WO2022140380A1 - Metabolomic signatures for predicting, diagnosing, and prognosing chronic pancreatitis - Google Patents

Abstract

A system and method for using new biomarkers to assess individual diseases is provided. In one embodiment of the present invention, absolute quantification of annotated metabolites by mass spectrometry is used to identify certain biomarkers and derivatives thereof (i.e., signatures), which are then used to screen for, diagnose, predict, prognose, and treat various diseases, including, but not limited to, chronic pancreatitis.

Description

METABOLOMIC SIGNATURES FOR PREDICITING, DIAGNOSING, AND PROGNOSING CHRONIC PANCREATITIS

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to new biomarkers for assessing chronic pancreatitis, and in particular to the use of certain metabolites (i.e., signatures) to screen for, diagnose, predict, prognose, and treat chronic pancreatitis, in addition to other diseases.

2. Description of Related Art

Chronic pancreatitis (CP) is an inflammatory syndrome of the pancreas in which repetitive episodes result in fibrotic tissue replacement, organ dysfunction and chronic pain. Multiple etiologies and risk factors lead to the development of CP in humans, which include immoderate alcohol consumption, tobacco smoke and variety of genetic predispositions. Affected patients are at high risk for developing exocrine pancreatic insufficiency leading to maldigestion, as well as endocrine insufficiency, leading to diabetes mellitus type 3c, and pancreatic cancer. Chronic pain, maldigestion and brittle diabetes will often lead to a significant reduction in quality of life, increased health care utilization and reduced life expectancy.

In spite of multiple etiologies and pathogeneses most CP patients will develop a similar clinical and histopathological phenotype. Following current guidelines, the diagnosis of CP is made when the following criteria are met: recurrent bouts of pancreatic pain with documented rise in amylase or lipase activity, and imaging evidence such as pancreatic calcifications, histological evidence of CP, unequivocal changes in pancreatic duct morphology, or severely abnormal pancreatic function tests with maldigestion. Sensitivity and specificity of imaging in CP is variable and stage dependent. Non-invasive biomarkers for diagnosis of CP have not been established and remain an unmet clinical need. Moreover, it is presently impossible to identify patients at risk, or at an early stage of CP, due to a lack of reliable biomarkers. While numerous genomic studies in large cohorts have identified a growing number of genetic modifiers and risk factors, transcriptome or proteome-based approaches have failed to produce robust diagnostic tools for CP.

Little is known about the potential role of metabolomic signatures including lipidomics of body fluids as a diagnostic tool for CP. The inventors have recently identified and validated a metabolomic signature to distinguish between pancreatic cancer and CP. This prompted the search for a biomarker signature that can discriminate between CP and controls including patients with non-pancreatic conditions.

Thus, in light of the foregoing, it would be advantageous to develop a system and method that uses targeted metabolomics, or absolute quantification of annotated metabolites by mass spectrometry, to identify certain biomarkers and derivatives thereof, such as ratios, etc. (i.e. , “signatures”) that can be used to screen for, diagnose, predict, prognose, and treat various diseases, including chronic pancreatitis.

SUMMARY OF THE INVENTION

The present invention provides a system and method for using new biomarkers to assess chronic pancreatitis. Preferred embodiments of the present invention include use of absolute quantification of annotated metabolites by mass spectrometry to identify certain biomarkers and derivatives thereof (i.e., “signatures”), which can then be used to screen for, diagnose, predict, prognose, and treat various diseases, including chronic pancreatitis.

In one embodiment of the present invention, the inventors identified 8 metabolites that were significant in distinguishing patients with chronic pancreatitis (CP) from patients that did not (referred to herein as the control group), including Beta carotene, Cryptoxanthin, Mannose, Behenic acid, Ceramide, lndole-3-acetic acid, Hippuric acid, and N-Acetylcytidine. The calculation of prediction scores revealed the relative importance of these metabolites, with beta-carotene and cryptoxanthin having the highest impact, as they discriminated best between groups.

While the inventors have found that the foregoing metabolites are beneficial in diagnosing and/or prognosing CP, the present invention is not so limited and other factors (or conditions) may also be considered in diagnosing and/or prognosing CP, including, for example, recurrent bouts of pancreatic pain, a documented rise in amylase or lipase activity over time, pancreatic calcifications, unequivocal changes in pancreatic duct morphology, diabetes, endocrine insufficiency, exocrine insufficiency, and/or cirrhosis of the liver, among others (e.g., sex, age, weight, BMI, overall health, etc.).

In preferred embodiments, blood samples can be analyzed using MxP® Global Profiling and MxP® Lipids. MxP® Global Profiling can be performed employing (i) gas chromatography-mass spectrometry (GC-MS) using an Agilent 6890 gas chromatograph coupled to an Agilent 5973 mass-selective detector and (ii) liquid chromatography-tandem mass spectrometry (LC-MS/MS) using an Agilent 1100 high-performance liquid chromatography system coupled to an Applied Biosystems API 4000 triple quadrupole mass spectrometer. Individual metabolites (e.g., the foregoing panel) are then Iog10-transformed to achieve an approximate normal distribution. Obviously, this is but one way to isolate and quantify individual metabolites, and thus the present invention is not so limited. As discussed in detail below, other methodologies are within the spirit and scope of the present invention.

The quantified, normalized (or otherwise measured) data can then be compared to known values, or a known score if an algorithm is used (e.g., an algorithm based on individual metabolites or derivatives thereof). In one embodiment, using a predicted score having an optimal calculated cut-off of 0.479, the biomarker signature detected CP in comparison to control patients with an AUC of 0.85 (95% Cl 0.79-0.91 ). The specificity was 0.86 and the sensitivity 0.71. The weighting of the metabolites can be used as coefficients to be multiplied with the respective concentrations of the eight metabolites in the biomarker signature (in μmol / L) to calculate the biomarker signature score. Whether the score is above or below the cut-off value of 0.479 determines whether the patient is evaluated as positive or negative for the diagnosis “chronic pancreatitis.”

The inventors also discovered that the biomarker signature score was more accurate in patients with advanced disease than in less severe cases. Because other clinical data regarding disease severity was less informative, the information whether the patients suffered from pancreatic endocrine or exocrine insufficiency was used, which is a good surrogate marker for severity and time since disease onset. 199 chronic pancreatitis patients from the identification and the first validation study for whom this information was available together were categorized in three groups: those without insufficiencies, those with either endo- or exocrine insufficiency, and those with both endo- and exocrine insufficiencies. The average biomarker signature score was 0.68 in patients without insufficiencies, 0.78 in patients with either endo-or exocrine insufficiency, and 0.90 in patients with both endo- and exocrine insufficiency. An ANOVA was employed to test whether the differences in the biomarker signature score were significant. While the group with one pancreatic insufficiency did not have a significantly different score compared to the other groups, the scores of the groups without pancreatic insufficiencies and with both endo- and exocrine insufficiencies were significantly different (p = 0.0018). This indicates that the biomarker signature score is higher in patients with more severe pancreatic disease.

As discussed above, the invention may be used as an early-diagnosis-tool that identifies patients with chronic pancreatitis in its earliest stages, when intervention offers the highest possibility of cure (or better treatment). The invention provides prognostic information and serves as a predictive test for clinical response. In doing this, the present invention provides for never described biomarkers (i.e., a new biomarker set) suitable for assessing chronic pancreatitis, including early and more advanced stages of disease and also provides biomarker sets that clearly discriminate, at baseline, patients with elevated risk of relapse after initial treatment. Out the outset, the invention may involve a patient visiting a doctor, clinician, technician, nurse, etc., where blood or a different sample is collected. The sample would then be provided to a laboratory for analysis, as discussed above (e.g., mass spectrometry, log-transformation, comparisons, etc.). In another embodiment, a kit can be used to obtain the sample, where the kit is made available to the patient via a medical facility, a drug store, the Internet, etc. In this embodiment, the kit may include one or more wells and one or more inserts impregnated with at least one internal standard. The kit can be used to gather the sample from a patient, where the sample is then provided to a laboratory for analysis.

For example, peripheral blood may be collected into EDTA-anticoagulant tubes. Plasma is isolated by centrifugation. Plasma samples may then be submitted for extraction and processing. In one embodiment, prepared samples will then undergo metabolite extraction (e.g., via Mass Spectrometry). The extracted data is then processed using computer software. For example, the data acquired may then be normalized (e.g., via log-transformation) and stored in a database that includes at least (i) patient identification, (ii) metabolite name, and (iii) quantification. If this data is on known individuals (individuals with known conditions), then it can be analyzed to determine signatures that can be used to assess a particular disease. If, however, the data is on a patient whose condition is unknown, then it can be compared to known signatures (e.g., stored in memory) to screen for, diagnose, prognose, and treat the patient.

The initial results can then be compared to known “signatures” for chronic pancreatitis, where similarities and differences are used to screen for, diagnose, prognose, treat, etc. CP. Results (e.g., assessments) are then provided to the patient directly (e.g., via mail, an electronic communication, etc.) or via the patient’s doctor, and can include screening information, diagnosis information, prognosis information, and treatment information.

In particular, the invention can be used to distinguish a sample from a patient having chronic pancreatitis from one that is normal. If positive for CP, then the invention can further be used to identify disease stage. This can be done using terminology (e.g., no insufficiencies, endo- or exocrine insufficiencies, or endo- and exocrine insufficiencies), at least one scale (e.g., 1 -10, 1-100, A-F, etc.), where one end of the scale is low grade (e.g., non-invasive) and the other end is high grade (lethal), or other visual forms (e.g., color coded, 2D or 3D model, etc.).

The invention can also be used to provide a prognosis. For example, in chronic pancreatitis, once the CP is identified, the invention can be used to provide gradations within the signature (or signatures), subcategorizing the patient into severity, treatability, etc. Again, prognosis could be provided using terminology (e.g., low risk, medium risk, high risk, etc.), at least one scale, or other visual forms.

Not only can the present invention be used to screen for and diagnose CP, but it can also be used to determine treatment, or viability of treatment (another form of prognosis). This could be a likelihood to respond to therapy (e.g., counseling (e.g., alcohol and tobacco cessation), opioids, surgery, etc.), which again could be provided using terminology, at least one scale, or other visual forms.

A more complete understanding of a system and method for using new biomarkers to assess chronic pancreatitis will be afforded to those skilled in the art, as well as a realization of additional advantages and objects thereof, by a consideration of the following detailed description of the preferred embodiment. Reference will be made to the appended sheets of drawings, which will first be described briefly.

BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1 provides an overview over participants in the validation study. The biomarker signature was identified on the metabolomic data from the identification study, comparing chronic pancreatitis (CP) patients with control patients. This data was used as a training set for the algorithm. Participants of the first validation study were recruited independently and their sample data served as a test set. For the second validation study, participants were recruited independently as well. In this study, liver cirrhosis patients (LC) were included as an additional control group; Figure 2 provides the metabolite distribution over ontology classes and number within class as analyzed in the identification study and the first validation study;

Figure 3 shows results of the validation studies, with Figure 3A showing a principal component analysis (PCA) score plot of the identification study and the first validation study sets (EDTA plasma). The best separation of the control group (medium grey) and the CP group (light grey) was observed plotting principal component (PC) 1 versus PC2. Together, these PCs account for 19% of the observed total variation within the dataset. Figure 3B shows a PCA score plot of the second validation study set (serum). A marked separation between the control group (medium grey), the CP group (light grey), as well as the liver cirrhosis group (dark grey) was observed plotting PC1 versus PC2. While there was some overlap between the CP and the liver cirrhosis groups, the control group is markedly separated from the other two. Together, these PCs account for 34% of the observed total variation within the dataset;

Figure 4 shows performance of the biomarker signature for detection of chronic pancreatitis, including receiver operating characteristics (ROC) and area under the curve (AUC) of the biomarker signature for differentiating chronic pancreatitis from control. In particular, Figure 4A shows results of the identification study (EDTA plasma), Figure 4B shows results of the first validation study (EDTA plasma), and Figure 4C shows results of the second validation study (serum);

Figure 5 provides boxplots of individual metabolite levels from the biomarker signature within the three studies, separated by group, i.e., Figure 5A being betaCarotene, Figure 5B being Cryptoxanthin, Figure 5C being N-Acetylcytidine, Figure 5D being lndole-3-acetic acid, Figure 5E being Hippuric acid, Figure 5F being Behenic acid (C22:0), Figure 5G being Mannosie, and Figure 5H being Ceramide (d18: 1 ,C24: 1 );

Figure 6 provides the distribution of age (Figures A and C) and BMI (Figures B and D) over biomarker signature score in the identification study (Figures A and B) and the first validation study (Figures C and D). Demographics are shown on the x axes, the biomarker signature score on the y axes. Stacked columns for age and side-by side columns for BMI are colored according to outcome (diagnosis). Horizontal lines/functions represent Gaussian approximation of the data;

Figure 7 provides boxplots of effect of enzyme supplementation on plasma carotenoid levels. Shown are data for chronic pancreatitis patients of the identification study (plasma). There was no significant increase of carotenoid levels in plasma of patients supplemented with enzymes to treat exocrine insufficiency;

Figure 8 provides boxplots of biomarker signature scores in chronic pancreatitis patients from the identification study and the first validation study, which were categorized depending on whether they suffer from endo- and/or exocrine insufficiencies as a measure of disease stage. The average biomarker signature score increased with disease severity and this increase was significant comparing patients without insufficiency and patients with both endo- and exocrine insufficiency; and

Figure 9 provides a weighting of the metabolites that can be used as coefficients to be multiplied with the respective concentrations of the eight metabolites in the biomarker signature (inμ pmol / L) to calculate the biomarker signature score.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Preferred embodiments of the present invention involve use of targeted metabolomics, or absolute quantification of annotated metabolites by mass spectrometry, to identify certain biomarkers (i.e., “signatures”) suitable for assessing various diseases, including, but not limited to chronic pancreatitis.

It should be appreciated that while the term “targeted metabolomics” is used herein, the present invention is not so limited, and, as discussed in greater detail below, other methodologies (e.g., untargeted metabolomics, etc.) are within the spirit and scope of the present invention. The identified “signatures” can be applied regardless of whether the approach is targeted or untargeted, and regardless of how “an amount” of each metabolite is determined (e.g., quantitative, semi-quantitative, etc.). For this reason, methodologies identified in U.S. Pat. No. 10,168,333 (“Means and Methods for Diagnosing Pancreatic Cancer in a Subject Based on a Metabolite Panel”) (“the ‘333 Patent”) are incorporated herein by reference. This includes the methodologies identified at column 5, line 31 - column 6, line 22 (definition of “sample”), column 6, line 44 - column 9, line 64 (definition of “determining the amount”), column 9, line 65 - column 12, line 15 (definition of “reference”), and column 12, line 16 - column 14, line 12 (definition of “comparing”). For the sake of brevity, the foregoing sections should be treated as if they are recited herein, in their entirety, as they include the inventors’ prior work in this field and are equally applicable to the diagnosis and prognosis of chronic pancreatitis. Also incorporated by reference are the methodologies recited on column 33, line 22 - column 36, line 7 of the 333 Patent.

It should also be appreciated that while a first disease (e.g., chronic pancreatitis) may have a first signature, and a second (different) disease may have a second, signature, the method used in identifying each signature is very similar, and in certain instances identical. Thus, while different diseases have been discussed below, for the sake of brevity, details concerning how a signature is identified and subsequently used to assess a particular disease are equally applicable to other signatures and other diseases unless stated otherwise.

It should further be appreciated that a disease may have more than one signature or portions thereof. For example, a first signature may be used for diagnoses, a second signature (or portion of the first signature) may be used for prognoses, etc. It should also be appreciated that while a disease may have more than one signature, there may be individual aspects (e.g., individual metabolites or derivatives thereof) that are common to several signatures, and can therefore provide, in and of themselves, information on diagnosis, prognosis, treatment, etc. Specifics concerning signatures will be discussed in the corresponding sections below.

It should also be appreciated that while the application focuses on chronic pancreatitis, the present invention is not so limited, and that those skilled in the art will understand that the methods disclosed herein can be used to identify signatures for, and assess, other diseases, including those not specifically mentioned herein. The present invention is also not limited to use of mass spectrometry, or any particular type of mass spectrometry (e.g., electrospray ionization (ESI) tandom mass spectrometry (MS/MS), etc.), and includes other methods for quantifying metabolites, such as chromatography or spectrometry (see also the foregoing citations to the 333 Patent, including the incorporation by reference therefrom). That being said, the inventors have found that there are benefits to using mass spectrometry, and in particular ESI MS/MS, and the data analysis described herein (e.g., log-transformation, ROC curves, etc.). As such, the methods described in detail herein are preferred embodiments, and should be treated as such.

Prior to discussing the inventions, including individual signatures, the methods used to identify the same, and assess various diseases, certain definitions of term or phrases used herein will first be provided.

Definitions

By employing the biomarkers (or specific sets thereof) and the methods according to the present invention it has become possible to assess a disease (e.g., chronic pancreatitis, etc.) with improved accuracy and reliability. It has surprisingly been achieved in the present invention to provide biomarkers or biomarker sets by measuring certain metabolites in samples, such as blood samples, of subjects (and/or relationships thereof (e.g., ratios, etc.)) that make it possible to diagnose and prognose diseases (e.g., chronic pancreatitis, etc.) in an improved manner and at an early stage of the disease.

In general, a biomarker is a valuable tool due to the possibility to distinguish two or more biological states from one another, working as an indicator of a normal biological process, a pathogenic process or as a reaction to a pharmaceutical intervention.

A metabolite is a low molecular compound (<1 kDa), smaller than most proteins, DNA and other macromolecules. Small changes in activity of proteins result in big changes in the biochemical reactions and their metabolites, whose concentrations, fluxes and transport mechanisms are sensitive to diseases and drug intervention.

This enables getting an individual profile of physiological and pathophysiological substances, reflecting both genetics and environmental factors like nutrition, physical activity, gut microbial and medication. Thus, a metabolic biomarker gives more comprehensive information than for example a protein or hormone, which are biomarkers, but not metabolic biomarkers.

In view thereof, the term “metabolic biomarker” or short “biomarker” as used herein is defined to be a compound suitable as an indicator of the presence and state of a disease (e.g., chronic pancreatitis) as well as its stage, being a metabolite or metabolic compound occurring during metabolic processes in the mammalian body.

The terms “biomarker” and “metabolic biomarker” are in general used synonymously in the context of the present invention and typically refer to the amount of a metabolite (quantitative or semi-quantitative) and/or the relationship between two or more metabolites. Hence, the term metabolic biomarker or biomarker is intended to also comprise ratios or other mathematical relationships between two or more metabolites.

The term “amount” typically refers to the concentration of a metabolite in a sample, such as blood sample, and is usually given in μmol/L, but may also be measured in other units typically used in the art, such as g/L, mg/dL, etc. Depending on the methodology used, it may refer to either absolute quantification or semi-quantitative data (see also the foregoing citations to the 333 Patent and the incorporation by reference therefrom). The term “sum” typically means the sum of molar amount of all metabolites as specified in the respective embodiment.

While a modified “signature” can be used, if one metabolite or one class of metabolites as specified for the respective biomarker combination is omitted or if the number thereof is decreased the assessment of the disease becomes less sensitive and less reliable.

This particularly applies for the early stages of the disease being not reliably detectable according to known methods using known biomarkers at all. In fact, the measurement of the metabolites contained in the respective sets of biomarkers at the same time allows a more accurate and more reliable assessment of a disease. Signatures for particular diseases, including the identification thereof and use of the same for assessing (e.g., screening, diagnosing, prognosing, treating, etc.) particular diseases, will now be discussed. Materials and Methods

Study design

The inventors conducted a type 3 study for multivariable prediction for individual prognosis according to the TRIPOD guidelines. A total of 670 patients and controls were prospectively enrolled in the three cohorts and the analysis was done retrospectively. The diagnosis “chronic pancreatitis” was made based on certain clinical and imaging criteria. Similar distribution of age and gender between patients and controls was attempted. Figure 1 and Table 1 (below) comprise an overview over the identification and validation cohorts.

Table 1: Cohort characteristics.

SD: standard deviation, BMI: Body mass index, CP: chronic pancreatitis, LC: liver cirrhosis; PEI: pancreatic enzyme insufficiency; PERT: pancreatic enzyme replacement therapy; NA: not applicable The identification study was performed in a case control cohort at a university referral center and included 80 CP patients and 80 non-pancreatic disease controls who underwent small, non-pancreas-related surgical procedures under general anesthesia (Table 1 and suppl. methods).

For the first validation study, 144 CP patients and 204 non-pancreatic controls were consecutively recruited from three different university referral centers.

For the second validation study, conducted to validate the robustness of the method using serum instead of plasma samples, CP, liver cirrhosis patients and healthy blood donors were enrolled at a fourth referral center. In this cohort some blood samples had low sample quality, identified by their extremely low glucose levels (<2800 μmol/L), and were consecutively excluded from analysis. The samples from 49 CP patients, 57 liver cirrhosis patients, and 56 healthy blood donors were included in the final analysis.

Clinical information on gender, age, and body mass index (BMI) were recorded. In addition, information on the disease etiology, disease duration, calcifications, endocrine insufficiency, and enzyme supplementation were recorded if available. All cohort characteristics can be found in Table 1.

All studies were designed and conducted in adherence to the Declaration of Helsinki and approved by the local ethics review boards of all four participating centers. All participants gave their written informed consent prior to inclusion. Further information on study details and sample processing can be found in the supplemental material and methods.

Metabolite profiling

All samples were analyzed with MxP® Global Profiling and MxP® Lipids. MxP® Global Profiling was performed employing (i) gas chromatography-mass spectrometry (GC-MS) using an Agilent 6890 gas chromatograph coupled to an Agilent 5973 mass- selective detector and (ii) liquid chromatography-tandem mass spectrometry (LC- MS/MS) using an Agilent 1100 high-performance liquid chromatography system coupled to an Applied Biosystems API 4000 triple quadrupole mass spectrometer, as has been described in detail before.

Up to 1449 metabolites were detected within the studies depending on the sample matrix and the analytical technique. The metabolites originated from 10 different ontology classes and comprised 838 known metabolites and 611 unknown spectral features. Only those metabolites that met specific quality criteria as described in were included in further statistical analyses. Furthermore, quality assessment of plasma samples was performed using the MxP® Biofluids Quality Control assay.

Statistics

All metabolite profiling data were Iog10-transformed before further analysis to achieve an approximate normal distribution. R 3.3.4 was used for data analyses, see supplemental methods for a list of R packages used.

For an exploratory multivariate analysis (Principal Component Analysis, PCA), the Iog10-transformed data of the identification and first validation study were centered and scaled to unit variance, and missing values were imputed before the analysis (see supplemental methods).

The Iog10-transformed, scaled and imputed ratios from the second validation study were also used for a PCA, which was visualized separately because of the different sample matrix. TIBCO® Spotfire® 7.12.0 was used to visualize the PCAs.

To differentiate between CP patients and controls depending on their metabolic profiles, a Naive Bayes model was fitted using the Iog10-transformed, median-imputed, centered, and scaled data from the identification study. Based on biomedical expertise a panel was nominated (see supplemental methods). An algorithm was trained with the data on the selected panel metabolites. The fitted model was evaluated with 10-fold cross-validation. Optimal coefficients were determined, and an optimal cut-off based on the criteria of a sensitivity of 0.8 was calculated in order to classify the patients.

To validate the generated model for patient classification, the algorithm was applied to log-10-transformed, centered and scaled data from the first validation set. For scaling of the first validation dataset, the mean and standard deviation of the identification dataset were applied. A prediction score was calculated for each patient and patients were classified according to their score being above or below the cut-off value. The cut-off established previously on the biomarker identification dataset was applied on the data from the first validation set without retraining, and the performance was measured in terms of area under the curve (AUC), sensitivity and specificity. Confidence levels for the AUC were calculated using the binormal model for the receiver operating characteristic (ROC) curve. Further information on the prediction model and more details on the statistical analysis can be found in the supplement.

To test the validity of this classification algorithm and the corresponding cut-off when the sample matrix is serum and not plasma, it was also applied separately to the Iog10-transformed, centered and scaled data from the second validation dataset in the same manner.

Independently, a simple linear model (multivariate analysis of variance (MANOVA)) was calculated using “disease”, “age”, “BMI”, and “gender” as independent variables and Iog10-transformed metabolite profiling data as dependent variable from all three studies separately. Each numerical independent variable was scaled prior to the analysis.

In the second validation study, BMI values were not available for all participants. For inclusion of BMI as confounding factor in MANOVA, missing BMI values were imputed. Significance level was set to 5%. The multiple test problem for the number of metabolites was addressed by calculating the false discovery rate (FDR) using the Benjamini & Hochberg method. The cut-off for the FDR was set at 20%.

Advancement criteria

The goal of the study was to design a biomarker that can discriminate between CP patients and controls with an AUC of at least 0.8. In addition, the specificity needed to be higher than the sensitivity because guidelines emphasize that diagnosis of CP comes with great burden and may induce stigmatization for the patient. For a successful validation, the inventors expected the confidence intervals for the performance (AUC) to overlap between the training and the test set.

Results

Study cohorts and patient characteristics

Clinical characteristics of all cohorts are shown in Table 1. All studies were unbalanced between male and female participants due to the nature of CP, which is much more frequent in men than in women.

Metabolomic analyses in samples from CP patients and non-pancreatic controls

The metabolomics data underwent a strict quality control after which 505 known and 115 unknown metabolites from plasma samples, and 498 known and 118 unknown metabolites from serum samples remained for statistical analysis. Most of the metabolites could be detected in both plasma and serum samples. Their distribution over the ontology classes is shown in Figure 2.

Discrimination of CP and control patients by multivariate statistics

The inventors investigated whether metabolic profiles of CP and control patients could generally be separated in an unsupervised multivariate statistical approach.

A principal component analysis was performed on all plasma sample data. The best separation of groups was obtained in the principal components (PC) 1 and 2, which accounted for 12% and 7% of the whole variance of the dataset, respectively (Figure 3A). The two groups showed a major overlap but samples from CP patients had a tendency towards lower scores in PC1 , which was remarkable for a heterogeneous cohort with high inter-individual variability due to diverse lifestyles, medications, and co-morbidities.

In the PCA obtained in the second validation study, liver cirrhosis patients were added as a third diagnosis group in addition to CP and controls (Figure 3B). The best separation between the groups was again observed in PC1 and PC2 (21 % and 13% of the observed variance). Remarkably, an almost complete separation of the control group from the other two could be observed. The CP patients tended to have higher scores in the principal component 2 than the liver cirrhosis patients, resulting in a visible separation between these groups.

While it is not common to perform a PCA in the validation cohorts, it was done in this case to show that the metabolomic profiles and the distribution of the CP versus control group patients in the identification study and the first validation study are very similar, proving that the two studies, although conducted independently, are actually comparable. The PCA of the second validation study yields a good overview on how the metabolic profile of the liver cirrhosis patients relate to the metabolic profiles of CP patients and controls.

Biomarker discovery and training

The observed separation tendencies in the multivariate approach indicated the possibility to compile a biomarker signature that allowed differentiation between CP and control patients.

As result of the Naive Bayes algorithm and biomedical expertise applied on the identification study a panel of eight metabolites (see Table 2 below) was nominated. Using the optimal calculated cut-off of 0.479 of the prediction score, the biomarker signature detected CP in comparison to control patients with an AUC of 0.85 (95% Cl 0.79-0.91 ) (see explanation in Figure 9). The specificity was 0.86 and the sensitivity 0.71 (Table 3); ROC curves see Figure 4a.

Table 2. List of metabolites selected for the biomarker signature.

Biomarker validation in independent validation cohorts

The biomarker signature was tested in a first validation study, comprising EDTA plasma samples from 348 patients. Cut-off and coefficient values for the metabolites were transferred from the identification study, and the diagnostic performance was evaluated. CP (in comparison to control patients) was detected with an AUC of 0.85 (95% Cl 0.81-0.89). This corresponded to a specificity of 0.66 and a sensitivity of 0.84 (Table 3). The confidence intervals of the ALICs in both studies were almost identical, indicating that the biomarker performance was robust enough to be successfully transferred to the first validation study. The ROC curve is shown in Figure 4b.

Having obtained a steady performance in the first validation study, the inventors tested whether the CP biomarker signature was valid in a different context and conducted a second validation study, including metabolomics data from 162 individuals. In this cohort serum samples were used, and liver cirrhosis patients as additional controls.

Cut-off and metabolite coefficients were again transferred from the identification study to the second validation study without retraining, and the diagnostic performance was assessed. The AUC for CP versus control was 0.87 (95% Cl 0.81-0.95). This confidence interval showed considerable overlap with the AUC confidence interval in the identification study, indicating a successful performance in the second validation. Specificity was 0.89 and sensitivity 0.78 (see Table 3 below). The ROC curve is shown in Figure 4c. The biomarker signature was thus robust enough to work with serum samples. When the algorithm was applied to liver cirrhosis samples, the results were more similar to the CP group than to the blood donor group.

Table 3. Performance characteristics for the biomarker signature.

Multivariate statistical analysis of the biomarker panel in the three study cohorts

Separate analysis of the three datasets revealed that the 8 chosen metabolites were all significantly altered in CP patients versus controls in both plasma-based studies (p<0.05; FDR <0.2), and 6 of them also in the serum-based study. The variance analysis results (fold-changes) for the panel metabolites are shown in Table 4 below. Of note, the fold-changes were in the same range across all studies. A striking feature was the very small p-values for the panel metabolites in the first validation study. Due to the larger sample number in this study, the statistical significance was higher also for metabolites with small shifts in concentration levels between the groups. In addition, the PCAs show that the groups in the second validation study were less homogenous than in the first validation study, leading to higher p values in the MANOVA.

Table 4. Statistical analysis results (linear model) of the signature metabolites (CP vs. control).

CP: Chronic pancreatitis The calculation of prediction scores revealed the relative importance of the panel metabolites, with beta-carotene and cryptoxanthin having the highest impact (Table 4). For example, as shown in the second validation study, indication of CP includes a Beta carotene amount that is at least 47% below the control, a Cryptoxanthin amount that is at least 48% below the control, a Mannose amount that is at least 280% above the control, etc. Boxplots indicate the inter-individual variability of the panel metabolite levels (Figure 5A-H) in the different studies. They illustrate why the carotenoids were chosen as the most important metabolites by the algorithm, as they discriminated best between groups.

Nevertheless, single metabolites were unable to discriminate between CP and controls. Only the computation of the biomarker panel yielded a sufficiently accurate diagnosis. The distribution of age and BMI over the biomarker signature score is shown in Figure 6. Thus, at least two metabolites (e.g., Beta-carotene and Cryptoxanthin), and preferably more, should be taken into consideration in screening, diagnosing, and/or prognosing for CP. This can be done by comparing metabolites individually (e.g., an amount of Beta-carotene in a sample derived from a patient to a known value (e.g., the cut-off value)), or comparing a score (a resultant of an algorithm whose variables are amounts of metabolites in a sample derived from a patient) to a known value (e.g., the cut-off value).

Effect of pancreatic insufficiency

Because the identification of beta-carotene and cryptoxanthin suggested a pathophysiological mechanism of maldigestion/malabsorption, the inventors analyzed whether pancreatic exocrine insufficiency (PEI) and enzyme supplementation (PERT) had an effect on plasma carotenoid levels (Figure 7). No significant increase of carotenoid plasma levels in PEI with PERT was observed. However, a significant increase of the biomarker signature score values was seen when comparing CP patient from the identification and first validation cohorts with and without pancreatic insufficiency, indicating correlation of this metabolic biomarker signature with disease stage (Figure 8).

Discussion

In inventors’ proof-of concept biomarker study following the TRIPOD guidelines it was show for the first time that a signature comprised of 8 metabolites of six different ontology classes can successfully differentiate between CP and controls with acceptable accuracy (AUC >0.8) in serum and EDTA-plasma samples.

There is no recommended blood-based biomarker for diagnosis of chronic pancreatitis in medical guidelines. Some proteins or miRNAs have been proposed to have a potential to take up this role, but validation studies in larger cohorts are still lacking. Those biomarkers analytically validated for diagnosis using mass spectrometry are reviewed, but diagnostic accuracy is mostly unknown. Studies in rodents employing spontaneous and pharmacologically induced models of CP suggested significant alterations to the pancreatic metabolome, including energy production, anabolism, lipid synthesis and ROS detoxification pathways. Small and due to their heterogeneity inconclusive NMR-spectroscopy-based human studies identified changes in citrate and adenosine levels in urine and 3-hydroxybutyrate, trimethylamine-N-oxide, acetate, acetone, isoleucine, acetylglycine, triglyceride and inosine levels in serum. This prompted the inventors to conduct a trial with a study design more adequate for biomarker development.

The main strength of the signature discovered in the presented study is its robustness. Large and well-characterized patient cohorts with adequate controls for training and external validation were used. The comparability of results yielded from EDTA-plasma and serum samples underscores the validity of the assays used, despite the described high intra- and inter-individual variability of the blood-metabolome. The signature yielded in acceptable diagnostic accuracy in the three presented sub-studies with AUC varying between 0.85 and 0.87. Of note to report, considerable variation regarding sensitivity and specificity in the first validation study which is explained by a certain heterogeneity when recruiting at multiple sites. The relevance of this finding needs exploration in further studies.

On closer inspections, the identified metabolites fit pathophysiological concepts of CP. Levels of fat-soluble vitamins, antioxidants and trace elements have been reported to be lower in CP patients when compared to controls. Whether the deficiency in beta-carotene and cryptoxanthin (exogenous compounds) is secondary to malabsorption remains unknown. The inventors could not discover an association between PEI or PERT and beta-carotene levels in the identification cohort. However, these data need to be interpreted with caution as previous studies have been inconsistent regarding the relationship of PEI and nutritional markers. This is the first study to describe a decrease in beta-Carotene levels as a diagnostic marker for CP.

Mannose and other carbohydrates were found to be significantly altered in a GC- MS based approach in studies identifying metabolic biomarkers for acute pancreatitis. Although studies did not find a further increase after repeated episodes of acute pancreatitis, it underscores the role of the pancreas in sugar metabolism during health and disease.

Ceramides and other components related to sphingomyelin-metabolism have been associated with metabolome changes in caerulein-induced pancreatitis in mice and were found to be a hallmark in a metabolome signature highly sensitive for the detection of pancreatic cancer in our previous studies. The reduced levels of the poorly absorbable long-chain saturated behenic acid, found in inventors’ CP subjects could be directly related to ceramide pathways by ceramide synthetase 2 (CERS2). To inventors’ knowledge, there is no published study linking behenic acid to pancreatitis. lndol-3-acetic acid, which belongs to the group of uremic toxins, is a tryptophan- derived byproduct of microbiota in the large intestine. It's bioavailability depends on the gut microbiome and has been linked to aryl hydrocarbon receptor (AhR) expression, which in turn regulates fibrosis of the pancreas. The microbiome composition is sensitive to pancreatic enzyme secretion, even in non-clinically manifested chronic pancreatitis. Similarly, plasma levels of hippuric acid, another uremic toxin, is sensitive to change of the gut microbiome related to diet, drugs and diseases.

N-acetylcytidine is a post-transcriptional mRNA modification that can induce more efficient translation and is implicated in inflammasome related IL1 β production in patients with chronic inflammation. Inflammasome activation, yet not a pancreas specific process, is involved in pancreatic healing and fibrosis. Taken together the signature is comprised of metabolites at least plausible to be involved in a variety of processes implicated with pathogenesis of chronic pancreatitis. Failure of a single biomarker in an individual patient could therefore be compensated by other components of the algorithm. This is further supported by the finding, that the biomarker score increases with presence of pancreatic insufficiency, a surrogate for disease stage.

A recent study by an U.S. American consortium used a 62-plex luminex assay to identify potential biomarkers for diagnosis of CP. In blood samples of 41 controls, 20 RAP and 40 CP patients they found that GM-CSF, IFNb, Leptin, PDGFB, and Resistin could distinguish between CP and control (AUC 0.86) and resistin, SCF, MIP-1 a, and IL-17F between RAP and CP (AUC 0.77). Although the results are not comparable due to methodological differences it appears clear that only a combination of markers rather than a single protein allows for adequate discrimination. Independent validation of these data is pending.

A potential weakness of the study is the fact that CP patients and controls were unmatched for age, gender and BMI due to disease heterogeneity and consecutive recruitment. CP is diagnosed predominantly in middle-aged males at risk for malnutrition. Non-pancreatic controls (e.g., day-surgery patients or blood donors) comprise by default a different cohort. Consequently, gender, BMI and age were not included in the prediction model because inventors aimed to avoid the pitfall that a shift in age alone could be sufficient to change a positive to a negative classification or vice versa.

It cannot be ruled out that some of the differences in the metabolic profiles between the groups are due to these features. Nevertheless, the MANOVA statistics were corrected for age, gender, and BMI, and still show a significant difference between the groups for all chosen metabolites. This supports inventors’ hypothesis that this metabolic signature is able to identify CP patients irrespective of gender, age, and BMI.

The inventors found the metabolic signature developed to distinguish CP from controls to be less discriminative when applied to liver cirrhosis samples. The similarity was interpreted as being likely due to activation of fibrosis and alcohol abuse, a common feature of both disorders. Fortunately, having to discriminate between liver cirrhosis and CP is uncommon and diseases rarely overlap. In the second validation study however, an almost complete separation of the control group from CP was seen. In this cohort, the control group was comprised healthy blood donors instead of non- pancreatic patients, which explains the significant improvement.

A potential clinical use of this metabolic signature is the identification of CP patients early in the disease course (early CP), of patients with unexplained abdominal symptoms and a history of pancreatic disease, but (yet) no definitive morphological signs of CP (probable CP), or of patients with recurrent acute pancreatitis (RAP) at risk for developing CP. These groups so far are only vaguely defined by international consensus diagnostic criteria. Inventors therefore recruited patients with definitive CP for the sake of biomarker development. Whether the presented metabolic signature is sensitive enough under the above circumstances needs further testing in trials with long-term follow-up, ideally in a design that includes disease staging via COPPS score. Whether a metabolic biomarker can distinguish between CP and cirrhosis which share a common etiology (alcohol), also needs to be addressed in future trials.

In conclusion, the inventors have identified and validated an LC-MS/MS-based human blood-metabolome signature, which successfully discriminates between healthy individuals and patients with chronic pancreatitis.

Supplemental Materials and Methods

Study details

Inventors conducted a type 3 study for multivariable prediction for individual prognosis according to the TRIPOD guidelines. Patients with chronic pancreatitis, liver cirrhosis, healthy blood donors and preoperative patients with non-pancreatic or liver disease were consecutively recruited from university referral centers in Greifswald, Dresden, Berlin, and Bochum, all in Germany. For the identification study, EDTA plasma samples were collected within a case control study from 80 patients with CP and 80 non-pancreatic control patients, who underwent small, non-pancreas-related surgical procedures under general anesthesia (see below). For the first validation study, 144 chronic pancreatitis patients and 204 non-pancreatic control patients were recruited at three different centers. Because it was acknowledged that CP patients are relatively young and mostly male, during patient recruitment, special care was taken to also recruit younger and mostly male patients for the control group with the aim to achieve a similar average patient age and a similar ratio of male to female subjects in both groups.

In the second validation study, a different sample type was utilized: serum samples taken from 49 chronic pancreatitis patients, 56 controls, and 57 liver cirrhosis patients were analyzed. These samples were collected in a fourth independent center. Furthermore, the control group consisted of healthy blood donors instead of patients waiting to undergo a small surgery.

The key study dates for the three studies were as follows: accrual for the identification study was started on 2009-01 -13, end of accrual was on 2013-08-01. Accrual for the first validation study was started on 2013-09-09 and ended on 2015-09- 28. Accrual for the second validation study started on 2002-10-23 and ended on 2010- 06-10.

The general inclusion criteria for all groups included written informed consent prior to any study procedures, age > 18 to 85 years and eight hours fasting prior to blood draw.

The general exclusion criteria for all groups included type I diabetes, pregnancy or lactation phase, known viral infections like hepatitis B, hepatitis C, HIV, major surgery within the last 4 weeks before sample collection, acute anemia (Hb<9 g/dl or <5,58 mmol/l), malignant tumors within the last 5 years.

Chronic pancreatitis patients were included if one or more of the following criteria were met and no other diagnosis was more likely: recurrent bouts of pancreatic pain with documented rise in amylase or lipase activity for a duration of more than one year plus radiological evidence supporting the diagnosis, pancreatic calcifications, histological proof of chronic pancreatitis, unequivocal changes in pancreatic duct morphology, severely abnormal pancreatic function tests with maldigestion. Calcifications were identified on CT74 scan, diabetes was diagnosed as suggested by the WHO definition and exocrine insufficiency was determined by either fecal elastase measurement or concurrent pancreatic enzyme supplementation. Pancreatitis patients were excluded if they had undergone pancreatitis surgery within 6 months before sample collection, bile duct stent placement or surgery, endoscopically assisted pancreatic aspiration <5 days before sample collection or had known liver cirrhosis.

Liver cirrhosis patients were included if preexisting liver cirrhosis had been diagnosed based on imaging and clinical chemistry. Liver cirrhosis patients were excluded if concomitant chronic pancreatitis was present.

Control patients were included if they were undergoing minor non-pancreatic surgery under general anesthesia. Control patients were excluded if they had chronic pancreatitis or liver cirrhosis or if a hernia was due to solid organ transplantation.

For the blood donors, only the standard blood donor inclusion criteria applied, i.e., the donors had to be in good general health, body weight at least 110 pounds. Participants with diabetes type II were excluded from the blood donor group because of the requirement of a fasting period of at least 8 hours which was not considered feasible for diabetics.

From the patients in the non-pancreatic control group in the identification study, 20 patients underwent vascular surgery, 18 patients received a hernia repair, 3 were resected for goiter and 39 received various other small surgical procedures under general anesthesia. From the patients in the non-pancreatic control group in the first validation study, 164 underwent hernia repair surgery and 40 were resected for thyroid goiter. None were operated in metabolically deranged state. Thus, the data in the first validation study differed from the identification study as it was a multicentric study, and in the composition of the non-pancreatic controls. The second validation study differed in the matrix used for analysis (serum instead of plasma), the center where the samples were obtained, the control group (healthy blood donors instead of non-pancreatic controls), and the inclusion of liver cirrhosis patients as an additional control group. Furthermore, 22.5% of the nonpancreatic controls in the identification study were diabetes type II patients, while 13.5% of the patients suffered from diabetes type II in the first validation study, and diabetes patients were excluded as control in the second validation study. As opposed to the identification study, the genesis of pancreatitis, calcifications, exocrine insufficiency, and enzyme supplementation were only partially available in the validation studies.

Sample Storage

Before freezing, the EDTA plasma samples and serum samples were aliquoted to avoid freeze-thaw cycles during the measurement period. Samples were stored at the respective center at -80°C until transport to the measurement location, which occurred on dry ice. Samples were stored at the measurement location at -80°C until measurement.

Our own work has shown that there were no significant differences in the metabolome of plasma samples due to storage when stored at -80°C for up to 7 years. Even though the sample collection for one of the studies employed here started in 2003 already, all samples were measured within 7 years after sample collection. The longest sample storage time before measurement was 1637 days (about 4.5 years). Thus, a marked influence of freeze-thaw cycles or sample age on the results of this study can be excluded.

Metabolite profiling details

Briefly, proteins were removed from the samples by precipitation, using three volumes of acetonitrile. Polar and nonpolar fractions were separated by adding water and a mixture of ethanol and dichloromethane (2:1 , v/v). For GC-MS analysis, the nonpolar fraction was treated with methanol under acidic conditions to yield the fatty acid methyl esters derived from both free fatty acids and hydrolyzed complex lipids. The polar and nonpolar fractions were further derivatized with O-methyl-hydroxylamine hydrochloride to convert oxo-groups to O-methyloximes, and subsequently with N- methyl-N-(trimethylsilyl)trifluoroacetamide prior to analysis. For LC-MS/MS analysis, both fractions were dried and reconstituted in appropriate solvent mixtures. High- performance liquid chromatography was performed by gradient elution using methanol/water/formic acid on reversed phase separation columns. Mass spectrometric detection technology was applied, which allows targeted and high-sensitivity multiple reaction monitoring (MRM) profiling in parallel to a full screen analysis. In brief, mass spectrometric detection was performed with repetitive cycles of MRM transitions for preselected metabolites followed by a full scan from a mass-to-charge ratio of 100 to 1000. The instrument was operated in positive ionization mode for metabolites in the nonpolar fraction, and in negative ionization mode for metabolites in the polar fraction. Metabolite identification was done by comparing sample data to authentic standards where applicable, as outlined previously.

MxP® Lipids covered profiling of sphingolipids (ceramides, sphingomyelins, and sphingobases). Total lipids were extracted from the sample by liquid/liquid extraction using chloroform/methanol. The lipid extracts were subsequently fractionated by normal phase liquid chromatography (NPLC) into different lipid groups according to. The fractions were analyzed by LC-MS/MS using electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) with detection of specific MRM transitions for preselected sphingolipids.

Metabolite profiling generated semi-quantitative data of metabolite concentrations calculated by determining metabolite levels in each study sample relative to metabolite concentrations in reference pool samples that were created from aliquots of all study samples. The normalization to reference pool samples compensates for inter- and intra-instrumental variation, i.e., variability that occurs when different analytical sequences were analyzed by different devices. To allow comparison of data sets between the different studies, the semiquantitative data were further normalized to the median of MxPool™ samples representing a pool of commercial human EDTA plasma containing more than 2,000 different metabolites of known concentrations. A one-point calibration was used to calculate quantitative absolute concentrations for those metabolites present in the MxPool. Both types of pooled reference samples were run in parallel through the entire process.

Biomarker selection

The metabolites for the biomarker panel were nominated based on biomedical expertise. In a first step, features that markedly differentiate CP patients from controls that could have an influence on the metabolome were considered. CP patients frequently suffer from lipid malabsorption and gut microbiome changes due to reduced bile acid secretion, reduced endocrine pancreatic function, pancreatic tissue fibrosis, and pancreatic inflammation. In a second step, metabolite groups that were expected to be different between CP patients and controls based on these physiological differences were collected: nutritional lipids that would be affected from malabsorption, microbiome-derived metabolites that could be affected by gut microbiome changes, carbohydrate metabolites that that would be affected by the reduced endocrine function, metabolites that would be altered in response to fibrosis, and metabolites that would be altered in response to inflammatory processes. In a third step, single representative metabolites from these groups were chosen for the signature panel based on methodical experience (the metabolites needed to allow for robust measurements above the limit of detection), available literature, and experience from previous experiments with CP patients and controls.

Prediction model

One prediction model was employed for all three study cohorts, i.e., the beta coefficients obtained from the first cohort were then applied to the individuals from the other 2 cohorts. The prediction model, consisting of the biomarker signature, the corresponding algorithm, and the established cut-off, predicts whether a patient suffers from chronic pancreatitis. The biomarker enables a clinical diagnosis, supporting the standard diagnostic means for diagnosis of chronic pancreatitis (see above). The biomarker is not designed to be applied for screening of the general population.

To avoid any bias when analyzing the concentrations of the metabolites present in the biomarker signature, the diagnosis was blinded to the scientists measuring the samples using mass spectrometry. The concentration values in the plasma samples of the 8 metabolites present in the biomarker signature are the only predictors used in the prediction model. Furthermore, the calculation of the biomarker score by the algorithm and selection of the cut-off was done fully automated, without human interference. After the initial calculation based on the identification study results, there were no subsequent interventions like patient exclusions, cut-off optimization, or re-training of the algorithm. Vice versa, the clinical diagnosis was established in the participating clinical centers according to the criteria mentioned above before the plasma samples were taken and analyzed in this study. Thus, the outcome obtained with the prediction model did not have any effect on the clinical diagnosis.

Statistical analysis details

Power analysis was performed to estimate an adequate sample size using representative metabolite profiling standard deviations that were determined by Metanomics GmbH in earlier studies. Primary goal of the study was to determine a 20% metabolic difference on a 5% significance level with approximately 72-99% power on the basis of the patient samples. Metabolic difference was defined as absolute or relative difference in concentrations of individual metabolites. Power estimates were based on t-test statistics.

Missing data were handled differently depending on the analysis. For the Naive Bayes algorithm and the principal component analysis, missing values were imputed with the NIPALS (Non-linear Iterative Partial Least Squares) algorithm. In the second validation study, BMI values were not available for all participants. For the inclusion of the BMI as a confounding factor in the MANOVA, the missing BMI values were imputed using K-means clustering for this purpose. Supplemental Results

Metabolomic analyses details

The metabolomics data underwent a strict quality control after which 505 known and 115 unknown metabolites remained for statistical analysis in the datasets based on plasma samples. Most of these metabolites could also be detected in the study conducted with serum samples. In this dataset, 498 known and 118 unknown metabolites remained for statistical analysis that met the quality control criteria.

Concentration data were missing for beta-carotin from 2 samples in the second validation study, for cryptoxanthin in 7 samples from the second validation study, for N220 acetylcytidine in 2 samples from the first and 1 sample from the second validation study, for behenic acid in 1 sample from the identification study, 6 samples from the first validation study, and 1 sample in the second validation study, for mannose in 16 samples from the first validation study and 5 samples from the second validation study, for indole-3-acetic acid for 1 sample in the identification study, for 28 samples in the first validation study, and for 7 samples in the second validation study, for hippuric acid for 1 sample in the second validation study, and for ceramide (d18:1 ,C24:1 ) for 1 sample in the first validation study and 17 samples in the second validation study. Normalized to the number of samples in each study, this means that a maximum of 1% of the values for a given metabolite were missing in the identification study, maximally 8% in the first validation study and maximally 10% in the second validation study. There were no cases where the outcome (diagnosis) was unknown or missing.

In total, 60 metabolites were not significantly different (p > 0.05 or q > 0.2) between CP and control groups in any of the three studies. 516 metabolites were significantly different in some of the studies and 39 metabolites were significantly (p < 0.05 and q < 0.2) different in all of the three studies. 6 of the 8 metabolites from the signature metabolite panel had a p value below the significance threshold (p < 0.05) in all three studies (see Table 4). The other 2 of the 8 metabolites from the panel (behenic acid and indole-3-acetic acid) were significantly different between the groups in the identification and the first validation study, but not in the second validation study that had serum as a sample matrix.

The panel metabolites were not the best 8 discriminators between CP patients and controls. Beta-carotene and cryptoxanthin were among the top 3 discriminators in the plasma-based studies (identification and first validation study), with lycopene being the best discriminator in the plasma studies. Looking at all three studies together, betacarotene, cryptoxanthin, and mannose were among the top 5 discriminators, with 3- h yd roxy butyrate being the best discriminator.

The distribution of age and BMI over the biomarker signature score is shown in supplemental Figure 6. The age gap between CP patients and non-pancreatic controls is markedly higher in the identification study than in the validation. In the first validation study, the age of the patients follows an even Gaussian distribution for both CP patients and nonpancreatic controls. In both studies, the score is markedly higher for CP patients compared to non-pancreatic controls irrespective of the age. As the BMI was calculated with a decimal, there are a lot of potential sublevels, which results in more data being needed for Gaussian curves. Nevertheless, the BMI of non-pancreatic controls in the validation study also follows a Gaussian distribution, while the BMI of CP patient is clearly skewed due to the increased frequency of patients with low BMI. This is an inherent feature of the disease concomitant with the malnutrition caused by CP. These trends can also be observed in the identification study. Despite the uneven BMI distribution, the graphs show that the biomarker score is markedly higher for CP patients compared to non-pancreatic controls irrespective of the BMI.

The full prediction model can be used universally. The weighting of the metabolites as shown in Figure 9 can be used as coefficients to be multiplied with the respective concentrations of the eight metabolites in the biomarker signature (in pmol/L) to calculate the biomarker signature score. Whether the score is above or below the cut-off value of 0.479 determines whether the patient is evaluated as positive or negative for the diagnosis “chronic pancreatitis”. Effect of exocrine insufficiency and enzyme supplementation on carotenoid levels

Because the identification of beta-carotene and cryptoxanthin suggested a pathophysiological mechanism of malabsorption, it was analyzed whether pancreatic exocrine insufficiency and enzyme supplementation had an effect on plasma carotenoid levels. This analysis was limited to the identification study because the full information was available for this cohort only. Almost all patients with exocrine insufficiency also received enzyme supplementation so that a separate comparison of the effect of exocrine insufficiency alone was not possible. As obvious from Figure 7, there was no significant increase of carotenoid levels in plasma of chronic pancreatitis patients supplemented with enzymes to treat exocrine insufficiency.

Biomarker signature score increases with disease severity

In clinical daily routine, patients that will are tested for chronic pancreatitis are not always as healthy as the control groups used in this study. It was therefore desirous to investigate whether the biomarker signature score was more accurate in patients with advanced disease than in less severe cases. Because other clinical data regarding disease severity was elusive, the information whether the patients suffered from pancreatic endocrine or exocrine insufficiency was used, which is a good surrogate marker for severity and time since disease onset. 199 chronic pancreatitis patients from the identification and the first validation study for whom this information was available together were categorized in three groups: those without insufficiencies, those with either endo- or exocrine insufficiency, and those with both endo- and exocrine insufficiencies. The distribution of biomarker signature score values in the three groups is shown in Figure 8. The average biomarker signature score was 0.68 in patients without insufficiencies, 0.78 in patients with either endo-or exocrine insufficiency, and 0.90 in patients with both endo- and exocrine insufficiency. An ANOVA was employed to test whether the differences in the biomarker signature score were significant. While the group with one pancreatic insufficiency did not have a significantly different score compared to the other groups, the scores of the groups without pancreatic insufficiencies and with both endo- and exocrine insufficiencies were significantly different (p = 0.0018). This indicates that the biomarker signature score is higher in patients with more severe pancreatic disease.

Using the established cut-off of 0.479, it was concluded for each patient in the three groups whether the diagnosis based on the signature score was correct or a false negative. A chi-squared test was employed to investigate whether the diagnosis and the severity were co-dependent. The result of p = 0.056 shows a clear trend towards a higher fraction of correct diagnoses in more severe cases, although there was no significant dependency between the group affiliation and the diagnosis.

Diagnosing and Treating Chronic Pancreatitis

Chronic pancreatitis (CP) is a disease, in which due to relapsing inflammatory processes, pancreatic parenchyma is substituted by fibrotic tissue. In parallel, complications are characteristic such as formation of pseudocysts, pancreatic duct stenosis, duodenal stenosis, vascular complications, compression of the distal bile duct, malnutrition, and a pain syndrome. Abdominal pain is the leading symptom of patients with CP. However, as discussed in greater detail below, other conditions can result in abdominal pain, hence a long felt need for the present invention.

Not only can CP be mistaken for other conditions, but it is important that it is properly diagnosed and treated. This is because CP is a known risk factor for the development of pancreatic cancer. CP can also reduce quality of life and life expectancy considerably. Consequences of a diagnosis and optimal treatment of CP with its cardinal symptom of opiate dependent abdominal pain range between symptombased treatment to total pancreatectomy with auto-islet transplantation. Total pancreatectomy with auto-islet transplantation, though burdened with considerable morbidity and mortality to the patient can be provided even to children in the absence of morphological changes in line with chronic pancreatitis on imaging to increase the yield of islet isolation warranting a diagnostic test with high accuracy in the presence of multiple differential diagnosis for safe disease management. Furthermore, to optimize treatment a blood-based signature diagnosing and excluding differential diagnosis of CP represents an urgent medical need.

The incidence of CP increases proportionally to alcohol consumption in the general population. The incidence worldwide is reported to be between 1.6 - 23 / 100,000 with an increasing prevalence. Although most patients with CP are treated under out-patient conditions, in the year 2008 there were 10,267 hospital admissions for CP in Germany alone. This does not include those patients who were coded as acute pancreatitis due to an acute exacerbation of CP (50,673 cases). This data substantiates the high socio-economic burden of the disease.

The aim of the present invention is two-fold: (1 ) to non-invasively diagnose CP in a population with unexplained abdominal pain to refer the patient for specific treatment; (2) to allow for stage adapted treatment. Currently, the diagnosis of CP is based on clinical, morphological and functional parameters. Due to the insufficient correlation of these three diagnostic arms, they can only be used in a complementary way and are often unspecific in early stages of the disease when the leading symptom is belt-like abdominal pain in the absence of morphological changes to the gland undetectable on conventional imaging. Clinical symptoms such as belt-like upper abdominal pain and vomiting, together with a more than 3-fold rise in serum amylase or lipase levels above normal, are a prerequisite for the diagnosis of acute pancreatitis or a relapse of CP. Relapses of pain in CP may, however, also be possible without an elevation of serum lipase. As mentioned above, there are numerous causes for pain (see tables below) as a cardinal symptom for CP which need to be excluded to adequately manage the patient and chronic pancreatitis.

Causes of epigastric abdominal pain

Table 5 Less common causes of abdominal pain

Table 6 Initial treatment of CP aims to address symptoms and improve quality of life. Patients are counseled for alcohol and tobacco cessation. These lifestyle changes alone can dramatically improve pain. First line pain control strategies aim to avoid opioid prescriptions, but patients often progress to requiring opioids. Interventions, such as an intrathecal pain pump or celiac plexus neurolysis are effective in select patients. A number of antioxidants have been studied for the treatment of CP pain. These antioxidants include vitamin A, C, E, selenium, and methionine. The goal of antioxidant treatment is to decrease ischemia and thereby ischemic induced inflammation and stimulus of peri-pancreatic nerves. Nutritional optimization often requires pancreatic enzyme replacement and commonly supplemental enteral nutrition via tube feeds.

Effective nutritional support requires counseling on healthy eating habits and positive lifestyle choices in addition to nutritional supplementation and food fortification. Treatment of pancreatogenic diabetes mellitus (type 3c) requires lifestyle modifications and anti-hyperglycemic agents. The diagnosis of pancreatogenic diabetes mellitus can be difficult and may coexist with type 1 or (more commonly) type 2 diabetes mellitus. Decreased insulin secretion, decreased pancreatic polypeptide response, hepatic and peripheral insulin resistance, and maldigestion of nutrients from pancreatic exocrine insufficiency (and the associated effect on incretin hormone response) contribute to the complexity in managing diabetes mellitus in chronic pancreatitis patients.

The approach to pain in CP typically follows a stepwise approach as recommended by the World Health Organization. This stepwise approach begins with non-steroidal anti-inflammatory drugs, followed by low potency opioids, followed by longer acting opioids. Celiac plexus blockade (CPB) is applied commonly in clinical practice. Pancreatic enzyme supplementation, although sometimes helpful, is typically not recommended as a specific treatment to improve pain. Endoscopic interventions are the first line in addressing pain and physical manifestations of chronic pancreatitis, including pancreatic duct stricture, lithiasis, and pseudocyst. Endoscopic intervention may include stricture dilation and stenting, extracorporeal shock wave lithotripsy with endoscopic removal of stones, and/or transpapillary or transmural pseudocyst drainage. Studies suggest that early surgical intervention (e.g., pancreatic drainage, pancreatic resection, duodenum-preserving pancreatic head resection, total pancreatectomy with islet cell auto-transplantation) yields superior results in CP.

In sum, consequences of a diagnosis and optimal treatment of CP with its cardinal symptom of opiate dependent abdominal pain range between symptom-based treatment to total pancreatectomy with auto-islet transplantation. Total pancreatectomy with auto-islet transplantation, though burdened with considerable morbidity and mortality to the patient can be provided even to children in the absence of morphological changes in line with chronic pancreatitis on imaging to increase the yield of islet isolation warranting a diagnostic test with high accuracy in the presence of multiple differential diagnosis for safe disease management. Furthermore, to optimize treatment a blood-based signature diagnosing and excluding differential diagnosis of chronic pancreatitis represents an urgent medical need.

To prevent progression and to ameliorate symptoms of chronic pancreatitis upon accurate diagnosis, the following treatment options are available following guidelines as reference above.

Determining and Providing Results

As discussed above, the present invention provides for new biomarkers for at least assessing chronic pancreatitis (CP), which allows for screening of pancreatitis in an early stage of disease progression with high accuracy and reliability. Optimally, the marker should be easily detectable in a biological sample such as in blood and its level should be consistently related to the stage of pancreatitis. Moreover, it is an object of the present invention to provide for a method for assessing CP in a biological sample, which allows for fast, convenient and high throughput performance. The invention may be used as an early-diagnosis-tool that identifies patients with

CP in its earliest stages, when intervention offers the highest possibility of cure (or better treatment). The invention provides prognostic information and serves as a predictive test for clinical response. In doing this, the present invention provides for never described biomarkers (i.e., a new biomarker set) suitable for assessing chronic pancreatitis, including early and more advanced stages of disease and also provides biomarker sets that clearly discriminate, at baseline, patients with elevated risk of relapse after initial treatment.

Out the outset, the invention may involve a patient visiting a doctor, clinician, technician, nurse, etc., where blood or a different sample is collected. The sample would then be provided to a laboratory for analysis, as discussed above (e.g., mass spectrometry, log-transformation, comparisons, etc.). In another embodiment, a kit can be used to obtain the sample, where the kit is made available to the patient via a medical facility, a drug store, the Internet, etc. In this embodiment, the kit may include one or more wells and one or more inserts impregnated with at least one internal standard. The kit can be used to gather the sample from a patient, where the sample is then provided to a laboratory for analysis.

For example, peripheral blood may be collected into EDTA-anticoagulant tubes. Plasma is isolated by centrifugation. Plasma samples may then be submitted for extraction and processing. In one embodiment, prepared samples will then undergo metabolite extraction (e.g., via Mass Spectrometry). The extracted data is then processed using computer software. For example, the data acquired may then be normalized (e.g., via log-transformation) and stored in a database that includes at least (i) patient identification, (ii) metabolite name, and (iii) quantification. If this data is on known individuals (individuals with known conditions), then it can be analyzed to determine signatures that can be used to assess a particular disease. If, however, the data is on a patient whose condition is unknown, then it can be compared to known signatures (e.g., stored in memory) to screen for, diagnose, prognose, and treat the patient.

It should be appreciated that the present invention is not limited to normalizing a quantified metabolite. In other words, other processes discussed herein and/or generally known to those skilled in the art may be performed either before or after normalization. It should also be appreciated that while certain processes can be performed manually, most (if not all) should preferably be performed using software, where initial results (data post mass spectrometry, post normalization), are stored in memory, presented on a display (e.g., computer monitor, etc.) and/or printed. The initial results can then be compared to known “signatures” for different diseases, where similarities and differences are used to screen for, diagnose, prognose, treat, etc. a particular disease. It should be appreciated that the sample may be assessed for a particular disease, or for multiple diseases, depending on the patient’s sex, age, etc. Thus, the software could be used to assess a particular disease or assess at least one disease from a plurality of diseases.

It should further be appreciated that the “comparing” step can be performed by (i) software, (ii) a human, or (iii) both. For example, with respect to the prior, a computer program could be used to compare sample results to known signatures and to use differences and/or similarities thereof to assess at least one disease, and provide diagnosis, prognosis, and/or treatment for the same. Alternatively, in the second embodiment, a technician could be used to compares sample results to known signatures (or aspects thereof) and make a diagnosis, prognosis, and/or treatment decision based on perceived similarities and/or differences. Finally, with respect to the latter, a computer program could be used to plot (e.g., on a computer display) sample results alongside known signatures (e.g., signatures of healthy patients, signatures of unhealthy patients, life expectancies, etc.). A technician could then view the same and make at least one diagnosis, prognosis, treatment recommendation, etc. based on similarities and/or differences in the plotted information.

Bottom line, it is the differences and/or similarities between known signatures that allows a disease to be assessed, whether that assessment is automated (e.g., performed by a computer), performed manually (e.g., done by a human), or a combination of the two.

Results (e.g., assessments) are then provided to the patient directly (e.g., via mail, email, text, etc.) or via the patient’s doctor, and can include screening information, diagnosis information, prognosis information, and treatment information.

In particular, the invention can be used to distinguish a sample from a patient having chronic pancreatitis from one that is normal. If positive for CP, then the invention can further be used to identify disease stage. This can be done using terminology (e.g., no insufficiencies, endo- or exocrine insufficiencies, or endo- and exocrine insufficiencies), at least one scale (e.g., 1 -10, 1-100, A-F, etc.), where one end of the scale is low grade (e.g., non-invasive) and the other end is high grade (lethal), or other visual forms (e.g., color coded, 2D or 3D model, etc.).

The invention can also be used to provide a prognosis. For example, in chronic pancreatitis, once CP is identified, the invention can be used to provide gradations within the signature (or signatures), subcategorizing the patient into severity, treatability, etc. Again, prognosis could be provided using terminology (e.g., low risk, medium risk, high risk, etc.), at least one scale, or other visual forms.

Not only can the present invention be used to screen for and diagnose CP, but it can also be used to determine treatment, or viability of treatment (another form of prognosis). This could be a likelihood to respond to therapy (e.g., counseling, opioids, surgery, etc.), which again could be provided using terminology, at least one scale, or other visual forms.

Thus, by way of example, the present invention may be used to determine (i) a high likelihood that a patient has pancreatitis (diagnosis), (ii) a high likelihood that the pancreatitis is chronic (diagnosis), and (iii) a high likelihood that it can be addressed by a total pancreatectomy with auto-islet transplantation (prognosis). Clearly this is exemplary, and other diseases, sub-categorizations, prognosis, and treatments can be identified (predicted) using the present invention.

The invention can also be used to screen for diseases. Medical screening is the systematic application of a test or inquiry to identify individuals at sufficient risk of a specific disorder to benefit from further investigation or direct preventative action (these individuals not having sought medical attention on account of symptoms of that disorder). The present invention uses metabolic signatures to screen for diseases in populations who are considered at risk.

It should be appreciated that while several examples have been provided as to what the present invention can discern from a blood sample (or the like), the present invention is not so limited, and other types of diagnosis and prognosis, including treatments, are within the spirit and scope of the present invention.

Once a sample has been received and processed (e.g., processed using techniques like the one used to identify the signatures in the first place, such as mass spectrometry (to quantify metabolites), log-transformation (or other mathematical manipulation to normalize the data), etc.), the initial results (e.g., metabolites and/or sets thereof) can then be compared to signatures (or portions thereof) that have been identified (by the inventors) as useful in assessing at least one disease. The signatures may be stored in memory, and the initial data (i.e., processed sample) may be compared to at least one signature either manually (e.g., by viewing the sample, or initial results thereof, against known signatures), automatically (e.g., using a computer program to discern differences and/or similarities between the sample, or initial results thereof, and known signatures), or both (e.g., a program determines at least one diagnosis/prognosis and a technician reviews the data to validate the same). Based on the results (i.e., comparison results), at least one diagnosis and/or prognosis, which may or may not include treatment, is identified and provided to the patient.

Conclusion

Having thus described several embodiments of a system and method for using new biomarkers for assessing different diseases, including chronic pancreatitis, it should be apparent to those skilled in the art that certain advantages of the system and method have been achieved. It should also be appreciated that various modifications, adaptations, and alternative embodiments thereof may be made within the scope and spirit of the present invention. The invention is solely defined by the following claims.

Claims

What is claimed is:

1. A method for assessing a human patient for chronic pancreatitis, comprising: using a technology selected from chromatography, spectroscopy, and spectrometry to quantify a plurality of metabolites included in a blood sample obtained from said human patient, including at least Beta-carotene and Cryptoxanthin; normalizing at least said Beta-carotene and said Cryptoxanthin, as quantified using said technology; comparing at least a result of an equation comprising at least said Betacarotene and said Cryptoxanthin, as normalized, to at least one predetermined value to both diagnose said human patient for said chronic pancreatitis and determine a prognosis for said human patient, wherein said diagnosis includes at least whether said human patient suffers from chronic pancreatitis and said prognosis includes at least a treatment for said chronic pancreatitis; and treating said human patient for said chronic pancreatitis, said treatment being selected from a group consisting of opioid treatment, endoscopic treatment, decompressive resection, total pancreatectomy followed by auto-islet transplantation, enzyme supplementation, and radiological transabdominal drainage.

2. The method of Claim 1 , further comprising the steps of quantifying and normalizing Behenic acid, wherein said equation further comprises at least said Behenic acid, as quantified and normalized.

3. The method of Claim 1 , further comprising the steps of quantifying and normalizing lndole-3-acetic acid, wherein said equation further comprises at least said lndole-3-acetic acid, as quantified and normalized.

4. The method of Claim 1 , further comprising the steps of quantifying and normalizing Hippuric acid, wherein said equation further comprises at least said Hippuric acid, as quantified and normalized.

5. The method of Claim 1 , further comprising the steps of quantifying and normalizing Mannose, wherein said equation further comprises at least said Mannose, as quantified and normalized.

6. The method of Claim 1 , further comprising the steps of quantifying and normalizing Ceramide, wherein said equation further comprises at least said Ceramide, as quantified and normalized.

7. The method of Claim 1 , further comprising the steps of quantifying and normalizing N-Acetylcytidine, wherein said equation further comprises at least said N- Acetylcytidine, as quantified and normalized.

8. The method of Claim 1 , wherein said step of normalizing at least said Beta-carotene and said Cryptoxanthin further comprises using at least a log transformation to normalize at least said Beta-carotene and said Cryptoxanthin.

9. The method of Claim 1 , wherein said step of treating said human patient for chronic pancreatitis further comprises [placeholder for further treatment step],

10. The method of Claim 1 , wherein said diagnosis further comprises a stage of said chronic pancreatitis.

11. The method of Claim 10, wherein said stage is selected from a group consisting of (i) no insufficiencies, (ii) endocrine or exocrine insufficiencies, and (iii) endocrine and exocrine insufficiencies.

12. A system for assessing a human patient for chronic pancreatitis, comprising: a computing system comprising at least one memory device for storing machine readable instructions adapted to perform the steps of: receive a plurality of quantified metabolites from a sample provided by said human patient, including at least Beta-carotene and Cryptoxanthin; normalize said plurality of quantified metabolites; compare at least a result of an equation comprising at least said Beta-carotene to said Cryptoxanthin, as normalized, to at least one predetermined value to determine at least one level of similarity therebetween; use said at least one level of similarity to determine a diagnosis and a prognosis for said human patient regarding said chronic pancreatitis, wherein said diagnosis includes at least whether said human patient suffers from chronic pancreatitis and said prognosis includes at least a treatment for said chronic pancreatitis; and treating said human patient for said chronic pancreatitis, said treatment being selected from a group consisting of opioid treatment, endoscopic treatment, decompressive resection, total pancreatectomy followed by auto-islet transplantation, enzyme supplementation, and radiological transabdominal drainage.

13. The system of Claim 12, wherein said quantified and normalized metabolites further include Behenic acid, and said equation further comprises at least said Behenic acid, as quantified and normalized.

14. The system of Claim 12, wherein said quantified and normalized metabolites further include lndole-3-acitic acid, and said equation further comprises at least said lndole-3-acitic acid, as quantified and normalized.

15. The system of Claim 12, wherein said quantified and normalized metabolites further include Hippuric acid, and said equation further comprises at least said Hippuric acid, as quantified and normalized.

16. The system of Claim 12, wherein said quantified and normalized metabolites further include Mannose, and said equation further comprises at least said Mannose, as quantified and normalized.

17. The system of Claim 12, wherein said quantified and normalized metabolites further include Ceramide, and said equation further comprises at least said Ceramide, as quantified and normalized.

18. The system of Claim 12, wherein said quantified and normalized metabolites further include N-Acetylcytidine, and said equation further comprises at least said N-Acetylcytidine, as quantified and normalized.

19. The system of Claim 12, wherein said diagnosis further comprises a stage of said chronic pancreatitis.

20. The system of Claim 19, wherein said stage is selected from a group consisting of (i) no insufficiencies, (ii) endocrine or exocrine insufficiency, and (iii) endocrine and exocrine insufficiency.