Abstract:
The invention relates to a method for the determination of a cancer diagnostic/therapeutic biomarker assay and drug-targets including the following steps: (a) identification of potential candidate protein/peptide biomarkers and drug-targets based on the measurement of protein/peptide constituent concentrations in tissue sample proteomes as well as serum, plasma or any other derivatives of blood, or blood itself sample proteomes derived from healthy non-human mammalian individuals as well as from cancerous non-human mammalian individuals and qualitatively selecting as potential candidate protein/peptide biomarkers those which show a pronounced differential behaviour between healthy and cancerous sample proteomes; (b) optional verification of the potential candidate protein/peptide biomarkers as identified in step (a) by quantitative mass spectrometric measurement of the potential candidate protein biomarkers in serum, plasma or any other derivatives of blood, or blood itself sample proteomes derived from healthy non-human mammalian individuals as well as from cancerous non-human mammalian individuals and selecting as candidate protein/peptide biomarkers those which show a mass-spectrometrically measurable quantitative differential behaviour between healthy and cancerous sample proteomes; (c) validation of the candidate protein/peptide biomarkers as identified in step (a), or as optionally verified in step (b), by mass spectrometric measurement and/or antibody-based assays such as an Enzyme-Linked Immunosorbent Assay (ELISA) determination of the candidate protein biomarkers in serum, plasma or any other derivatives of blood, or blood itself sample proteomes derived from healthy human individuals as well as from cancerous human individuals and selecting as protein/peptide biomarkers those which show a mass-spectrometrically measurable and/or antibody-based assay detectable differential behaviour between healthy and cancerous sample proteomes; (d) application of statistical methods to uncover single or groups of protein/peptide biomarkers as validated in step (c) as signatures for the detection of patients with cancer. The invention furthermore relates to specific biomarker assays for the highly reliable diagnosis of cancer, specifically of localized or non-localized prostate cancer, using human serum, plasma or any other derivatives of blood, or blood itself.

Description:
TECHNICAL FIELD 
       [0001]    The present invention relates to the field of methods for the determination of biomarker assays and/or drug-targets for the diagnosis of cancer and its treatment and/or prognosis, specifically of prostate cancer, be it localized or non-localized prostate cancer. A further object of the present invention is to propose specific biomarker assays for these diagnostic purposes and/or patient stratification as well as methods for diagnosis using these specific biomarker assays. 
       BACKGROUND OF THE INVENTION 
       [0002]    The diagnosis and treatment of prostate cancer, despite decennial research efforts, are still a major challenge in the clinics. Prostate cancer progression is unfortunately silent, and an early detection of faster progressing and potentially dangerous lesions is crucial for the patient&#39;s health, since complete remission and cure from the disease is possible only at early stages of the disease. 
         [0003]    The best noninvasive diagnostic test available for prostate cancer is the detection of the Prostate Specific Antigen (PSA) in the blood coupled with digital rectal examination (DRE). PSA is a protein produced by the epithelial cells of the prostate gland. PSA is also known as kallikrein III, seminin, semenogelase, γ-seminoprotein and P-30 antigen and it is a 34 kD glycoprotein present in small quantities in the serum of normal men, and is often elevated in the presence of prostate cancer and in other prostate disorders. A blood test to measure PSA coupled with DRE is the most effective test currently available for the early detection of prostate cancer. Higher-than-normal levels of PSA are associated with both localized (loc) and metastatic (met) prostate cancer (CaP). 
         [0004]    The diagnostic accuracy of PSA alone is only around 60% and the methodology has major drawbacks in specificity (too many false positives cases that undergo unneeded prostate biopsy or surgery). Indeed PSA levels can be also increased by prostate infection, irritation, benign prostatic hypertrophy (enlargement) or hyperplasia (BPH), and recent ejaculation, producing a false positive result. 
         [0005]    A reliable and non-invasive diagnostic/prognostic procedure is thus still lacking, even tough novel methodologies based on the simultaneous measurement of various parameters (e.g. free and total PSA) are emerging as tools to increase the overall diagnostic accuracy. Most PSA in the blood is bound to serum proteins. A small amount is not protein bound and is called free PSA. In men with prostate cancer the ratio of free (unbound) PSA to total PSA is decreased. The risk of cancer increases if the free to total ratio is less than 25%. The lower the ratio, the greater the probability of prostate cancer. However, both total and free PSA increase immediately after ejaculation, returning slowly to baseline levels within 24 hours, and also other mechanisms not related to CaP can influence the free to total PSA ratio. 
         [0006]    Similar to diagnosis, treatment and/or prognosis of prostate cancer remains a major challenge due to heterogeneity of the disease. Although multiple mechanisms of prostate cancer have been suggested, the lack of suitable signatures able to stratify patients and key target proteins for therapeutic intervention cures are still not within reach. 
       SUMMARY OF THE INVENTION 
       [0007]    The object of the present invention is therefore to provide an improved method for the determination of biomarker assays and/or drug-targets for diagnosis, prognosis, treatment as well as for monitoring of treatment of cancer, and/or for the stratification of patients, specifically of prostate cancer, be it localized or non-localized prostate cancer. It should be noted that from a principle point of view the proposed method is not limited to cancer but can be applied to any kind of human or animal disease or dysfunction. From a practical point of view the only limitation can sometimes be that a model system should be available which can be used for the translational approach as described below. 
         [0008]    It should be noted that not only candidates from a mouse (or generally animal, e.g. non-human model) are part of the invention. Potential marker candidates can be determined from a variety of sources, including also human tissue, proximal fluids, animal models cell lines, data mining etc. 
         [0009]    There is however a rather distinctive advantage of the animal model, in the more general context of a systems biology approach to biomarker discovery. Assuming that in different cancers (that affect different tissues) cellular networks are perturbed. Assuming further that different manifestations of cancer can have the same or overlapping perturbations, an animal such as a mouse model allows to specifically apply one perturbation in isolation or defined combinations of perturbations to determine how the target tissue reacts to this perturbation. If one then furthermore assumes that some of the proteins that constitute this response, (either direct effects of the perturbation, e.g. loss of phosphopeptides if a kinase is deleted or mutated) or compensatory effects leave specific fingerprints in the target tissue, some of these fingerprints are detectable in serum using the methods we describe here. A distinctive feature of a genetically defined mouse model allows to define changes associated with a specific gene mutation (in our case e.g. PTEN) that we know is mutated also in human cancer and thus immediately suggests a subclass of patients to be looked at and treated (personalized medicine). In planning clinical trials it is often important to have solid knowledge of the prevalence and frequency of molecular marker species in the diseased population. Those patients with a high likelihood of good response may be selected in a so called patient stratification process. Based on this information the size of the available cohort can be estimated for a given strict marker profile. Retrospective studies in archived tissues e.g. allow determining those parameters fast and early before the design for the clinical phase has to be fixed and committed. 
         [0010]    A further object of the present invention is to propose specific biomarker assays for these diagnostic/therapeutic/monitoring/prognostic/patient stratification purposes as well as methods for diagnosis/therapy/monitoring/prognosis/patient stratification using these specific biomarker assays. 
         [0011]    The present invention according to a first aspect thus relates to a method for the determination of a cancer (or generally speaking disease/dysfunction) diagnostic/therapeutic/monitoring/prognostic/patient stratification biomarker assay including the following steps: 
         [0000]    (a) identification of potential candidate protein/peptide biomarkers based on the measurement of protein/peptide constituent concentrations (abundances) in tissue sample proteomes as well as sample proteomes of serum, plasma or any other derivatives of blood, or blood itself derived from healthy non-human mammalian individuals as well as from cancerous non-human mammalian individuals and qualitatively selecting as potential candidate protein/peptide biomarkers those which show a pronounced differential behaviour between healthy and cancerous sample proteomes. As pointed out above, in this step not necessarily non-human samples have to be used, also human sources can be used for this step such as human tissue, proximal fluids etc. 
         [0012]    This step can optionally be followed by step (b): verification of the potential candidate protein/peptide biomarkers as identified in step (a) by quantitative mass spectrometric measurement of the potential candidate protein biomarkers in sample proteomes of serum, plasma or any other derivatives of blood, or blood itself derived from healthy non-human mammalian individuals as well as from cancerous non-human mammalian individuals and selecting as candidate protein/peptide biomarkers those which show a mass-spectrometrically measurable quantitative differential behaviour between healthy and cancerous sample proteomes. Of course mass spectroscopy is just one and indeed the preferred way of measurement in this verification step. Also different methods for example using an affinity reagents, can be used in a way similar or identical to the one that has finally to be used for the diagnosis/prognosis/therapy. 
         [0013]    Then follows a step (c): validation of the candidate protein/peptide biomarkers as identified in step (a), or as optionally verified in step (b), by mass spectrometric measurement and/or antibody-based determination of the candidate protein biomarkers in sample proteomes of serum, plasma or any other derivatives of blood, or blood itself derived from healthy human individuals as well as from cancerous human individuals and selecting as protein/peptide biomarkers those which show a mass-spectrometrically measurable and/or affinity reagent-based assay, preferably antibody-based assay detectable differential behaviour between healthy and cancerous sample proteomes; 
         [0000]    (d) application of statistical methods to uncover single or groups of protein/peptide biomarkers as validated in step (c) as signatures for the detection of patients with cancer. 
         [0014]    Preferably, the affinity reagent-based determination is, as mentioned above, an antibody-based determination method/assay, and is for example selected to be an Enzyme-Linked Immunosorbent Assay (ELISA) or a Multiplex Bead Array Assay or other methodologies aiming at measuring a particular protein concentration. 
         [0015]    As mentioned above, the method can not only be applied for the determination of cancer biomarker systems but also to the determination of biomarker systems for other kinds of diseases or dysfunctions of an organism. In these cases in the above methods (and also in the discussion further below of the specification) the expression “cancerous” (for example for the sample) is essentially to be replaced by an expression “diseased” or “dysfunctional”. 
         [0016]    One of the gists of the present invention is therefore the concept to increase the accuracy of the non-invasive diagnostic procedure for the detection of (prostate) cancer on the one hand, and to identify new therapeutical/imaging targets used in the clinical practice. We have established a protocol for (prostate) cancer biomarkers and/or drug-targets identification, which is summarized in  FIG. 1 , which will be discussed in more detail further below. This approach is based on three major aspects: 
         [0000]    (I) a translational approach based on the initial identification of candidate biomarkers and/or drug-targets, in vivo using a defined genetic mouse model and subsequent validation in human clinical samples;
 
(II) cutting edge mass spectrometry-based methodologies and bioinformatics methods established in our lab for the isolation, identification and quantitation of N-linked glycoproteins followed by
 
(III) multivariate statistical methods to uncover particular signatures for the detection of patients with prostate cancer.
 
         [0017]    According to a first preferred embodiment of the proposed method, it is applied to the diagnosis of prostate cancer. To this end, the cancerous sample proteomes are selected to be sample proteomes of individuals with prostate cancer. Furthermore the tissue samples are prostate tissue samples, wherein these can be samples with localized or non-localized prostate cancer. Correspondingly the derived protein/peptide biomarkers are selected to be diagnostic of prostate cancer, can be used for the therapy of prostate cancer or for the monitoring of the therapy of prostate cancer. 
         [0018]    According to a further embodiment of the proposed method, in step (a) proteins derived from the sample proteomes are selected to be exclusively glycoproteins, preferably N-linked glycoproteins, as these constitute a sub proteome which is highly relevant in the context of cancer drug-target and biomarker discovery. 
         [0019]    Preferably in a first step of this step (a) the proteome of the corresponding sample is digested, preferably by using trypsin and/or Lys C (other digestive systems however being possible), and subsequently extracted using solid-phase extraction (preferably using the method SPEG as will be discussed in more detail below). The determined biomarkers are correspondingly preferred to be N-linked glycoproteins and/or peptide fragments thereof. 
         [0020]    In principle it would be possible to use cell culture systems at least for step (a) and specifically for the tissue samples thereof. However, in order to mimic more closely the complexity of a human disease or dysfunction it is preferred to select the samples to be derived from in vivo sources, and most preferably the non-human mammalian individuals are selected to be mice, and preferably a murine prostate tissue for the samples in step (a) is perfused for complete removal of blood from the prostate tissue prior to the analysis and/or further treatment of the proteome (in case of other diseases or dysfunctions the corresponding tissue or organ can be treated analogously). 
         [0021]    As already pointed out above, for several reasons animal models are preferred. The three main points for this preference are as follows:
       the samples are homogeneous, i.e. the individuals from which the samples originate are genetically identical and have the same lesion   the lesion corresponds to a lesion observed in human cancer and thus accurately models tumor development   reproducible: very similar samples can be prepared over and over which is not possible in humans   defined perturbation. In humans we have no control over the perturbations that lead to cancer. In animal models single or a combination of perturbations can be applied in a tissue specific and time specific manner.       
 
         [0026]    After the mere identification of proteins/fragments thereof within step (a), preferably only those proteins/fragments thereof selected which show a well distinguishable differential behaviour between healthy and cancerous sample sources. To this end, the differential behaviour of the measured signals (differential abundance) is observed and only those signals (corresponding to specific protein/fragments thereof) which showed sufficient differential behaviour will be selected for the next step for further evaluation. 
         [0027]    Differential behaviour can either be a situation, in which a specific signal is sufficiently increased/decreased when comparing the healthy with the cancerous samples signals, it can however also be a situation, in which there is no signal in the cancerous or the healthy sample signals, and a clearly detectable signal in the healthy or the cancerous sample signals, respectively. According to a preferred embodiment therefore, the selection criteria for the determination of the presence of sufficient differential behaviour in step (a) are selected from the following group: 
         [0000]    biomarkers regulated in prostate tissue and serum; potential biomarkers regulated in prostate tissue and detected in serum; potential biomarkers regulated in prostate tissue and secreted; potential biomarkers exclusively detected in prostate tissue and sera of mice with cancer; potential biomarkers, specific for prostate and regulated in cancer tissue or serum; potential biomarkers specific for prostate and secreted; potential biomarkers highly regulated in prostate tissue or serum, preferably by a factor of more than four; potential biomarkers, prior knowledge-based selection, preferably characterised by known biological function during cancer progression; or a combination thereof, preferably a combination of at least five or most preferably of all of these criteria is used. Of these preferably specifically the following combination of criteria leads to biomarker systems which can finally be used for human diagnosis/therapy: biomarkers regulated in prostate tissue and serum; potential biomarkers regulated in prostate tissue and detected in serum; potential biomarkers regulated in prostate tissue and secreted; potential biomarkers highly regulated in prostate tissue or serum, preferably by a factor of more than four; potential biomarkers, prior knowledge-based selection, preferably characterised by known biological function during cancer progression. 
         [0028]    Preferably selection takes place (selection meaning that the corresponding protein/fragment thereof (meaning protein or a fragment of such a protein) will enter the next step) if the factor between signals of healthy and signals of cancerous samples is either larger than 1.5 or smaller than 0.75. This in particular applies to the first three above-mentioned selection criteria. 
         [0029]    Typically, in step (a) the proteins/peptides of the digested proteins of the samples are in a first step identified by using a (shotgun) mass spectrometric technique, and in a second step a combined liquid chromatography/mass spectrometry technique, preferably a label-free quantitation technique, is used for the identification of the differential properties (normally differential abundance) between healthy and cancerous samples. Preferably, within step (a) the mass spectrometrically detected proteins/protein fragment signals are attributed to the corresponding proteins by using database information attributing mass spectrometric signals to specific proteins/protein fragments. 
         [0030]    According to a further preferred embodiment, in step (b) absolute quantification is achieved by using a quantitative internal standard, preferably a specifically synthesised internal standard. 
         [0031]    It is further preferred to use in step (b) and/or in step (c) tandem mass spectrometry techniques, preferably selected reaction monitoring (SRM), preferably in combination with liquid chromatography, as mass spectrometry method. As concerns these techniques and their definitions and parameters, for keeping the present specification within reasonable boundaries, reference is made to the publication B. Domon and R. Aebersold, entitled Mass Spectrometry and Protein Analysis (Science 312, 121 (2006)) and the corresponding references cited therein. The disclosure of these documents is expressly included into this specification as concerns these analytical tools for the analysis of the proteome. 
         [0032]    The present invention furthermore relates to a cancer diagnostic biomarker assay and/or therapeutic target which can be determined using a method as outlined above, or specifically determined using such a method. Specifically such a biomarker assay and/or therapeutic target may consist of the set as outlined further below in the context of the description of the corresponding methods, so for example a cancer diagnostic/therapeutic biomarker assay for localized prostate cancer can be based on, for the monitoring of localized prostate cancer, in particular for the distinction from benign prostate hyperplasia, a combined measurement of the concentration of at least two, preferably at least three proteins and/or fragments of proteins selected from the group derived from: ASPN; VTN; AOC3; LOX; PGCP; PSAP; THBS1; CFH; CLU; KIT; TFRC; LGALS3BP; GOLPH2; HYOU1; CTSD; OLFM4; AKAP13; CP; CPE; CPM; ICAM1; MSMB; TM9SF3; GALNTL4 in human serum, plasma or a derivative of blood, or blood itself. Gene names as given here, Entry names, Protein names (shortened) and Accession numbers as generally used in all this specification are as defined according to the UniProt Consortium, which is comprised of the European Bioinformatics Institute (EBI), the Swiss Institute of Bioinformatics (SIB), and the Protein Information Resource (PIR). Preferably the measurement is carried out using tandem mass spectrometry techniques, preferably selected reaction monitoring (SRM), more preferably in combination with liquid chromatography, and/or Enzyme-Linked Immunosorbent Assays (ELISA) for the detection of these proteins/fragments thereof. 
         [0033]    The present invention furthermore relates to a cancer diagnostic biomarker assay and/or therapeutic target which can be determined using a method as outlined above, or specifically determined using such a method. Specifically such a biomarker assay and/or therapeutic target may consist of the set as outlined further below in the context of the description of the corresponding methods, so for example a cancer diagnostic/therapeutic biomarker assay for localized prostate cancer can be based on ASPN, and optionally VTN, in combination with one of AOC3; LOX; PGCP; PSAP; THBS1; CFH; CLU; KIT; TFRC; LGALS3BP; GOLPH2, HYOU1; CTSD; OLFM4 derived proteins/fragments thereof. 
         [0034]    Furthermore the present invention relates to a cancer diagnostic/therapeutic biomarker assay for the diagnosis, therapy and/or the therapeutic monitoring of (human) diseases or dysfunctions, preferably of cancer, and most preferably of prostate cancer (localized or non-localized) comprising the measurement of at least two, preferably at least three or at least five protein/peptide biomarkers (as for example determined according to a method as given above) in human serum, plasma or any other derivatives of blood, or blood itself. The assay can for example be an antibody-based assay such as an Enzyme-Linked Immunosorbent Assay, it can however also be an LC-SRM assay. 
         [0035]    To increase the reliability of such cancer diagnostic biomarker assay, it can be combined with an affinity reagent-based assay, e.g. an antibody-based assay such as Enzyme-Linked Immunosorbent Assay (ELISA) for the detection of further systems such as Prostate Specific Antigen (PSA). Also multiplexing techniques of a series of antibodies for example using bead techniques are possible in this respect. 
         [0036]    The present invention furthermore relates to a method for the diagnosis of localized prostate cancer using a (preferably combined) measurement of the concentration of 
         [0037]    ASPN derived protein/fragments thereof as well as VTN derived protein/fragments thereof in human serum, plasma or any other derivatives of blood, or blood itself. For increasing the accuracy, it is preferred to carry out one (or even several) further measurements, namely the measurement of one further protein/fragments thereof selected from the group derived from: AOC3; LOX; PGCP; PSAP; THBS1. If combined with a PSA-measurement, the further protein/fragments thereof can additionally be selected from: CFH; CLU; KIT; TFRC; LGALS3BP; GOLPH2. 
         [0038]    Preferably in such a method the measurement is carried out using tandem mass spectrometry techniques, preferably selected reaction monitoring (SRM), typically in combination with preceding liquid chromatography. Alternatively or additionally it is possible to use an antibody based assay such as an Enzyme-Linked Immunosorbent Assays (ELISA) for the detection of these proteins/fragments thereof. Combined approaches are possible, so for example one system (or group of systems) can be determined using SRM (if for example no ELISA is available), and the remaining system(s) can be determined by using antibody based techniques such as ELISA-techniques. 
         [0039]    In such a method, typically for a positive diagnosis of localized prostate cancer 
         [0000]    the concentration of ASPN derived protein/fragments thereof has to be more than 55 ng/ml, preferably more than 60 ng/ml, and optionally at the same time,
 
the concentration of VTN derived protein/fragments has to be less than 3500 ng/ml, preferably less than 3300 ng/ml.
 
         [0040]    If, as preferred, additionally one of the above-mentioned additional systems is measured, 
         [0000]    the concentration of AOC3 derived protein/fragments thereof has to be less than 250 ng/ml, preferably less than 220 ng/ml,
 
and/or the concentration of LOX derived protein/fragments thereof has to be less than 580 ng/ml, preferably less than 550 ng/ml,
 
and/or the concentration of PGCP derived protein/fragments thereof has to be more than 550 ng/ml, preferably more than 570 ng/ml,
 
and/or the concentration of PSAP derived protein/fragments thereof has to be less than 33000 ng/ml, preferably less than 32500 ng/ml, most preferably less than 32250 ng/ml,
 
and/or the concentration of THBS1 derived protein/fragments thereof has to be more than 12500 ng/ml, preferably more than 13000 ng/ml, most preferably more than 13500 ng/ml
 
and/or the concentration of LGALS3BP derived protein/fragments thereof has to be more than 390 ng/ml, preferably more than 400 ng/ml
 
and/or the concentration of GOLPH2 derived protein/fragments thereof has to be more than 80 ng/ml, preferably more than 90 ng/ml
 
and/or the concentration of HYOU1 derived protein/fragments thereof has to be more than 35 ng/ml, preferably more than 40 ng/ml,
 
and/or the concentration of CTSD derived protein/fragments thereof has to be less than 32 ng/ml, preferably less than 25 ng/ml,
 
and/or the concentration of OLFM4 derived protein/fragments thereof has to be less than 20 ng/ml, preferably less than 15 ng/ml.
 
         [0041]    Preferably in such a method the measurement is carried out for the diagnosis and/or for the therapy and/or for the monitoring of localized prostate cancer for the distinction from benign prostate hyperplasia, using a combined measurement of the concentration of at least three proteins and/or fragments of proteins selected from the group derived from: ASPN; HYOU1; CTSD; OLFM4; in human scrum, plasma or a derivative of blood, or blood itself. For the diagnosis/monitoring preferably additionally the concentration of the Prostate Specific Antigen (PSA) in the human serum, plasma or a derivative of blood, or blood itself is measured using an affinity reagent-based, preferably an antibody-based assay such as an Enzyme-Linked Immunosorbent Assay (ELISA). Further preferably for a positive diagnosis the concentration of the Prostate Specific Antigen has to be more than 2 ng/ml, preferably more than 4 ng/ml. 
         [0042]    In this context, preferably for a positive diagnosis or the monitoring of localized prostate cancer the concentration of ASPN derived protein/fragments thereof has to be more than 55 ng/ml, preferably more than 60 ng/ml; and/or the concentration of HYOU1 derived protein/fragments thereof has to be more than 35 ng/ml, preferably more than 40 ng/ml; and/or the concentration of CTSD derived protein/fragments thereof has to be less than 32 ng/ml, preferably less than 25 ng/ml; and/or the concentration of OLFM4 derived protein/fragments thereof has to be less than 20 ng/ml, preferably less than 15 ng/ml. 
         [0043]    It should be noted in the context of the threshold concentrations as given above as well as a detailed further below that these may depend on the specific measurement technique, as for example the methods used here, namely SRM, will measure the total species, so e.g. free and bound species, while for example an antibody-based assay such as ELISA might be able to distinguish between these two forms leading to different threshold concentrations if the latter methods are used. The values given here therefore in particular relate to measurements using SRM-methods, and they might have to be adapted by analogy if different methods are being used. This is however a matter of conversion which is within the realm of the skills of the person skilled in the art in this field. 
         [0044]    The present invention furthermore relates to an extremely high accuracy method for the diagnosis of metastatic prostate cancer using a (preferably combined) measurement of the concentration of ASPN and CTSD and THBS1 and GALNTL4 as well as VTN derived protein/fragments thereof in human serum, plasma or any other derivatives of blood, or blood itself, preferably in combination with the measurement of one further protein/fragments thereof selected from the group derived from: PSAP; GSPT1; CEACAM1; HYOU1; EFNA5; KIT. 
         [0045]    Preferably, as in the above case of the methods for diagnosis of localized prostate cancer, the measurement is carried out using tandem mass spectrometry techniques, preferably selected reaction monitoring (SRM), more preferably in combination with liquid chromatography, and/or antibody based methods such as Enzyme-Linked Immunosorbent Assays (ELISA) for the detection of these proteins/fragments thereof. 
         [0046]    For a positive diagnosis of non-localized (metastatic) prostate cancer 
         [0000]    the concentration of ASPN derived protein/fragments thereof has to be more than 60 ng/ml, preferably more than 65 ng/ml, most preferably more than 68 ng/ml and at the same time
 
the concentration of CTSD derived protein/fragments has to be more than 120 ng/ml, preferably more than 130 ng/ml, most preferably more than 133 ng/ml and at the same time
 
the concentration of THBS1 derived protein/fragments has to be less than 12000 ng/ml, preferably less than 11500 ng/ml, most preferably less than 10750 ng/ml and at the same time
 
the concentration of GALNTL4 derived protein/fragments has to be more than 1400 ng/ml, preferably more than 1600 ng/ml, most preferably more than 1650 ng/ml and at the same time
 
the concentration of VTN derived protein/fragments has to be more than 3000 ng/ml, preferably more than 3150 ng/ml, most preferably more than 3300 ng/ml.
 
         [0047]    If, as preferred, additionally one of the above-mentioned additional systems is measured, 
         [0000]    the concentration of PSAP derived protein/fragments thereof has to be more than 33000 ng/ml, preferably more than 34000 ng/ml,
 
and/or the concentration of GSPT1 derived protein/fragments thereof has to be more than 450 ng/ml, preferably more than 500 ng/ml, more preferably more than 510 ng/ml,
 
and/or the concentration of CEACAM1 derived protein/fragments thereof has to be more than 35 ng/ml preferably more than 38 ng/ml, (this threshold value being the only one calculated in relation of ELISA determination)
 
and/or the concentration of HYOU1 derived protein/fragments thereof has to be more than 80 ng/ml, preferably more than 89 ng/ml,
 
and/or the concentration of EFNA5 derived protein/fragments thereof has to be more than 60 ng/ml, preferably more than 65 ng/ml,
 
and/or the concentration of KIT derived protein/fragments thereof has to be more than 90 ng/ml, preferably more than 95 ng/ml.
 
         [0048]    As mentioned above, it can be advantageous to combine the measurement of the above-mentioned systems, be it for localized or non-localized prostate cancer diagnosis, with the measurement of further parameters of the serum, plasma or any other derivatives of blood, or blood itself which are not the result of a biomarker determination method as outlined above. It is for example possible that for the diagnosis additionally the concentration of the Prostate Specific Antigen (PSA) in the human serum, plasma or any other derivatives of blood, or blood itself is measured using a corresponding antibody-based assay such as an Enzyme-Linked Immunosorbent Assay (ELISA), wherein for a positive diagnosis the concentration of the Prostate Specific Antigen (PSA) normally has to be more than 2 ng/ml, preferably more than 4 ng/ml. 
         [0049]    Further embodiments of the present invention are outlined in the dependent claims. 
     
    
     
       SHORT DESCRIPTION OF THE FIGURES 
         [0050]    In the accompanying drawings preferred embodiments of the invention are shown in which: 
           [0051]      FIG. 1  is an overview of the integrated proteomic approach for biomarker discovery, verification and validation. The scheme is divided in two main sections: First the discovery and verification phases performed using an animal model and second the validation phase with human patient samples; the numbers in italics indicate the number of glycoproteins that were identified and considered for the next step; wherein in a): selective enrichment of N-glycopeptides was performed from tissue and serum from healthy and cancerous mice to discover in vivo CaP-specific signatures using prostate tissue from a mouse model of CaP; this allows to create a catalogue of 785 glycoproteins which served as a resource for the later steps; MS-based label-free quantification was performed on the same murine tissue and serum samples; this resulted in a relative quantification of 352 glycoproteins comparing cancerous vs. benign samples; 164 glycoproteins matching criteria were then chosen for further investigation; wherein b): 41 of these biomarker candidates could be validated in sera of mice and wherein c): 43 candidates in human patients by MS based selected reaction monitoring (SRM) and ELISA; generally, the boundaries between the human and animal steps are flexible; e.g. it is possible to do the verification step also in human samples provided such a collection is actually available; 
           [0052]      FIG. 2  shows an overview of the Mouse Glycoproteome Catalog, wherein the number of proteins identified in the mouse prostate tissue and serum are shown as a Venn diagram; the number of proteins that could be quantified is shown below; and 
           [0053]      FIG. 3  shows the discriminant accuracy of selected candidates in multivariate approaches; a patient is classified following a rule generated by the statistical software; the % of correct predictions is defined as accuracy of the model; as indicated above, Gene names are as defined according to Bioinformatics Institute (EBI), the Swiss Institute of Bioinformatics (SIB), and the Protein Information Resource (PIR); the shaded entries in the first lines indicate which systems can be interchanged within one assay. 
       
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
       [0054]    Referring to the drawings, which are for the purpose of illustrating the present preferred embodiments of the invention and not for the purpose of limiting the same,  FIG. 1  shows an overview of the integrated proteomic approach for biomarker discovery, verification and validation. The scheme is divided in two main sections: First the to discovery and verification phases (a) and (b) performed using an animal model (mice) and second the validation phase (c) with human patient samples. 
         [0055]    In the following example the method is applied to the determination of biomarkers for prostate cancer. As outlined above, this shall however not be construed to the actual gist of the invention, as the method may equivalently be applied to other types of cancer such as breast cancer, lung cancer, ovarian cancer and the like and it may also equivalently be applied generally to other types of diseases or dysfunctions such as diabetes (mellitus and other types), neurodegenerative diseases: such as Alzheimer&#39;s disease, Parkinson&#39;s disease, Huntington&#39;s disease, Creutzfeldt-Jakob disease; autoimmune diseases: such as multiple sclerosis, rheumatoid arthritis; infectious diseases: such as malaria, HIV; cardiovascular disease: such as hypertension, atherosclerosis. As outlined above, the main strength of the animal model work is that specific, defined perturbations can be applied and that the consequences of these are being measured. The same perturbations can be also be relevant to other types of cancers, which means that it is possible to look at the markers as a rationale consequence of the induced perturbation as opposed to what the general term like disease related might suggest. 
         [0056]    In the initial discovery phase, prostate tissue samples, serum samples and both on the one hand of healthy mice and of prostate cancerous mice were used, so four different series of experiments. For the determination of the differential behaviour only tissue samples of healthy/cancerous mice were compared, and on the other hand serum samples of healthy/cancerous mice were compared. 
         [0057]    In a first step (a) the tissue (prepared as described in more detail below) and the serum samples were digested using trypsin, and from the corresponding proteome digests the N-linked glycosylated protein fragments were selected and extracted using the SPEG-technique (described in more detail below). 
         [0058]    Subsequently glycoprotein identification was carried out using mass spectrometry, specifically a shotgun approach, without determining differential behaviour in this stage. This resulted in a total of 532 detected glycoproteins in prostate tissue and 253 detected glycoproteins in serum. A total of 785 glycoproteins were detected, as 110 proteins were detected in the tissue as well as in the serum (graphically illustrated in  FIG. 2 ). 
         [0059]    The next step designated with label-free quantitation aims at the detection of the differential behaviour of the signals of the protein fragments. The experiment is a combined liquid chromatography/mass spectrometry experiment in which mass spectrometry is carried out according to the elution profile of the chromatography. Using this experiment one can track the differential behaviour between healthy/cancerous samples. Those signals/protein fragments which in this label-free quantitation step did not show differential behaviour were rejected from the above-mentioned set of 785 glycoproteins, leading to 352 quantified proteins, of which 279 originate from the tissue samples and 160 from the serum samples (illustrated in  FIG. 2 ). 
         [0060]    These 352 quantified proteins are now further selected to only keep those which show a pronounced differential behaviour, and which comply with at least one of the eight rationales as given and discussed in the context of table 1 below. This after the filtering using the rationales leads to 164 potential candidate biomarker systems, which are resulting from the attribution of the signals to the specific glycoproteins by using electronic annotation. 
         [0061]    In the second step (b), which is optional, verification or rather qualification using the nonhuman system takes place, wherein the final analytical tools which are to be used for the final biomarker assay method are used. In this step (b) correspondingly only serum of healthy/cancerous mice is analysed, it is again digested and the glycoproteins extracted as described in the context of (a), but subsequently selected reaction monitoring (SRM), i.e. a liquid chromatography/tandem mass spectrometry method is used for absolute quantitation of the systems using specifically provided (synthesised) internal standards for absolute quantitation. 
         [0062]    Out of the 164 systems which have entered step (b) only 41 could be absolutely quantified mainly for practical reasons. The corresponding 41 systems are given in table 2 discussed in more detail below. 
         [0063]    Therefore for the next step all the 164 systems having resulted from step (a) are used for the final step of validation (c). The results of step (b) are further verified by using RT-PCR, immunohistochemistry, western blot. 
         [0064]    Within step (c) essentially the same procedure is carried out as within step (b) however this time using serum samples of human origin of healthy/cancerous individuals. From the SRM-side this leads to 37 candidates. Wherever possible, the 164 candidates having entered step (c) are furthermore validated using available ELISA assays, leading to an additional 11 possible candidates. 
         [0065]    Due to the fact that certain systems result from the SRM verification as well as from the ELISA validation, this results in a final number of 43 candidate biomarker systems. These are listed in table 3. 
         [0066]    From a principal point of view any of these, possibly in combination with one or several, can be used in an assay for the detection of prostate cancer. 
         [0067]    In view of reducing the number of necessary measurements by at the same time keeping an as high as possible accuracy, statistical methods (for a more detailed discussion see further below) were applied to all 43 systems in correlation with the patient&#39;s data collection leading to the final assays as given in  FIG. 3 . 
         [0068]    Five particularly high accuracy assays are given in  FIG. 3  A), and one notes that in all of them ASPN as well as VTN are present. Correspondingly therefore glycoproteins derived from these genes or rather the fragments of these glycoproteins are highly indicative for the distinction between benign prostate hyperplasia (BPH) and localized prostatic cancer (locPCa). The corresponding accuracies are above 80%, so roughly around 20% higher than the accuracy of present state of the art PSA-methods. 
         [0069]    Using additional incorporation of PSA-measurements using ELISA, one statistically finds further nine biomarker assays for discriminating between BPH and locPCa as given in  FIG. 3  B). Again in all of these systems ASPN as well as VTN derived glycoproteins are present. The accuracies of these combined measurements are another 10% higher than without taking PSA-measurements into account, leading to a so far unreached exceedingly high accuracy for the detection of prostate cancer. 
         [0070]    Using additional incorporation of PSA-measurements using ELISA and more data, one statistically finds further 5 biomarker assays for discriminating between BPH and locPCa as given in  FIG. 3  C). In each of these systems three out of the systems of the group: ASPN, OLFM4, HYOU1, CTSD derived glycoproteins are present. The corresponding accuracies are again above 80%, so roughly around 20% higher than the accuracy of present state of the art PSA-methods. 
         [0071]    Finally in  FIG. 3  D) the statistical results for biomarker assays for the discrimination between locPCa and metCPa is given. Using the combined measurements of six systems in each assay one reaches 100% accuracy. 
         [0072]    The above shows that the proposed method not only provides a powerful tool for targeted development of biomarker assays with high accuracy. It furthermore shows that for the specific situation of prostate cancer the correspondingly determined biomarker assays show an unexpectedly high accuracy which exceeds anything so far reported in the literature. 
       EXPERIMENTAL DETAILS 
       [0073]    (I) Translational approach: The translational approach is based on the initial identification of interesting candidate biomarkers in a mouse model for prostate cancer and the validation of such candidates in human clinical samples. To identify candidate biomarkers to be used in the clinics as defined diagnostic or therapeutical targets, we started to analyze the prostate tissue and blood from genetically defined mice that develop prostate cancer (Pten conditional knockout, cKO, see e.g. US 2006/0064768) and control mice that have intact Pten alleles and do not develop such tumors. 
         [0074]    Rationales for using a mouse model: We decided to use the genetically defined Pten conditional knockout model because these mice develop early stage epithelial prostate cancers following deletion of the tumor suppressor gene Pten. The phenotype is closely related to human localized prostate cancer and is thus an ideal starting point for the identification of novel biomarkers that could distinguish human localized prostate cancer from benign hyperplastic lesions (Benign Prostatic Hyperplasia or BPH). Moreover, the use of a Pten cKO mouse model allows to identify therapeutical/imaging targets and biomarkers to be used specifically for stratified patients having PTEN mutations or any imbalance derived by mutations along the PTEN signaling pathway. The use of a mouse model facilitates the initial identification of candidate biomarkers since the prostate tissue is very homogeneous and major variables such as environmental conditions and timing can be controlled, in contrast to the highly heterogeneous human tissues. Interestingly, the ratio between prostate cancer tissue volume and total blood volume is 40-40000× higher in mice compared to men. This is of course an intrinsic advantage since variations in the blood proteome are expected to be better uncovered in such a model than in human patient samples. Finally only the murine tissue can be efficiently perfused in order to eliminate blood contaminations (see below). Blood protein contaminations in the tissues often mask the identification of proteins present at particular low concentration. Moreover, the absence of blood in the tissue following perfusion allows to apply comparative proteomics (blood-tissue) without any potential bias (see table 1, rationales 1, 2, 4, 7). 
         [0000]                                                                          TABLE 1               Selection of interesting proteins for validation in human Sera                                    Discriminant       List of rationales:   factor                    1   potential biomarkers regulated in prostate tissue AND serum   &gt;1.5 or &lt;0.75       2   potential biomarkers regulated in prostate tissue AND detected   &gt;1.5 or &lt;0.75           in serum       3   potential biomarkers regulated in prostate tissue AND secreted   &gt;1.5 or &lt;0.75       4   potential biomarkers exclusively detected in prostate tissue           AND sera of mice with cancer       5   potential biomarkers, specific for prostate AND regulated in           cancer tissue or serum       6   potential biomarkers specific for prostate AND secreted       7   potential biomarkers, top regulated in prostate tissue or serum           (&gt;4x)       8   potential biomarkers, prior knowledge-based selection           (biological function during cancer progression)                                            annotated or                               predicted cellular           Ratio-   Gene           Accession   localization           nale   name   Entry name   Protein name   number   (ref 6)               1   1   Ecm1   ECM1_MOUSE   Extracellular matrix protein 1   Q61508   secreted       2   1   Egfr   EGFR_MOUSE   Epidermal growth factor   Q01279   plasma membrane/                       receptor       secreted       3   1   Trf   TRFE_MOUSE   Serotransferrin   Q921I1   secreted       4   1   Pdia6   PDIA6_MOUSE   Protein disulfide-isomerase   Q922R8   ER                       A6       5   1   Hsp90b1   ENPL_MOUSE   Endoplasmin   P08113   ER       6   1   Rnase1   RNAS1_MOUSE   Ribonuclease pancreatic   P00683   secreted       7   1   Lifr   LIFR_MOUSE   Leukemia inhibitory factor   P42703   plasma membrane                       receptor       8   2   Ighg1   IGH1M_MOUSE   Ig gamma-1 chain C region,   P01869   secreted                       membrane-bound form       9   2   Clu   CLUS_MOUSE   Clusterin   Q06890   secreted       10   2   Cfh   CFAH_MOUSE   Complement factor H   P06909   secreted       11   2   H2-L   HA1L_MOUSE   H-2 class I histocompatibility   P01897   plasma membrane                       antigen, L-D alpha chain       12   2   Col12a1   COCA1_MOUSE   Collagen alpha-1(XII) chain   Q60847   secreted       13   2   Dpp7   DPP2_MOUSE   Dipeptidyl-peptidase 2   Q9ET22   lysosomal       14   2   Pgcp   O70216_MOUSE   Plasma glutamate   Q9WVJ3   secreted?                       carboxypeptidase       15   2   Cp   CERU_MOUSE   Ceruloplasmin   Q61147   secreted       16   2   Cfb   CFAB_MOUSE   Complement factor B   P04186   secreted       17   2   Lrp1   LRP1_MOUSE   Low-density lipoprotein   Q91ZX7   secreted                       receptor-related protein 1       18   2   Col1a1   CO1A1_MOUSE   Collagen alpha-1(I) chain   P11087   secreted       19   2   Itgav   ITAV_MOUSE   Integrin alpha-V   P43406   plasma membrane       20   2   Lama3   LAMA3_MOUSE   Laminin subunit alpha-3   Q61789   secreted       21   2   Fn1   FINC_MOUSE   Fibronectin   P11276   secreted       22   2   Anpep   AMPN_MOUSE   Aminopeptidase N   P97449   plasma membrane       23   2   Ctse   CATE_MOUSE   Cathepsin E   P70269   endosomal       24   2   Ctsa   PPGB_MOUSE   Lysosomal protective protein   P16675   lysosomal       25   2   Ceacam1   CEAM1_MOUSE   Carcinoembryonic antigen-   P31809   plasma membrane                       related cell adhesion                       molecule 1       26   2   Ace   ACET_MOUSE   Angiotensin-converting   P22967   plasma membrane/                       enzyme, testis-specific       secreted                       isoform       27   2   Pdia3   PDIA3_MOUSE   Protein disulfide-isomerase   P27773   ER                       A3       28   2   Sslp1   SSLP1_MOUSE   Secreted seminal-vesicle Ly-   Q3UN54   secreted                       6 protein 1       29   2   Btd   BTD_MOUSE   Biotinidase   Q8CIF4   secreted       30   2   Atp1b2   AT1B2_MOUSE   Sodium/potassium-   P14231   plasma membrane                       transporting ATPase subunit                       beta-2       31   2   Hspa5   GRP78_MOUSE   78 kDa glucose-regulated   P20029   ER                       protein       32   2   Psap   SAP_MOUSE   Sulfated glycoprotein 1   Q61207   secreted       33   2   Thbs1   TSP1_MOUSE   Thrombospondin 1   P35441   secreted       34   2   Adcy3   ADCY3_MOUSE   Adenylate cyclase type 3   Q8VHH7   plasma membrane       35   2   Ctbs   DIAC_MOUSE   Di-N-acetylchitobiase   Q8R242   lysosomal       36   2   Ggh   GGH_MOUSE   Gamma-glutamyl hydrolase   Q9Z0L8   secreted/lysosomal       37   2   Serping1   IC1_MOUSE   Plasma protease C1 inhibitor   P97290   secreted/plasma?       38   2   L1cam   L1CAM_MOUSE   Neural cell adhesion   P11627   plasma membrane                       molecule L1       39   2   1100001   PLBL1_MOUSE   Putative phospholipase B-   Q8VCI0   secreted               H23Rik       like 1       40   2   Qsox1   QSOX1_MOUSE   Sulfhydryl oxidase 1   Q8BND5   secreted/Golgi                               membrane       41   2   Lrg1   Q91XL1_MOUSE   Leucine-rich alpha-2-   Q91XL1   secreted/plasma?                       glycoprotein       42   2   Lgals3bp   O35649_MOUSE   Cyclophilin C-associated   O35649   plasma membrane                       protein       43   2   Cd44   CD44_MOUSE   CD44 antigen   P15379   plasma membrane       44   3   Col14a1   COEA1_MOUSE   Collagen alpha-1(XIV) chain   Q80X19   secreted       45   3   Fam3d   FAM3D_MOUSE   Protein FAM3D   P97805   secreted       46   3   Pon3   PON3_MOUSE   Serum paraoxonase/   Q62087   secreted                       lactonase 3       47   3   Timp1   TIMP1_MOUSE   Metalloproteinase inhibitor 1   P12032   secreted       48   3   Abca16   Q6XBG1_MOUSE   ATP-binding cassette   Q6XBG1   plasma? Membrane/                       transporter sub-family A       secreted?                       member 16       49   3   Fbn1   FBN1_MOUSE   Fibrillin-1   Q61554   secreted       50   3   Lum   LUM_MOUSE   Lumican   P51885   secreted       51   3   Lamb2   LAMB2_MOUSE   Laminin subunit beta-2   Q61292   secreted       52   3   Vcan   CSPG2_MOUSE   Versican core protein   Q62059   secreted       53   3   Bgn   PGS1_MOUSE   Biglycan   P28653   secreted       54   3   Enpp5   ENPP5_MOUSE   Ectonucleotide   Q9EQG7   secreted                       pyrophosphatase/phosphodi-                       esterase family member 5       55   3   Erap1   ERAP1_MOUSE   Endoplasmatic reticulum   Q9EQH2   secreted                       aminopeptidase 1       56   3   Pxdn   PXDN_MOUSE   Peroxidasin homolog   Q3UQ28   secreted/ER       57   3   Col6a3   O88493_MOUSE   Type VI collagen alpha 3   O88493   secreted                       subunit       58   3   Emilin1   EMIL1_MOUSE   EMILIN-1   Q99K41   secreted       59   3   Mfap4   MFAP4_MOUSE   Microfibril-associated   Q9D1H9   secreted                       glycoprotein 4       60   3   Agrn   O08860_MOUSE   Agrin   O08860   secreted       61   3   Prelp   PRELP_MOUSE   Prolargin   Q9JK53   secreted       62   3   Lamc1   LAMC1_MOUSE   Laminin subunit gamma-1   P02468   secreted       63   3   Lama1   LAMA1_MOUSE   Laminin subunit alpha-1   P19137   secreted       64   3   Lama5   LAMA5_MOUSE   Laminin subunit alpha-5   Q61001   secreted       65   3   Lama2   LAMA2_MOUSE   Laminin subunit alpha-2   Q60675   secreted       66   3   Col6a5   A6H586_MOUSE   Collagen type VI alpha 5   A6H586   secreted       67   3   Lamb1-1   LAMB1_MOUSE   Laminin subunit beta-1   P02469   secreted       68   3   Creg1   CREG1_MOUSE   Protein CREG1   O88668   secreted       69   3   Sva   Q64367_MOUSE   Seminal vesicle autoantigen   Q64367   secreted       70   3   Serpinb6   SPB6_MOUSE   Serpin B6   Q60854   plasma membrane?/                               secreted?       71   3   Cpe   CBPE_MOUSE   Carboxypeptidase E   Q00493   secretory granules       72   3   9530002   SPIKL_MOUSE   Serine protease inhibitor   Q8CEK3   secreted               K18Rik       kazal-like protein       73   3   Olfm4   OLFM4_MOUSE   Olfactomedin-4   Q3UZZ4   secreted       74   3   Lama4   LAMA4_MOUSE   Laminin subunit alpha-4   P97927   secreted       75   3   Fcgbp   A1L0S2_MOUSE   LOC100037259 protein   A1L0S2   secreted/ER?/                               golgi?       76   3   Dmbt1   DMBT1_MOUSE   Deleted in malignant brain   Q60997   secreted/plasma                       tumors 1 protein       membrane       77   3   Wfdc3   Q14AE4_MOUSE   Wap four-disulfide core   Q14AE4   secreted                       domain 3       78   3   Spink5   Q5K5D4_MOUSE   Spink5 protein   Q5K5D4   secreted       79   3   Ngp   Q61903_MOUSE   Myeloid secondary granule   Q61903   secreted                       protein       80   3   Col7a1   CO7A1_MOUSE   Collagen alpha-1(VII) chain   Q63870   secreted       81   3   Itih5   ITIH5_MOUSE   Inter-alpha-trypsin inhibitor   Q8BJD1   secreted                       heavy chain H5       82   3   Hyal6   Q8CDQ9_MOUSE   Hypothetical Glycoside   Q8CDQ9   secreted?                       hydrolase family 56                       containing protein       83   3   BC023744   Q0P6B3_MOUSE   BC023744 protein   Q0P6B3   secreted       84   3   Aspn   ASPN_MOUSE   Asporin   Q99MQ4   secreted       85   4   Postn   POSTN_MOUSE   Periostin   Q62009   secreted       86   4   Fmr1   FMR1_MOUSE   Fragile X mental retardation   P35922   secreted?/                       protein 1 homolog       cytoplasmic       87   4   Golga5   GOGA5_MOUSE   Golgin subfamily A member 5   Q9QYE6   Golgi       88   4   Grn   GRN_MOUSE   Granulins   P28798   secreted       89   4   Man2b1   MA2B1_MOUSE   Lysosomal alpha-   O09159   lysosomal                       mannosidase       90   4   Nav1   NAV1_MOUSE   Neuron navigator 1   Q8CH77   cytoplasmic       91   4   Ramp3   RAMP3_MOUSE   Receptor activity-modifying   Q9WUP1   plasma membrane                       protein 3       92   5   Tspan1   Q99J59_MOUSE   Tetraspanin 1   Q99J59   plasma membrane       93   5   5430419   Q8BZE1_MOUSE   hypothetical Speract receptor   Q8BZE1   plasma membrane               D17Rik       94   5   Grk5   GRK5_MOUSE   G protein-coupled receptor   Q8VEB1   cytoplasmic                       kinase 5       95   5   Azgp1   ZA2G_MOUSE   Zinc-alpha-2-glycoprotein   Q64726   secreted       96   6   Spink3   ISK3_MOUSE   Serine protease inhibitor   P09036   secreted                       Kazal-type 3       97   6   Egf   EGF_MOUSE   Pro-epidermal growth factor   P01132   plasma membrane/                               secreted       98   6   Msmb   MSMB_MOUSE   Beta-microseminoprotein   O08540   secreted       99   6   Creld2   CREL2_MOUSE   Cysteine-rich with EGF-like   Q9CYA0   secreted/ER                       domain protein 2       100   6   Pbsn   PBAS_MOUSE   Probasin   O08976   secreted       101   6   Sbp   SPBP_MOUSE   Prostatic spermine-binding   P15501   secreted                       protein       102   7   Ermp1   ERMP1_MOUSE   Endoplasmatic reticulum   Q3UVK0   ER membrane                       metallopeptidase 1       103   7   Pigr   PIGR_MOUSE   Polymeric-immunoglobulin   O70570   plasma membrane                       receptor       104   7   Cadm1   CADM1_MOUSE   Cell adhesion molecule 1   Q8R5M8   plasma membrane       105   7   Golph2   GOLM1_MOUSE   Golgi phosphoprotein 2   Q91XA2   Golgi       106   7   Tspan8   Q8R3G9_MOUSE   Tspan8   Q8R3G9   plasma membrane       107   7   Adam3   Q62287_MOUSE   Cyritestin   Q62287   plasma membrane       108   7   Thy1   THY1_MOUSE   Thy-1 membrane   P01831   plasma membrane                       glycoprotein       109   7   Mme   NEP_MOUSE   Neprilysin   Q61391   plasma membrane       110   7   Apmap   APMAP_MOUSE   Adipocyte plasma   Q9D7N9   plasma membrane                       membrane-associated protein       111   7   Ergic3   ERGI3_MOUSE   Endoplasmatic reticulum-   Q9CQE7   ER/Golgi                       Golgi intermediate                       compartment protein 3       112   7   9530003J   Q8BM27_MOUSE   Weakly similar to   Q8BM27   secreted               23Rik       LYSOZYME C, TYPE M       113   7   Ceacam10   CEAMA_MOUSE   Carcinoembryonic antigen-   Q61400   secreted                       related cell adhesion                       molecule 10       114   7   Plxna3   P70208_MOUSE   Plexin 3   P70208   plasma membrane       115   7   Vmn2r10   O35204_MOUSE   Putative phermone receptor   O35204   plasma membrane       116   7   Hyou1   HYOU1_MOUSE   Hypoxia up-regulated protein   Q9JKR6   secreted/ER                       1       117   7   Defb50   BD50_MOUSE   Beta-defensin 50   Q6TU36   secreted       118   7   Fcgbp   Q8BZG2_MOUSE   hypothetical von Willebrand   Q8BZG2   secreted                       factor type D protein       119   7   Rai2   RAI2_MOUSE   Retinoic acid-induced   Q9QVY8   nuclear                       protein 2       120   7   Pnliprp1   LIPR1_MOUSE   Pancreatic lipase-related   Q5BKQ4   secreted                       protein 1       121   7   Pdia2   Q14AV9_MOUSE   Pdia2 protein   Q14AV9   ER membrane       122   7   Hp   HPT_MOUSE   Haptoglobin   Q61646   secreted/plasma       123   7   Cpm   CBPM_MOUSE   Carboxypeptidase M   Q80V42   plasma membrane       124   7   Pigs   PIGS_MOUSE   GPI transamidase component   Q6PD26   ER                       PIG-S       125   7   Mup3   MUP3_MOUSE   Major urinary protein 3   P04939   secreted       126   7   Gc   VTDB_MOUSE   Vitamin D-binding protein   P21614   secreted       127   7   Prom1   PROM1_MOUSE   Prominin-1   O54990   plasma membrane       128   7   Vtn   VTNC_MOUSE   Vitronectin   P29788   secreted       129   7   Aoc3   AOC3_MOUSE   Membrane copper amine   O70423   plasma membrane                       oxidase       130   8   Lamp1   LAMP1_MOUSE   Lysosome-associated   P11438   lysosomal                       membrane glycoprotein 1       131   8   Lamp2   LAMP2_MOUSE   Lysosome-associated   P17047   lysosomal                       membrane glycoprotein 2       132   8   Itgb1   ITB1_MOUSE   Integrin beta-1   P09055   plasma membrane       133   8   Itgae   ITAE_MOUSE   Integrin alpha-E   Q60677   plasma membrane       134   8   Flt4   VGFR3_MOUSE   Vascular endothelial growth   P35917   plasma membrane                       factor receptor 3       135   8   Tnc   TENA_MOUSE   Tenascin   Q80YX1   secreted       136   8   Fap   SEPR_MOUSE   Seprase   P97321   secreted       137   8   Asph   Q6P8S1_MOUSE   Aspartate-beta-hydroxylase   Q6P8S1   ER       138   8   Asah1   ASAH1_MOUSE   Acid ceramidase   Q9WV54   lysosomal       139   8   Atrn   ATRN_MOUSE   Attractin   Q9WU60   plasma membrane       140   8   Cacna2d1   CA2D1_MOUSE   Voltage-dependent calcium   O08532   plasma membrane                       channel subunit alpha-                       2/delta-1       141   8   Chl1   CHL1_MOUSE   Neural cell adhesion   P70232   plasma membrane/                       molecule L1       secreted       142   8   Ctsd   CATD_MOUSE   Cathepsin D   P18242   lysosomal       143   8   Dpp4   DPP4_MOUSE   Dipeptidyl peptidase 4   P28843   plasma membrane/                               secreted       144   8   Gba   GLCM_MOUSE   Glucosylceramidase   P17439   lysosomal       145   8   Ncam1   NCA12_MOUSE   Neural cell adhesion   P13594   plasma membrane                       molecule 1       146   8   Plxnb2   Q3UH76_MOUSE   Plexin B2   Q3UH76   plasma membrane       147   8   Ptprj   PTPRJ_MOUSE   Protein-type tyrosine-protein   Q64455   plasma membrane                       phosphatase eta       148   8   Ptprk   PTPRK_MOUSE   Receptor-type tyrosine-   P35822   plasma membrane                       protein phosphatase kappa       149   8   Sirpa   SHPS1_MOUSE   Tyrosine-protein phosphatase   P97797   plasma membrane                       non-receptor type substrate 1       150   8   Kit   KIT_MOUSE   Mast/stem cell growth factor   P05532   plasma membrane                       receptor       151   8   Sema4d   SEM4D_MOUSE   Semaphorin-4D   O09126   plasma membrane       152   8   Apob48r   AB48R_MOUSE   Apolipoprotein B-100   Q8VBT6   plasma membrane                       receptor       153   8   Agtr1   AGTRA_MOUSE   Type-1A angiotensin II   P29754   plasma membrane                       receptor       154   8   Tm9sf3   TM9S3_MOUSE   Transmembrane 9   Q9ET30   plasma membrane?                       superfamily member 3       155   8   Galntl4   GLTL4_MOUSE   Polypeptide n-   Q8K1B9   Golgi                       acetylgalactosaminyl-                       transferase       156   8   Efna5   EFNA5_MOUSE   Ephrin-a5   O08543   plasma membrane       157   8   F5   O88783_MOUSE   Coagulation factor V   O88783   secreted       158   8   Nptn   NPTN_MOUSE   Neuroplastin   P97300   plasma membrane       159   8   Lox   LYOX_MOUSE   Protein-lysine 6-oxidase   P28301   secreted       160   8   Mmel1   MMEL1_MOUSE   Membrane metallo-   Q9JLI3   plasma membrane                       endopeptidase-like 1       161   8   Tfrc   TFR1_MOUSE   Transferrin receptor   Q62351   plasma membrane       162   8   Gspt1   Q8K2E1_MOUSE   G1 to S phase transition 1   Q8K2E1       163   8   Akap13   Q3T998_MOUSE   A kinase (PRKA) anchor   Q3T998                       protein 13       164   8   Vasn   VASN_MOUSE   Vasorin   Q9CZT5   plasma membrane       165   8   Icam1   ICAM1_MOUSE   Intercellular adhesion   P13597   plasma membrane                       molecule 1               Table 1: Selection of interesting proteins for validation in human sera. 165 glycoproteins detected in the mouse serum and tissue were selected for verification through targeted mass spectrometry and later validation in human clinical samples. Gene names, Entry names, Protein names (shortened) and Accession numbers as generally used in this specification are as defined according to the UniProt Consortium, which is comprised of the European Bioinformatics Institute (EBI), the Swiss Institute of Bioinformatics (SIB), and the Protein Information Resource (PIR). The annotated or predicted cellular localization is according to Emanuelsson O, Brunak S, von Heijne G, Nielsen H. (2007) Locating proteins in the cell using TargetP, SignalP and related tools. Nat Protoc. 2, 953-71.            
Rationales for using mice and not cell culture systems: Proteomics techniques are easily applied to cell lines in vitro, whereas the use of in vivo models requires more complex handling and initial trouble-shootings. We decided to use an in vivo model however, because this mimics more closely the complexity of the human disease compared to in vitro models. The approach presented here is thus unique as very few screens today are applied to freshly isolated organs.
 
         [0075]    Tissue and blood extraction procedure: Mice are anesthetized and blood is extracted by pinning the left heart ventricle. Mice are subsequently heart perfused. This allows for the complete removal of blood from the prostate tissue. Tissue samples are then dissected and pure prostate tissue is readily snap-frozen and pulverized by using a mortar and pestle in the presence of liquid nitrogen. Serum is extracted from the blood and stored at −80° C. until use. 
         [0076]    (II) Cutting edge mass-spectrometry (MS) and bioinformatics: Rationales for focusing on the N-linked glycoproteome: In order to find candidate biomarkers, we decided to focus on a particular and highly relevant subproteome, the N-linked glycosylated proteins. Protein glycosylation has long been recognized as a common post-translational modification. Typically, glycans are linked to serine or threonine residues (O-linked glycosylation) or to asparagine residues (N-linked glycosylation). N-linked glycosylation sites generally fall into the N×S/T sequence motif in which x denotes any amino acid except proline. The glycosylation of proteins is a characteristic post-translational modification of proteins residing in the extracellular space. This means that the vast majority of proteins that are specifically secreted or shed by the tumor and released into the bloodstream (which makes them highly valuable biomarker candidates) are glycosylated. Moreover, the enrichment of glycoproteins enables to unmask interesting candidates present at particular low concentration because highly abundant, non-glycosylated and non-relevant proteins such as cytoskeletal proteins in tissue samples as well as albumin (present at 35-50 mg/ml) in the serum samples are excluded from the measurements. 
         [0077]    N-linked glycopeptide extraction procedure and quantification: To identify N-linked glycoproteins, we employed a method for the solid phase extraction of N-glycopeptides (SPEG) from tissue and serum according to Zhang, H., Li, X. J., Martin, D. B., and to Aebersold, R. (2003) Identification and quantification of N-linked glycoproteins using hydrazide chemistry, stable isotope labeling and mass spectrometry; Nat Biotechnol 21, 660-666, the disclosure of which is expressly included into the specification as concerns SPEG. Glycopeptides are coupled to a solid support via their glycan moieties. Non-glycosylated peptides are then washed away and N-glycopeptides can be specifically released using the enzyme PNGase F. The method can be applied to tissue and serum alike. 
         [0078]    The high mass accuracy and retention time reproducibility of the mass spectrometer instrument setup used (LTQ-FT instrument), in combination with the trans proteomic pipeline (TPP) software suite and SuperHirn (see e.g. Mueller et al. An Assessment of Software Solutions for the Analysis of Mass Spectrometry Based Quantitative Proteomics Data. J Proteome Res (2008) vol. 7 (1) pp. 51-61), allowed for the identification and direct label-free quantification of common peptide features. Thereby, peptide elution profiles from different runs were compared and glycoprotein ratios were calculated from the N-glycopeptides belonging to the same protein. Verification and validation phase: In order to verify our findings from the initial discovery phase, a list of interesting proteins selected by various rationales were quantified in the corresponding murine sera by targeted mass spectrometry via selected reaction monitoring (SRM, see e.g. Stahl-Zeng, J., Lange, V., Ossola, R., Eckhardt, K., Krek, W., Aebersold, R., and Domon, B. (2007) High sensitivity detection of plasma proteins by multiple reaction monitoring of N-glycosites. Mol Cell Proteomics 6, 1809-1817.). 
         [0079]    This novel approach allows the simultaneous detection and quantification of proteins comparable in sensitivity to classical immunodetection procedures (e.g. Enzyme-Linked ImmunoSorbent Assay, ELISA), but with the advantage of not requiring tedious optimization steps for each biomarker candidate and generation of new antibodies. The SRM experiment is accomplished by specifying the parent mass of the compound for MS/MS fragmentation and then specifically monitoring for a single fragment ion. Thus, SRM delivers a unique fragment ion that can be monitored and quantified in the midst of a very complicated matrix. Stable isotope labeled peptides corresponding to the targeted N-glycosites (A peptide that was N-glycosylated in the intact protein in its de-glycosylated form) were synthesized and used as internal standards. This allowed for the absolute quantification of endogenous glycoproteins present in the mice sera (Table 2). 
         [0000]                                                                        TABLE 2                   List of 41 serum glycoproteins measured by SRM in murine sera from controls and mice with prostate cancer at 8       and 18 weeks of age. p-values below 0.05 indicate a statistical significant difference between the normal mice       (n = 3) and mice with prostate cancer (n = 3) for the corresponding protein. Experiments were performed on 8 and       18-week old mice. Gene name, Protein name (shortened) and Accession number are defined as given in Table 1.       Table 2 Glycoproteins measured by SRM in murine sera                        Accession   p-value   p-value           Gene name   Protein name   number   8 weeks   18 weeks                        1   Anpep   Aminopeptidase N   P97449   0.4524   0.8517       2   Asah1   Acid ceramidase   Q9WV54   0.6247   0.0186       3   Aspn   Asporin   Q99MQ4   0.2619   0.0068       4   Atp1b2   Sodium/potassium-transporting ATPase subunit beta-2   P14231   0.6055   0.0894       5   Atrn   Attractin   Q9WU60   0.7464   0.0079       6   Cacna2d1   Voltage-dependent calcium channel subunit alpha-2/delta-1   O08532   0.6186   0.1576       7   Cadm1   Cell adhesion molecule 1   Q8R5M8   0.8260   0.0670       8   Chl1   Neural cell adhesion molecule 1   P70232   0.9771   0.0258       9   Clu   Clusterin   Q06890   0.7098   0.1500       10   Cpm   Carboxypeptidase M   Q80V42   0.3680   0.1460       11   Ctsd   Cathepsin D   P18242   0.5680   0.0176       12   Dpp4   Dipeptidyl peptidase 4   P28843   0.2811   0.1521       13   Ecm1   Extracellular matrix protein 1   Q61508   0.9629   0.0322       14   Fap   Seprase   P97321   0.6198   0.1000       15   Flt4   Vascular endothelial growth factor receptor 3   P35917   0.9818   0.1180       16   Fn1   Fibronectin   P11276   0.7536   0.2586       17   Gba   Glucosylceramidase   P17439   0.2033   0.0070       18   Golph2   Golgi phosphoprotein 2   Q91XA2   0.2742   0.0114       19   Hyou1   Hypoxia up-regulated protein 1   Q9JKR6   0.4711   0.0352       20   L1cam   Neural cell adhesion molecule L1   P11627   0.5814   0.7871       21   Lamp1   Lysosome-associated membrane glycoprotein 1   P11438   0.7962   0.0939       22   Lamp2   Lysosome-associated membrane glycoprotein 2   P17047   0.0206   0.0504       23   Lgals3bp   Cyclophilin C-associated protein   O35649   0.4300   0.0800       24   Lifr   Leukemia inhibitory factor receptor   P42703   0.3391   0.0066       25   Lrp1   Low-density lipoprotein receptor-related protein 1   Q91ZX7   0.6288   0.0336       26   Ncam1   Neural cell adhesion molecule 1   P13594   0.7807   0.0412       27   Nptn   Neuroplastin   P97300   0.3157   0.7977       28   Pgcp   Plasma glutamate carboxypeptidase   Q9WVJ3   0.8894   0.0635       29   Pigr   Polymeric-immunoglobulin receptor   O70570   0.4333   0.3961       30   Plxnb2   Plexin B2   Q3UH76   0.4965   0.0243       31   Pnliprp1   Pancreatic lipase-related protein 1   Q5BKQ4   0.9855   0.0194       32   Postn   Periostin   Q62009   0.2395   0.0954       33   Prom1   Prominin-1   O54990   0.8580   0.3555       34   Psap   Sulfated glycoprotein 1   Q61207   0.6845   0.1519       35   Ptprj   Receptor-type tyrosine-protein phosphatase eta   Q64455   0.7613   0.1003       36   Ptprk   Receptor-type tyrosine-protein phosphatase kappa   P35822   0.6358   0.0095       37   Sirpa   Tyrosine-protein phosphatase non-receptor type substrate 1   P97797   0.7780   0.1677       38   Thbs1   Thrombospondin 1   P35441   NA   0.0110       39   Tnc   Tenascin   Q80YX1   0.9564   0.1920       40   Vasn   Vasorin   Q9CZT5   0.4737   0.1717       41   Vtn   Vitronectin   P29788   0.3433   0.2021                    
Highly sensitive and selective analyses were performed by monitoring fragmentation channels specific to each peptide of interest in the sera of control mice (healthy) and mice with prostate cancer (cancerous). The human orthologues of the potential biomarkers detected in mouse were then validated in human sera using standard ELISA techniques and again targeted mass spectrometry.
 
         [0080]    (III) Multivariate statistical methods: Rationales and advantages on using multivariate methods: Signatures or combination of biomarker detection can lead to increased diagnostic accuracy, when compared with the use of single biomarker detection. This is the case when total and free PSA are used at the same time to diagnose prostate cancer. In our case, we have measured a panel of candidate biomarkers and we can now ask what signatures can best discriminate between BPH and localized prostate cancer (locPCa) or between localized and non-localized, i.e. metastatic prostate cancer (metPCa). Moreover we can find out what are the biomarkers commonly shared in all signatures, making them highly valuable in terms of intellectual property. In order to classify patients based on a biomarker signature, we performed quadratic discriminance analysis. The goal of the discriminance analysis is to determine a rule by which an individual is allocated to one of 2 or more groups (e.g. BPH and locPCa), based on the independent variables (biomarkers) that are measured in such an individual. The parameters that describe this rule are computed from the analysis of variables of all individuals with already known classification. In order to estimate the bias of the discriminant rule, we apply Jacknife leave one-out cross validation. Analyses were performed using the statistical software packages SYSTAT 12 and SPSS14.0. 
       Results: 
       [0081]    Initially, we extracted N-glycopetides from the perfused prostate tissue and serum of both control and cancer-bearing mice. We identified in total 642 glycoproteins from prostate tissue and 253 glycoproteins from serum. 110 proteins were commonly detected. We could thus generate a catalog comprising of 785 N-glycoproteins in total. From the initial mouse glycoprotein catalog, we could quantify 279 glycoproteins from tissue and 160 glycoproteins from serum comparing samples from mice with cancer and their respective controls ( FIG. 2 ). Out of these proteins, 165 glycoproteins fulfilling at least one of the rationales listed in Table 1 were found to be potential biomarkers and therefore chosen for further verification. 
         [0082]    Using SRM on the murine scrum samples, we could verify and quantify 41 out of the 165 initial candidates. (Table 2) 
         [0083]    46 candidate biomarkers which were either already tested in mice sera via SRM or promising candidates that showed up in the initial discovery phase from murine prostate tissue (Table 3) were further validated on 52 human serum samples. This was done by applying ELISA and SRM. 
         [0000]    
       
         
               
             
               
               
               
               
               
             
               
               
               
               
               
             
           
               
                 TABLE 3 
               
             
             
               
                   
               
               
                 List of 46 serum glycoproteins measured in human sera. The selected biomarker 
               
               
                 candidates were either analyzed by SRM or ELISA. Gene name, Protein name 
               
               
                 (shortened) and Accession number are defined as given in Table 1. 
               
               
                 Table 3 List of 43 serum glycoproteins measured in human sera 
               
             
          
           
               
                   
                   
                   
                 Accession 
                 Technique used 
               
               
                   
                 Gene name 
                 Protein name 
                 number 
                 for analysis 
               
               
                   
                   
               
             
          
           
               
                 1 
                 AGTR1 
                 Type-1 angiotensin II receptor 
                 P30556 
                 SRM 
               
               
                 2 
                 AKAP13 
                 A-kinase anchor protein 13 
                 Q12802 
                 SRM 
               
               
                 3 
                 AOC3 
                 Membrane copper amine oxidase 
                 Q16853 
                 SRM 
               
               
                 4 
                 APOB 
                 Apolipoprotein B-100 
                 P04114 
                 SRM 
               
               
                 5 
                 ASPN 
                 Asporin 
                 Q9BXN1 
                 SRM 
               
               
                 6 
                 ATRN 
                 Attractin 
                 O75882 
                 SRM 
               
               
                 7 
                 AZGP1 
                 Zinc-alpha-2-glycoprotein 
                 P25311 
                 SRM 
               
               
                 8 
                 CADM1 
                 Cell adhesion molecule 1 
                 Q9BY67 
                 SRM 
               
               
                 9 
                 CEACAM1 
                 Carcinoembryonic antigen-related cell adhesion 
                 P13688 
                 ELISA, SRM 
               
               
                   
                   
                 molecule 1 
               
               
                 10 
                 CFH 
                 Complement factor H 
                 P08603 
                 SRM 
               
               
                 11 
                 CLU 
                 Clusterin precursor 
                 P10909 
                 SRM 
               
               
                 12 
                 CP 
                 Ceruloplasmin 
                 P00450 
                 SRM 
               
               
                 13 
                 CPM 
                 Carboxypeptidase M 
                 P14384 
                 SRM 
               
               
                 14 
                 CTSD 
                 Cathepsin D 
                 P07339 
                 SRM 
               
               
                 15 
                 ECM1 
                 Extracellular matrix protein 1 
                 Q16610 
                 ELISA, SRM 
               
               
                 16 
                 EFNA5 
                 Ephrin-A5 
                 P52803 
                 SRM 
               
               
                 17 
                 F5 
                 Coagulation factor V 
                 P12259 
                 SRM 
               
               
                 18 
                 FAM3D 
                 Protein FAM3D 
                 Q96BQ1 
                 ELISA 
               
               
                 19 
                 GALNTL4 
                 Putative polypeptide N-acetylgalactosaminyltransferase- 
                 Q6P9A2 
                 SRM 
               
               
                   
                   
                 like protein 4 
               
               
                 20 
                 GOLPH2 
                 Golgi phosphoprotein 2 
                 Q8NBJ4 
                 SRM 
               
               
                 21 
                 GRN 
                 Granulins 
                 P28799 
                 ELISA 
               
               
                 22 
                 GSPT1 
                 Eucariotic peptide chain release factor GTP-binding 
                 P15170 
                 SRM 
               
               
                   
                   
                 subunit ERF3A 
               
               
                 23 
                 HYOU1 
                 Hypoxia up-regulated protein 1 
                 Q9Y4L1 
                 SRM 
               
               
                 24 
                 KIT 
                 Mast/stem cell growth factor receptor 
                 P10721 
                 SRM 
               
               
                 25 
                 KLK3 
                 Prostate-specific antigen 
                 P07288 
                 ELISA, SRM 
               
               
                 26 
                 L1CAM 
                 Neural cell adhesion molecule L1 
                 P32004 
                 SRM 
               
               
                 27 
                 LGALS3BP 
                 Galectin-3-binding protein 
                 Q08380 
                 ELISA, SRM 
               
               
                 28 
                 LOX 
                 Protein-lysine 6-oxidase 
                 P28300 
                 SRM 
               
               
                 29 
                 LRP1 
                 Prolow-density lipoprotein receptor-related protein 1 
                 Q07954 
                 SRM 
               
               
                 30 
                 MME 
                 Neprilysin 
                 P08473 
                 ELISA 
               
               
                 31 
                 MMP1 
                 Interstitial collagenase 
                 P03956 
                 SRM 
               
               
                 32 
                 NCAM1 
                 Neural cell adhesion molecule 1 
                 P13591 
                 SRM 
               
               
                 33 
                 OLFM4 
                 Olfactomedin-4 
                 Q6UX06 
                 SRM 
               
               
                 34 
                 PGCP 
                 Plasma glutamate carboxypeptidase 
                 Q9Y646 
                 SRM 
               
               
                 35 
                 PIGR 
                 Polymeric immunoglobulin receptor 
                 P01833 
                 ELISA 
               
               
                 36 
                 POSTN 
                 Periostin 
                 Q15063 
                 ELISA 
               
               
                 37 
                 PSAP 
                 Proactivator polypeptide 
                 P07602 
                 SRM 
               
               
                 38 
                 SEMA4D 
                 Semaphorin-4D 
                 Q92854 
                 SRM 
               
               
                 39 
                 TFRC 
                 Transferrin receptor protein 1 
                 P02786 
                 SRM 
               
               
                 40 
                 THBS1 
                 Thrombospondin-1 
                 P07996 
                 ELISA, SRM 
               
               
                 41 
                 TIMP1 
                 Metalloproteinase inhibitor 1 
                 P01033 
                 ELISA, SRM 
               
               
                 42 
                 TM9SF3 
                 TM9SF3 protein 
                 Q8WUB5 
                 SRM 
               
               
                 43 
                 VTN 
                 Vitronectin 
                 P04004 
                 SRM 
               
               
                 44 
                 ICAM1 
                 Intercellular adhesion molecule 1 
                 P05362 
                 SRM 
               
               
                 45 
                 CPE 
                 Carboxypeptidase E 
                 P16870 
                 ELISA 
               
               
                 46 
                 MSMB 
                 Beta-microseminoprotein 
                 P08118 
                 ELISA 
               
               
                   
               
             
          
         
       
     
       Statistical Analysis 
       [0084]    Following statistical analysis, we could identify a 3-biomarker signature comprising of Asporin (ASPN), Vitronectin (VTN) and Membrane copper amine oxidase (AOC3). The Signature had an accuracy of 81% in discriminating between BPH (n=15) and locPCa (n=16) patients; this means that 81% of the patients analyzed were correctly diagnosed by our 3-biomarker signature. AOC3 was found to be the weakest contributor. Thus we substituted this protein with other potential biomarkers and kept the ones gaining similar or higher accuracy (≧80%). The following proteins could be individually added in this way: LOX, PGCP, PSAP, THBS1 ( FIG. 3  A). 
         [0085]    The discrimination of PSA itself was measured as well which resulted in an accuracy of 71% discriminating between BPH (n=15) and locPCa (n=16) patients. 
         [0086]    Additionally, we added PSA data to the core signature of ASPN and VTN. By including one of the following proteins: AOC3, CFH, CLU, KIT, LOX, TFRC, THBS1, LGALS3BP, GOLPH2, accuracies of up to 90% was achieved ( FIG. 3  B). 
         [0087]    Following statistical analysis using more data, we could further identify a 5-biomarker signature comprising of Asporin (ASPN), Cathepsin D (CTSD), Hypoxia up-regulated protein 1 (HYOU1) and Olfactomedin-4 (OLFM4). The Signature had an accuracy of 87% in discriminating between BPH (n=35) and locPCa (n=41) patients; this means that 87% of the patients analyzed were correctly diagnosed by our 5-biomarker signature. The discrimination of PSA itself was measured as well which resulted in an accuracy of 72% discriminating between BPH (n=41) and locPCa (n=64) patients ( FIG. 3C ). 
         [0088]    Additionally, by removing in each case only one of these four proteins an accuracy of up to 83% was achieved ( FIG. 3C ). 
         [0089]    Using the same dataset and applying a somewhat less stringent criterion for selection out of the systems according to table 3, a refined list of biomarkers was determined and is collected in table 4. An assay with a group of at least three of the systems given in table 4 in combination with a PSA (ELISA) measurement leads to an accuracy of around 80% or even higher. A selection of at least four of the systems given in table 4 in combination with a PSA (ELISA) measurement even leads to an accuracy of around 85% or higher. 
         [0090]    The threshold values for each of the systems given in table 4 indicates the concentration threshold above or below (as indicated) which a positive diagnosis can be issued. If all of the markers in one assay (for example in a group of 3 biomarkers selected from table 4) exceed in concentration above these concentration values a positive diagnosis can be issued with the accuracies as given above. 
         [0000]                                                                                TABLE 4                   List of 15 serum glycoproteins measured in human sera                            Technique                           Accession   used   basic   preferred           Gene name   Protein name   number   for analysis   conc   conc                        1   AKAP13   A-kinase anchor protein 13   Q12802   SRM   &gt;2500   &gt;2800       2   ASPN   Asporin   Q9BXN1   SRM   &gt;55   &gt;60       3   CFH   Complement factor H   P08603   SRM   &lt;250000   &lt;231500       4   CP   Ceruloplasmin   P00450   SRM   &lt;120000   &lt;101500       5   CPE   Carboxypeptidase E   P16870   ELISA   &gt;0.05   &gt;0.075                           (OD)   (OD)       6   CPM   Carboxypeptidase M   P14384   SRM   &lt;110   &lt;95       7   CTSD   Cathepsin D   P07339   SRM   &lt;32   &lt;25       8   HYOU1   Hypoxia up-regulated protein 1   Q9Y4L1   SRM   &gt;35   &gt;40       9   ICAM1   Intercellular adhesion molecule   P05362   SRM   &lt;360   &lt;340               1       10   LGALS3BP   Galectin-3-binding protein   Q08380   SRM   &lt;400   &lt;390       11   MSMB   Beta-microseminoprotein   P08118   ELISA   &gt;0.12   &gt;0.15                           (OD)   (OD)       12   OLFM4   Olfactomedin-4   Q6UX06   SRM   &lt;20   &lt;15       13   TM9SF3   TM9SF3 protein   Q8WUB5   SRM   &gt;8   &gt;10       14   VTN   Vitronectin   P04004   SRM   &lt;3500   &lt;3300       15   GALNTL4   Putative polypeptide N-   Q6P9A2   SRM   &lt;15   &lt;10               acetylgalactosaminyltransferase-               like protein 4               Table 4: Refined list of 15 serum glycoproteins measured in human sera after statistical analysis (BPH (n = 35) and locPCa (n = 41)). Gene name, Protein name (shortened) and Accession number are defined as given in Table 1. In a first column the basic concentration threshold values in ng/ml are given, and in a second column the preferred concentration threshold in ng/ml values are given. Where OD is indicated measurement takes place at 405 nm and relative values are given using commercially available antibodies (CPE: R&amp;Dsystems, polyclonal: Nr. AF3587 and R&amp;Dsystems, monoclonal: MAB3587; MSMB: R&amp;Dsystems, polyclonal: Nr. AF3780 and Abnova, monoclonal: H00004477-M08).            
b) Using a biomarker signature comprising of the following biomarkers: Asporin (ASPN), Vitronectin (VTN), Cathepsin D (CSTD), Polypeptide N-acetyl-galactosaminyltransferase GALNTL4, Proactivator polypeptide (PSAP), and Thrombospondin-1 (THBS-1), we could correctly distinguish between locPCa (n=16) and metPCa (n=21) patients in 100% of the cases. PSAP was found to be the weakest contributor. Leaving it out, still 97% accuracy in the discriminant analysis was achieved. Thus we substituted this protein with other potential biomarkers and kept the ones ameliorating the accuracy (&gt;97%). The following protein could be individually added in this way: CEACAM1, EFNA5, GSPT1, HYOU1, KIT (all gaining an accuracy of 100%) ( FIG. 3 ).
 
         [0091]    It should be noted that any of the systems as given in table 3, preferably at the combination of two, most preferably as a combination of at least three (or exactly 3), of at least four (or exactly 4) or of at least five (or exactly 5) glycoproteins can be an assay which shall be covered by the present invention. The specific statistically evaluated systems as outlined above are just those which for the diagnostic aspects addressed in these statistical tests could be shown to be most powerful. For different diagnostic/prognostic/therapeutic aspects or using different statistical evaluation methods, different combinations might also be possible and shall be regarded as according to the present invention.