EP3987290A1 - Series of metabolites as biomarkers for the diagnosis of pancreatic cancer - Google Patents

Abstract

The present invention is comprised in the field of medicine, pharmacology, chemistry, biochemistry and biotechnology, and it relates to different series of metabolites present in serum capable of differentiating healthy patients from patients with pancreatic cancer (PC). The invention has an application in the clinical diagnosis of a pathology such as PC.

Description

Series of metabolites as biomarkers for the diagnosis of pancreatic cancer

Field of the invention

The present invention is comprised in the field of medicine, pharmacology, chemistry, biochemistry and biotechnology, and it relates to different series of metabolites present in serum capable of differentiating healthy patients from patients with pancreatic cancer (PC). The invention has an application in the clinical diagnosis of a pathology such as PC.

Background of the invention

Today, the early diagnosis of PC is a healthcare priority as its rate of survival in the first year after diagnosis is 19%, reaching only 5% when the first five years are considered (Zhang et al ., 2016). Its late diagnosis is due to several factors: 1) wide-ranging symptomatology with few specific and visible signs of the disease, and 2) the non-existence of reliable markers for its detection in the first phases of the disease, which means that only 8% of PC cases are detected in early stages, during which phases they are still resectable given their size (15-20% of the cases) (Giovannetti et al. , 2012). This late diagnosis is an essential factor for its poor prognosis, since the main cause of death in this pathology is the infiltration into neighboring tissues and/or the presence of distant metastasis (Kamisawa et al. , 2016; Sharma et al. , 2011).

Today, the treatment of PC includes chemotherapy which, while not completely effective, allows a sufficiently considerable reduction of the tumor mass to take a surgical approach, significantly increasing survival particularly in localized stages (Lee and Park, 2016). Surgical resection with clear margins is the ideal treatment for these patients though it is uncommon due to the late diagnosis (Erkan et al. , 2012). Therefore, in order to improve the prognosis of PC, it is essential to not only identify new biomarkers of disease but also to develop systems which make their detection reliable and applicable to practice.

Biomarkers and pancreatic cancer. There is no reliable biomarker available today for the diagnosis of PC. Nevertheless, the advent of genetic, high-throughput proteomic and metabolomic technologies is making it easier to discover new biomarkers, particularly in cancer (Le et al. , 2016). The development of a diagnostic system for PC requires: 1) high sensitivity for identifying all or most of the patients; 2) being based on a determination with low invasiveness for the patient, and c) having the lowest possible cost for public healthcare (Riickert et al. , 2010).

At present, the only marker approved by the FDA for use as an element for a diagnostic approach to this pathology is CA19-9 (Winter et al. , 2013). However, this marker has serious limitations: 1) a low sensitivity (about 80%) and 2) a lack of specificity (73%, respectively), which impedes it from differentiating PC from other pathologies in which high levels are also observed (Goggins et al ., 2005). In fact, CA19-9 is elevated in only 65% of people with resectable PC and may not discern between patients with PC and with PC and/or other malignant lesions. Hence, there is evidence which justifies the doubts raised with respect to the use of this marker in PC, which has been considered rather unreliable by the scientific community (Duffy et al ., 2010). For this reason, a number of studies in recent years have set their objective on finding molecules that can act like biomarkers of this pathology whether it is in tissues (MUC4, MUC1, CECAM1) or in peripheral blood (MIC-1, NGAL, telomerase and microRNAs) (Liang et al, 2009).

Conventional genetic markers of other tumors such as mutated KRAS and p53 have been widely tested in PC, and their limitations, which include low sensitivity, a high number of false positives, and low throughput in the case of obstruction of the pancreatic duct are known (Teich and Mossner, 2004). Studies of genes altered in PC show a large number of overexpressed molecules both in early and in late lesions, including SI OOP, MMP7, MUC4, FSCN1, and MUC5AC (Harsha et al. , 2009). A combination of four messenger RNA biomarkers (KRAS, MBD3L2, ACRV1, and DPMI) which seem to differentiate PC patients from healthy subjects with high sensitivity and specificity (90 and 95%) has been identified using saliva from patients with PC as a sample and conducting transcriptome studies. However, a large-scale analysis of salivary secretions will be required for its validation (Zhang et al. , 2010). More recently, studies based on miRNA have demonstrated that while miR-21 and miR-155 are overexpressed, expression of miR-216 in PC tissues, pancreatic juice, and feces decreases compared with controls, which could represent an association of biomarkers for the diagnosis of the disease (Yang et al, 2014).

In this context, the search for the differential expression of genes has been a tool for detecting biomarkers in different types of tumors, including PC (Caba et al. , 2012). Alteration of the gene expression profile in peripheral blood cells has been seen as a chance for the early detection and determination of the progression of the disease (Honda et al. , 2012). The authors of the present invention already demonstrated the existence of a specific pattern of gene expression in the peripheral blood of patients with PC that differentiates them from healthy people (patent WO2014/076342 Al) (Caba et al., 2014; Irigoyen et al., 2016). Other authors have also found significant differences in gene expression between both groups, although the low number of samples used makes it necessary to conduct a broader study for validation (Yan et al, 2011).

Moreover, the determination of protein expression levels whether in tissue or in serum has been examined for the purpose of obtaining PC biomarkers. Preliminary studies with the anti- MUC (PAM4) monoclonal antibody have demonstrated that it presents higher diagnostic sensitivity than CA19-9 (91%, 86%, and 62% for stage 3/4, stage 2, and the disease in stage 1, respectively) although it needs more extensive clinical validation (Gold et al ., 2010). Today, the stroma is known to play a crucial role in supporting growth, angiogenesis, and resistance to CD drugs. Some proteins such as SPARC, which is involved in cell matrix interactions (Hwang et al ., 2008), could act like biomarkers of a poor prognosis for these patients. The possibility of combining PC stromal markers with CA19-9 to increase their sensitivity has recently been analyzed (Franklin et al. 2015).

Furthermore, the development of PC involves an aggressive inflammatory process (Amedei et al. , 2013) which may, on one hand, have an influence on the effectiveness of treatment with cytotoxic agents, and on the other, release cytokine and chemokine signals (Jimenez-Luna C et al. , 2016), which not only modulate the response of the inflammatory cells themselves, but also the processes of growth, angiogenesis, and metastasis, all of which are essential processes for development of the tumor and, therefore, the prognosis of the patient (Neese et al, 2011; Luo et al, 2012).

It should also be pointed out that in studies of this type the sample used is serum from patients, and this is a quite important aspect from the clinical application viewpoint. Obtaining this sample represents, in the case of the PC, a huge advantage given the difficulty in accessing this type of tumor (Bournet et al , 2012). The possibility of detecting biomarkers either in formed elements of the blood, such as peripheral blood mononuclear cells (PBMCs) or in the serum of patients, has opened up the possibility to the development of new diagnostic methods that are easier to apply (Caba et al, 2012).

Finally, it is important to point out that not all the avenues of research for the determination of biomarkers in PC have been successful, as seen with the results of the search for serum autoantibodies of patients demonstrating low sensitivity. The attempt to use multiple serum autoantibodies has seen a low diagnostic performance, though it is pending validation (Dumstrei et al, 2016).

Metabolites as serum biomarkers by means of LC/MS. A new step in this direction is the massive analysis of metabolites (metabolomics) for detecting new tumor pathology markers. This analysis is a new approach useful for the search for tumor markers as demonstrated by the growing number of studies with this technology (Nishiumi et al, 2014). The objective of untargeted global metabolic analysis of tissues and biofluids (metabolic fingerprinting) is to quantify metabolites and their changes, for the purpose of discovering new potential biomarkers and disclosing information about the metabolic state of an organism (Nicholson et al, 2002). There is a broad consensus in relation to the importance of metabolomics (human metabolic profile) as a rapid non-invasive tool for the early diagnosis of many diseases. “Metabolic fingerprinting” is obtained with combined liquid chromatography and high-resolution mass spectrometry (LC-HRMS) techniques (Dunn et al, 2011) which allow obtaining a large amount of information in the form of molecular mass variables from a relatively small number of observations (Ranjbar et al, 2011). This data requires holistic tools which allow, in contrast with conventional statistical techniques, the simultaneous consideration of many variables for the purpose of reducing, clustering, and displaying the data (Tautenhahn et al. , 2011).

Moreover, metabolomics allows determining the changes in markers according to the metabolic state of the cell, with the advantage that these changes can occur even before significant variations in the levels of proteins takes place (Medina et al. , 2014). This is why metabolomics is being applied to the development of kits in biomedical fields as varied as the measurement of the antithrombotic activity of nonsteroidal anti-inflammatory drugs through the detection of the thromboxane A2 metabolites (US 8168400 B2), or the application of the detection of metabolites which allow identifying the presence of drugs (US 6180414 Bl). Furthermore, the detection of metabolites is also already being applied for early diagnosis in investigation areas as important as neurology, as demonstrated in recent patents which determine oxidized dopamine metabolites in the plasma of patients with Parkinson’s (WO 2011152699 A2).

Metabolomics in cancer. In the area of cancer, metabolomics is becoming increasingly more important. Changes in cell metabolism, including those occurring during tumor transformation processes, result in altered levels of different metabolites which can now be detected in a highly reliable manner. The identification of these patterns of changes between healthy people and patients with cancer, including PC, provides valuable information that allows not only understanding the disease better (Pirman et al. , 2013) but also allows early detection. In that sense, determination by mass spectrometry (MALDI-TOF/ MS) of three metabolites (m/z 1465, 1206, and 1020) has recently been proposed for discriminating between patients with PC and those associated with diabetes (Kim 2015). This method is pending clinical validation. In gastric cancer, the determination in different fluids of a group of metabolites has been recently patented as a diagnostic method (Patent WO/2015/093800). Zhu et al. have performed mass spectrometry and liquid chromatography analysis using serum from colon cancer patients which allowed them to differentiate, with only 13 metabolites, patients with colon cancer from healthy controls, and with 14 metabolites, patients with colon cancer from patients with polyps, with high sensitivity (between 96 and 89%) and specificity (80 and 88%) (Zhu et al, 2014; 2015). Brief description of the invention

The present invention describes the existence of a series of metabolites in serum capable of differentiating healthy subjects from those with PC. These series are intended for clinical application for the early diagnosis of said pathology.

Brief description of the Figure

Figure 1. ROC curve for combined biomarkers.

Detailed description of the invention

Diagnostic methods of the Invention

Before the present methods are described, it is to be understood that this invention is not limited to particular methods, and experimental conditions described, methods and conditions may vary. It is also to be understood that the terminology used herein is for purposes of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only in the appended claims.

The authors of the present invention have identified a series of metabolic markers present in serum, plasma or blood samples collected from patients diagnosed with pancreatic cancer and healthy controls (HC). These metabolic markers selected are significantly differentiated between Healthy Controls (HC) and patients diagnosed with pancreatic cancer. These metabolic markers can thus be used in a non-invasive diagnostic method for identifying and classifying patients. In particular, the invention relates to a diagnostic method to distinguish between pancreatic cancer patients versus HC based on the different serum, plasma or blood biomarker profiles. Each of these biomarker profiles is identified and explained below.

Serum, plasma or blood biomarker profile for identifying and classifying pancreatic cancer patients versus HC

The authors of the present invention have determined that by using the different subsets of metabolites identified below, and preferably applying PLS-DA, the discrimination results illustrated in Table 2 were obtained for pancreatic cancer patients versus HC. Therefore, a first aspect the invention relates to an in vitro method to classify a subject in need thereof, between patients suffering from or having pancreatic cancer, that is preferably suffering the symptomatology of the disease, vs HC (healthy controls and/or subjects not having M. pancreatic cancer) (hereinafter“first classification method of the invention”), that comprises the in vitro determination of the levels of at least one of any of the following markers: Glycochenodeoxycholic acid 7-sulfate, phenylalanine-phenylalanine, adrenal acid, dehydroepiandrosterone sulfate, androsterone sulfate, triacylglycerol (22:2), lysophosphatidylethanolamine (18: 1), lysophosphatidylethanolamine (18:2), 13- hydroxyoctadecadienoic acid, 3-oxo-octadecanoic, or 4-oxo-retinoic acid, or any combination thereof, in a serum, plasma or blood sample taken from the subject. Preferably, the in vitro classification method is based on the in vitro determination of the levels of all of the following markers: Glycochenodeoxycholic acid 7-sulfate, phenylalanine-phenylalanine, adrenal acid, dehydroepiandrosterone sulfate, androsterone sulfate, triacylglycerol (22:2), lysophosphatidylethanolamine (18: 1), lysophosphatidylethanolamine (18:2), 13- hydroxyoctadecadienoic acid, 3-oxo-octadecanoic, and 4-oxo-retinoic acid, in a serum, plasma or blood sample taken from the subject.

A preferred embodiment of the first aspect the invention relates to a method to classify a subject in need thereof, between pancreatic cancer patients versus HC subjects which comprises determining in a serum, plasma or blood sample of the subject the levels of at least Glycochenodeoxycholic acid 7-sulfate, phenylalanine-phenylalanine, adrenal acid, dehydroepiandrosterone sulfate, androsterone sulfate, triacylglycerol (22:2), lysophosphatidylethanolamine (18: 1), lysophosphatidylethanolamine (18:2), 13- hydroxyoctadecadienoic acid, 3-oxo-octadecanoic, or 4-oxo-retinoic acid, or any combination thereof, and comparing the levels of said markers with respect to the levels of the same markers in a HC or with respect to the reference value ranges for the biomarkers for a HC, wherein the subject is classified as suffering from pancreatic cancer if different levels of the biomarkers compared to the reference value ranges for the biomarkers for a HC indicate that the subject has pancreatic cancer.

In a preferred embodiment, the in vitro method of the invention further comprises the in vitro determination of the levels of at least Phosphatidylserine (PS) (12:0/15: 1), Triglyceride (TG) (22:2/15:0/18:3), all-trans-Decaprenyldiphosphate, Lysophosphatidylcholine (LysoPC) (18:2), or any combination thereof, and comparing the levels of said markers with respect to the levels of the same markers in a HC or with respect to the reference value ranges for the biomarkers for a HC, wherein the subject is classified as suffering from PC if different levels of the biomarkers compared to the reference value ranges for the biomarkers for a HC indicate that the subject has PC.

It is noted that the first classification method of the invention aids in the diagnosis of the subject and therefore, in a preferred embodiment, the first classification method of the invention aids in the diagnosis of a subject in need thereof, in particular aids in determining whether or not a subject suffers from pancreatic cancer (hereinafter“first diagnosis method of the invention”). The term“diagnosis”, as used herein, refers both to the process of attempting to determine and/or identify a possible disease in a subject, i.e. the diagnostic procedure, and to the opinion reached by this process, i.e. the diagnostic opinion. As such, it can also be regarded as an attempt at classification of an individual’s condition into separate and distinct categories (such as predicting the“increasing risk” of suffering a disease, meaning“increasing risk” as an increased chance of developing or acquiring a disease compared with a normal individual) that allow medical decisions about treatment and prognosis to be made. It is to be understood that the method, in a preferred embodiment, is a method carried out in vitro , i.e. not practiced on the human or animal body. In particular, the diagnosis to determine pancreatic cancer patients relates to the capacity to identify and classify pancreatic cancer patients. This diagnosis, as understood by a person skilled in the art, does not claim to be correct in 100% of the analyzed samples. However, it requires that a statistically significant amount of the analyzed samples is classified correctly. The amount that is statistically significant can be established by a person skilled in the art by means of using different statistical tools; illustrative, non-limiting examples of said statistical tools include determining confidence intervals, determining the p-value, the Chi-Square test discriminating functions, etc. Preferred confidence intervals are at least 90%, at least 97%, at least 98%, at least 99%. The p-values are preferably less than 0.1, less than 0.05, less than 0.01, less than 0.005, or less than 0.0001. The teachings of the present invention preferably allow correctly diagnosing in at least 60%, in at least 70%, in at least 80%, or in at least 90% of the subjects of a determining group or population analyzed.

The first diagnostic method of the invention comprises comparing the level(s) of the metabolic marker(s) identified above, with a reference value. The term“reference value”, as used herein, relates to a predetermined criterion used as a reference for evaluating the values or data obtained from the samples collected from a subject. The reference value or reference level can be an absolute value, a relative value, a value that has an upper or a lower limit, a range of values, an average value, a median value, a mean value, or a value as compared to a particular control or baseline value. A reference value can be based on an individual sample value or can be based on a large number of samples, such as from population of subjects of the chronological age matched group or based on a pool of samples including or excluding the sample to be tested.

In the context of the present invention, the terms“subject”,“patient” or“individual”‘ are used herein interchangeably to refer to all the animals classified as mammals and includes but is not limited to domestic and farm animals, primates and humans, for example, human beings, non human primates, cows, horses, pigs, sheep, goats, dogs, cats, or rodents. Preferably, the subject is a male or female human being of any age or race.

In the context of the present invention, the term“metabolic marker” or“metabolite” or “biomarker”, are used herein interchangeably to refers to small molecule compounds, such as substrates for enzymes of metabolic pathways, intermediates of such pathways or the products obtained by a metabolic pathway, the occurrence or amount of which is characteristic for a specific situation, for example pancreatic cancer. The metabolic markers useful for the first diagnostic method of the invention are those defined in Table 1 and Table 2. Table 2 contains the acommon names of the metabolites. The metabolic markers of Table 2 are intended to refer to any isomer thereof, including structural and geometric isomers. The term“structural isomer”, as used herein, refers to any of two or more chemical compounds, having the same molecular formula but different structural formulas. The term“geometric isomer” or“stereoisomer” as used herein refers to two or more compounds which contain the same number and types of atoms, and bonds (i.e., the connectivity between atoms is the same), but which have different spatial arrangements of the atoms, for example cis and trans isomers of a double bond, enantiomers, and diastereomers. The common name of the amino acid or protein corresponds to the Amino Acid name or Protein to which it belongs followed by an accession number described in the Human Metabolome Database HMDB (http://www.hmdb.ca).

In a preferred embodiment the first diagnostic method of the invention further comprises confirming the diagnosis of pancreatic cancer by means of the clinical examination of the patient. In the context of the present invention, the term“level” or“presence”, as used herein, refers to the quantity of a biomarker detectable in a sample. Techniques to assay levels of individual biomarkers from test samples are well known to the skilled technician, and the invention is not limited by the means by which the components are assessed. In one embodiment, levels of the individual components of the metabolomic profile include, without limitation, refractive index spectroscopy (RI), Ultra-Violet spectroscopy (UV), fluorescent analysis, radiochemical analysis, Infrared spectroscopy (IR), Nuclear Magnetic Resonance spectroscopy (NMR), Light Scattering analysis (LS), Mass Spectrometry, Pyrolysis Mass Spectrometry, Nephelometry, Dispersive Raman Spectroscopy, gas chromatography combined with mass spectroscopy, liquid chromatography combined with mass spectroscopy, supercritical fluid chromatography combined with mass spectroscopy, MALDI combined with mass spectroscopy, ion spray spectroscopy combined with mass spectroscopy, capillary electrophoresis combined with mass spectrometry, NMR combined with mass spectrometry and IR combined with mass spectrometry. Preferably, levels of the individual components of the biomarker profile are assessed using a proton NMR spectrum.

Therefore, in a further preferred embodiment of the first aspect of the invention, the method is carried out by determining a measure of any of the subsets of biomarkers identified in the first aspect of the invention or in any of its preferred embodiments, in a serum, plasma or blood biological sample, using +/- 0.02 ppm, the biomarker peak regions identified in Table 1 of a proton NMR high field spectrum for each biomarker.

In another preferred embodiment of the first aspect of the invention, the method is carried out by determining a measure of any of the subsets of biomarkers identified in the first aspect of the invention or in any of its preferred embodiments, in a serum, plasma or blood biological sample, using +/- 0.02 ppm, the biomarker peak regions identified in Table 5 of a proton NMR low field spectrum for each biomarker.

It is further noted that in the context of the present invention, the assessment of the levels of the individual components can be expressed as absolute or relative values and may or may not be expressed in relation to another component, a standard, an internal standard, or another molecule of compound known to be in the sample. If the levels are assessed as relative to a standard or internal standard, the standard may be added to the test sample prior to, during or after sample processing. In the context of the present invention, to assess levels of the individual components of the subject, a serum, plasma or blood sample is taken from the subject. The sample may or may not be processed prior to assaying levels of the components of the metabolic profile. The sample may or may not be stored, e.g., frozen, prior to processing or analysis. Once the sample has been processed, the first method of the invention involves the determination of the levels of the biomarker in the sample. The expression“determining the levels of the biomarker”, as used herein, refers to ascertaining the absolute or relative amount or concentration of the biomarker in the sample. There are many ways to collect quantitative or relational data on biomarkers or metabolites, and the analytical methodology does not affect the utility of metabolite concentrations in assessing a diagnosis. Suitable methods for determining the levels of a given metabolite were already indicated above.

Method for determining the efficacy of a therapy for pancreatic cancer

In a second aspect, the invention relates to a method for determining the efficacy of a therapy for pancreatic cancer, which method comprises determining in a serum, plasma or blood sample of a subject suffering from pancreatic cancer and having been treated with said therapy, the level(s) of the serum, plasma or blood biomarker profiles of the first aspect of the invention, wherein such level(s) with respect to HC or with respect to a reference value are indicative of whether or not that said therapy is effective against pancreatic cancer.

The term“therapy for pancreatic cancer” as used herein, refers to the attempted remediation of a health problem, usually following a diagnosis, or to prevention of the onset of a health problem. As such, it is not necessarily a cure, i.e. a complete reversion of a disease. Said therapy may or may not be known to have a positive effect on a particular disease. This term includes both therapeutic treatment and prophylactic or preventative measures, in which the object is to prevent or stop (reduce) an undesired physiological change or disorder. For the purpose of this invention, beneficial or desired clinical results include, without limitation, relieving symptoms, stabilizing pathological state (specifically not worsening), slowing down or stopping the progression of the disease, improving or mitigating the pathological state and remission (both partial and complete), both detectable and undetectable. It can also involve prolonging survival, disease free survival and symptom free survival, in comparison with the expected survival if treatment is not received. Those subjects needing treatment include those subjects already suffering the condition or disorder, as well as those with the tendency to suffer the condition or disorder or those in which the condition or disorder must be prevented. In a particular embodiment, the determination of the level of the one or more metabolic markers is carried out by mass spectrometry or by using a proton NMR spectrum.

Method for monitoring the progression of pancreatic cancer

In a third aspect, the invention relates to a method for monitoring the progression of a subject suffering from pancreatic cancer, which method comprises determining in a serum, plasma or blood sample of a subject suffering from this disease, over the course of a therapy or not, the level(s) of the serum, plasma or blood biomarker profiles of the first aspect of the invention, wherein such level(s) with respect to a reference value determined in a urine sample from the same subject at an earlier time point are indicative that the pancreatic cancer condition/disease is progressing.

The term“monitoring the progression”, as used herein, refers to the determination of the progression of the disease in a subject diagnosed with pancreatic cancer, i.e., whether the pancreatic cancer is worsening or whether it is ameliorating.

The term“progression in the pancreatic cancer”, as used herein, is understood as a worsening of the disease, i.e., that the disease is progressing to a later stage with respect to a stage at an earlier time point measured.

Diagnostic kit

In a final aspect of the invention, the determination of the level of the one or more metabolic markers, to practice any of the aspects of the present invention, can be carried out by any suitable method, such as refractive index spectroscopy (RI), Ultra-Violet spectroscopy (UV), fluorescent analysis, radiochemical analysis, Infrared spectroscopy (IR), Nuclear Magnetic Resonance spectroscopy (NMR), Light Scattering analysis (LS), Mass Spectrometry, Pyrolysis Mass Spectrometry, Nephelometry, Dispersive Raman Spectroscopy, gas chromatography combined with mass spectroscopy, liquid chromatography combined with mass spectroscopy, supercritical fluid chromatography combined with mass spectroscopy, MALDI combined with mass spectroscopy, ion spray spectroscopy combined with mass spectroscopy, capillary electrophoresis combined with mass spectrometry, NMR combined with mass spectrometry and IR combined with mass spectrometry. Preferably, levels of the individual components of the biomarker profile are assessed using a proton NMR spectrum. In particular, by using a kit suitable for mass spectrometry assay preparation or proton NMR spectrum assay preparation, such kit should preferably deliver the widest range of metabolomic information available from a single targeted assay, covering a large number of key metabolites from main metabolic pathways. This kit should thus quantitatively analyze a large number of metabolites that have already been identified herein as part of key biochemical pathways, providing fundamental data to link changes in the metabolome to biological events. Preferably, such kit should preferably comprise at least one, preferably all, of the following components; a kit’s plate, a silicone mat cover for the plate, solvents preferably in sealed glass ampoules, quality controls, standards, a deep well capture plate, a memory stick having a software to link changes in the metabolome to biological events and a user manual.

Yet another aspect of the present invention includes a kit for aiding in the diagnosis of pancreatic cancer, comprising: biomarker detecting reagents for determining a differential expression level of the specific combinations of biomarkers identified in any of the aspects of the present invention.

In one preferred embodiment of this aspect of the invention, the kit further comprises instructions for use in diagnosing risk for pancreatic cancer, wherein the instructions comprise step-by-step directions to compare the expression level of the specific combinations of biomarkers identified in any of the aspects of the present invention, when measuring the expression of a serum, plasma or blood sample obtained from a subject suspected of having pancreatic cancer with the expression level of a sample obtained from a normal subject, wherein the normal subject is a healthy subject not suffering from pancreatic cancer, or with a reference value. In another aspect, the kit further comprises tools, vessels and reagents necessary to obtain urine samples from a subject.

Yet another aspect of the present invention includes a computer program suitable for implementing any of the methods of the present invention. In addition, a device comprising the above-mentioned computer program also forms part of the present invention as well as its use for the diagnosis of pancreatic cancer in a human subject. In this sense, the assignment of a patient into a specific group of patients, such as patients with pancreatic cancer, by any of the methods of the invention can be done by a computer program, preferably, after introducing the data into said program. Thus, in another preferred embodiment, the step of assigning a patient into a specific group of patients, such as patients with pancreatic cancer, according to any of the methods described in the present specification, is a computer implemented step wherein the data obtained in the previous steps of the method are inserted in a computer program and the program assigns the patient into one of the groups of patients.

The present invention is further illustrated by the following examples which merely illustrate the invention and do not limit same.

Examples

1. Methodology

Patients and samples

A total of 119 samples were collected for this study: 59 from patients with PC and 60 from healthy controls. The sera of patients with PC were obtained at Hospital Universitario Virgen de las Nieves and the healthy controls were obtained through the Andalusian Public Healthcare System Biobank.

Blood samples were obtained from all the patients before the start of any specific therapy. The diagnosis of PC was based on the clinical evaluation and imaging studies, which were later histologically confirmed by surgery or biopsy. The healthy controls showed no sign of actual disease or oncologic history. All the serum samples were stored at -80°C after being collected using standard techniques and protocols.

The study was approved by the Ethics Committee of Hospital Universitario Virgen de las Nieves and all the clinical investigations were performed according to the principles expressed in the Declaration of Helsinki. Written informed consent was obtained from all the patients and controls before they were enrolled in the study.

Obtaining the metabolites

All the serum samples were kept at 4°C throughout the entire analytical process. The proteins were removed from serum samples using acetonitrile (AcN) ([1 :3] [serum: AcN]) and were stirred for 60 seconds. They were later centrifuged at 13,300 rpm for 15 minutes.

LC-HRMS conditions

The supernatants were then evaporated and the dry residues were reconstituted in 50% (water: AcN). These solutions were transferred to analytical vials, stored in the automatic injector at 4°C, and analyzed by LC-HRMS. Next, chromatographic separation was performed by means of the Agilent Series 1290 LC system using a Waters XBridge BEH Amide column (2.1 mm x 150 mm) at 25°C in ESI (+) and a 45°C in ESI (-). Mass detection was achieved using an AB TOEO SCIEX Triple 600 quadruple time-of-flight mass spectrometer (Q-TOF-MS) in ESI (+) and ESI (-). The volume of the injected sample was 3 pi. The mobile phase consisted of 0.1% formic acid [water: AcN] [90: 10] (eluent A) and 0.1% formic acid [AcN:water] [90: 10] (eluent B). The gradient elution was performed as follows: 0-0.1 min 99% eluent B; 0.1-7 min 30% eluent B; 7-7,10 min, eluent B and 7,10-10 min 99% eluent B. The elution flow rate was 0.4 ml/min.

The TOF 5600 was operated using a method of information-dependent acquisition to compile complete information about HRMS and MS/MS for simultaneous screening. Accurate mass calibration is performed automatically each ten injections. It must be observed that the sequence in the samples of the samples is randomly injected to avoid any possible artificial aggregation of the sample due to analytical deviation.

Creation of the dataset

Before the creation of the dataset, reproducibility of both the mass/charge (m/z) and retention time (RT) was evaluated, as they play an important role in the successful processing of metabolomic data, particularly in the peak alignment step. Although minimization of the fluctuation of m/z values was assured by means of the regular calibration of the mass spectrometer, the stability of RT and m/z must be examined in batches. For this purpose, RT and m/z variability of three peaks eluting at RT 1.26, 4.75, and 8.59 min (with m/z 830.5657, 200.0392, and 537.3933, respectively) for HILIC ESI (+), RT 1.37, 3.37, and 5.35 min (with m/z 178.8807, 796.4157 and 434.87026, respectively) for HILIC ESI (-). In addition to obtaining complete screening mass spectra, the simultaneous automatic acquisition of the collision-induced dissociation (CID) mass spectra for ions that exceed the intensity threshold was performed and allowed obtaining additional information about the respective markers. MarkerView (version 1.2.1) software was used to process the raw LC-HRMS data. This is an adaptive processing software package designed for LC-HRMS data which performs peak detection, alignment, and data filtering, generating a table of characteristics where the intensity of m/z ions, mean RT, and the intensity of integrated ions are defined. Data mining was performed by means of an automated algorithm in the RT interval of 0.75-9.5 min in HILIC. Next, RT and m/z tolerances of 0.1 min and 15 ppm were used for the alignment of the respective peaks. The background noise (50 cps) was eliminated by the MarkerView software tool. To identify the actual molecular characteristics, the algorithm uses the mass measurement precision to cluster related ions with the state of charge and isotopic distribution. Identification of the marker compounds

To evaluate the LC-HRMS data obtained in LC-QTOF-MS and to estimate the elemental formulas of the preselected markers, PeakView (version 1.0) software was used. The estimation of the elemental formula was achieved from MS/MS and simple HRMS mass spectra, which was followed by a search in spectral databases for structural identification. Automatic estimation of the elemental formula was carried out using: (i) a single exact HRMS mass of the original ion, (ii) an isotopic profile of the original ion, and (iii) fragments of precise MS/MS fragments. The following atoms were considered for the calculations: C (n>50), H (n>100), N (n>10), O (n>20), P (n>15) and S (n>5). The formula search software (AB SCIEX) used in this study allowed classifying the proposed formulas according to“MS rank” and“MS/MS rank”, reflecting the differences between the calculated and measured m/z values for the original ions as the fragments, and the experimental and theoretical isotropic pattern match in terms of isotopic spacing and relative intensities. In the following step, a search by steps of the molecular formula of the candidates was conducted using several online databases (MassBank, Metlin, Human Metaboloma Database, Lipid Maps, PubChem, ChemSpider) and MS/MS. Of all the compounds obtained, only the candidates whose presence was probable in human beings were examined, comparing the mass spectra of experimental fragmentation with those already included in databases (MassBank, Metabolome Database, Metlin, NIST 2012 MS/MS library) and / or in the scientific literature.

Metabolomic analysis by LC-HRMS

Despite numerous breakthroughs in instrumentation, the complexity of biological samples is still an important challenge in metabolomic experiments, which reflect both the large number of metabolites and the wide range of their expression levels. For untargeted metabolomics of biological samples, deproteinization with an organic solvent is often carried out. It has been demonstrated that AcN-based extraction methods provide the richest information for low molecular weight lipid species and are also effective for metabolites such as the TCA cycle, glycolysis, amino acid metabolism, fatty acid metabolism, glutamine metabolism. These fractions are usually analyzed separately using LC-HRMS.

Under the present experimental conditions, several observations could be made from the total ion current (TIC) chromatograms of a QC sample. The HILIC TIC chromatograms showed a clear differential peak profile depending on the ionization mode. Specifically, the most intense ions in this separation mode were observed in ESI (+) between 1 and 2 min of RT, corresponding to poorly retained compounds (Figure 1 A). In the HILIC chromatography of serum samples, the classes of compounds eluted at such retention time have been related to classes of phosphatidylglycerol compounds. In contrast, more intense signals are observed in the range of retention time of 2.1 to 4.7 min for the HILIC ESI (-) mode (Figure IB). It has been reported that under weak acid conditions negative ionization of molecules such as organic acids with -COOH functional groups and phosphate groups is favored, and therefore intense mass signals are expected for such classes of compounds.

Based on the data of the selected chromatographic peaks, it was found that the typical RT window and mass tolerance were less than 3 sec and 10 ppm, respectively, which can be considered acceptable values. As a result of the selection and peak alignment methods, a data array containing the intensity of the mass signals was obtained for each chromatographic and ionization mode.

2. Results

The most discriminatory metabolites for the identification between both groups was the series made up of:

Glycochenodeoxycholic acid 7-sulfate, phenylalanine-phenylalanine, adrenal acid, dehydroepiandrosterone sulfate, androsterone sulfate, triacylglycerol (22:2), lysophosphatidylethanolamine (18: 1), lysophosphatidylethanolamine (18:2), 13- hydroxyoctadecadienoic acid, 3-oxo-octadecanoic acid, and 4-oxo-retinoic acid.

The m/z values thereof were those identified in Table 1.

Table 1. M/z values of the most discriminatory metabolites.

The values of the changes in expression (FD), area under the curve (AUC), p-value and FDR achieved by the different markers were those illustrated in Table 2.

Table 2. Values of the changes in expression area under the curve (AUC), p-value

and FDR achieved by the most discriminatory metabolites.

When the proposed series was used in a combined manner, the value of the area under the curve (AUC) achieved for enabling discrimination between both groups of subjected reached 97.5%. Moreover, a validation process was carried out in order to evaluate several candidates by univariate receiver-operating characteristic curves (ROC). As a result, nine biomarkers were confirmed as showing a strong capacity for differentiating PDAC form HC patients, with AUC values always above 0.8 (see Tables 3 and 4). Consequently, it is herein proposed the individual use of any of the biomarkers included in Tables 3 and 4, or any combination thereof comprising less than nine biomarkers, to classify a subject in need thereof, between patients suffering from pancreatic cancer vs subjects not suffering from pancreatic cancer. Moreover, according to the present invention, the biomarkers included in Tables 3 and 4 can be combined with any of the biomarkers included in Tables 1 or 2. In addition, the biomarkers included in Tables 3 and 4 give rise to a multivariate model based on the combination of nine candidate markers that discriminates unresectable PDAC patients from HC with an AUC value of 0.992 (95% Cl of 0.972-1.000).

Table 3. M/z values of the most discriminatory metabolites.

Table 4. Values of the changes in expression (PDAC/HC), area under the curve (AUC) and p(FDR) achieved by the most discriminatory metabolites.

ESI: electrospray ionization; FDR: false discovery rate; PDAC: pancreatic ductal adenocarcinoma patients; HC: healthy controls; AUC: area under the curve; PDAC/HC ratio shows increased (†) or decreased ( j) levels of each marker in PDAC group compared to HC and based in fold change ratio.

Literature

- Amedei A, Prisco D, D' Elios MM. The use of cytokines and chemokines in the cancer immunotherapy. Recent Pat Anticancer Drug Discov. 2013;8: 126Y142.

- Bathe O, Shaykhutdinov R, Kopciuk K, Weljie AM, McKay A, Sutherland FR, Dixon E, Dunse N, Sotiropoulos D, Vogel HJ . Feasibility of Identifying Pancreatic Cancer Based on Serum Metabolomics. Cancer Epidemiol., Biomark. Preven. 2011;20, 140-147.

- Bournet B, Pointreau A, Souque A, Oumouhou N, Muscari F, Lepage B, Senesse P, Barthet M, Lesavre N, Hammel P, Levy P, Ruszniewski P, Cordelier P, Buscail L. Gene expression signature of advanced pancreatic ductal adenocarcinoma using low density array on endoscopic ultrasound-guided fine needle aspiration samples. Pancreatology 2012; 12, 27-34.

- Caba O, Ortiz R, Alvarez P, Prados J, Melguizo C, Hita F, Rojas I, Perez-Florido J, Prieto A, Aranega A. Patented Biomarkers of Peripheral Blood for the Early Detection of Cancer. Recent Patents on Biomarkers 2012;2, 17-28.

- Caba O, Ortiz R, Alvarez P, Prados J, Melguizo C, Hita F, Rojas I, Perez-Florido J, Prieto A, Aranega A. Transcriptional Profiling of Peripheral Blood in Pancreatic Ductal Adenocarcinoma. Digestive diseases and sciences 2014;59, 2714-20.

- Duffy MJ, Sturgeon C, Lamerz R, Haglund C, Holubec VL, Klapdor R, Nicolini A, Topolcan O, Heinemann V. Tumor markers in pancreatic cancer: a European Group on Tumor Markers (EGTM) status report. Ann Oncol. 2010; 2E441Y447.

- Dunn, W. B., Broadhurst, D., Begley, P., Zelena, E.,Francis-McIntyre, S., Anderson, N., Brown, M., Knowles, J. D., Halsall, A., Haselden, J. N., Nicholls, A.W., Wilson, I. D., Kell, D. B., Goodacre, R., Consortium, H. S. M. H., Nat. Protoc. 2011, 6: 1060-1083.

- Dumstrei K, Chen H, Brenner H. A systematic review of serum autoantibodies as biomarkers for pancreatic cancer detection. Oncotarget. 2016;7: 11151-64. - Erkan M, Hausmann S, Michalski CW, Fingerle AA, Dobritz M, Kleeff J, Friess H. The role of stroma in pancreatic cancer: diagnostic and therapeutic implications. Nat Rev Gastroenterol Hepatol. 2012;9, 454-67.

- Franklin O., Ohlund D, Lundin C, Oman M, Naredi P, Wang W, Sund M. Combining conventional and stroma-derived tumour markers in pancreatic ductal adenocarcinoma. Cancer Biomarkers. 2015; 15, 1-10.

- Giovannetti E, van der Velde A, Funel N, Vasile E, Perrone V, Leon LG, De Lio N, Avan A, Caponi S, Pollina LE, Galla V, Sudo H, Falcone A, Campani D, Boggi U, Peters GJ. High- Throughput MicroRNA (miRNAs) Arrays Unravel the Prognostic Role of MiR-211 in Pancreatic Cancer. PLoS One 2012;7:e49145.

- Goggins M. Molecular markers of early pancreatic cancer. J Clin Oncol. 2005;23(20):4524-31. Harsha HC, Kandasamy K, Ranganathan P, Rani S, Ramabadran S, Gollapudi S, et al. A Compendium of Potential Biomarkers of Pancreatic Cancer. PLoS Med. 2009;6:el000046.

- Gold DV., Goggins M, Modrak DE, Newsome G, Liu M, Shi C, Hruban RH, Goldenberg DM. Detection of early-stage pancreatic adenocarcinoma. Cancer Epidemiology, Biomarkers and Prevention, 2010; 19, 2786-2794.

- Honda M, Sakai Y, Yamashita T, Yamashita T, Sakai A, Mizukoshi E, Nakamoto Y, Tatsumi I, Miyazaki Y, Tanno H, Kaneko S; Hokuriku Liver Study Group. Differential gene expression profiling in blood from patients with digestive system cancers. Biochem Biophys Res Commun. 2010;40, 7-15.

- Hwang R.F., Moore T, Arumugam T, Ramachandran V, Amos KD, Rivera A, Ji B, Evans DB, Logsdon CD. Cancer-associated stromal fibroblasts promote pancreatic tumour progression. Cancer Research, 2008; 68, 918-926.

- Irigoyen A, Caba O, Benavides M, Jimenez-Luna C, Gallego J, Ortuno FM, Guillen-Ponce C, Rojas I, Aranda E, Torres C, Prados J. Integrative multi-platform meta-analysis of gene expression profiles in pancreatic ductal adenocarcinoma patients for identifying novel diagnostic biomarkers. BMC Genomics (Submitted). - Jimenez-Luna C, J. Prados, Caba O, Ortiz R., Melguizo C, Torres C, Caba O. Current status of immunotherapy treatments for pancreatic cancer. J. Clin. Gastroenterol (Accepted for publication)

Kamisawa T, Wood LD, Itoi T, Takaori K. Pancreatic cancer. Lancet. 2016. doi: 10.1016/S0140- 6736(16)00141-0.

- Kim K.. Detection of PDAC biomarkers using mass spectrometry. Cancer Informatics, 2015; 13, 45-53.

- Le N, Sund M, Vinci A, GEMS collaborating group of Pancreas 2000. Prognostic and predictive markers in pancreatic adenocarcinoma. Dig Liver Dis. 2016;48, 223-30.

- Lee Has and Park SW. Systemic Chemotherapy in Advanced Pancreatic Cancer. Gut Liver. 2016;10, 340-7.

- Liang JJ, Kimchi ET, Staveley-O'Carroll KF, Tan D.. Diagnostic and prognostic biomarkers in pancreatic carcinoma. Source. Int J Clin Exp Pathol. 2009;2,1-10.

- Liu X, Wang J, Sun G. Identification of key genes and pathways in renal cell carcinoma through expression profiling data. Kidney Blood Press Res. 2015;40, 288-97.

- Luo G, Long J, Zhang B, Liu C, Xu J, Ni Q, Yu X. Stroma and pancreatic ductal adenocarcinoma: an interaction loop. Biochim Biophys Acta. 2012;1826,170Y178.

- Neesse A, Aigul H, Tuveson DA, Gress TM. Stromal biology and therapy in pancreatic cancer: a changing paradigm.. Gut. 2015, 64,1476-84.

- Nicholson, J. K., Connelly, J., Lindon, J. C., Holmes, E., Nat. Rev. Drug Discov. 2002, 1 : 153- 161.

- Nishiumi S, Suzuki M, Kobayashi T, Matsubara A, Azuma T, Yoshida M. Metabolomics for Biomarker Discovery in Gastroenterological Cancer. Metabolites.201, 4, 547-571. - Ranjbar,M. R. N., Yue,W., Ressom, H.W., Bioinformatics and Biomedicine Workshops (BIBMW), 2011 IEEE International Conference on 2011, pp. 1034-1036.

- Rios S, Diaz C, Marquez C, Caba O, Jimenez-Luna C, Melguizo C, Prados J,Genilloud O, de Vicente F, Perez del Palacio J. Untargeted lc-tofhrms-based metabolomics for searching new biomarkers of pancreatic ductal adenocarcinoma: a pilot study. J B S (Accepted for publication).

- Ruckert F, Pilarsky C, Griitzmann R. Serum Tumor Markers in Pancreatic Cancer-Recent Discoveries. Cancers 2010;2,1107-1124.

- Sarkar FH, Baneijee S, Li Y. Pancreatic cancer: pathogenesis, prevention and treatment. Toxicol Appl Pharmacol. 2007;224:326Y336.

- Sharma NK, Regine WF. Radiation Therapy in Resectable/Resected Pancreatic Adenocarcinomas: Clearing Up the Fog. Oncology 2011;25, 715-21.

- Tautenhahn, R., Patti, G. J., Kalisiak, E., Miyamoto, T., Schmidt, M., Lo, F. Y., McBee, J., Baliga, N. S., Siuzdak, G, Anal. Chem. 2011, 83: 696-700.

- Teich N and Mossner J. Molecular analysis of pancreatic a helpful tool to differentiate benign and malignant pancreatic tumours? Digestive Diseases. 2004;22, 235-239.

- Torres C, Linares A, Alejandre MJ, Palomino-Morales RJ, Caba O, Prados J, Aranega A, Delgado JR, Irigoyen A, Martinez-Galan J, Ortuno FM, Rojas I, Perales S. Serum cytokine profile in patients with pancreatic cancer. Pancreas. 205, 43, 1042-9.

- Torres C, Linares A, Alejandre MJ, Palomino-Morales RJ, Caba O, Prados J, Aranega A, Delgado JR, Irigoyen A, Martinez-Galan J, Ortuno FM, Rojas I, Perales S. Prognosis Relevance of Serum Cytokines in Pancreatic Cancer. Biomed Res Int. 2015;2015, 518284.

- Yan H, Lu D, Xu L, Xie Q, Dong X, Wu Y. Increased expression level of 01factomedin4 in peripheral blood mononuclear cells of pancreatic adenocarcinoma patients. Hepatogastroenterology 2011;58, 1354-9. - Yang J.Y, Sun YW, Liu DJ, Zhang JF, Li J, Hua R. MicroRNAs in stool samples as potential screening biomarkers for pancreatic ductal adenocarcinoma cancer American Journal of Cancer Research.2014; 4, 663-673. - Winter J,M Yeo CJ, Brody JR. Diagnostic, prognostic, and predictive markers in pancreatic cancer. Journal of Surgical Oncology, 107 (2013), 15-22.

- Zhang Q, Zeng L, Chen Y, Lian G, Qian C, Chen S, Li J, Huang K. Pancreatic Cancer Epidemiology, Detection, and Management. Gastroenterol Res Pract. 2016;8962321.

- Zhang L., Farrell JJ, Zhou H, Elashoff D, Akin D, Park NH, Chia D, Wong DT. Salivary transcriptomic biomarkers for detection of resectable pancreatic cancer. Gastroenterology, 2010; 138, 949-957. - Zhu J, Djukovic D, Deng L, Gu H, Himmati F, Chiorean EG, Raftery D. Colorectal cancer detection using targeted serum metabolic profiling. J Proteome Res. 2014; 13:4120-30.

- Zhu J, Djukovic D, Deng L, Gu H, Himmati F, Abu Zaid M, Chiorean EG, Raftery D. Targeted serum metabolite profiling and sequential metabolite ratio analysis for colorectal cancer progression monitoring. Anal Bioanal Chem. 2015b; 407, 7857-63.

- Zhai LL, Cai CY, Wu Y, Tang ZG. Correlation and prognostic significance of MMP-2 and TFPI-2 differential expression in pancreatic carcinoma. Int J Clin Exp Pathol. 2015;8, 682-91.

Claims

Claims

1. An in vitro method to classify a subject in need thereof, between patients suffering from pancreatic cancer (PC) vs HC (subjects not suffering from pancreatic cancer), that comprises the in vitro determination of the levels of at least Glycochenodeoxycholic acid 7-sulfate, phenylalanine-phenylalanine, adrenal acid, dehydroepiandrosterone sulfate, androsterone sulfate, triacylglycerol (22:2), lysophosphatidylethanolamine (18:1), lysophosphatidylethanolamine (18:2), 13-hydroxyoctadecadienoic acid, 3-oxo- octadecanoic, or 4-oxo-retinoic acid, or any combination thereof, in a serum, plasma or blood sample taken from the subject.

2. The in vitro method of claim 1 , wherein the in vitro classification method comprises determining in a serum, plasma or blood sample of the subject the levels of at least Glycochenodeoxycholic acid 7-sulfate, phenylalanine-phenylalanine, adrenal acid, dehydroepiandrosterone sulfate, androsterone sulfate, triacylglycerol (22:2), lysophosphatidylethanolamine (18:1), lysophosphatidylethanolamine (18:2), 13- hydroxyoctadecadienoic acid, 3-oxo-octadecanoic, or 4-oxo-retinoic acid, or any combination thereof, and comparing the levels of said markers with respect to the levels of the same markers in a HC or with respect to the reference value ranges for the biomarkers for a HC, wherein the subject is classified as suffering from PC if different levels of the biomarkers compared to the reference value ranges for the biomarkers for a HC indicate that the subject has PC.

3. The in vitro method of any of the claims 1 or 2, which further comprises the in vitro determination of the levels of at least Phosphatidylserine (PS) (12:0/15:1), Triglyceride (TG) (22:2/15:0/18:3), all-trans-Decaprenyldiphosphate, Lysophosphatidylcholine (LysoPC) (18:2), or any combination thereof, and comparing the levels of said markers with respect to the levels of the same markers in a HC or with respect to the reference value ranges for the biomarkers for a HC, wherein the subject is classified as suffering from PC if different levels of the biomarkers compared to the reference value ranges for the biomarkers for a HC indicate that the subject has PC.

4. The in vitro method of any of the preceding claims, for aiding in the diagnosis of whether or not a subject suffers from PC, and optionally confirming the diagnosis of PC by means of the clinical examination of the patient.

5. The in vitro method of any of the preceding claims, wherein the in vitro determination of the levels is carried out by using any of the techniques selected from the list consisting of: refractive index spectroscopy (Rl), Ultra-Violet spectroscopy (UV), fluorescent analysis, radiochemical analysis, Infrared spectroscopy (IR), Nuclear Magnetic Resonance spectroscopy (NMR), Light Scattering analysis (LS), Mass Spectrometry, Pyrolysis Mass Spectrometry, Nephelometry, Dispersive Raman Spectroscopy, gas chromatography combined with mass spectroscopy, liquid chromatography combined with mass spectroscopy, supercritical fluid chromatography combined with mass spectroscopy, MALDI combined with mass spectroscopy, ion spray spectroscopy combined with mass spectroscopy, capillary electrophoresis combined with mass spectrometry, NMR combined with mass spectrometry and IR combined with mass spectrometry.

6. The in vitro method of claim 5, wherein the in vitro determination of the levels is carried out by using a proton NMR spectrum.

7. A method for determining the efficacy of a therapy for PC, which method comprises determining in a serum, plasma or blood sample of a subject suffering from any of these diseases, and having been treated with said therapy, the level(s) of the biomarkers identified in any of claims 1 to 6, wherein such level(s) with respect to HC or with respect to a reference value are indicative of whether or not that said therapy is effective against PC.

8. A method for monitoring the progression of a subject suffering from PC, which method comprises determining in a serum, plasma or blood sample of a subject suffering from this disease, over the course of a therapy or not, the level(s) of the biomarkers identified in any of claims 1 to 7, wherein such level(s) with respect to a reference value determined in a serum, plasma or blood sample from the same subject at an earlier time point are indicative that the PC condition/disease is progressing.

9. A computer program for assigning a patient into a specific group of patients, such as patients with PC, according to any of the methods of claims 1 to 7, after introducing the levels of the biomarkers identified in any of claims 1 to 8 into said program.

10. A device comprising the computer program of claim 9, and the use of said device for the diagnosis of PC in a human subject.