WO2013155605A1

WO2013155605A1 - Method for predicting a pathophysiological condition in an animal

Info

Publication number: WO2013155605A1
Application number: PCT/CA2013/000370
Authority: WO
Inventors: John D. Lewis; Hon Sing LEONG; Andries Zijlstra
Original assignee: Innovascreen Inc.
Priority date: 2012-04-19
Filing date: 2013-04-19
Publication date: 2013-10-24
Also published as: WO2013155605A8

Abstract

Disclosed is a method for predicting a pathophysiological condition in a animal, The method involves isolating microparticles from a biological sample from an animal using flow cytometry. These isolated microparticles are sorted into two or more groups. Within the sorted microparticlesj a subpopulation of microparticles are identified, which express a biomarker that is predictive of a pathophysiological condition in the animal This method can be used to determine whether prostate cancer within a subject has metastasized. In this case, Prostate Specific Membrane Antigen (PSMA) positive microparticles from a patient with prostate cancer are isolated and the presence of CD151 positive microspheres within the PSMA positive microsphere population indicates metastasis of the cancer,

Description

METHOD FOR PREDICTING A PATHOPHYSIOLOGICAL CONDITION IN AN ANIMAL

FIELD OF THE INVENTION

[0001] The present invention generally relates to a method for predicting a pathophysiological condition in an animal. More specifically the invention relates to the use of biomarkers on microparticles to predict the pathophysiological condition.

BACKGROUND OF THE INVENTION

[0002] Diagnostic and prognostic tests that attempt to determine whether a particular phenotype is being expressed in a disease or disorder are becoming more important as the value of personalized medicine is becoming more widely accepted. The development of such tests have allowed for early detection of disease, and to predict the therapeutic value of certain treatment regimes.

[0003] In the case of cancer, diagnostic tests designed to detect particular relative disease phenotypes have allowed for cancers to be categorized with relative precision. Moreover, detection of specific antigens associated with the cancer cells makes them mote susceptible to certain therapeutic inventions,

[0004] Prostate cancer (PCa) is the most common malignancy in North American men, accounting for 10% of all cancer-related deaths in 2010.[1] With the ubiquitous use of prostate specific antigen (PSA) testing and PCa screening programs, an increasing number of low grade early PCa are being diagnosed. Due to the low overall risk of these cancers developing into clinically-relevant disease, many patients opt for active surveillance rather than radiation or radical retropubic prostatectomy (RRP). In a significant number of these cases, disease progression can accelerate and pose an elevated risk that may have been avoided with earlier intervention. [2] The vast majority of cancer-related mortalities are due to metastatic disease,[3] Current diagnostic tools such as PSA, digital rectal examination (DRE), biopsy, Gleason score, TNM stage, Partin's nomogram and tables,[4] and D'Amico's risk stratification scheme[5] accurately predict PCa-specific mortality.[6] However, these tools do not accurately identify those cases that harbour occult metastatic disease. [7-9] [0005] As a screening tool, the performance characteristics of PSA are limited by the fact that infection, trauma, and benign prostatic hyperplasia (BPH) are more common causes of elevated serum PSA than cancer.[10] BPH is present in more than 50% of men >50 years of age, significantly confounding PSA as a cancer biomarker.[l l] Because of this high false-positive rate, PSA-based screening for prostate cancer demonstrates a positive predictive value (PPV) of only 25%.[12] The current diagnostic regimen for prostate cancer results in more than 1 million prostate biopsy procedures each year in North America, of which roughly 25% result in a diagnosis of cancer. Prostate biopsies are associated with a number of complications, and the likelihood of hospitalization in the 30 days following prostate biopsy is significantly increased (from 2.7% to 6,9%).[13] A diagnostic biomarker test with higher cancer predictive value is needed to improve the effectiveness of prostate cancer screening and to decrease the number of unnecessary biopsies, To put this into perspective, each 5% increase in PPV for a prostate screening test would eliminate approximately 165,000 unnecessary biopsies and 6,930 hospitalizations each year in North America, Beyond the potential impact on prostate cancer screening efforts, this equates to significant health care savings and improved patient quality of life.

[0006] The presence or absence of metastases is the only individual prognostic factor in multivariate analysis of prostate cancer outcomes.[14] While the five year survival for localized prostate cancer in North America is close to 100%, the survival rate drops to 31.9% for metastatic disease.[15]. The success of metastatic dissemination is determined by a tumor cell's ability to travel to a distant site, arrest, enter the stroma surrounding the vasculature and grow in a new rnicroenvironment.[16] Nevertheless, without the initial entry into the circulation, subsequent rate-limiting steps become inconsequential and distant metastases fail to form, The appearance of circulating tumor cells in patients with metastatic disease and experimental animals bearing metastatic tumors suggests the intravasation is an important component of the metastatic process. [17, 18] The importance of intravasation to metastasis is further emphasized by the discovery that the integrin-associated protein CD 151 prevents intravasation and consequently, inhibits metastasis by more than 90%. These data strongly suggest that CD151- mediated cell migration is an important rate-limiting step in metastasis, and as such, CD151 and other factors involved in this process may represent key biomarkers of metastasis in prostate and other cancers. [0007] CD151 (also known as GP27, SFA-1, PETA 3) is one of 33 members in the mammalian Tetraspanin family and is characterized by four transmembrane domains. It is ubiquitously expressed, but is predominantly found in the plasma membrane of epithelial cells, endothelial cells, smooth muscle cells, and platelets. [19-22] Several related functions have been attributed to CD151, including platelet aggregation,[23] cell adhesion,[24] cell migration,[25, 26] tumour cell invasion and metastasis, [22, 27, 28] Its loss in humans and experimental mouse models leads to basement membrane failure, including skin blistering and renal failure. [29, 30] CD151 is frequently upregulated in cancer. [22] Its expression is associated with poor outcome in several cancers, including esophageal cancer,[31] hepatic cancer,[32, 33] lung cancer,[34] and clear cell renal carcinoma[35]. Improved prognostic biomarkers could substantially improve patient management after a diagnosis of prostate cancer. Growing evidence now suggests that low risk patients benefit from "active surveillance". It is estimated that active surveillance could spare an estimated -40% of all diagnosed prostate cancer patients [15, 36-38] from treatments that have significant side effects but are not likely to improve survival. A simple non-invasive diagnostic test that accurately and confidently distinguishes low-risk from high-risk patients would be of great clinical benefit to help choose which patients should opt for active surveillance. Furthermore, an improved test to detect early metastatic disease could provide a window of therapeutic opportunity prior to the full manifestation of metastasis and potentially improve overall survival for those living with advanced cancer.

SUMMARY OF THE INVENTION

[0008] According to an aspect of the present invention there is provided a method comprising the steps of: distinguishing microparticles from a biological sample from an animal into two or more groups using flow cytometry; and identifying a subpopulation of microparticles from the two or more groups. The subpopulation of microparticles is predicative or indicative of a pathophysiological condition in the animal.

[0009] In one embodiment₅ the step of distinguishing the microparticles comprises isolating at least one microparticle population from the overall population of microparticles by selecting for a biomarker selective for the at least one microparticle population. [0010] In a second embodiment, the biomarker is detected by an antibody, peptide or small molecule.

[0011] In a third embodiment_s the step of distinguishing the microparticles comprises isolating prostate cancer microparticles from the overall population of microparticles.

[0012] In a fourth embodiment, the prostate cancer microparticles are isolated from the overall population of microparticles by detecting the presence of a Prostate Specific Membrane Antigen (PSMA) or prostate stem cell antigen (PSCA) on the prostate cancer microparticle.

[0013] In a fifth embodiment) the PSMA is detected using an anti-PSMA antibody.

[0014] In a subsequent embodiment, the step of identifying the subpopulation of microparticles comprises identifying a biomarker selective for the subpopulation of microparticles.

[0015] In a further embodiment, the biomarker is detected by an antibody, peptide or small molecule.

[0016] In a yet further embodiment, the step of identifying the subpopulation of microparticles comprises identifying a biomarker selective to a pathological stage of prostate cancer.

[0017] In a still further embodiment, the biomarker is selected from CD151, CD166 (ALCAM) and cytokeratin.

[0018] In another embodiment, CD151 is detected using an anti-CD151 antibody, such as 1A5.

[0019] In a further embodiment, the animal is a human and the biological sample is a component of blood, in particular serum or plasma.

[0020] According to another aspect of the present invention, there is provided a method comprising the steps of: distinguishing prostate cancer derived microparticles from other microparticles in a biological sample using flow cytometry; and identifying microparticles from the prostate cancer derived microparticles which express a marker conelating with cancer metastasis. The presence of microparticles which express the marker correlating with cancer metastasis is predictative or indicative of metastatic prostate cancer.

[0021] In one embodiment, the prostate cancer derived microparticles express a prostate cancer specific antigen or marker, or a metastasis specific marker,

[0022] In another embodiment, the prostate cancer antigen is PSMA or PSCA. The PSMA is detected by an anti-PSMA monoclonal antibody, such as 3 E7, as described in US2009/0041789 (the contents of which are incorporated herein by reference).

[0023] In a further embodiment, the marker correlating with cancer metastasis is selected from CD151, CD166(ALCAM) and cytokeratin, The CD151 marker is detected by an anti- CD151 monoclonal antibody, such as 1A5.

[0024] In a further embodiment, the prostate cancer derived microparticles and the prostate cancer derived microparticles which express a marker conelating with cancer metastasis are detected concurrently.

[0025] In a yet further embodiment, the prostate cancer derived microparticles and the prostate cancer derived microparticles which express a marker correlating with cancer metastasis are detected simultaneously,

[0026] According to another aspect of the present invention there is provided a method comprising the steps of; distinguishing microparticles from a biological sample into two or more groups using flow cytometry; and identifying a subpopu on of microparticles from two or more groups based on a microparticle-, tissue-, diagnosis-, disease progression-, and/or cancer metastasis-specific biomarkers. The subpopulation of microparticles being predictive or indicative of a pathophysiological condition in an animal,

[0027] In one embodiment, the tissue-specific biomarker is selected from: carcinoembryonic antigen-related cell adhesion molecule 7 (CEACA 7), chloride channel accessory 1 (CLCA1), glycoprotein A33 (transmembrane) (GPA33), zymogen granule protein 16 (ZG16); iroquois homeobox 5 (IRX5), lysosomal-associated membrane protein 3 (LAMP3), microfibrillar-associated protein 4 (MFAP4), transmembrane protein 100 (TMEMIOO); pancreatic tissue can be detected by the presence of aquaporin 8 (AQP8), carboxyl ester lipase (CEL), chymotrypsin-like elastase family, member 2A (CELA2A), chymotrypsin-like elastase family, member 2B (CELA2B), chymotrypsin-like elastase family, member 3B (CELA3B), carboxypeptidase Al (pancreatic) (CPA1), carboxypeptidase A2 (pancreatic) (CPA2), carboxypeptidase Bl (tissue) (CPB1), chymotrypsinogen Bl (CTRB1), chymotrypsinogen B2 (CTRB2), chymotrypsin C (caldecrm) (CTRC), CUB and zona pellucida-like domains 1 (CUZDl), glucagon (GCG), islet amyloid polypeptide (IAPP), insulin (INS), kallikrein 1 (KL 1), pancreatic lipase (PNLIP), pancreatic lipase-related protein 1 (PNLIPRP1), pancreatic lipase-related protein 2 (PNLIPRP2), pancreatic polypeptide (PPY), protease, serine, 1 (trypsin 1) (PRSSl), protease, serine, 3 (PRSS3), regenerating islet-derived 1 beta (REG1B), regenerating islet-derived 3 gamma (REG3G), solute carrier family 30 (zinc transporter), member 8 (SLC30A8), syncollin (SYCN); glutamyl aminopeptidase (aminopeptidase A) (ENPEP), solute carrier family 2 (facilitated glucose transporter), member 9 (SLC2A9), solute carrier family 12 (sodium/potassium chloride transporters), member 1 (SLC12A1), uromodulin (UMOD), cubilin (intrinsic factor-cobalamin receptor) (CUBN), solute carrier family 13 (sodium-dependent dicarboxylate transporter), member 3 (SLC13 3), transmembrane protein 27 (TMEM27), claudin 2 (CLDN2), prostate specific membrane (PSMA), prostate stem cell antigen (PSCA), epidermal growth factor receptor (EGFR), vascular endothelial growth factor (VEGF), HER-2/neu, polymorphic epithelial mucin (MUCI), folate hydrolase I (FOLH1), kaltikrein- related peptidase 2 (KLK2), kallikrein-related peptidase 3 (KLK3), solute carrier family 45 member 3 (SLC45A3) and vascular endothelial growth factor (VEGF),

[0028] In another embodiment, the microparticle-specific biomarker is selected from: CD9, CD63, CD81, Alix, TSglOl, CD40 ligand, and Selectin,

[0029] In a further embodiment, the diagnosis-specific biomarker is selected from:

CECAM, CA125, PSMA, ECAM, VECAM, CD13, CD20, CD30, c- IT, ER, AR, Alphafetoprotein, Carcinoembryonic antigen, CA-125, MUC-1, Epithelial tumor antigen, Tyrosinase, Melanoma-associated antigen, abnormal products of ras, and p53. [0030] In a still further embodiment, the disease progression- or metastasis-specific biomarker is selected from: CD44, CD166, CD133, L1CAM, CD151, ITGA2, ITGA3, ITGA6, MMP14, ADAM17, and ADAM 12.

[0031] ϊη a yet further embodiment, the cancer metastasis-specific biomarker is selected from: CD151, .ALCAM, and ανβ6 integrin,

[0032] According to another aspect of the present invention there is provided a method comprising the steps of: distinguishing microparticles from a biological sample into two or more groups using flow cytometry; identifying a subpopulation of microparticles from the two or more groups; and quantifying the relative number of microparticles in the subpopulation to the number of microparticles in the two or more groups. The relative number of microparticles in me subpopulation to the number of microparticles in the two or more groups being predictive or indicative of a pathophysiological condition in an animal.

[0033] In one embodiment, the quantifying step involves comparing the number of microparticles to a known control sample. The control sample can be an isotype antibody.

BRIEF DESCRIPTION OF THE DRAWINGS

[0034] These and other features, aspects and advantages of the present invention will become better understood with regard to the following description and accompanying drawings wherein:

[0035] FIG. 1 is a representative FACS histogram of a microparticles from a subject with localized prostate cancer (top panels) and a subject with metastatic prostate cancer (bottom panels);

[0036] FIG. 2 is a graphical representation of the mean total number of prostate specific membrane antigen (PSMA) per 1 ml of plasma f om subjects having localized prostate cancer (PCa) and metastatic PCa. Bars are the mean of 10 samples ± SEM, P^e0.0973 as determined by Mann- Whitney test; [0037] FIG. 3 is a graphical representation of the mean total number of PSMA and

CD 151- positive microparticles. Bars are the mean of 10 samples ± SEM, P=0.Q012 as determined by Mann-Whitney test;

[0038] FIG. 4 is a graphical representation of the mean percentage of prostate cancer microparticles that express CD151. Bars are the mean of 10 samples ± SEM, P=0.0006 as determined by Mann- Whitney test;

[0039] FIG. 5 is a representative FACS histogram of a microparticles from a subject with localized prostate cancer (top panels) and a subject with benign prostatic hyperplasia (BPH) (bottom panels). Sizing analysis of PSMA-positive microparticles (left panel) reveals a prostate microparticle diameter distribution of 110-235 nm; and FIG. 6 is a graphical representation of prostate cancer microparticles (PSMA positive) that bind Ghrelin probe enumerated in patient plasmas by flow cytometry, N=7 for each group, Mann- Whitney test. * p < 0.05;

[0040] FIG, 6 is a graphical representation of prostate cancer microparticles (PSMA positive) that bind Ghrelin probe were enumerated in patient plasmas by flow cytometry. N~& for each group, Mann-Whitney test; and

[0041] FIG. 7 is a (A) micrograph of CD151 immunohistochemistry with mAB 11GA or 1 A5 on adjacent benign, adjacent normal and tumor tissue; (B) represents Kaplan-Meier curves of recurrence-free survival generated using the CD151 free immunoreactivity in patients that received a radical retropubic prostatectomy (RRP) after diagnosis (cohort #1, N-99). Log-Rank test p^=s0.023; and (C) represents Kaplan-Meier curves generated of metastasis-free survival in biopsy specimens from patients that did not receive RRP after diagnosis (cohort #2, N=38). Log- Rank test p=0.001.

DESCRIPTION OF THE INVENTION

[0042] The following description is of a preferred embodiment by way of example only and without limitation to the combination of features necessary for carrying the invention into effect. [0043] The method of the present invention uses microparticles contained within a biological sample to predict or COnfirm a pathophysiological condition in an animal. The method involves analyzing microparticles from a biological sample from an animal using flow cytometry. Once the microparticles are analyzed from the biological sample, they are distinguished into two groups, based on whether the micxoparticle has a particular characteristic or not. Using either the group of microparticles having the characteristic or those that do not have the primary characteristic, a further distinguishing characteristic on the microparticles is used to determine whether a subpopulation of microparticles exists. The relative abundance of the subpopulation of microparticles can be used to predict or confirm a pathophysiological condition in the animal.

[0044] Microparticles, for the purpose of this discussion, are defined as cell-derived vesicular fragments of less than 1.0 um in diameter. They are also referred to as microvesicles in some cases. In practice, the size distribution of the microparticles typically range from 50 to 500 nm (Dean WL et al, J Thromb Haemost 102:711-8, 2009; Yuana Y et al, J Thromb Haemost 8:315-323, 2009). Microparticles can be identified in the present method by gating the flow cytometer to detect such elements, The microparticles can be derived from any type of cell, including those native or endogenous to the animal, or those foreign to the subject animal. For example, native or endogenous microparticles include, but are not limited to, tissue-specific cells, blood cells and immunological cells. Generally, foreign cells are any type of cell that is not found in the animal under normal circumstances. For example, cells from invading bacteria, viruses, or other pathogens can be considered to be foreign. These microparticles are typically found in the blood of an animal. To decrease the noise in the sample, serum or plasma is preferred over whole blood, However, it is contemplated that whole blood may be used in the method, especially if the pathophysiological condition stems from or involves a type of blood cell. Other forms of biological samples may also contain microparticles, such as sputum, cerebrospinal fluid, lymph, urine and semen, In the method of the present invention, the biological sample is exposed a marker that identifies a primary characteristic of the cell or tissue of interest. For example, colon tissue can be detected by the presence of carcinoembryonic antigen-related cell adhesion molecule 7 (CEACAM7), chloride channel accessory 1 (CLCAl), glycoprotein A33 (transmembrane) (GPA33), and/or 2ymogen granule protein 16 (ZG16); lung tissue can be detected by the presence of iroquois homeobox 5 (IRX5), lysosomal-assooiated membrane protein 3 (LAMP3), microfibrillar-associated protein 4 (MFAP4), and/or transmembrane protein 100 (TMEMIOO); pancreatic tissue can be detected by the presence of aquaporin 8 (AQP8), carboxyl ester lipase (CEL), chymotrypsin-like elastase family, member 2A (CELA2A), chymotrypsin-like elastase family, member 2B (CELA2B), chymotrypsin-like elastase family, member 3B (CELA3B), carboxypeptidase Al (pancreatic) (CPA1), carboxypeptidase A2 (pancreatic) (CPA2), carboxypeptidase Bl (tissue) (CPB1), chymotrypsinogen Bl (CTRB1), chymotrypsinogen B2 (CTRB2), chymotrypsin C (caldecrin) (CTRC), CUB and zona pellucida-Iike domains 1 (CUZ 1), glucagon (GCG), islet amyloid polypeptide (IAPP), insulin (INS), kallikrein 1 ( LKl), pancreatic lipase (PNUP), pancreatic lipase-related protein 1 (PNLIPRPl), pancreatic lipase-related protein 2 (PNLIPRP2), pancreatic polypeptide (PPY), protease, serine, 1 (trypsin 1) (PRSS1), protease, serine, 3 (PRSS3), regenerating islet-derived 1 beta (REG1B), regenerating islet-derived 3 gamma (REG3G), solute carrier family 30 (zinc transporter), member 8 (SLC30A8), and/or syncollin (SYC ); kidney tissue can be detected by the presence of glutamyl aminopeptidase (aminopeptidase A) (ENPEP)_S solute carrier family 2 (facilitated glucose transporter), member 9 (SLC2A9), solute carrier family 12 (so um/potassium/chloride transporters), member 1 (SLC12A1), uromodulin (UMOD), cubilin (intrinsic factor-cobalamin receptor) (CUBN), solute carrier family 13 (sodium-dependent dicarboxylate transporter), member 3 (SLC13A3), transmembrane protein 27 (TMEM27) and or claudin 2 (CLDN2),

[0045] If microparticles stemming from prostate cancer cells are desired, then a prostate cancer cell marker would be used. In the case of prostate cancer, several antigens or markers have been identified that correlate with this type of cell. For example, prostate specific membrane (PSMA), prostate stem cell antigen (PSCA), epidermal growth factor receptor (EGFR), vascular endothelial growth factor (VEGF), HER-2/neu, polymorphic epithelial mucin (MUCI), folate hydrolase I (FOLH1), kallikrein-related peptidase 2 (KLK2), kallikrein-related peptidase 3 ( LK3), solute carrier family 45 member 3 (SLC4SA3) and vascular endothelial growth factor (VEGF) can all be used to identify prostate cancer cells. Of these, PSMA is preferred to identify prostate cancer cells, since some of the other antigens are present on other cells or correlate with only a specific form of prostate cancer. [0046] The antigens on the microparticles of interest are typically detected by labelled antibodies. However, direct labels may be used that recognize and bind certain microparticle membrane structures. Moreover, labeled peptides or small molecules may be used in some methods such as insulin, RGD and other commercially available peptide ligands or commercially available small molecule drugs such as imatinib or gefitinib, Normally, the antibodies used to detect the primary characteristic of interest on the microparticles are labelled with a fluorophore. Fiuorophores include, but are not limited to, Indo-1, Cascade Blue, AMCA, DAPI, Alexa 350, Hoeschst 33342, PacificBlue, MarinaBlue, eCFP, Cascade Yellow, Propidmm Iodide, Alexa 430, eGFP, FITC, Alexa 488, Phodamine 123, RPE, Acridine Orange, eYFP, PE, DsRed2, Ds- Red, PE-Texas Red, 7-AAD, Per-CP, PE-Cy5, DRAQ5, PE-Cy5.5, PE-Cy7, Alexa 633, To-Pro- 3, APC, Cy5, Alexa 647, Alexa 660, Cy5.5, Alexa 680 and APC-Cy7. Other labels, such as quantum dots and radioisotopes can also be used in conjunction with an antibody that recognizes the primary characteristic of interest.

[0047] In order to detect a subpopulation of these primary labelled microparticles, a marker specific to a secondary characteristic is used to label the microparticles having the desired characteristic. In most cases, the secondary marker is another fluorescently labelled antibody that recognizes an antigen that is unique to a subpopulation of microparticles having the first characteristic, If this is the case, then the excitation wavelength of each marker should be spectrally distinct. For example, the antibody used to detect the primary characteristic of the cell of interest could be labelled with a fluorescein(FITC)-conjugated antibody and the secondary characteristic of the cell of interest could be labelled with a R-Phycoerythrin (RPE)-conjugated antibody, since the excitation/emission spectra for FITC is 494 nm 520 nm and 546 nm/575 nm for RPE.

[0048] The application of the primary and secondary markers can be done at the same time, or done consecutively.

[0049] Flow cytometry is used to isolate the labelled microparticles from the biological sample. Preferably, a fluorescence-activated cell sorting (FACS^®) instrument is used. In this case, the microparticles would be fluorescently labelled. Microparticles can be identified by a forward scatter below that of 1.0 urn, [0050] The subpopulation of microparticles having the unique characteristic recognized by the second marker can be used to identify or predict a pathophysiological condition in the animal. In other words, the presence of the antigen can either identify or predict changes of normal, mechanical, physiological and biochemical functions, either caused by a disease or disorder, or resulting from an abnormal syndrome. This can be accomplished by selecting biomarkers that are specific for the microparticle, tissue, diagnosis, disease progression and/or cancer metastases. Examples of such biomarkers include, but not limited to: microparticle- specific biomarkers CD9, CD63, CD81, Alix, TSglOl, CD40 ligand, and Selectin; diagnosis- specific biomarkers CECAM, CA125, PSMA, ECAM, VECAM, CD13, CD20, CD30, c-ΚΓΓ, ER, AR, Alphafetoprotein, Carcinoembryonic antigen, CA-125, MUC-1, Epithelial tumor antigen, Tyrosinase, Melanoma-associated antigen, abnormal products of ras, and p53; metastatic- or disease-progression specific CD44, CD166, CD133, LI CAM, CD151, ITGA2, ITGA3, ITGA6, MMP14, ADAM17, and ADAM12; and cancer metastau specific biomarkers CD151, ALCAM, and ανβ6 integrin. In the case of cancers, the presence of certain antigens can be used to confirm or predict whether the cancer has metastasized or would be subject certain therapeutic interventions.

[0051] With respect to diagnosis, the method can be used to differentiate between potentially lethal diseases, such as cancer, from treatable conditions or abnormalities; or normal tissue. For example, serum PSA levels can be elevated PSA by the fact that infection, trauma, and benign prostatic hyperplasia (BPH) cause PSA levels to be elevated. In fact, infection, trauma and BPH are more common causes of elevated serum PSA than cancer. Accordingly, the method described herein can be used in conjunction with or to confirm the results of a PSA test. In this case, biomarkers for both prostate tissue, such as, but not limited to, PSMA, and prostate cancer, such as, but not limited to, Ghrelin, are used to label the circulating microparticles,

[0052] In a preferred embodiment, the present method can be used to detect metastatic prostate cancer in a human, In this case, microparticles are isolated from a biological sample from a human. In most cases, the biological sample will be blood, or some component of blood, such as serum, [0053] The microparticles derived from prostate cancer cells are isolated by detecting the presence of prostate cancer cell marker on the surface of the microparticle. Examples, of suitable prostate cancer cell markers include: PSMA, FOLH1, KLK2, KLK3, PSCA, SLC45A3 and PSA. Of these, PSMA is preferred. A fiuorophore-conjugated antibody to the prostate cancer cell markers are used to identify the prostate cancer cell specific antigen. In the case of PSMA, anti-PSMA antibodies can be used to detect the prostate cancer cells, In order to increase sensitivity of the assay, monoclonal antibodies to the antigen should be used, For example, the monoclonal anti-PSMA antibodies 3/A12, 3 E7 and 3 F11, as described in WO 2006/125481, the contents of which are incorporated herein by reference, can be used to detect the prostate cancer cells.

[0054] The anti-PSMA antibodies can be conjugated to a fluorophore, such as RPE or FITC. When selecting a suitable fluorophore, it is important to keep in mind that the excitation wavelength of the anti-PSMA fluorophore should be distinct from that of the fluorophore conjugated to the antibody recognizing the secondary characteristic of the microparticle. For example, if RPE is conjugated to the anti-PSMA antibody, then FITC should be conjugated to the antibody recognizing the secondary characteristic of the microparticle.

[0055] The prostate cancer cell derived microparticles are further exposed to a marker, such as a second monoclonal antibody, that recognizes some antigen that correlates with prostate cancer metastasis. An example of such an antigen is CD151 (GP27, SFA-1, PETA-3). Monoclonal antibodies to GDI 51 can be used to detect whether the prostate cancer positive microparticle expresses the CD 151 antigen. An example of such an antibody would be the monoclonal antibody to PETA-3/CD151 described in US Patent No. 6,245,898, the contents of which are incorporated by reference. If R-PE is used to label the prostate cancer microparticle, then FITC can be conjugated to the CD1 1 antibody.

[0056] Depending on the information that is required for the diagnosis, the microparticles can be first identified for the diagnosis-specific biomarker, and then sorted for the tissue-specific biomarker, [0057] In another embodiment, more than two biomarkers can be used to categorize the microparticles for the purpose predicting or confirming a pathophysiological condition in an animal.

[0058] As described above, the microparticles can be separated from the rest of the biological sample by flow cytometry. A fluorescence-activated cell sorting (FACS^®) instrument can be used to separate those microparticles being PSMA positive from those microparticles derived from other cells, as well as, CD151 positive microparticles from those PSMA positive microparticles not expressing CD151. In this case, the microparticles would be fluorescently labelled with distinct fluorophores, such as RPE and FITC, Microparticles can be identified by a forward scatter below that of 1.0 um.

[0059] A biological sample that is both PSMA positive and CD151 positive is indicative of the fact that the prostate cancer has metastasized,

[0060] The present invention has been described with regard to one or more embodiments, However, it will be apparent to persons skilled in the art that a number of variations and modifications can be made without departing from the scope of the invention as defined by the claims.

EXAMPLE

[0061 ] Analysis of patient samples

[0062] Patient blood processing to generate plasma

[0063] Patient blood was collected into two Vacutainers (10 mL limit), a vacutainer called the CellSave tube for circulating tumor cell enumeration (Veritex Inc.) and a sodium heparin vacutainer. The latter vacutainer was centrifuged at 2,000xg's for 10 minutes at room temperature. The yellow upper fraction, called platelet-poor plasma (PPP) was transferred to 2.0 mL Cryo-vial tubes for storage at -20"C until analyzed,

[0064] PPP from fifty male human subjects were used in this study and all blood samples were anonymously labelled. Twenty-five of the subjects had been diagnosed with localized prostate cancer and twenty-five were confirmed to have metastasis from the prostate cancer, as confirmed by bone scan. Diagnosis of each PPP sample was not known to the operator at the time of analysis.

[0065] For each patient plasma sample, 20 ί of PPP was labelled with a set of antibodies specific for prostate specific membrane antigen (PSMA, 3/E7 mAb clone, mouse IgG- RPE conjugated with FITC), CD151 (IAS mAb clone, mouse IgG-FITC), and pan-cytokeratin (C 19 mAb clone, mouse IgG-APC), Another 20 of PPP was incubated with isotype negative control antibodies, such as mouse IgG-FITC, mouse IgG-RPE, and mouse IgG-APC. For each antibody, 1 μ of antibody is used. All plasmas were incubated in the dark at room temperature for 20 rriinutes. After incubation with antibodies, 500 μΐ, of PBS containing a known concentration of beads was added to each stained plasma sample_s vortexed for 5 seconds and then transferred to a 5.0 mL polypropylene test tube and capped and kept in the dark in room temperature until analysis. Overall, each plasma sample had an isotype control tube (mouse IgG- FITC/RPE/APC) and an experimental tube (containing 3/E7-RPE, 1-A5-FITC, CK19-APC mouse IgG's) for flow cytometric analysis.

[0066] PSMA CDl51-positive microparticles confirmed metastasis

[0067] In order to determine the number of positive microparticles in each sample and the volume of plasma analyzed during each run, A488 polystyrene beads (1,0 una diameter) were subjected to flow cytometry. A PSMA monoclonal antibody (clone 3/E7) from University of Freiburg, Germany was conjugated to R-PE and used to detect PSMA-positive microparticles. A CD151 monoclonal antibody (clone 1A5) from the State University of New York, Stony Brook was conjugated to FITC and used to detect CD151-positive microparticles.

[0068] To optimize the flow cytometer for analysis of events exhibiting a diameter less than 1.0 micron, fluorescently-labeled Alexa488 microspheres of 1.0 μιη diameter were analyzed so that events exhibiting a forward scatter less than the 1.0 μπι diameter microspheres may be identified and analyzed. These microspheres were diluted into filtered phosphate buffered saline (pH 7.2) such that when 10 μί, of this suspension is used for analysis by a hemacytometer under a widefield fluorescence microscope, the number of fluorescent green beads is within the 10- 80x10⁴ beads mL range. PBS containing a known concentration of fluorescent beads was used to dilute all plasma samples being analyzed. The bead concentration value was used to determine the amount of sample analyzed by the flow cytometer. tn order to visualize the 1.0 um diameter microspheres, the instrument's neutral density filter was removed, or if this was not present, then the FS and SS threshold was adjusted to minimal levels (near 0), The gain on the FS was modulated until the microspheres appear as a tight population on a FS log vs. SS log histoplot in the middle of the histoplot. Once set, a gate was drawn that encompasses the entire lower half of the microsphere population, Any events appearing within this gate were considered microparticles and subsequently analyzed for binding of antigen-specific antibodies conjugated to user defined fluorophores as described below.

[0069] The isotype control tube was analyzed first with the channels of interest set to FITC, RPE, and APC. The PMTs for each of these channels were modulated such that the majority of all events determined by the FS log gate (as previously described) form a peak within the first log of relative fluorescence intensity (RFI). At least 30,000 events were analyzed to generate the negative isotype control peaks. Subsequently, the experimental tube was analyzed using the same parameters as the control tube, Based on the previous isotype control tubes, histoplots of events for FITC vs, RPE, and RPE vs. APC allow the number of events with significant dual fluorescence, such as 1-A5 and 3/E7 co-expression on microparticles (1A5 bound to prostate cancer microparticles), 3/E7 and CK19 co-expression on microparticles (prostate cancer microparticles) to be enumerated in each patient sample. The number of A488 microspheres, present as a tight peak in the FITC channel, was determinate of how many microliters of diluted sample was analyzed.

[0070] The microparticles were separated from the remainder of the sample by fluorescence-activated cell sorting (FACS^®) instrument. As can be seen in FIG. 1, the gated histogram from a patient having localized prostate cancer had only approximately 1.34% of the microparticles in the second quadrant. The microparticles in this quadrant were both PSMA and CD151 positive.

[0071] In contrast, the gated histogram from a patient having metastatic prostate cancer had 13.1% of the microparticles in the second quadrant, Again, the microparticles in this quadrant were both PSMA and CD151 positive. In each case, each plasma sample had its own IgG-FITC/RPE control analyzed to set FITC PE gates,

[0072] As can be seen in FIG. 2, the overall number of PSMA-positive microparticles per

1 ml of plasma was not statistically significant (p=0.0973) to differentiate metastatic prostate cancer from that which was localized. However, the number of dual positive (PSMA and CD151) microparticles was significantly increased (p ^¾ 0.0012) in those subject having metastatic prostate cancer from those with the localized form. In addition, when the PSMA positive microparticles were separated from all other microparticles, and then the CD151 antigen identified in this group of PSMA-positive microparticles. the ability to detect metastatic prostate cancer was further enhanced (p = 0.0006) (see FIG. 3).

[0073] The use of dual positive microparticles was a better predictor of metastatic prostate cancer than circulating tumor cells (CTCs). As can be seen in Table 1 , in many cases no CTC could be detected, yet the percentage of PSMA/CDlSl-positive microparticles could successfully predict or confirm whether the subject had metastatic prostate cancer.

Table 1

Patient ID CTC Count %1A5+/PSMA+ Metastatic PCa

JL1 81 72.3% Y

JL4 0 48.4% Y

JL5 0 44.3% Y

JL7 0 15.9% N

JL9 1 14.3% N

JL10 0 22% N

JL16 0 15% N JL56 0 65.5% Y

JL46 0 62.7% Y

JL55 0 51.9% Υ

JL35 0 24.9% Ν

JL45 0 9.6% Ν

JL15 0 12.9% Ν

JL25 0 63.7% Υ

JL13 0 14.1% Ν

JL23 0 9% Ν

JL24 0 10.3% Ν

JL33 0 47.4% Υ

JL12 0 43.1% Υ

JL43 0 42.7% Υ

Detection of Prostate Cancer in Prostate Tissue

[0074] To determine whether prostate cancer could be detected in blood samples and to determine whether prostate cancer could be distinguished from benign prostatic hyperplasia (BPH) prostate tissue, PSMA and Ghrelin were used as the biomarkers in the method described herein.

[0075] Ghrelin is a natural growth hormone secretagogue (GHS) whose receptor, GHSR, is highly expressed in human prostate cancer. [0076] A circulating microparticle (CMPs) assay was developed whereby patient plasmas were incubated with anti-PSMA antibodies and a fluorescein-ghrelin probe prior to flow cytometry analysis of the microparticle populations using the Apogee A-50 micro flow cytometer (FIG, 4). Preliminary enumeration of these PSMA+ GHSR+ microparticles in the plasma of 45 patients in three patient cohorts ( >1 each; BPH, localized prostate cancer, metastatic prostate cancer) indicated that metastatic and localized prostate cancer patients have a significantly higher population of PSMA+ GHSR+ microparticles compared to either BPH patients or healthy volunteers (FIG. 5). Ghrelin probe specifically detects prostate cancer over BPH (FIG. 6).

Detection of CD 151*" in prostate cancer corresponds with poor patient outcome

[0077] Micrograph of CD151 immunohistochemistry with mAB 11GA or 1A5 on adjacent benign, adjacent normal and tumor tissue (FIG. 7A). Kaplan-Meier curves of recurrence-free survival were generated using the GDI 51 free immunoreactivity in patients that received a radical retropubic prostatectomy (RRP) after diagnosis (cohort #1, N=99). Log-Rank test p=0,023 (FIG. 7B). Kaplan-Meier curves were also generated of metastasis-free survival in biopsy specimens from patients that did not receive RRP after diagnosis (cohort #2, N=38). Log-Rank test p=0.023. (FIG. 7C) These patients were monitored longitudinally after nonsurgical intervention for disease progression. Detection of CDISI⁶*⁶ in prostate cancer corresponds with poor patient outcome.

REFERENCES

[1] Jemal A, Siegel R, Xu J, Ward E. Cancer statistics. CA: a cancer journal for clinicians. 2010 Sep-Oct;60(5):277-300.

[2] Makarov DV, Loeb S, Getzenberg RH, Partin AW, Biomarkers for prostate cancer. Annual review of medicine. 2009;60:139-51.

[3] Hanahan D, Weinberg RA. The hallmarks of cancer. Cell.2000 Jan 7;100(l):57-70.

[4] Partin AW, Mangold LA, Lamm DM, Walsh PC, Epstein JI, Pearson JD. Contemporary update of prostate cancer staging nomograms (Partin Tables) for the new millennium. Urology. 2001 Dec;58(6):843-8.

[5] DAmico AV, Whittington R, Malkowicz SB, Schultz D, Blank K, Broderick GA, et al. Biochemical outcome after radical prostatectomy, external beam radiation therapy, or interstitial radiation therapy for clinically localized prostate cancer. Jama. 1998 Sep 16;280(11):969-74.

[6] Eggener SE, Scardino PT, Walsh PC, Han M, Partin AW, Trock BJ, et al. Predicting 15-year prostate cancer specific mortality after radical prostatectomy. The Journal of urology. 2011 Mar;l85(3):869-75.

[7] Schroder FH, Hugosson J, Roobol MJ, Tammela TL, Ciatto S, Nelen V, et al. Screening and prostate-cancer mortality in a randomized European study. The New England journal of medicine, 2009 Mar 26;360(13): 1320-8.

[8] Lee MC, Dong F, Stephenson AJ, Jones JS, Magi-Galluzzi C, Klein EA. The Epstein criteria predict for organ-confined but not insignificant disease and a high likelihood of cure at radical prostatectomy. Eur Urol. 2009 Jul;58(l):90-S.

[9] Loeb S_? Roehl KA, Thaxton CS, Catalona WJ. Combined prostate-specific antigen density and biopsy features to predict "clinically insignificant" prostate cancer. Urology. 2008 Jul;72(l):143-7. [10] Brawer, M. ., Prostate-specific antigen: current status. CA Cancer J Clin 1999, 49 (5), 264-81.

[11] Wolf, P.; Freudetiberg, N.; Buhler, P.; Alt, K.; Schultze-Seemann, W.; Wetterauer, U.; EIsasser-Beile, U., Three conformational antibodies specific for different PSMA epitopes are promising diagnostic and therapeutic tools for prostate cancer. Prostate 2010, 70 (5), 562-9, [12] Schroder, F. H.; Carter, H. B.; Wolters, T.; van den Bergh, R. C; Gosseiaar, C; Bangma, C. H.; Roobol, M. J., Early detection of prostate cancer in 2007. Part 1: PSA and PSA kinetics. Eur Urol 2008, 53 (3), 468-77.

[13] Loeb, S.; Carter, H, B.; Berndt, S. I.; Ricker, W,; Schaeffer, E. M._s Complications after prostate biopsy: data from SEER-Medicare. J Urol 2011, 186 (5), 1830-4.

[14] Berner, A.; Harvei, S.; Skjorten, F, J., Follow-up of localized prostate cancer, with emphasis on previous undiagnosed incidental cancer. BJU Int 1999, 83 (1), 47-52.

[15] Klotz;, L., Active surveillance; the Canadian experience. Curr Opin Urol 2012, 22 (3),

222^30.

[16] Straoke, M. L.; Liotta, L. A., Multi-step cascade of tumor cell metastasis. In Vivo 1992, 6 (4), 30946.

[17] Wang, Y.; Xue, H.; Cutz, J. C; Bayani, J.; Mawji, N. R.; Chen, W, G.; Goetz, L. J.;

Hayward, S. W.; Sadar, M. D.; Gilks, C. B.; Gout, P. W.j Squire, J. A.; Cunha, G, R.; Wang, Y. Z., An orthotopic metastatic prostate cancer model in SCID mice via grafting of a transplantable human prostate tumor line. Lab Invest 2005, 85 (11), 1392-404. [18] Weiss, L., Metastatic inefficiency, Advances in cancer research 1 90, 54, 159-211.

[19] Sincock PM, Fitter S, Partem RG, Bemdt MC, Gamble JR, Ashman LK. PETA-3/CD151, a member of the transmembrane 4 superfamily, is localised to the plasma membrane and endocytic system of endothelial cells, associates with multiple integrins and modulates cell function, J Cell Sci. 1 99 Mar.l 12 ( Pt 6):833-44.

[20] Sterk LM, Geuijen CA, Oomen LC, Calafat J, Janssen H, Sonnenberg A. The tetraspan molecule CD151, a novel constituent of hemidesmosomes, associates with the integrin alpha6beta4 and may regulate the spatial organization of hemidesmosomes. The Journal of ceil biology, 2000 May l5;149(4):969-82.

[21] Zijlstra A. Tetraspanins in Cancer. In: Zent R, Pozzi A, eds. Cell-Extracellular Matrix Interactions in Cancer. Nashville: Springer 2009:217-43,

[22] Palmer T, Zijlstra A. CD151 UCSD Molecular Pages. 201 l(doi:10.1038/mp.a004123.01).

[23] Lau LM, Wee JL, Wright MD, Moseley GW, Hogarth PM, Ashman LK, et al. The tetraspanin superfamily member CD151 regulates outside-in integrin alphallbbeta3 signaling and platelet function. Blood. 2004 Oct 15;104(8):2368-75.

[24] HasegawaH, Nomura T, Kishimoto , Yanagisawa K, Fujita S. SFA-l/PETA-3 (CD151), a member of the transmembrane 4 superfamily, associates preferentially with alpha 5 beta 1 integrin and regulates adhesion of human T cell leukemia virus type 1-infected T cells to fibronectin, J Immunol. 1998 Sep 15;161(6):3087-95.

[25] Yanez-Mo M, Alfranca A, Cabanas C, Marazuela M, Tejedor R, Ursa MA, et al. Regulation of endothelial cell motility by complexes of tetraspan molecules CD81 TAPA-1 and CD151/PETA-3 with alpha3 betal integrin localized at endothelial lateral junctions. The Journal of cell biology, 1998 May 4;141(3):791-804.

[26] Liu L, He B, Liu WM, Zhou D, Cox JV, Zhang XA. Tetraspanin CD151 promotes cell migration by regulating integrin trafficking. J Biol Chem. 2007 Oct 26;282(43):31631-42.

[27] Chien CW, Lin SC, Lai YY, Lin BW, Lin SC, Lee JC, et al. Regulation of CD151 by hypoxia controls cell adhesion and metastasis in colorectal cancer. Clin Cancer Res. 2008 Dec 15;14(24):8043-51.

[28] Zijlstra A, Lewis J, Degryse B, Stuhlmann H, Quigley JP, The inhibition of tumor cell intravasation and subsequent metastasis via regulation of in vivo tumor cell motility by the tetraspanin CD151. Cancer cell. 2008 Mar;13(3):221^34.

[29] Wright MD, Geary SM, Fitter S, Moseley GW, Lau LM, Sheng KC, et al. Characterization of mice lacking the tetraspanin superfamily member CD1 1. Molecular and cellular biology, 2004 JuL24(13):5978-88.

[30] Sachs N, Kreft M, van den Bergh Weerman MA, Beynon AJ, Peters TA, Weening JJ, et al. Kidney failure in mice lacking the tetraspanin CD151, The Journal of cell biology. 2006 Oct 9;175(l):33-9.

[31] Suzuki S, Miyazaki T, Tanaka N, Sakai M, Sano A, Inose T, et al. Prognostic Significance of CD151 Expression in Esophageal Squamous Cell Carcinoma with Aggressive Cell Proliferation and Invasiveness. Ann Surg Oncol. 2011 Oct 27.

[32] Ke AW, Shi GM, Zhou J, Wu FZ, Ding ZB, Hu MY, et al. Role of overexpression of CD151 and/or c-Met in predicting prognosis of hepatocellular carcinoma, Hepatology (Baltimore, Md. 2009 Feb;49(2):491-503.

[33] Shi GM, Ke AW, Zhou J, Wang XY, Xu Y, Ding ZB, et al. CD151 modulates expression of matrix metalloproteinase 9 and promotes neoangiogenesis and progression of hepatocellular carcinoma, Hepatology (Baltimore, Md. 2010 Jul;52(l): 183-96.

[34] Tokuhara T, Hasegawa H, Hattori N, Ishida H, Taki T, Tachibana S, et al. Clinical significance of CD151 gene expression in non-small cell lung cancer. Clin Cancer Res. 2001 Dec;7(12):4109-14.

[35] Yoo SH, Lee K, Chae JY, Moon KC. CD151 expression can predict cancer progression in clear cell renal cell carcinoma. Histopathology. 2011 Jan;58(2):1 1-7.

[36] Albertsen, P. C; Hanley, J. A.; Fine, J,, 20-year outcomes following conservative management of clinically localized prostate cancer. JAMA 2005, 293 (17), 2095-101, [37] Carroll, P. R., Early stage prostate cancer-do we have a problem with over-detection, overtreatment or both? J Urol 2005, 173 (4), 1061-2.

[38] Cooperberg, M. R-; Lubeck, D. P.; Meng, M. V.; Mehta, S. S.; Carroll, P. R„ The changing face of low-risk prostate cancer; trends in clinical presentation and primary management. J Clin Oncol 2004, 22 (11), 2141-9.

Claims

WHAT IS CLAIMED IS:

1. A method comprising the steps of: distinguishing microparticles from a biological sample into two or more groups using flow cytometry; and identifying a subpopulation of microparticles from the two or more groups, wherein the subpopulation of microparticles is predicative or indicative of a pathophysiological condition in an animal.

2. The method of claim 1, wherein the step of distinguishing the microparticles comprises identifying at least one microparticle population from the overall population of microparticles by selecting for a biomarker selective for the at least one microparticle population.

3. The method of claim 2, wherein the biomarker is detected by an antibody, peptide or small molecule.

4. The method of claim 1, wherein the step of distinguishing the microparticles comprises identifying prostate cancer microparticles from the overall population of microparticles.

5. The method of claim 4, wherein the prostate cancer microparticles are distinguished from the overall population of microparticles by detecting the presence of a Prostate Specific Membrane Antigen (PSMA) on the prostate cancer microparticle.

6. The method of claim 5, wherein the PSMA is detected using an anti-PSMA antibody.

7. The method of claim 2, wherein the step of identifying the subpopulation of microparticles comprises identifying a biomarker selective for the subpopulation of microparticles.

8. The method of claim 7, wherein the biomarker is detected by an antibody, peptide or a small molecule.

9. The method of claim 4, wherein the step of identifying the subpopulation of microparticles comprises identifying a biomarker selective to a pathological stage of prostate cancer.

10. The method of claim 9, wherein the biomarker is CD151,

11. The method of claim 10, wherein CD151 is detected using an anti-CD151 antibody.

12. The method of any one of claims 1 to 11, wherein the animal is a human.

13. The method of any one of claims 1 to 12, wherein the biological sample is a component of blood,

14. The method of claim 13 , wherein the component of blood is serum ,

15. A method comprising the steps of: disungm^'shing microparticles from a biological sample from a human as cancer derived microparticles or other microparticles using flow cytometry; and identifying microparticles from the cancer derived microparticles which express a marker correlating with cancer metastasis, wherein the presence of microparticles which express the marker correlating with cancer metastasis is predictative or indicative of metastatic cancer.

16. The method of claim 15, wherein the cancer is prostate cancer,

17. The method according to claim 16, wherein the prostate cancer derived microparticles express a prostate cancer antigen,

18. The method according to claim 17, wherein the prostate cancer antigen is prostate specific membrane antigen (PSMA).

19. The method according claim 18, wherein the PSMA is detected by an anti-PSMA monoclonal antibody.

20. The method according to claim 19, wherein the anti-PSMA monoclonal antibody is 3E6.

21. The method according to any one of claims 15 to 20, wherein the marker correlating with cancer metastasis is CD151.

22. The method according to claim 21, wherein the CD151 is detected by an anti-CD151 monoclonal antibody.

23. The method according to claim 22, wherein the anti-CD151 monoclonal antibody is 1A5,

24. The method according to any one of claims 16 to 23, wherein the prostate cancer derived microparticles and the prostate cancer derived microparticles which express a marker correlating with cancer metastasis are detected concurrently.

25. The method according to any one of claims 16 to 23, wherein the prostate cancer derived microparticles and the prostate cancer derived microparticles which express a marker correlating with cancer metastasis are detected simultaneously,

26. A method comprising:

&straguishing microparticles from a biological sample into two or more groups using flow cytometry; and identifying a subpopulation of microparticles from the two or more groups based on the presence or absence of a microparticle specific biomarker, wherein the subpopulation of microparticles is predicative or indicative of a pathophysiological condition in an animal.

27. The method of claim 26, wherein the step of distinguishing the microparticles comprises identifying at least one microparticle population from the overall population of microparticles by selecting for a biomarker selective for the at least one microparticle population.

28. The method of claim 27, wherein the biomarker is detected by an antibody, peptide or small molecule.

29. The method of claim 26, wherein the microparticle specific biomarker is selected from CD9, CD63, CD81, Alix, TSglOl, CD40 Ugand and Selectin.

30. The method of claim 29, wherein the microparticle specific biomarker is detected by an antibody, peptide or a small molecule.

31. The method of claim 29, wherein the biomarker is CD151.

32. The method of claim 31, wherein CD 151 is detected using an anti-CDl 51 antibody.

33. The method of any one of claims 29 to 32, wherein the animal is a human.

34. The method of any one of claims 29 to 33, wherein the biological sample is a component of blood.

35. The method of claim 34, wherein the component of blood is serum.

36. A method comprising: distinguishing microparticles from a biological sample into two or more groups using flow cytometry; and identifying a subpopulation of microparticles from the two or more groups based on the presence or absence of a tissue specific biomarker, wherein the subpopulation of microparticles is predicative or indicative of a pathophysiological condition in an animal.

37. The method of claim 36, wherein the step of distinguishing the microparticles comprises identifying at least one microparticle population from the overall population of microparticles by selecting for a biomarker selective for the at least one microparticle population.

38. The method of claim 37, wherein the biomarker is detected by an antibody, peptide or small molecule.

39. The method of claim 36, wherein the tissue specific biomarker is selected from carcinoembryonic antigen-related cell adhesion molecule 7 (CEACAM7), chloride channel accessory 1 (CLCAl), glycoprotein A33 (transmembrane) (GPA33), zymogen granule protein 16 (ZG16); iroquois homeobox 5 (IRX5), lysosomal-associated membrane protein 3 (LAMP3), rnicrofibrillar-associated protein 4 (MFAP4), transmembrane protein 100 (TMEM100); pancreatic tissue can be detected by the presence of aquaporin 8 (AQP8), carboxyl ester lipase (CEL), chymotr psin-like elastase family, member 2A (CELA2A), chymotrypsin-like elastase family, member 2B (CELA2B), chymotrypsin-like elastase family, member 3B (CELA3B), carboxypeptidase Al (pancreatic) (CPA1), carboxypeptidase A2 (pancreatic) (CPA2), carboxypeptidase Bl (tissue) (CPB1)₃ chymotrypsmogen Bl (CTRB1), chymotrypsinogen B2 (CTRB2), chymotrypsin C (caldecrin) (CTRC), CUB and zona pellucida-like domains 1 (CUZD1), glucagon (GCG), islet amyloid polypeptide (IAPP), insulin (INS), kallikrein 1 (KLKl), pancreatic lipase (PNLIP), pancreatic lipase-related protein 1 (PNLIPRPl), pancreatic lipase-related protein 2 (PNLIPRP2), pancreatic polypeptide (PPY), protease, serine, 1 (trypsin 1) (PRSS1), protease, serine, 3 (PRSS3), regenerating islet-derived 1 beta (REG1B), regenerating islet-derived 3 gamma (REG3G), solute carrier family 30 (zinc transporter), member 8 (SLC30A8), syncollin (SYCN); glutamyl arninopeptidase (aminopeptidase A) (ENPEP), solute carrier family 2 (facilitated glucose transporter), member 9 (SLC2A9), solute carrier family 12 (sodium/potassium/chloride transporters), member 1 (SLC12A1), uromodulin (UMOD), cubilin (intrinsic factor-cobalamin receptor) (CUBN), solute carrier family 13 (sodium-dependent dicarboxylate transporter), member 3 (SLC13A3), transmembrane protein 27 (TMEM27), claudin 2 (CLDN2), prostate specific membrane (PSMA), prostate stem cell antigen (PSCA), epidermal growth factor receptor (EGFR), vascular endothelial growth factor (VEGF), HER-2/neu, polymorphic epithelial mucin (MUCI), folate hydrolase I (FOLH1), kallikrein- related peptidase 2 (KLK2), kallikrein-related peptidase 3 (KLK3), solute carrier family 45 member 3 (SLC45A3) and vascular endothelial growth factor (VEGF).

40. The method of claim 39, wherein the tissue specific biomarker is detected by an antibody, peptide or a small molecule.

41. The method of claim 39, wherein the biomarker is PSMA.

42. The method of claim 31, wherein PSMA is detected using an anti-PSMA antibody.

43. The method of any one of claims 39 to 42, wherein the animal is a human.

44. The method of any one of claims 39 to 43, wherein the biological sample is a component of blood,

45. The method of claim 44, wherein the component of blood is serum.

46. A method comprising: distinguishing microparticles from a biological sample into two or more groups using flow cytometry; and identifying a subpopulation of microparticles from the two or more groups based on the presence or absence of a diagnosis specific biomarker, wherein the subpopulation of microparticles is indicative of a pathophysiological condition in an animal.

47. The method of claim 46, wherein the step of distmguishing the microparticles comprises identifying at least one microparticle population from the overall population of microparticles by selecting for a biomarker selective for the at least one microparticle population.

48. The method of claim 47, wherein the biomarker is detected by an antibody, peptide or small molecule.

49. The method of claim 46, wherein the diagnosis specific biomarker is selected from CECAM, CA125, PSMA, ECAM, VECAM, CD13, CD20, CD30, c-KIT, ER, AR, Alphafetoprotein, Carcinoembryonio antigen, CA-125, MUC-1, Epithelial tumor antigen, Tyrosinase, Melanoma-associated antigen, abnormal products of ras and p53.

50. The method of claim 49, wherein the diagnosis specific biomarker is detected by an antibody, peptide or a small molecule,

51. The method of claim 49, wherein the biomarker is detected by an antibody, peptide or small molecule.

52. The method of any one of claims 46 to 51, wherein the animal is a human.

53. The method of any one of claims 46 to 52, wherein the biological sample is a component of blood.

54. The method of claim 53, wherein the component of blood is serum,

55. A method comprising: distinguishing microparticles from a biological sample into two or more groups using flow cytometry; and identifying a subpopulation of microparticles from the two or more groups based on the presence or absence of a disease progression specific biomarker, wherein the subpopulation of microparticles is predictive of a pathophysiological condition in an animal.

56. The method of claim 55, wherein the step of distinguishing the microparticles comprises identifying at least one microparticle population from the overall population of microparticles by selecting for a biomarker selective for the at least one microparticle populatioa

57. The method of claim 56, wherein the biomarker is detected by an antibody, peptide or small molecule.

58. The method of claim 55, wherein the progression disease specific biomarker is selected from CD4, CD166, CD133, LICAM, CD151. ITGA2, ITGA3, ITGA6, MMP14, ADAM17 and ADAM12.

59. The method of claim 58, wherein the progression specific biomarker is detected by an antibody, peptide or a small molecule.

60. The method of claim 59, wherein the biomarker is detected by an antibody, peptide or small molecule.

61. The method of any one of claims 55 to 60, wherein the animal is a human.

62. The method of any one of claims 55 to 61, wherein the biological sample is a component of blood.

63. The method of claim 62, wherein the component of blood is serum.

64. A method comprising: distinguishing microparticles from a biological sample into two or more groups using flow cytometry; and identifying a subpopulation of microparticles from the two or more groups based on the presence or absence of a cancer metastasis specific biomarker, wherein the subpopulation of microparticles is indicative of a pathophysiological condition in an animal,

65. The method of claim 64, wherein the step of distinguishing the microparticles comprises identifying at least one microparticle population from the overall population of microparticles by selecting for a biomarker selective for the at least one microparticle population.

66. The method of claim 65, wherein the biomarker is detected by an antibody, peptide or small molecule.

67. The method of claim 64, wherein the cancer metastasis specific biomarker is selected from CD151, ALCAM and ανβ6 integrin.

68. The method of claim 67, wherein the biomarker is CD151.

69. The method of claim 68, wherein CD151 is detected using an anti-CD 151 antibody.

70. The method of any one of claims 64 to 69, wherein the animal is a human,

71. The method of any one of claims 64 to 70, wherein the biological sample is a component of blood,

72. The method of claim 71 , wherein the component of blood is serum.

73. A method comprising: distinguishing microparticles from a biological sample into two or more groups using flow cytometry; identifying a subpopulation of microparticles from the two or more groups; and quantifying the relative number of microparticles in the subpopulation to the number of microparticles in the two or more groups, wherein the relative number of microparticles in the subpopulation to the number of microparticles in the two or more groups is predictive or indicative of a pathophysiological condition in an animal.

74. The method of claim 73, wherein the quantifying step comprises comparing the number of microparticles to a known control sample.

75. The method of claim 74, wherein the known control sample is an iso type antibody.

76. The method of any one of claims 73 to 75, wherein the microparticles are distinguished in the biological sample by identification of a tissue-specific biomarker.

77. The method of any one of claims 73 to 76, wherein the subpopulation of microparticles is based on the presence or absence of a pathological condition specific biomarker,

78. The method of claim 76, wherein the tissue-specific biomarker is Prostate Specific Membrane Antigen (PSMA).

79. The method of claim 78, wherein the PSMA is detected by an anti-PSMA antibody.

80. The method of any one of claims 77 to 79, wherein the pathological condition specific biomarker is CD151.

81. The method of claim 80, wherein the CD 151 is detected by an anti-CD 151 antibody.

82. The method of any one of claims 73 to 81, wherein the pathophysiological condition is prostate cancer metastasis.