the present invention provides kits for qualifying mesothelioma status, which kits are used to detect biomarkers according to the invention. In one embodiment, the kit comprises a solid support, such as a biochip, a microtiter plate or a bead or resin having a capture reagent attached thereon, wherein the capture reagent binds a biomarker of the invention. Thus, for example, the kits of the present invention can comprise mass spectrometry probes for SELDI, such as ProteinChip arrays. In the case of biospecific capture reagents, the kit can comprise a solid support with a reactive surface, and a container comprising the biospecific capture reagent.

[0103] The kit can also comprise a washing solution or instructions for making a washing solution, in which the combination of the capture reagent and the washing solution allows capture of the biomarker or biomarkers on the solid support for subsequent detection by, e.g., mass spectrometry. The kit may include more than type of adsorbent, each present on a different solid support.

[0104] 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, how to wash the probe or the particular biomarkers to be detected.

[0105] In yet another embodiment, the kit can comprise one or more containers with biomarker samples, to be used as standard(s) for calibration.

9. USE OF BIOMARKERS FOR IMAGING

[0106] Non-invasive medical imaging techniques such as Positron Emisson Tomography (PET) or single photon emission computerized tomography (SPECT) imaging are particularly useful for the detection of cancer, coronary artery disease and brain disease. PET and SPECT imaging shows the chemical functioning of organs and tissues, while other imaging techniques - such as X-ray, CT and MRI - show structure. The use of PET and SPECT imaging has become increasingly useful for qualifying and monitoring the development of mesothelioma. See, e.g., Haberkom, Lung Cancer 45(7):S73-6, 2004; Nanni et al., Cancer Biother Radiopharm. 19(2): 149-54, 2004; Wang et al., Radiographics 24(1): 105-19, 2004.

[0107] The peptide biomarkers disclosed herein, or fragments thereof, can be used in the context of PET and SPECT imaging applications. For example, antibodies that recognize the biomarkers of the present invention can be labeled with appropriate tracer residues for PET or SPECT applications.

[0108] Antisense technology may be used to detect expression of transcripts whose translation is correlated with the biomarkers identified herein. For example, the use of antisense peptide nucleic acid (PNA) labeled with an appropriate radionuclide, such as ¹¹¹In, and conjugated to a brain drug-targeting system to enable transport across biologic membrane barriers, has been demonstrated to allow imaging of endogenous gene expression in brain cancer. See Suzuki et al., Journal of Nuclear Medicine 10: 1766-1775, 2004. Suzuki et al. utilize a delivery system comprising monoclonal antibodies that target transferring receptors at the blood-brain barrier and facilitate transport of the PNA across that barrier.

10. DETERMINING THERAPEUTIC EFFICACY OF PHARMACEUTICAL DRUG

[0109] In another embodiment, this invention provides methods for determining the therapeutic efficacy of a pharmaceutical drug. These methods are useful in performing clinical trials of the drug, as well as monitoring the progress of a patient on the drug. Therapy or clinical trials involve administering the drug in a particular regimen. The regimen may involve a single dose of the drug or multiple doses of the drug over time. The doctor or clinical researcher monitors the effect of the drug on the patient or subject over the course of administration. If the drug has a pharmacological impact on the condition, the amounts or relative amounts (e.g., the pattern or profile) of the biomarkers of this invention changes toward a non-disease profile. For example, ApoCl and ApoA2 are decreased with disease. Therefore, one can follow the course of the amounts of these biomarkers in the subject during the course of treatment. Accordingly, this method involves measuring one or more biomarkers in a subject receiving drug therapy, and correlating the amounts of the biomarkers with the disease status of the subject. One embodiment of this method involves determining the levels of the biomarkers for at least two different time points during a course of drug therapy, e.g., a first time and a second time, and comparing the change in amounts of the biomarkers, if any. For example, the biomarkers can be measured before and after drug administration or at two different time points during drug administration. The effect of therapy is determined based on these comparisons. If a treatment is effective, then the biomarkers will trend toward normal, while if treatment is ineffective, the biomarkers will trend toward disease indications.

11. USE OF BIOMARKERS FOR MESOTHELIOMA IN SCREENING ASSAYS AND METHODS OF TREATING MESOTHELIOMA

[0110] The methods of the present invention have other applications as well. For example, the biomarkers can be used to screen for compounds that modulate the expression of the biomarkers in vitro or in vivo, which compounds in turn may be useful in treating or preventing mesothelioma in patients. In another example, the biomarkers can be used to monitor the response to treatments for mesothelioma.

[0111] Compounds suitable for therapeutic testing may be screened initially by identifying compounds which interact with ApoCl and ApoA2. By way of example, screening might include recombinantly expressing a biomarker, purifying the biomarker, and affixing the biomarker to a substrate. Test compounds would then be contacted with the substrate, typically in aqueous conditions, and interactions between the test compound and the biomarker are measured, for example, by measuring elution rates as a function of salt concentration. Certain proteins may recognize and cleave one or more biomarkers of Table 1 or Table 2, in which case the proteins may be detected by monitoring the digestion of one or more biomarkers in a standard assay.

[0112] Test compounds capable of modulating the activity of any of the biomarkers of Table 1 or Table 2 may be administered to patients who are suffering from or are at risk of developing mesothelioma or other cancer. For example, the administration of a test compound which increases the activity of a particular biomarker may decrease the risk of mesothelioma in a patient if the activity of the particular biomarker in vivo prevents the accumulation of proteins for mesothelioma. Conversely, the administration of a test compound which decreases the activity of a particular biomarker may decrease the risk of mesothelioma in a patient if the increased activity of the biomarker is responsible, at least in part, for the onset of mesothelioma.

[0113] At the clinical level, screening a test compound includes obtaining samples from test subjects before and after the subjects have been exposed to a test compound. The levels in the samples of one or more of the biomarkers listed in Table 1 or Table 2 may be measured and analyzed to determine whether the levels of the biomarkers change after exposure to a test compound. The samples may be analyzed by mass spectrometry, as described herein, or the samples may be analyzed by any appropriate means known to one of skill in the art. For example, the levels of one or more of the biomarkers listed in Table 1 or Table 2 may be measured directly by Western blot using radio- or fluorescently-labeled antibodies which specifically bind to the biomarkers. Alternatively, changes in the levels of mRNA encoding the one or more biomarkers may be measured and correlated with the administration of a given test compound to a subject. In a further embodiment, the changes in the level of expression of one or more of the biomarkers may be measured using in vitro methods and materials. For example, human tissue cultured cells which express, or are capable of expressing, one or more of the biomarkers of Table 1 or Table 2 may be contacted with test compounds. Subjects who have been treated with test compounds will be routinely examined for any physiological effects which may result from the treatment. In particular, the test compounds will be evaluated for their ability to decrease disease likelihood in a subject. Alternatively, if the test compounds are administered to subjects who have previously been diagnosed with mesothelioma, test compounds will be screened for their ability to slow or stop the progression of the disease.

12. EXAMPLES

12.1. Methods

12.1.1. Mesothelioma patients and controls

[0114] Pleural fluid was collected after informed consent from patients who presented with large pleural effusions. Removal of effusion was performed to treat patients' shortness of breath. Pleural effusions were due to the following conditions: 41 patients with mesothelioma and 48 age-matched patients with effusions due to other causes (non-infective inflammatory exudates (n=3), transudates (n=3), a still unknown cause (n=2), and other malignancies (n=40)) with no documented asbestos exposure (Table 3). The non- mesothelioma cancers with pleural involvement can be divided into carcinoma (28), NSCLC (6), sarcoma (3), lymphoma (2), and melanoma (1). From some patients, effusions were taken at multiple time-points. Informed consent was obtained from all subjects. The study was approved by the Human Ethics committee of the Erasmus MC, Rotterdam, The Netherlands. TABLE 3 Patients Characteristics

Group 1. Group 2.

Mesothelioma effusions Other effusions

Number of patients 41 48

1 x thoracentesis 34 patients 44 patients

2 x thoracentesis 3 patients 2 patients

3 x thoracentesis 2 patients 2 patients

4 x thoracentesis 2 patients 0 patients

Number of samples 54 54

Age range (mean) 44 - 80 (62.5) yr 31 - 84 (61.1) yr

Sex S : ? 39 : 2 20 : 28

Volume effusion range 10 - 4000 (1077) ml 10 - 2000 (603) ml

(mean)

Diagnose Exudative pleural effusion Exudative pleural effusion number of patients) Malignancy Malignancy

Pleural mesothelioma (41) Carcinoma (28)

NSCLC (6)

Sarcoma (3)

Non-hodgkin lymphoma (2)

Melanoma (1)

Lymphatic abnormalities (3)

Undiagnosed effusions (2)

Transudatieve pleural effusion

Renal failure (2)

Lungembolie (1)

12.1.2. Antibodies and proteins

[0115] All antibodies and calibrator proteins were purchased from US Biologicals, US: Goat anti-human apolipoprotein C-I (Academy Bio-Medical Company, cat # A2299-61); Apolipoprotein C-I, Human (Apo C-I), purified from human plasma (Academy Bio-Medical Company, cat # A2290-60). ProteinA HyperD beads were purchased from BioSepra (Cergy-Saint-Christophe, France).

12.1.3. Processing of pleural effusions

[0116] Irrespective of cause, all effusions were collected, processed, and stored in the same way. Prior to the pleural fluid removal procedure, patients were given a local anesthetic (Lidocaine 1%). After introducing a metallic needle in the pleural cavity, fluid was gently aspirated and collected in sterile tubes without anticoagulant or other additives. Total volumes varied from 10 ml to 4 L. Pleural cells were removed by centrifugation at 400xg for 10 min at 4⁰C and supernatant was then subjected to a second centrifugation at 3000xg for 20 min at 4⁰C and the resulting supernatant was stored in aliquots at -8O⁰C until further analysis. No infectious agents were observed in the pleural fluid and bacterial cultures were negative in all cases.

12.1.4. Protein expression profiling

[0117] Pleural effusion was fractionated using anion exchange chromatography according to the protocol described by Gilbert et al (Gilbert et al., Methods MoI Biol. 264: 259-69, 2004). Briefly, pleural effusion was applied to Q HyperD F anion exchange resin, and fractions were eluted using a descending stepwise pH gradient. Fraction 1 = pH 9, Fraction 2 = pH 7, Fraction 3 = pH 5, Fraction 4 = pH 4, Fraction 5 = pH3 and Fraction 6 = organic wash. Each of these fractions was applied to IMAC30 and CMlO ProteinChip arrays. The arrays were read in PBS Hc ProteinChip readers, a time-lag focusing, linear, laser desorption/ionization- time of flight mass spectrometer. All spectra were acquired in the positive-ion mode. Time-lag focusing delay times were set at 400 ns for low-mass scans and 1900 ns for high-mass scans. Ions were extracted using a 3 kV ion extraction pulse and accelerated to a final velocity using 20 kV of acceleration potential. The system employed a pulsed nitrogen laser at repetition rates varying from 2-5 pulses per second. Typical laser fluence varied from 30-150 mJ/mm2. An automated analytical protocol was used to control the data acquisition process in most of the sample analysis. Each spectrum was an average of at least 50 laser shots and externally calibrated against a mixture of known peptides or proteins.

12.1.5. Data analysis

[0118] Data preprocessing was performed in CiphergenExpress, version 2.1. Spectra were baseline subtracted using a fitting window of 8 times expected peak width. Data were normalized using an external coefficient of 1, and peak detection was performed using the automated Biomarker Wizard algorithm. Univariate analysis was performed using the Mann-Whitney test for each pairwise comparison. Multivariate analysis was performed using PAM (Prediction Analysis for Microarrays) (Tibshirani et al., PNAS 99: 6567-6572, 2002). It is a nearest shrunken centroid method that can be used in high-dimensional classification problems. Each prediction variable (i.e., peak) is standardized by the within- group standard deviation so that higher weight is given to the peaks whose intensity is homogeneous within the same group. This algorithm shrinks the class/group centroids toward the overall centroid. The optimal amount of shrinkage is determined by cross- validation. This method can be used to perform feature selection and classification simultaneously. In this study, we emphasize the feature selection functionality. Permutation testing was performed to determine the threshold for calling a peak significant based on random class assignments. By assuming that each of those test statistics is to occur equally likely, the original test statistic (without any permutation) is compared with the test statistics distribution and the significance probability is calculated. Hothorn and Lausen (2003) (Hothorn and Lausen, Computational Statistics & Data Analysis 43: 121-137, 2003) describe the detailed algorithm about permutation tests used in this study. The optimal peaks were analyzed by ROC (Receiving Operating Characteristics Curve) analysis. Principal component analysis was performed to visualize the separating power of the best peaks. All analyses were performed using the statistical package R.

12.1.6. Antibody-capture based confirmation of pleural effusion profiling biomarker candidates' identities

[0119] The appropriate antibodies were coupled to Protein A HyperD beads (BioSepra) as follows: the antibody was diluted to 0.05 mg/ml in PBS. In a 96 well 0.45μm filter plate per necessary well a 50 μl aliquot of diluted antibody was mixed with 2 μl of Protein A HyperD beads for 50 minutes at room temperature. The beads were washed 3 times with 200 μl of PBS in the filter plate wells by means of a vacuum manifold. The antibody-coupled beads were then used to specifically capture proteins from samples: 10 μl of pleural effusion was diluted with 40 μl of PBS, added to the beads, and incubated for 30 minutes on a microtiter plate shaker (form 21, amplitude 7) (MicroMix5, Diagnostic Products Corporation, Gwynedd, UK). After the incubation step the beads were washed three times with 200 μl of PBS, two times with 200 μl (50 mM Tris, pH 7.5, 1 M urea, 0.2% CHAPS, 0.5M NaCl), three times with 200 μl of PBS and finally once with 200 μl of 5mM Hepes pH 7.4.

[0120] Finally the proteins were eluted with 30 μl of 0.1 M acetic acid. Per sample a volume of 3μl eluate fraction was profiled on NP20 ProteinChip arrays by incubation on-spot in a total volume of lOμl (water added until final volume) for 30min in a humid chamber, followed by two on-spot water washes, an air-drying step and two consecutive applications of one microliter sinapinic acid (dissolved in a 400μl volume of 50% acetonitrile, 0.5% TFA). [0121] To ascertain that the captured molecule indeed is Apo C-I the outcome of the eluate fraction analyses was compared to corresponding volumes of the depleted pleural effusion samples and of the non-depleted effusion samples when analyzed on the NP20 surface type. Five (out of fifty) microliter volumes of the depleted fraction and two (out of ten) μl fractions of the non-depleted (original) effusion volume were analyzed in comparison to 3 out of 30 μl of the captured target.

12.2. Results

[0122] Pleural effusion samples were subjected to anion exchange chromatography, generating six fractions containing subsets of the effusions' protein contents. Each fraction was applied to three ProteinChip array types (IMAC30, H50, and CMlO), resulting in 18 fraction-array combinations.

[0123] Peaks detected by the Expression Difference Mapping module in CiphergenExpress software were analyzed using the software package Prediction Analysis of Microarrays (PAM) to determine peaks with the greatest between-class variance while minimizing within class variance. The five peaks with the best discriminating power identified by PAM were at m/z 6614 (found on CMlO, fraction 1), m/z 6626 (found on H50, fraction 1), m/z 6656 (found on CMlO, fraction 1), m/z 6821 (found on CMlO, fraction 1), m/z 8799 (found on H50, fraction 6). Each of these peaks had P values of 10-5 or better when calculated with permutation test methodologies. Table 4 shows for each of these peaks the mean and within-class variance in the mesothelioma subject group and the group suffering pleural effusions due to other causes.

TABLE 4 The Statistic of Pleural Effusion Fluid Markers for Different Groups

[0124] Each of these five peaks is down-regulated in the mesothelioma group. These peaks are highly correlated to each other, with correlation coefficients between 0.5 and .95. Based on previous experience with peaks at these m/z values eluting from these fractions and binding to these arrays, it was hypothesized that the first four peaks were apolipoprotein Cl or adducts of apolipoprotein Cl, and that the m/z 8799 peak was apolipoprotein AIL

[0125] To confirm the identity of the five candidate markers, hypothesized as isoforms or adducts of Apo C-I and Apo A-II, immunoprecipitation followed by analysis with mass spectrometry (immuno-MS) was performed. For this purpose negative control and specific antibodies were coupled to ProteinA HyperD beads (BioSepra) and the antibodies' captured target antigens were eluted. Analyses of the eluates showing molecular weights (m/z) corresponding to the calibrator proteins' experimental molecular weights confirmed the observed markers' identities as Apo C-I and Apo A-II (Figure 1). The observed identity similarity between captured target and calibrator protein was further corroborated by the partial removal of the target antigen in the depleted pleural effusions and the presence of the target antigen in the original non-depleted sample (Figure 2). In addition to the expected molecular weight for both the calibrator protein and the observed marker candidate, the calibrator protein and the capture experiments also revealed a common quadruplet peak cluster around 6.4 kDa for the Apo C-I target. The 6.6 and 6.4 kDa peak clusters captured do represent Apo C-I and the postulated ApoC-I form lacking the aminoterminal Thr-Pro sequence. The 6.8 kDa ApoC-I SPA adduct was not visible in these immuno-MS spectra. In contrast to obvious expression level differences for the Apo C-I isoforms in the 6.6 kDa cluster between the mesothelioma and non-mesothelioma group the expression level difference for the peaks in the 6.4 kDa cluster are less pronounced considering selected patients with high (non-mesothelioma patients 80, 78 and 100) and low (mesothelioma patients 53, 52 and 9) extremes for the Apo C-I 6.6 kDa cluster peaks (Figure 1, Figure 3).

[0126] ROC analysis revealed that both ApoCl and Apo All have area under the curves of between 66 and 69%. Figure 4A shows the separating power of the two main contributing vectors in a principal component analysis and Figure 4B shows scatter plots for the m/z 6614 peak and the m/z 8799 peak, or the ApoCl and ApoAII peaks respectively. [0127] Patients with low levels of both the Apo C-I and Apo A-II protein signals were exceedingly likely to have mesothelioma versus other conditions. However, because of the high correlation in amount of the two proteins, it was unfeasible to construct multivariable models encompassing both proteins or any other protein features in the generated SELDI-TOF-MS spectra, that had significantly better classifying ability than either of the two proteins alone.

12.3. Discussion

[0128] Table 5 provides an overview of biomarkers said to correlate with malignant mesotehlioma diagnosis.

TABLE 5 Literature overview: Biomarkers correlating with malignant mesothelioma diagnosis

[0129] By means of SELDI-TOF-MS, pleural effusion fluid samples (n=108) of both pleural mesothelioma diagnosed subjects (n=41, from which few had undergone between 1 to 4 thoracenteses, leading to a total of 54 samples) and of subjects with other malignancies, lymphatic abnormalities, and transudative pleural effusions correlated with diagnosed renal failures and a lung emboli status were investigated

[0130] For several of the TOF-MS spectral features (6614 Da, 6656 Da and 6821 Da in pleural effusion fractions 1 applied to CMlO surface type; 6626 Da in effusion fraction 1 and the 8799 Da feature in effusion fraction 6 on the H50 surface type) the average intensities in the pleural mesothelioma group showed to be decreased significantly in comparison to the pleural effusions correlated with other causes.

[0131] Experience from earlier observations and earlier protein identification work for similar spectral features in serum (combinations of molecular weight labels and ProteinChip array surface type) suggested that the molecules were Apolipoprotein C-I and Apolipoprotein A-II isoforms.

[0132] Confirmation of that hypothesized identity was obtained by means of SELDI-based antibody capture approaches: with an anti-(Apo C-I) antibody method Apo C-I was successfully captured from pleural effusions from selected subjects (n=6) of both the pleural mesothelioma (n=3) and other cause (n=3) exudative effusion groups. The additionally captured 6.4 kDa peak is postulated to be ApoC-I lacking the aminoterminal Thr- Pro dipeptide. A similar Apo C-I peptide was also observed and identified in serum samples by means of SELDI-TOF-MS (Leonardo et al., Proteomics 6(2):709-720, 2006); Rossi et al postulated this form to be Des Thr-Val ApoC-I, although the second amino acid of ApoCI (SwissProt # P02654) is Proline. Data for a similar peptide with the same MW from actual MS/MS sequencing experiments has shown to contain Pro (personal communications) as the second amino acid rather than the VaI residue suggested by Rossi et al (Leonardo et al., Proteomics 6(2):709-720, 2006). Further investigation would be required to understand the origin of the Des Thr-Pro ApoCI form: it could either be due to exoproteolytic digestion of the full-length 6.6 kDa ApoCI during the sample handling or alternatively could be caused by cleavage of the signal sequence prior to polypeptide secretion. This 6.4 kDa form is also present in the capture of the purified calibrator protein dilution in PBS, where no exoprotease activity is added as part of a lysate. In addition: there is also a significant difference of the 6.4 to 6.6 kDa peak ratio in the MMs vs non-MMs: where the MM effusions show mainly the 6.4 kDa Des Thr-Pro peak, from the non-MM pleural effusions a similar amount of both the 6.4 N-terminally cut form and the intact 6.6 kDa peptide are captured in an equal ratio.

[0133] The Apo C-I forms in pleural effusions perform reasonably as single biomarkers for separation of pleural mesothelioma subjects from subjects with other pleural affections: their performance is characterized by ROC AUCs between 0.666 and 0.690, and by a sensitivity of 71% and specificity of 70%. ApoC-I isoforms can also be considered as candidates in multi-marker panels.

[0134] For example, in one embodiment, an initial triage of all pleural effusions could consist of the IHC staining of the effusion's cellular components with antibodies against CEA, Ber-EP4 and CD138 (syndecan-1). These three target molecules have been described as negative markers after showing only limited staining reaction in malignant mesothelioma cells, in comparison to the other tumor types tested (Saqi et al., Diagn Cytopathol. 33(2): 65-70, 2005; Soomro et al., J Pak Med Assoc. 55(5):205-9, 2005; Dejmek and Hjerpe, Diagn Cytopathol. 32(3): 160-6, 2005). So far no reports have been published on determination of soluble forms of these molecules in serum or pleural effusion fluids. Upon a negative outcome the pathologist could consider going down the route of further confirmation of potential mesothelioma diagnosis.

[0135] The next steps towards positive diagnosis of pleural malignant mesothelioma could comprise, for example, the combined analyses of ApoC-I isoforms and pleural SMRP levels in the fluid part of the pleural effusion, and a parallel IHC assessment of the cell-attached mesothelin form of the malignant mesothelioma cells. Mesothelin is a cell surface protein present on a restricted set of normal adult mesothelial tissue cells lining the body cavities, but is aberrantly expressed by several tumor types (ovarian epithelial, serous papillary ovarian cancers, pancreatic adenocarcinomas, endometrioid uterine adenocarcinomas, mesotheliomas, and squamous cell carcinomas of the esophagus, lung and cervix) (Chang et al., Int J Cancer 50: 373-381, 1992; Chang et al., Am J Surg Pathol 16: 259-268, 1992; Chang and Pastan, Int J Cancer 57: 90-97, 1994; Argani et al., Clin Cancer Res 7: 3862-3868, 2001; Frierson et al., Hum Pathol 34: 605-609, 2003; Ordonez, Mod Pathol 16: 192-197, 2003; Ordonez, Am J Surg Pathol 27: 1418-1428, 2003). Full length mesothelin (-69 kD) can be proteolytically cleaved to release a -33 kD soluble protein corresponding to megakaryocyte potentiating factor (MPF), also called soluble mesothelin- related protein (SMRP) (Yamaguchi et al., / Biol Chem 269: 805-808, 1994; Kojima et al., / Biol Chem 270: 21984-21990, 1995). Pleural SMRP levels are significantly higher in mesothelioma in comparison to benign lesions and pleural metastases (Scherpereel et al., Am J Respir Crit Care Med 2006 (Epub ahead of print).

[0136] An alternative or additional candidate marker to an ApoC-I and SMRP/mesothelin marker panel could be, for example, osteopontin. Initially, osteopontin's mRNA level has been described as increased in tumorous tissue vs non-tumorous tissue of a rat model system upon asbestos-induced carcinogenesis (Sandhu et al., Carcinogenesis 21(5): 1023-1029, 2000). The parallel increased mRNA transcription levels for the signaling phosphoprotein zyxin and integrin-linked protein kinase (Sandhu et al., Carcinogenesis 21(5): 1023-1029, 2000) suggest a link with disturbance of integrin βl cell signaling systems (Hannigan et al., Nature 379(6560): 91-6, 1996), leading to anchorage-independent growth of the epithelial cells in the pleural cavity (Radeva et al., / Biol Chem 272: 13927-13944, 1997).

[0137] The serum derivate of blood extractions can also be used as an alternative matrix for determination of SMRP and osteopontin levels. For both molecules, SMRP and osteopontin, enhanced immuno assays in the serum matrix have been reported (Pass et al., N Engl J Med, 15: 353; Robinson et al., Lung Cancer. 49(1): S109-11, 2005; Hassan et al., Clin Cancer Res 12: 447-453, 2006; Scherpereel et al., Am J Respir Crit Care Med 2006 (Epub ahead of print)). However, the caveat that the serum levels measured might only partially mirror the SMRP and osteopontin levels in the pleural cavity is corroborated by Scherpereel and co-workers' results showing that the levels of SMRP are typically higher in the pleural fluids in comparison to the corresponding serum samples (Scherpereel et al., Am J Respir Crit Care Med 2006 (Epub ahead of print)), and that therefore the discrepancy between the AUC for serum SMRP (AUC = 0.693) differentiating between mesothelioma and pleural metastasis is lower than based on the pleural SMRP values (AUC = 0.793).

[0138] However, serum SMRP levels have been described as tumor- size related and to decrease upon surgical cytoreduction interventions (Hassan et al., Clin Cancer Res 12: 447-453, 2006). This leads to the suggestion that SMRP analysis values in the pleural effusion fluid matrix might be preferable for positive primary diagnosis while the serum SMRP values might contain an opportunity as a treatment monitoring tool in a follow-up population. Those treatments could encompass administering of the mesothelin — targeting anti-tumor immunotoxin Kl-LysPE38QQR (Hassan et al., J Immunother 23(4): 473-479, 2000) or other established or investigated treatment compounds and regimens (Robinson and Lake, N Engl J Med 353(15): 1591-1603, 2005). In these treated cases the pleural edema might be completely absent or not severe enough to permit the invasive protocol for effusion fluid collection.

[0139] Further alternative diagnostic marker candidates include, for example, podoplanin, calretinin and its 22 kDa splicing form and an oncofetal protein against which the D2-40 monoclonal antibody has been targeted. For each of these targets successful immunohistochemistry approaches have been described, resulting in high sensitivity detections of mesothelioma tumors (Ordonez, Hum Pathol. 36(4):372-80, 2005; Kimura and Kimura, Pathol Int. 55(2): 83-6, 2005; Barberis et si., Acta Cytol. 41(6):1757-61, 1997). However, all of these markers are available as IHC approaches only. These methods do require laborious cell isolation and preparation from difficult to obtain cells and visual evaluation might potentially result in interpreter-biased diagnosis.

[0140] 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, patents, and patent applications cited herein are hereby incorporated by reference in their entirety for all purposes.

Claims

WHAT IS CLAIMED IS:

1. A method for qualifying mesothelioma status in a subject comprising:

(a) measuring one or more biomarkers in a biological sample from the subject, wherein at least one biomarker is ApoCl or ApoA2; and

(b) correlating the measurement or measurements with a mesothelioma status selected from mesothelioma and non-mesothelioma.

2. The method of claim 1, comprising measuring a plurality of biomarkers in the biological sample, wherein the plurality of biomarkers comprises ApoCl and ApoA2.

3. The method of claim 2, wherein ApoCl is mature ApoCl and ApoA2 is mature ApoA2.

4. The method of claim 2, wherein the plurality of biomarkers further comprises at least one biomarker selected from the group consisting of: SMRP (soluble mesothelin-related protein), osteopontin and cytokeratin 8.

5. The method of claim 1, wherein ApoCl is mature ApoCl and ApoA2 is mature ApoA2.

6. The method of claim 1, wherein the one or more biomarkers is measured by mass spectrometry.

7. The method of claim 6, wherein mass spectrometry is SELDI-MS.

8. The method of claim 1, wherein the at least one biomarker is measured by immunoassay.

9. The method of claim 1, wherein the sample is pleural fluid.

10. The method of claim 1, wherein the correlating is performed by executing a software classification algorithm.

11. The method of claim 1, wherein non-mesothelioma is non- mesothelioma presenting with pleural effusion.

12. The method of claim 1, wherein non-mesothelioma is a cancer.

13. The method of claim 1, wherein the subject has been treated for mesothelioma and the mesothelioma is recurrence of cancer.

14. The method of claim 1, further comprising: (c) reporting the status to the subject.

15. The method of claim 1, further comprising: (c) recording the status on a tangible medium.

16. The method of claim 1, further comprising: (c) managing subject treatment based on the status.

17. The method of claim 16, further comprising: (d) measuring the at least one biomarker after subject management and correlating the measurement with disease progression.

18. A method for determining the course of mesothelioma comprising:

(a) measuring, at a first time, one or more biomarkers in a biological sample from the subject, wherein at least one biomarker is ApoCl or ApoA2;

(b) measuring, at a second time, the at least one biomarker in a biological sample from the subject; and

(c) comparing the first measurement and the second measurement; wherein the comparative measurements determine the course of the mesothelioma.

19. A method comprising measuring ApoCl and ApoA2 in a sample from a subject.

20. The method of claim 19, further comprising measuring at least one of SMRP (soluble mesothelin-related protein), osteopontin and cytokeratin 8 in the sample.

21. A kit comprising:

(a) a solid support comprising at least one capture reagent attached thereto, wherein the capture reagent binds ApoCl or ApoA2; and (b) instructions for using the solid support to detect ApoCl or ApoA2.

22. The kit of claim 21, wherein the solid support comprising a capture reagent is a SELDI probe.

23. The kit of claim 21, further comprising a standard reference of ApoCl or ApoA2.

24. A kit comprising:

(a) at least one solid support comprising at least one capture reagent attached thereto, wherein the capture reagent binds or reagents bind ApoCl and ApoA2; and

(b) instructions for using the solid support or supports to detect ApoCl and ApoA2.

25. The kit of claim 24, wherein the solid support comprising a capture reagent is a SELDI probe.

26. The kit of claim 24, further comprising a standard reference of ApoCl and ApoA2.

27. 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, wherein at least one biomarker is ApoCl or ApoA2; and

(b) code that executes a classification algorithm that classifies the mesothelioma status of the sample as a function of the measurement.

28. The software product of claim 27, wherein the at least one biomarker comprises ApoCl and ApoA2.

29. The software product of claim 27, wherein the at least one biomarker further comprises at least one biomarker selected from SMRP (soluble mesothelin-related protein), osteopontin and cytokeratin 8.

30. A method comprising communicating to a subject a diagnosis relating to mesothelioma status determined from the correlation of at least one biomarker in a sample from the subject, wherein at least one biomarker is ApoCl or ApoA2.

31. The method of claim 30, wherein the at least one biomarker comprises ApoCl and ApoA2.

32. The method of claim 30, wherein the diagnosis is communicated to the subject via a computer-generated medium.

33. A method for identifying a compound that interacts with ApoCl or ApoA2, wherein said method comprises:

(a) contacting ApoCl or ApoA2 with a test compound; and

(b) determining whether the test compound interacts with ApoCl or ApoA2.