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Patent Analysis of

Method for biomarker and drug-target discovery for prostate cancer diagnosis and treatment as well as biomarker assays determined therewith

Updated Time 12 June 2019

Patent Registration Data

Publication Number

US10151755

Application Number

US15/170876

Application Date

01 June 2016

Publication Date

11 December 2018

Current Assignee

ETH ZURICH,KANTONSSPITAL ST. GALLEN

Original Assignee (Applicant)

ETH ZURICH,KANTONSSPITAL ST. GALLEN

International Classification

G01N33/53,G01N33/574,G01N33/68

Cooperative Classification

G01N33/57434,G01N33/57407,G01N33/6848,G01N2333/8146,G01N2333/47

Inventor

KREK, WILHELM,CIMA, IGOR,AEBERSOLD, RUDOLF,SCHIESS, RALPH,CERNY, THOMAS,GILLESSEN, SILKE

Patent Images

This patent contains figures and images illustrating the invention and its embodiment.

US10151755 Method biomarker drug-target 1 US10151755 Method biomarker drug-target 2 US10151755 Method biomarker drug-target 3
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Abstract

The invention relates to biomarker assays based on protein/peptide biomarkers which show a pronounced differential behaviour between healthy and cancerous sample proteomes e.g. by mass spectrometric measurement and/or antibody-based assays such as an Enzyme-Linked Immunosorbent Assay (ELISA) determination of the protein biomarkers in serum, plasma or blood itself.

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Claims

1. A method comprising measuring HBS1 protein, ICAM1 protein, and Prostate specific antigen (PSA) protein in human serum, plasma, or blood.

2. A method comprising measuring CTSD protein ICAM1 protein, and Prostate specific antigen (PSA) protein in human serum, plasma, or blood.

3. The method of claim 1, further comprising measuring at least one protein biomarker selected from the group consisting of ASPN, VTN, AOC3, LOX, PGCP, PSAP, CFH, CLU, KIT, TFRC, LGALS3BP, GOLPH2, AKAP13, PC, CPE, CPM, MSMB, TM9SF3, GALNTL4, GSPT1, CEACAM1, EFNA5, HYOU1, CTSD, OLFM4, and TIMP1.

4. The method of claim 2, further comprising measuring at least one protein biomarker selected from the group consisting of ASPN, VTN, AOC3, LOX, PGCP, PSAP, CFH, CLU, KIT, TFRC, LGALS3BP, GOLPH2, AKAP13, PC, CPE, CPM, MSMB, TM9SF3, GALNTL4, GSPT1, CEACAM1, EFNA5, THBS1, HYOU1, OLFM4, and TIMP1.

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Claim Tree

  • 1
    1. A method comprising
    • measuring HBS1 protein, ICAM1 protein, and Prostate specific antigen (PSA) protein in human serum, plasma, or blood.
    • 3. The method of claim 1, further comprising
      • measuring at least one protein biomarker selected from the group consisting of ASPN, VTN, AOC3, LOX, PGCP, PSAP, CFH, CLU, KIT, TFRC, LGALS3BP, GOLPH2, AKAP13, PC, CPE, CPM, MSMB, TM9SF3, GALNTL4, GSPT1, CEACAM1, EFNA5, HYOU1, CTSD, OLFM4, and TIMP1.
  • 2
    2. A method comprising
    • measuring CTSD protein ICAM1 protein, and Prostate specific antigen (PSA) protein in human serum, plasma, or blood.
    • 4. The method of claim 2, further comprising
      • measuring at least one protein biomarker selected from the group consisting of ASPN, VTN, AOC3, LOX, PGCP, PSAP, CFH, CLU, KIT, TFRC, LGALS3BP, GOLPH2, AKAP13, PC, CPE, CPM, MSMB, TM9SF3, GALNTL4, GSPT1, CEACAM1, EFNA5, THBS1, HYOU1, OLFM4, and TIMP1.
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Description

TECHNICAL FIELD

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

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's health, since complete remission and cure from the disease is possible only at early stages of the disease.

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).

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.

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.

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

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.

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.

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.

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.

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 as say including the following steps:

(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.

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.

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;

(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.

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.

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”.

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:

(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.

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.

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.

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.

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).

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.

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.

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:

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.

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.

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.

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.

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.

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 (www.uniprot.org), 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.

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.

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.

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.

The present invention furthermore relates to a method for the diagnosis of localized prostate cancer using a (preferably combined) measurement of the concentration of 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.

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.

In such a method, typically for a positive diagnosis 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 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.

If, as preferred, additionally one of the above-mentioned additional systems is measured,

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.

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 serum, 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.

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.

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.

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.

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.

For a positive diagnosis of non-localized (metastatic) prostate cancer

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.

If, as preferred, additionally one of the above-mentioned additional systems is measured,

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.

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.

Further embodiments of the present invention are outlined in the dependent claims.

SHORT DESCRIPTION OF THE FIGURES

In the accompanying drawings preferred embodiments of the invention are shown in which:

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;

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

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 the UniProt Consortium (www.uniprot.org), which is comprised of the European 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

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 discovery and verification phases (a) and (b) performed using an animal model (mice) and second the validation phase (c) with human patient samples.

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's disease, Parkinson's disease, Huntington'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.

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.

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).

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).

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).

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.

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.

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.

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.

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.

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.

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.

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's data collection leading to the final assays as given in FIG. 3.

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.

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.

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.

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.

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

(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.

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).


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
>1.5 or <0.75
2
potential biomarkers regulated in prostate tissue AND detected
>1.5 or <0.75
in serum
3
potential biomarkers regulated in prostate tissue AND secreted
>1.5 or <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
(>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 (www.uniprot.org), 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.

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.

(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 NxS/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.

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 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.

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.). 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).


TABLE 2
Glycoproteins measured by SRM in murine sera
Gene
Accession
p-value
p-value
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-
O08532
0.6186
0.1576
2/delta-1
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
P97797
0.7780
0.1677
substrate 1
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
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.

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.

(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:

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.

Using SRM on the murine serum samples, we could verify and quantify 41 out of the 165 initial candidates. (Table 2)

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.


TABLE 3
List of 43 serum glycoproteins measured in human sera
Technique
Gene
Accession
used
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
P13688
ELISA, SRM
adhesion 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-acetylgalactosaminyl-
Q6P9A2
SRM
transferase-like protein 4
20
GOLPH2
Golgi phosphoprotein 2
Q8NBJ4
SRM
21
GRN
Granulins
P28799
ELISA
22
GSPT1
Eucariotic peptide chain release factor
P15170
SRM
GTP-binding 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-
Q07954
SRM
related protein 1
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
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.
Statistical analysis

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.

Statistical Analysis

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).

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.

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).

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).

Additionally, by removing in each case only one of these four proteins an accuracy of up to 83% was achieved (FIG. 3C).

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.

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.


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
>2500
>2800
2
ASPN
Asporin
Q9BXN1
SRM
>55
>60
3
CFH
Complement factor H
P08603
SRM
<250000
<231500
4
CP
Ceruloplasmin
P00450
SRM
<120000
<101500
5
CPE
Carboxypeptidase E
P16870
ELISA
>0.05
>0.075
(OD)
(OD)
6
CPM
Carboxypeptidase M
P14384
SRM
<110
<95
7
CTSD
Cathepsin D
P07339
SRM
<32
<25
8
HYOU1
Hypoxia up-regulated protein 1
Q9Y4L1
SRM
>35
>40
9
ICAM1
Intercellular adhesion molecule
P05362
SRM
<360
<340
1
10
LGALS3BP
Galectin-3-binding protein
Q08380
SRM
<400
<390
11
MSMB
Beta-microseminoprotein
P08118
ELISA
>0.12
>0.15
(OD)
(OD)
12
OLFM4
Olfactomedin-4
Q6UX06
SRM
<20
<15
13
TM9SF3
TM9SF3 protein
Q8WUB5
SRM
>8
>10
14
VTN
Vitronectin
P04004
SRM
<3500
<3300
15
GALNTL4
Putative polypeptide N-
Q6P9A2
SRM
<15
<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&Dsystems, polyclonal: Nr. AF3587 and R&Dsystems, monoclonal: MAB3587; MSMB: R&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 (>97%). The following protein could be individually added in this way: CEACAM1, EFNA5, GSPT1, HYOU1, KIT (all gaining an accuracy of 100%) (FIG. 3).

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.

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Citation

Patents Cited in This Cited by
Title Current Assignee Application Date Publication Date
Determining cancer-linked genes and therapeutic targets using molecular cytogenetic methods AVALON PHARMACEUTICALS,STROVEL, JEFFREY, W.,CAIN, COLYN, B.,HORRIGAN, STEPHEN, K.,AUGUSTUS, MEENA 23 June 2005 05 January 2006
Methods for quantitative proteome analysis of glycoproteins INSTITUTE FOR SYSTEMS BIOLOGY, THE 03 June 2003 05 February 2004
糖タンパク質を定量的プロテオ-ム分析する方法 ザ インスティテュート フォー システムズ バイオロジー 03 June 2003 02 March 2006
In-vitro method for screening accessible biological markers in pathological tissues CASTRONOVO VINCENT,WALTREGNY DAVID,KISCHEL PHILIPPE 13 December 2006 25 June 2009
Compositions and methods for treating and diagnosing cancer REGENTS OF THE UNIVERSITY OF MICHIGAN, THE 13 June 2006 03 May 2007
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