WO2015157557A1

WO2015157557A1 - Methods of diagnosing pancreatic cancer and methods related thereto

Info

Publication number: WO2015157557A1
Application number: PCT/US2015/025179
Authority: WO
Inventors: Karri Lynn BALLARD; Ralph Leon MCDADE; Michael Douglas SPAIN; Samuel Thomas LABRIE; James Preston MAPES; Michael Graham Walker; Jing Shi; Matt FIRPO; Sean MULVIHILL
Original assignee: Myriad Rbm, Inc.; University Of Utah Research Foundation
Priority date: 2014-04-09
Filing date: 2015-04-09
Publication date: 2015-10-15

Abstract

The invention provides for diagnostic methods and, particularly, biomarkers for diagnosing pancreatic cancer and methods of use thereof.

Description

METHODS OF DIAGNOSING PANCREATIC CANCER AND METHODS RELATED

THERETO

FIELD OF THE INVENTION

[0001] The invention generally relates to diagnostic methods and particularly to diagnostic methods utilizing pancreatic cancer biomarkers.

BACKGROUND OF THE INVENTION

[0002] Cancer is a major public health problem, accounting for roughly 25% of all deaths in the United States. Pancreatic cancer is the fourth leading cause of cancer death in the United States. Rebecca Siegel et al, Cancer Statistics, 2013, CA: A Cancer Journal for Clinicians 1 1 (2013). Pancreatic cancer has a 5-year survival rate of only 5%. Because the disease is largely asymptomatic at early stages, early detection of pancreatic cancer is rare, and over 85% of pancreatic cancer patients have tumors that cannot be surgically resected at the time of diagnosis. However, resection of pancreatic tumors at early stages dramatically improves survival rates, up to a 5-year survival of 75% when tumors are less than 10mm at diagnosis and resection. Therefore, early detection of pancreatic cancer in a patient may increase the likelihood of successful treatment and survival.

SUMMARY OF THE INVENTION

[0003] The present disclosure includes methods of screening for pancreatic cancer, wherein the methods comprise determining from a biological sample of a patient the levels of a panel of biomarkers, comparing the level of each biomarker to threshold levels for each biomarker and determining the likelihood the patient has pancreatic cancer. In some embodiments the biomarkers are selected from Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14. In some embodiments the biomarkers selected consist of both positive and negative biomarkers. In some embodiments, the methods include determining the presence or absence of a biomarker that has either a level above a threshold level for each particular positive biomarker or a level below a threshold level for each particular negative biomarker and conducting endoscopic ultrasonography or magnetic resonance

cholangiopancreatography if the patient has a biomarker that has either a level above the threshold level for each particular positive biomarker or a level below the threshold level for each particular negative biomarker. In some embodiments, the methods include conducting endoscopic ultrasonography or magnetic resonance

cholangiopancreatography if the patient has at least 4 biomarkers that have either a level above the threshold level for that particular positive biomarkers or a level below the threshold level for that particular negative biomarkers.

[0004] The present disclosure also includes methods of screening a biological sample, wherein the method includes obtaining a biological sample, determining in the biological sample the levels of a panel of biomarkers selected from a panel of biomarkers, comparing the level of each biomarker to threshold levels for each biomarker, and determining the presence or absence of a biomarker that has either a level above a threshold level for each particular positive biomarker or a level below a threshold level for each particular negative biomarker. In some embodiments, the panel comprises any one of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14. In some embodiments, the methods include screening patients with a first-degree relative diagnosed with pancreatic cancer. In some embodiments, the methods include screening a sample from a patient with a mutation in a BRCA2, PALB2, p l 6, STK1 1 , MLH 1 , MSH2, MSH6, PMS2, or EPCAM gene. In some embodiments, the methods include screening patient with a diagnosis of hereditary non- polyposis colorectal cancer. In some embodiments, the methods include screening patient with a diagnosis of Lynch syndrome, Peutz-Jeghers syndrome, acute

pancreatitis, chronic pancreatitis, or recent onset diabetes.

[0005] The present disclosure also includes methods of diagnosing pancreatic cancer, wherein the methods comprise determining from a biological sample of a patient a level of a panel of biomarkers. The methods may further comprise comparing the level of the panel of biomarkers to threshold levels for each biomarker. The methods may further comprise determining the presence or absence of the panel of biomarkers that have a level either above a threshold level for each particular positive biomarker or below a threshold level for each particular negative biomarker, wherein the presence of the panel of such biomarkers indicates a likelihood of pancreatic cancer. In some embodiments, the biomarkers are at least 3 biomarkers selected from of any one of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14, wherein the panel comprises positive biomarkers, negative biomarkers, or both.

[0006] In some embodiments, methods of diagnosing pancreatic cancer further comprise determining the number of family members of the patient diagnosed with pancreatic cancer and determining the degree of relationship to the patient of any such family member; determining the presence or absence of a mutation in a BRCA2, PALB2, pl6, STK1 1, MLH1, MSH2, MSH6, PMS2, or EPCAM gene of the patient, or determining whether the patient has hereditary non-polyposis colorectal cancer.

[0007] In some embodiments, methods of diagnosing pancreatic cancer further comprise determining from a biological sample of the patient the level of each biomarker of a panel of biomarkers for pancreatic cancer, wherein the panel comprises positive biomarkers, negative biomarkers, or both, and comparing the level of each biomarker to threshold levels for each biomarker.

[0008] In some embodiments, methods of diagnosing pancreatic cancer further comprise determining whether the patient has or is symptomatic of Lynch syndrome, chronic pancreatitis, recent onset diabetes, acute pancreatitis, or Peutz-Jeghers syndrome.

[0009] In some embodiments, methods of diagnosing pancreatic cancer further comprise determining the number of patient biomarkers that have either a level above a threshold level for each particular positive biomarker or a level below a threshold level for each particular negative biomarker; determining the magnitude of the difference in level for each biomarker of the patient that is either above a threshold level for that particular positive biomarker or below a threshold level for that particular negative biomarker; and calculating the probability of early- stage pancreatic cancer based on at least (i) the number of family members of the patient diagnosed with pancreatic cancer, (ii) the degree of relationship to the patient of any such family member, (iii) the presence or absence of a mutation in a BRCA2, PALB2, pl6, STK11, MLH1, MSH2, MSH6, PMS2, or EPCAM gene or whether the patient has hereditary non-polyposis colorectal cancer, (iv) the number of patient biomarkers that have either a level above the threshold level for each particular positive biomarkers or a level below the threshold level for each particular negative biomarkers, and (v) the magnitude of the difference in level for each biomarker of the patient that is either above a threshold level for that particular positive biomarker or below a threshold level for that particular negative biomarker.

[0010] In some embodiments, methods of diagnosing pancreatic cancer further comprise determining the existence of pancreatic cancer by computed tomography (CT), somatostatin receptor scintigraphy (SRS), positron emission tomography (PET), ultrasonography (ultrasound), endoscopic retrograde cholangiopancreatography (ERCP), angiography, or biopsy.

[0011] In some embodiments, the methods further include treating the patient for pancreatic cancer. In some embodiments the patient is treated by surgical resection of the tumor, chemotherapy, radiation therapy, ablative techniques, or some combination thereof.

[0012] The present disclosure also includes determining the probability of early stage pancreatic cancer in a patient, wherein the method comprises determining from a biological sample of a patient a level of a panel of biomarkers. The methods may further comprise comparing the level of the panel of biomarkers to threshold levels for each biomarker. The methods may further comprise determining the presence or absence of the panel of biomarkers that have a level either above a threshold level for each particular positive biomarker or below a threshold level for each particular negative biomarker, wherein the presence of the panel of such biomarkers indicates a likelihood of pancreatic cancer. In some embodiments, the biomarkers are selected from any one of Panels A through L, or any panel of 3, 4, or 5 biomarkers in

Megatables 3 through 14, wherein the panel comprises positive biomarkers, negative

biomarkers, or both. In some embodiments, the methods include screening patients with a first-degree relative diagnosed with pancreatic cancer. In some embodiments, the methods include screening patient with a mutation in a BRCA2, PALB2, p l 6, STK1 1 , MLH 1 , MSH2, MSH6, PMS2, or EPCAM gene. In some embodiments, the methods include screening patient with a diagnosis of hereditary non-polyposis colorectal cancer. In some embodiments, the methods include screening patient with a diagnosis of Lynch syndrome, Peutz-Jeghers syndrome, acute pancreatitis, chronic pancreatitis, or recent onset diabetes.

[0013] The present disclosure also includes computer-assisted methods of determining the probability of a patient having early-stage pancreatic cancer, wherein the methods comprise providing biomarker information from a biomarker information database module on a data processing device, wherein the biomarker information comprises threshold level information for each biomarker of a panel of biomarkers, wherein the panel of biomarkers comprises positive biomarkers, negative biomarkers, or both, wherein a level above a threshold level for positive biomarkers is indicative of the presence of pancreatic cancer in a patient and a level below a threshold level for negative biomarkers is indicative of the presence of pancreatic cancer in a patient. The methods may further comprise providing patient information from a patient information database module on a data processing device, wherein the patient information comprises biomarker level information for the patient. The methods may further comprise comparing with a comparison module on a data processing device biomarker information and patient information. In some embodiments patient information may include family history, and the patient's medical history. The methods may further comprise determining with an evaluation module on a data processing device operably connected to the comparison module a number of patient biomarkers with either a level above a threshold level for each particular positive biomarker or a level below a threshold level for each particular negative biomarker. The methods may further comprise determining with a calculation module on a data processing device operably connected to the evaluation module a probability of early-stage pancreatic cancer based on the above-determined number. The methods may further comprise displaying a graphical representation of the patient's probability of early-stage pancreatic cancer.

[0014] The present disclosure also includes methods of assessing the likelihood of pancreatic cancer in a patient, wherein the methods comprise determining from a biological sample of a patient the levels of a panel of biomarkers. The methods may further comprise comparing the levels for each biomarker measured in a control subject. The methods may further comprise comparing the levels of biomarkers in the patient and in the control subject and determining the likelihood of pancreatic cancer in the subject. In some embodiments the biomarkers are selected from any Panel A through L. In some embodiments the biological sample is blood or plasma. In some embodiments the control subjects are healthy, or have a diagnosis of other cancer or diabetes. In some embodiments the subject has one first degree relative with a pancreatic cancer diagnosis. In some embodiments the subject has a mutation in one of the pl6, BRCA2, PALB2, or STK11 genes. In some embodiments the subject is symptomatic of or diagnosed with Lynch syndrome, chronic pancreatitis, acute pancreatitis, diabetes, or Peutz-Jeghers syndrome. In some embodiments the biomarkers are selected by performing statistical analysis and selecting biomarkers with a t-test p-value equal to or less than 0.05, an ANOVA p-value equal to or less than 0.05, a Wilcoxon rank sum p-value equal to or less than 0.05, or a Kruskal-Wallis p-value equal to or less than 0.05. In some embodiments, a model or algorithm is created which incorporates the expression level of biomarkers and calculates a probability that an individual suffers from pancreatic cancer.

[0015] In some embodiments, the likelihood of pancreatic cancer in the patient is confirmed by biopsy, Computed tomography (CT) scan, Somatostatin

Receptor Scintigraphy (SRS), Positron Emission Tomography (PET) scan,

ultrasonography (ultrasound), Endoscopic Retrograde Cholangiopancreatography (ERCP), or Angiography. In some embodiments the patient is treated with surgical resection, chemotherapy, radiation therapy, or ablative techniques.

[0016] The present disclosure also includes methods of treating a patient wherein the methods comprise measuring the levels of a panel of biomarkers in a patient, comparing the levels of the panel of biomarkers in the patient with a threshold level for each biomarker, and treating the patient for pancreatic cancer. In some embodiments, the methods further comprise correlating a level of biomarker above the threshold for a positive biomarker or below the threshold for a negative biomarker in any of said panel of biomarkers to an increased likelihood of pancreatic cancer, or correlating a level of biomarker below the threshold for a positive biomarker or above the threshold for a negative biomarker in any of said panel of biomarkers to no increased likelihood of pancreatic cancer, and recommending, prescribing, or administering a treatment or care to the patient for pancreatic cancer. In some embodiments, the biomarkers are selected from any panel A through L. In some embodiments the panel of biomarkers is at least three biomarkers from Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14. In some embodiments pancreatic cancer in the patient is confirmed by biopsy, Computed tomography (CT) scan, Somatostatin Receptor Scintigraphy (SRS), Positron Emission Tomography (PET) scan, ultrasonography (ultrasound), Endoscopic Retrograde

Cholangiopancreatography (ERCP), or Angiography. In some embodiments the patient is treated with surgical resection, chemotherapy, radiation therapy, or ablative techniques.

[0017] The present disclosure also includes methods of diagnosing pancreatic cancer wherein the methods comprise reacting a biological sample from the subject with at least three reagents, each one of which specifically binds to a different biomarker in a panel and determining whether the selected markers is over expressed or under expressed in the sample, thereby providing a diagnosis for pancreatic cancer in the subject. In some embodiments, over or under expression is determined by comparing expression level to a predetermined threshold. In some embodiments, the panel of biomarkers is determined by comparing the levels in the patient to a control group, the control group can consist of healthy age-matched individuals, individuals diagnosed with early stage pancreatic cancer, late stage pancreatic cancer, other cancers, diabetes, or a combination thereof. In some embodiments the subject has been diagnosed with or is symptomatic of Lynch syndrome, chronic pancreatitis, new onset diabetes, acute pancreatitis, or Peutz-Jeghers syndrome; has at least one first degree relative with a pancreatic cancer diagnosis; has a germ line germ-line mutation in at least one of the p i 6, BRCA2, PALB2, or STKl 1 genes, or some combination thereof. In some embodiments, determining an expression level comprises determining protein expression, or RNA expression. In some embodiments, determining an expression level comprises determining protein concentration in a biological sample.

[0018] The present disclosure also includes methods of screening a biological sample, wherein the methods comprise obtaining a biological sample and determining a level in the biological sample of at least 3, 4, or 5 biomarkers in any one of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14, wherein the panel comprises positive biomarkers, negative biomarkers, or both. The methods may further comprise comparing the level of each biomarker to threshold levels for each biomarker. The methods may further comprise determining the presence or absence of a biomarker that has either a level above a threshold level for each particular positive biomarker or a level below a threshold level for each particular negative biomarker. In some of such embodiments, the methods comprise determining a level in the biological sample of each biomarker of the panel.

[0019] The present disclosure also includes methods of screening a biological sample, wherein the methods comprise detecting the level of each biomarker of any one of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14 in a biological sample of an individual identified as having (i) a first-degree relative diagnosed with pancreatic cancer, (ii) a mutation in a BRCA2, PALB2, pl6, STKl 1, MLH1, MSH2, MSH6, PMS2, or EPCAM gene, (iii) a diagnosis of hereditary non-polyposis colorectal cancer, (iv) chronic pancreatitis, (v) diabetes, or (vi) a combination of (i)-(v).

[0020] The present disclosure also includes methods of screening a biological sample, wherein the methods comprise detecting in a biological sample of an individual identified as having (i) a first-degree relative diagnosed with pancreatic cancer, (ii) a mutation in a BRCA2, PALB2, pl6, STKl 1, MLH1, MSH2, MSH6, PMS2, or EPCAM gene, (iii) a diagnosis of hereditary non-polyposis colorectal cancer, or (iv) a combination of (i)-(iii), the level of at least 4 biomarkers of any one of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14.

[0021] The present disclosure also includes a kit for detecting the presence of early-stage pancreatic cancer in a patient, the kit comprising reagents useful, sufficient, or necessary for determining the level of each biomarker of any one of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14.

[0022] Other features and advantages of the invention will be apparent from the following Detailed Description, and from the Claims.

BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 illustrates a system for performing computer-assisted methods of determining the probability of a patient having early-stage pancreatic cancer.

[0024] Figure 2 shows sample boxplots for the biomarker analysis performed on Panel A in Example 1.

[0025] Figure 3 shows the mean AUCs calculated in Example 1 for

differentiation of PDAC from CON and ChPT from 100 multivariate analysis bootstraps using lasso, random forest, logistic regression (log it), or boosting, and the best 2 to 15 biomarkers, including CA 19-9.

[0026] Figure 4 shows the mean AUCs calculated in Example 1 for

differentiation of PDAC from CON from 100 multivariate analysis bootstraps using lasso, random forest, log it, or boosting, and the best 2 to 15 biomarkers, including CA 19-9.

[0027] Figure 5 shows the mean AUCs calculated in Example 1 for

differentiation of PDAC from ChPT from 100 multivariate analysis bootstraps using lasso, random forest, log it, or boosting, and the best 2 to 15 biomarkers, including CA 19-9.

[0028] Figure 6 shows the mean AUCs calculated in Example 1 for

differentiation of CON from ChPT from 100 multivariate analysis bootstraps using lasso, random forest, log it, or boosting, and the best 2 to 15 biomarkers, including CA 19-9.

[0029] Figure 7 shows the mean AUCs calculated in Example 1 for

differentiation of PDAC from CON and ChPT from 100 multivariate analysis bootstraps using lasso, random forest, log it, or boosting, and the best 2 to 15 biomarkers, excluding CA 19-9.

[0030] Figure 8 shows the mean AUCs calculated in Example 1 for

differentiation of PDAC from CON from 100 multivariate analysis bootstraps using lasso, random forest, log it, or boosting, and the best 2 to 15 biomarkers, excluding CA 19-9. [0031] Figure 9 shows the mean AUCs calculated in Example 1 for differentiation of PDAC from ChPT from 100 multivariate analysis bootstraps using lasso, random forest, log it, or boosting, and the best 2 to 15 biomarkers, excluding CA 19-9.

[0032] Figure 10 shows the mean AUCs calculated in Example 1 for differentiation of CON from ChPT from 100 multivariate analysis bootstraps using lasso, random forest, log it, or boosting, and the best 2 to 15 biomarkers, excluding CA 19-9.

[0033] Figure 1 1 shows a heatmap depicting Pair-wise Pearson correlation coefficients among significant analytes for Example 1.

[0034] Figure 12 shows boxplots for the biomarker analysis performed on

Panel F in Example 2.

[0035] Figure 13 shows the mean AUCs calculated in Example 2 for differentiation of PDAC from CON and ChPT from 100 multivariate analysis bootstraps using lasso, random forest, log it, or boosting, and the best 2 to 15 biomarkers.

[0036] Figure 14 shows the mean AUCs calculated in Example 2 for differentiation of PDAC from CON from 100 multivariate analysis bootstraps using lasso, random forest, log it, or boosting, and the best 2 to 15 biomarkers.

[0037] Figure 15 shows the mean AUCs calculated in Example 2 for differentiation of PDAC from ChPT from 100 multivariate analysis bootstraps using lasso, random forest, log it, or boosting, and the best 2 to 15 biomarkers.

[0038] Figure 16 shows the mean AUCs calculated in Example 2 for differentiation of CON from ChPT from 100 multivariate analysis bootstraps using lasso, random forest, log it, or boosting, and the best 2 to 15 biomarkers.

[0039] Figure 17 shows the mean AUCs calculate in Example 2 for differentiation of late stage PDAC from CON and ChPT from 100 multivariate analysis bootstraps using lasso, random forest, log it, or boosting, and the best 2 to 15 biomarkers.

[0040] Figure 18 shows the mean AUCs calculate in Example 2 for differentiation of late stage PDAC from CON and ChPT from 100 multivariate analysis bootstraps using lasso, random forest, log it, or boosting, and the best 2 to 15 biomarkers.

[0041] Figure 19 shows the mean AUCs calculate in Example 2 for differentiation of late stage PDAC from CON and ChPT from 100 multivariate analysis bootstraps using lasso, random forest, log it, or boosting, and the best 2 to 15 biomarkers. [0042] Figure 20 shows the mean AUCs calculate in Example 2 for

differentiation of early stage PDAC from CON and ChPT from 100 multivariate analysis bootstraps using lasso, random forest, log it, or boosting, and the best 2 to 15 biomarkers.

[0043] Figure 21 shows the mean AUCs calculate in Example 2 for

differentiation of early stage PDAC from CON from 100 multivariate analysis bootstraps using lasso, random forest, log it, or boosting, and the best 2 to 15 biomarkers.

[0044] Figure 22 shows the mean AUCs calculate in Example 2 for

differentiation of early stage PDAC from ChPT from 100 multivariate analysis bootstraps using lasso, random forest, log it, or boosting, and the best 2 to 15 biomarkers.

[0045] Figure 23 shows a heatmap of pair- wise Pearson correlation coefficients among significant analytes for Example 2.

[0046] Figure 24 shows a cluster dendrogram of Hierarchical clustering of PDAC samples sets from Example 1 and Example 2.

DETAILED DESCRIPTION OF THE INVENTION

Aspects of the Invention

[0047] Accordingly, one aspect of the present invention provides a method for screening patients. Generally, the method includes at least the following steps: (1) measuring the levels of a panel of biomarkers in a sample taken from a patient, said panel of biomarkers comprising at least three biomarkers in any of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14; and (2) determining the likelihood that the individual has pancreatic cancer based at least in part on the levels of the panel of biomarkers in the sample. In some embodiments, the methods may be used to determine the individual has early stage pancreatic cancer.

[0048] In another aspect the present invention provides a method for diagnosing pancreatic cancer, which comprises (1) measuring the levels of a panel of biomarkers in a sample taken from a patient, said panel of biomarkers comprising at least three biomarkers in any of Panels Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14; and (2) diagnosing the patient with pancreatic cancer based at least in part on the levels of the panel of biomarkers in the sample. In some embodiments, the methods may be used to diagnose early stage pancreatic cancer. In some embodiments the methods may be used to classify individuals with pancreatic cancer from individuals with other diseases such as other cancers, diabetes, Peutz-Jeghers syndrome, lynch syndrome, chronic pancreatitis, or acute pancreatitis.

[0049] In another aspect the present invention provides a method for treating pancreatic cancer, which comprises (1) measuring the levels of a panel of biomarkers in a sample taken from a patient, said panel of biomarkers comprising at least three biomarkers in any of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14; and (2) treating the patient for pancreatic cancer. In some embodiments the methods may be used to treat early stage pancreatic cancer.

[0050] In another aspect the present invention provides a method for screening patients. Generally, the method includes at least the following steps: (1) measuring the levels of a panel of biomarkers in a sample taken from a patient, said panel of biomarkers comprising at least three biomarkers in any of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14; and (2) determining the likelihood that the patient has pancreatitis based at least in part on the levels of the panel of biomarkers in the sample.

[0051] In another aspect the present invention provides a method for diagnosing pancreatitis, which comprises (1) measuring the levels of a panel of biomarkers in a sample taken from a patient, said panel of biomarkers comprising at least three biomarkers in any of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14; and (2) diagnosing the patient with pancreatitis based at least in part on the levels of the panel of biomarkers in the sample. In some embodiments, the methods may be used to diagnose acute or chronic pancreatitis. In some embodiments the methods may be used to classify patients with pancreatitis from patients with other diseases such as pancreatic cancer, other cancers, diabetes, Peutz-Jeghers syndrome, or Lynch syndrome.

[0052] In another aspect the present invention provides a method for treating pancreatitis, which comprises (1) measuring the levels of a panel of biomarkers in a sample taken from a patient, said panel of biomarkers comprising at least three biomarkers in any of Panels A - F; and (2) treating the patient for pancreatitis. In some embodiments the methods may be used to treat acute pancreatitis. In other embodiments, the methods may be used to treat chronic pancreatitis.

[0053] In another aspect, the present invention provides a method for

differentially diagnosing between pancreatic cancer and pancreatitis. In general, the method includes at least the following steps: (1) measuring the levels of a panel of biomarkers in a sample taken from a patient, said panel of biomarkers comprising at least three biomarkers in any of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14; and (2) diagnosing the patient with either pancreatitis or pancreatic cancer based at least in part on the levels of the panel of biomarkers in the sample. In some embodiments the methods may also include: (3) differentially treating the patient for either pancreatitis or pancreatic cancer based at least in part on the diagnosis of step (2).

[0054] In another aspect, the present invention provides for a computer program that uses biomarker levels to assesses the probability of a patient having pancreatic cancer or pancreatitis, comprising providing biomarker information from a biomarker information database module on a data processing device, wherein the biomarker information comprises threshold level information for each biomarker of a panel of biomarkers, wherein the panel of biomarkers comprises positive biomarkers, negative biomarkers, or both, wherein a level above a threshold level for positive biomarkers is indicative of the presence of pancreatic cancer or pancreatitis in a patient and a level below a threshold level for negative biomarkers is indicative of the presence of pancreatic cancer or pancreatitis in a patient. In some embodiments the computer program assesses the probability of a patient having early stage pancreatic cancer. In other embodiments, the computer program assesses the probability of a patient having a differential diagnosis of either pancreatitis or pancreatic cancer. In some embodiments, a statistically generated model or algorithm is used to assess the likelihood that a patient suffers from pancreatic cancer or pancreatitis.

[0055] In another aspect the invention provides a system for measuring the levels of biomarkers in a sample, comprising (1) a first computer program for receiving test biomarker data on the panel of biomarkers and (2) a second computer program for comparing the levels of biomarkers to one or more reference levels. In some embodiments the system comprises a computer program for determining the likelihood a patient has pancreatic cancer based at least in part on the comparison of the levels of biomarkers in the sample with said one or more reference levels. In some embodiments the system comprises a computer program for determining a differential diagnosis of either pancreatitis or pancreatic cancer for a patient based at least in part on the comparison of the levels of biomarkers in the sample with said one or more reference levels. In additional embodiments, a statistically generated model is used. In other

embodiments, other clinical variables in addition to the biomarkers are incorporated into the model.

[0056] In another aspect, the invention provides methods for combining the biomarker analysis as described above with analysis of other cancer risk factors, such as the patient's family and/or personal history of cancer, genetic analysis, or the patient's health history. In some embodiments the invention provides a method for determining whether a patient has pancreatic cancer, which comprises (1) evaluating the patient's personal and family history risk factors for pancreatic cancer (2) classifying the patient as at risk for pancreatic cancer (3) measuring the levels of a panel of biomarkers in a sample taken from a patient, said panel of biomarkers selected from any of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14; and (4) determining the likelihood that the patient has pancreatic cancer based on the patient's risk factors and the levels of the panel of biomarkers in the sample. In some embodiments the invention provides a method for differentially diagnosing whether a patient has pancreatic cancer or pancreatitis, which comprises (1) evaluating the patient's personal and family history risk factors for pancreatic cancer or pancreatitis (2) classifying the patient as at risk for pancreatic cancer or pancreatitis (3) measuring the levels of a panel of biomarkers in a sample taken from a patient, said panel of biomarkers selected from any of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14; and (4) determining a differential diagnosis of either pancreatic cancer or pancreatitis for the patient based on the patient's personal and family history risk factors and the levels of the panel of biomarkers in the sample. Personal risk factors may include, age, history of smoking, cancer diagnosis, new-onset diabetes diagnosis, diabetes diagnosis, Lynch syndrome diagnosis, Peutz- Jeghers syndrome diagnosis, chronic pancreatitis diagnosis, acute pancreatitis diagnosis, or genetic mutations in BRCA2, PALB2, pl6, STK1 1, MLH1, MSH2, MSH6, PMS2, or EPCAM. Family history risk factors can include a relative with pancreatic cancers, a relative with early onset cancer, a relative with multiple primary cancers, etc.

[0057] Several aspects of the invention described herein involve a step of correlating a particular assay or analysis result or output (e.g., increased or decreased level of individual biomarkers in a panel of biomarkers compared to thresholds ) to some likelihood (e.g., increased, not increased, decreased, etc.) of some clinical feature (e.g., pancreatic cancer, pancreatitis, etc.). Throughout this document, wherever such an aspect is described, an alternative aspect of the invention may involve, in addition to or instead of a correlating step, one or more of the following steps: (a) concluding that the patient has or does not have the clinical feature based at least in part on the assay or analysis result; (b) communicating that the patient has or does not have the clinical feature based at least in part on the assay or analysis result; (c) treating the patient based at least in part on the assay or analysis result; or (d) referring the patient for additional screening. Embodiments of these Aspects

[0058] Various embodiments of the preceding aspects of the invention are provided. Unless otherwise stated, the invention may apply each of these embodiments to each of the preceding aspects.

Indications

[0059] In some embodiments, the methods include evaluating the likelihood of pancreatic cancer in a patient at risk for developing pancreatic cancer. In some embodiments, the patient has at least one first degree relative with a pancreatic cancer diagnosis. In some embodiments, the individual has been diagnosed with or is symptomatic of Lynch syndrome. In some embodiments the individual has been diagnosed with or is symptomatic of chronic pancreatitis. In some embodiments the individual has been diagnosed with or is symptomatic of acute pancreatitis. In some embodiments the individual has been recently diagnosed with or is symptomatic of diabetes. In some embodiments the individual has been diagnosed with or is symptomatic of Peutz-Jeghers syndrome. In some embodiments, the individual has a mutation in a BRCA2 gene. In some embodiments, the individual has a mutation in a PALB2 gene. In some embodiments, the individual has a mutation in a pi 6 gene. In some embodiments, the individual has a mutation in a STK1 1 gene. In some embodiments, the individual has a mutation in a MLH1 gene. In some embodiments, the individual has a mutation in a MSH2 gene. In some embodiments, the individual has a mutation in a MSH6 gene. In some embodiments, the individual has a mutation in a PMS2 gene. In some embodiments, the individual has a mutation in an EPCAM gene. In some embodiments, the individual has any combination of the above indications.

[0060] In some embodiments, the methods include evaluating the likelihood of pancreatitis in a patient at risk for developing pancreatitis. In some embodiments, the patient has a personal history of chronic pancreatitis. In some embodiments, the patient has episodes of abdominal pain. In some embodiments, the patient has a personal history of heavy alcohol use. In some embodiments, the patient has a hereditary disorder of the pancreas or hereditary pancreatitis. In some embodiments, the patient has cystic fibrosis. In some embodiments, the patient has hypercalcemia, hyperlipidemia and/or hypertriglyceridemia. In some embodiments, the patient has certain autoimmune disorders that cause the patient to be more likely to develop pancreatitis. In some embodiments, the patient has any combination of the above indications. In other embodiments, the patient has a family history that causes the patient to be more likely to have any combination of the above indications. [0061] In some embodiments, the methods include evaluating the likelihood of developing pancreatic cancer in a patient with pancreatitis. In some embodiments, the patient has been diagnosed with acute pancreatitis. In some embodiments, the patient has been diagnosed with chronic pancreatitis. In some embodiments of these methods, the patient has risk factors for developing pancreatitis including any one or more of: history of chronic pancreatitis, episodes of abdominal pain, personal history of heavy alcohol use, hereditary disorder of the pancreas, hereditary pancreatitis, cystic fibrosis, hypercalcemia, hyperlipidemia,

hypertriglyceridemia, and/or autoimmune disorder(s) that increase the likelihood of developing pancreatitis. In some embodiments of these methods, the patient has risk factors for developing pancreatic cancer including any one or more of: a first degree relative with pancreatic cancer, a diagnosis or symptoms of Lynch syndrome, a diagnosis of new-onset diabetes, a diagnosis of diabetes, and/or a diagnosis or symptoms of Peutz-Jeghers syndrome. In some embodiments, a patient has at least one mutation in any one or more of the following genes: BRCA2, PALB2, pl6, STK11, MLH1, MSH2, MSH6, PMS2, and/or EPCAM. In some embodiments, the patient has any combination of the above indications.

Selecting Biomarkers

[0062] The methods, systems and kits of the invention will comprise measuring a panel of biomarkers. Thus in some embodiments, the levels of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 75, 80, 85, 90, 95, or 100 biomarkers are measured.

[0063] The panel of biomarkers analyzed in the methods, systems and kits of the invention will comprise at least some of the biomarkers listed in Panels A-L. Thus, in some embodiments, the panel of biomarkers comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 75, 80, 85, 90, 95, or 100 biomarkers listed in Panels A-L. In some embodiments, the biomarkers chosen from Panels A-L comprise at least some percentage, e.g., 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 60%, 70%, 80%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100%, of the panel of biomarkers to be analyzed. [0064] In some embodiments, the panel of biomarkers may be selected by comparing the levels of the biomarkers in a control group with the level of biomarkers in a group with early stage pancreatic cancer. In some embodiment, the biomarkers may be selected by comparing the levels of biomarkers in control group with the level of biomarkers in a group of individuals with late stage pancreatic cancer. In some embodiments, the control group may consist of healthy age matched individuals. In some embodiments, the control group may further consist of individuals with other cancers. In some embodiments, the control group may further consist of individuals with diabetes.

[0065] In some embodiments biomarkers may be selected by performing statistical analysis on the measured levels of biomarkers in a group of individuals with pancreatic cancer compared to controls. Biomarkers may be selected based on their t-test p- value, where said value is equal to or less than 0.05. In some embodiments biomarkers may be selected based on their ANOVA p-value, where said value is equal to or less than 0.05. In some embodiments biomarkers may be further selected based on their Wilcoxon p-value, where said value is equal to or less than 0.05. In some embodiments biomarkers may be further selected based on their Kruskal-Wallis p-value, where said value is equal to or less than 0.05. In some embodiments biomarkers may be selected based on their q- value, where said value is equal to or less than 0.05. In some embodiments biomarkers may be selected based on their area under the receiver operating characteristic curve ("AUC") value.

[0066] In some embodiments, the biomarkers are selected from Neuropilin-1,

Peptidase D (PEPD), Neutrophil Gelatinase-Associated Lipocalin (NGAL), Dipetidyl Peptidase IV (DPPIV), Cancer Antigen 19-9 (CA 19-9), Complement Component Clq Receptor (ClqRl), Interleukin-6 Receptor Subunit Beta (IL-6R beta), Fatty Acid-Binding Protein Liver (FABP liver), Lumican, Cathespin D, Lactoferrin ( LTF), Cartilage Oligomeric Matrix Protein (COMP), Squamous Cell Carcinoma Antigen- 1 (SCCA-1), Cadherin-1, Mast/Stem Cell Growth Factor Receptor (SCFR), Monokine Induced by Gamma Interferon (MIG), Vascular Endothelial Growth Factor C (VEGF-C), Osteopontin, Antileukopronteinase (ALP), Pulmonary Surfactant- Associated Protein (SP-D), Interlukein-6 ( IL-6), Cancer Antigen 125 (CA-125), Insulin-Like Growth Factor Binding Protein 5 (IGFBP5), Cystatin-B, Phosphoserine Aminotransferase (PSAT), Thrombospondin-4 (TSP4), Pancreatic Secretory Trypsin Inhibitor (TATI), Tissue Inhibitor of Metalloproteinases 2 (TIMP-2), Interluekin-2 Receptor Alpha (IL-2 receptor alpha), Carcrinoembryonic Antigen-Related Cell Adhesion Molecule 1 (CEACAM1), Stromal Cell- Derived Factor- 1 (SDF-1), Osteoprotegerin (OPG), Urokinase-Type Plasminogen Activator Receptor (uPAR), Ezrin, YKL-40, and Glucose-6-Phosphte Isomerase (G6PI). [0067] In some embodiments, the biomarkers are selected from Neuropilin-1,

PEPD, NGAL, DPPIV, CA 19-9, FABP liver, IL-6R beta, ClqRl, VEGF-C, Lumican,

Cathespin D, LTF, CA-125, IL-6, SCCA-1, TATI, MIG, OPG, Cystatin- B, SP-D, Cadherin-1, COMP, ALP, Osteopontin, IGFBP5, SCFR, PSAT, TIMP-2, TSP4, YKL-40, uPAR, Leptin, G6PI, IL-2 receptor alpha, SDF-1, Tissue Type Plasminogen Activator (tPA), Tyrsosine Kinase with IG and EGF Homology Domains 2 (TIE-2), and Ezrin.

[0068] In some embodiments, the biomarkers are selected from Neuropilin-1,

PEPD, NGAL, IL-6R beta, Cathespin D, Carcrinoembryonic Antigen-Related Cell Adhesion Molecule 6 (CEACAM6), Cancer Antigen 9 (CA-9), FABP liver, ClqRl, Oseoponitin, TATI, CEACAM1, IL-6, Lumican, uPAR, Galectin-3, SCFR, CA-125, B Cell-Activating Factor (BAFF), OPG, Tumor Necrosis Factor Receptor 1 (TNF Rl), VEGF-C, MIG, IL-2 receptor alpha, ALP, IGFBP5, COMP, YKL-40, Cadherein-1, Midkine, SDF-1, Vascular Endothelial Growth Factor Receptor 1 (VEGFR-1), Prostasin, SCCA-1, LTF, Leptin, Interleukin- 18 Binding Protein (IL-18bp), TIE-2, Vascular Endothelial Growth Factor (VEGF), TSP4, HE4, PSAT, Cystatin-B, Cathepsin B (CTSB), Hepsin, Carcinoembryonic antigen (CEA), SP-D, Aldose Reductase, Maspin, Insulin-like Growth Factor Binding Protein 4 (IGFBP4), Decorin,

Hepatocyte Growth Factor (HGF), Collagen IV, 6Ckine, TIMP-2, Macrophage Inflammatory Protein 3 beta (MIP-3 beta), Tenascin-C (TN-C), Vascular Endothelial Growth Factor D (VEGF- D), Glucose-6-Phosphate Isomerase (G6PI), Protein S100-A6 (S100-A6), and Tetranectin.

[0069] In some embodiments, the biomarkers are selected from Neuropilin-1,

PEPD, DPPIV, CA 19-9, NGAL, CA-9, CEACAM6, galectin-3, cathespin-D, FABP liver, lumican, BAFF, TATI, OPG, uPAR, IL-6R beta, IL-6, ClqRl, CA-125, SCFR, osetopontin, VEGF, CEACAM1, TNF RI, YKL-40, SDF-1, midkine, leptin, VEGF-C, IL-2 receptor alpha, MIG, IGFBP5, prostasin, LTF, cystatin B, SCCA-1, IL-18bp, CEA, HE4, COMP, hespin, G6PI, CTSB, TIE-2, aldose reductase, VEGFR-1, HGF, Cadherin-1, SP-D, ALP, IGFBP4, TIMP-2, decorin, maspin, , Vascular Endothelial Growth Factor Receptor 3 (VEGFR-3), PSAT, MIP-3 beta, and TSP4.

Measuring Biomarkers

[0070] Those skilled in the art are familiar with various techniques for measuring the levels of biomarkers in a sample. Useful techniques include, but are not limited to antibody tests, Western Blot, ELISA, PCR, or quantitative PCR. [0071] In some embodiments biomarkers are measured by using Enzyme-Linked

Immunosorbent Assay (ELISA), liquid chromatography mass spectrometry (LCMS), surface enhanced laser desorption ionization time, surface enhanced laser desorption ionization time of flight mass spectrometry (SELDI-TOF), two-dimension gel electrophoresis (2-DE), and or any combinations thereof.

[0072] In some embodiments biomarkers are measured using bead-based multiplex immune assays. In some embodiments the bead-based multiplex immune assays further comprise combining Luminex technology with automated liquid handling. In some embodiments the methods includes detection of blood proteins. In some embodiments the methods include using small sample volumes. In some embodiments the sample volume is as low as lOul. In some embodiments the methods include measuring biomarkers in a dynamic range of fg/ml to mg/ml.

[0073] In some embodiments biomarkers are measured using a multiplexed assay. In some embodiments the multiplexed assay further comprises a microsphere-based assay. In some embodiments multiplex assays are performed in a single reaction vessel. In some embodiments multiplex assays are performed by combining optical classification schemes, biochemical assays, flow cytometry, and advanced digital signal processing hardware and software. In some embodiments as many as 100 multiplex assays are performed in a single reaction vessle.

[0074] In some embodiments the multiplex assay inculdes analyte-specific microspheres labeled with a unique flourescent signature. In some embdiments microbeads are labled with red flourecent dye. In some embodiments microbeads are labeled with far red flourescent dye. In some embodiments microbeads are lableled with varying intensitiy of dye.

[0075] In some embdiments the multiplex assay inculdes an assay specific capture reagent that is covalently conjugted to a set of unique microspheres. In some embodiments conjugation occurs with carbodimide chemistry. In some embodiments conjugation occurs with carboxyl functional groups on the surmace of the microsphers. In some embodiments conjugation occurs with primary amines in the capture agent. In some embodiments the assay specific capture reagent is an antibody. In some embodiments the assay specific capture reagent is an antigen, receptor, peptide or enzyme substrate. In some embodiments the assay-specific capture reagent on each individual microsphere binds the analyte of interest. [0076] In some embodiments a biotinylated detecting reagent is added to the assay. In some embodiments the biotinylated detecting reagent is an antibody, antigen or ligand. In some embodiments the biotinlyated detecting moledule is assay-specific. In some

embodiments, a streptavidin-labeled floursecent molecule is added to the assay. In some embodiments, the streptavidin-labeled floursecent molecule is phycoerythrin.

[0077] In some embodiments the assay kinetics are near solution-phase. In some embdiments the multiplex assay is washed to remove unbound detecting reagents. In some embodiments the multiplex reaction is passed through an analyzer comprising laser beams. In some embodiments the analyzer uses hydrodynamic focusing to pass microspheres through the analzyer in single file. In some embodiments the indivudal mircosphers pass through at least one excitation beams. In some emboimdnets miscrosphere size is measured. In some embodiments microsphere size is determined by measuring 90-degree light scatter. In some embodiments microsphere size is deteremined by passing the mirospheres through a red diode, 633 nm, laser. In some embodiments miscosphere aggregates are removed from analysis. In some embodiments the encoded floursecent signaure of the mincrobead is analyzed. In some embodiments the flouresent signature is determined by passing the microspheres through a red diode laser. In some embodiments the flouresent signature is measured using avalance photodiodes. In some embodiments miscrosphere flourescence is measured. In some embodiments microsphere flouresence is measured by passing the microspheres through a green diode, 532 nm, laser. In some embodiments a flourescent reporter signal is genereated in proportion to the analyte concentration. In some embodiments the flourescens is measured using a photomultiplier tube. In some embodiments at least 100 microspheres from each analyte set are analyzed. In some embodiments the median value of the analyte-specific flourescent is measured. In some embodiments analyte flourecemce is calibrated using internal controls of known quantity.

Samples

[0078] In some embodiments the patient is human. In some embodiments the biological sample is taken from a patient or individual.

[0079] In some embodiments the sample is blood. In some embodiments the sample from is plasma. In some embodiments the sample from the patient may include urine, blood, plasma, serum, buffy coat, saliva or buccal swabs. [0080] In some embodiments, the sample is serum, plasma or blood.

[0081] In some embodiments, the biological sample is stored and/or shipped at room temperature with or without stabilizers. In some embodiments, the biological sample is stored and/or shipped frozen. In some embodiments, the biological sample is stored and/or shipped chilled. In some embodiments, serum, plasma or whole blood arebe dried on a material for storage and or shipping. In a related embodiment, the biological sample is eluted from the storage material when ready to be tested at the testing site.

Comparing Biomarker Levels

[0082] The methods of the invention generally involve measuring the levels of a panel of biomarkers described herein. With modern techniques, it is often possible to measure the levels of tens or hundreds of biomarkers. Once such a global assay has been performed, one may then informatically analyze one or more subsets of biomarkers (panels, or sub-panels of biomarkers). After measuring the levels of tens or hundreds of biomarkers in a sample, for example, one may analyze (e.g., informatically) the levels of a panel or sub-panel of test biomarkers comprising primarily biomarkers in any of Panels A-L according to the present invention. Additionally, in some embodiments, a specific targeted panel of biomarkers is selected and only those biomarkers are measured in the biological sample. In related embodiments, those specific panels comprise at least 3 biomarkers selected from of any one of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14

[0083] In some embodiments, the levels of the biomarkers in the individual are compared to a threshold or a reference standard. In some embodiments, the threshold for each biomarker is determined by measuring the levels of the biomarker in a healthy age- and sex- matched individual or group of healthy age matched individuals. In some embodiments, the threshold for each biomarker is determined by measuring the levels of the biomarker in an individual or group of individuals with a known early stage pancreatic cancer diagnosis. In some embodiments, the threshold for each biomarker is determined by measuring the levels of the biomarker in an individual or group of individuals with a known late stage pancreatic cancer diagnosis. In some embodiments, the threshold for each biomarker is further determined by measuring the levels of the biomarker in an individual or group of individual with a cancer diagnosis other than pancreatic cancer. In some embodiments, the threshold for each biomarker is further determined by measuring the levels of the biomarker in an individual or group of individuals diagnosed with diabetes. In some embodiments, the threshold for each biomarker is further determined by measuring the levels of the biomarker in an individual or group of individuals diagnosed with acute pancreatitis. In some embodiments, the threshold for each biomarker is further determined by measuring the levels of the biomarker in an individual or group of individuals diagnosed with chronic pancreatitis. In some embodiments, the threshold for each biomarker is further determined by measuring the levels of the biomarker in an individual or group of individuals diagnosed with acute pancreatitis and chronic pancreatitis.

[0084] In some embodiments, the levels of the biomarkers in the individual are compared to levels measured in one or more control group(s). In some embodiments, the control group consists of healthy, age-matched individuals. In some embodiments, the control group consists of individuals with an early stage pancreatic cancer diagnosis. In some embodiments, the control group consists of individuals with a late stage pancreatic cancer diagnosis. In some embodiments, the control group consists of individuals with a cancer diagnosis other than pancreatic cancer. In some embodiments, the control group consists of individuals with diabetes. In some embodiments, the control group consists of individuals with acute pancreatitis. In some embodiments, the control group consists of individuals with chronic pancreatitis. In some embodiments, the control group consists of individuals with acute and chronic pancreatitis.

[0085] In some embodiments, the determination of the likelihood an individual has pancreatic cancer is based on determining the levels of biomarkers in the individual's sample are significantly different than those measured in the control group. In some embodiments, the determination of the likelihood an individual has pancreatic cancer is based on the magnitude of the difference between the levels of biomarkers in the individual's sample and the levels of biomarkers in the control sample. In some embodiments, the determination of the likelihood an individual has pancreatic cancer is based on determining the levels of biomarkers in the individual's sample are elevated when compared to a control group. In some embodiments, the determination of the likelihood an individual has pancreatic cancer is based on determining the levels of biomarkers in the individual's sample are decreased when compared to a control group.

[0086] In some embodiments, the determination of the likelihood an individual has pancreatic cancer is based on determining the levels of biomarkers in the individual's sample are above or below the threshold level. In some embodiments, the determination of the likelihood an individual has pancreatic cancer is based on the magnitude of the difference between the levels of biomarkers in the individual's sample and the threshold. [0087] In some embodiments the levels of biomarkers in the individuals sample indicate early stage pancreatic cancer. In some embodiments the levels of biomarkers indicate stage 2 pancreatic cancer. In some embodiments the levels of biomarkers indicated stage 3 pancreatic cancer.

[0088] In some embodiments, the determination of the likelihood that an individual has chronic pancreatitis is based on determining that the levels of biomarkers in the patient's sample are significantly different than those measured in the control group. In some embodiments, the determination of the likelihood that a patient has pancreatitis is based on the magnitude of the difference between the levels of biomarkers in the patient's sample and the levels of biomarkers in the control sample. In some embodiments, the determination of the likelihood that a patient has pancreatitis is based on determining the levels of biomarkers in the patient's sample are elevated when compared to a control group. In some embodiments, the determination of the likelihood that a patient has pancreatitis is based on determining the levels of biomarkers in the patient's sample are decreased when compared to a control group.

[0089] In some embodiments, the determination of the likelihood that a patient has pancreatitis is based on determining that the levels of biomarkers in the patient's sample are above or below the threshold level. In some embodiments, the determination of the likelihood that an individual has pancreatic cancer is based on the magnitude of the difference between the levels of biomarkers in the individual's sample and the threshold. In some embodiments the levels of biomarkers in the individuals sample indicate acute pancreatitis. In some embodiments the levels of biomarkers indicate chronic pancreatitis.

[0090] In some embodiments, comparing a biomarker in a sample to a control or reference comprises generating a statistical model to determine the likelihood an individual suffers from pancreatic cancer or chronic pancreatitis. In some embodiments, the statistical tools of lasso, random forest, logistic regression, or boosting are used to generate a statistical model to differentiate pancreatic cancer or chronic pancreatitis or control populations with a given sensitivity and specificity. In some embodiments, a linear model is used to generate a score which indicates an individual's probability of having pancreatic cancer or chronic pancreatitis.

[0091] In some embodiments, the expression levels measured in a sample are used to derive or calculate a value or score, as described above. This value may be derived solely from expression levels or optionally derived from a combination of the expression value scores with other components (e.g., clinical staging, etc.) to give a potentially more comprehensive value/score. Thus, in every case where an embodiment of this disclosure involves determining the status of a biomarker, or comparing a biomarker to a control or reference value, related embodiments involve deriving or calculating a value or score from the measured status, where that score is based on a statistical analysis of diseased and control populations as described above.

[0092] In some such embodiments, multiple scores (e.g., expression test value and clinical parameters, such as clinical staging) can be combined into a more comprehensive score. Single component (e.g., biomarker) or combined test scores for a particular patient can be compared to single component or combined scores for reference populations, with differences between test and reference scores being correlated to or indicative of some clinical feature. Thus, in some embodiments this disclosure provides methods comprising (1) obtaining the measured expression levels of a panel of genes in a sample from the patient, (2) calculating a test value from these measured expression levels, (3) comparing said test value to a reference value calculated from measured expression levels of the panel of genes in a reference population of patients, and (4)(a) correlating a test value greater than the reference value to a diagnosis of pancreatic cancer, (4)(b) correlating a test value equal to or less than the reference value to a diagnosis of chronic pancreatitis or (4)(c) correlating a test value equal to or less than the reference value to a benign diagnosis.

[0093] In some such embodiments the test value is calculated by averaging the measured expression of the panel genes (as discussed below). In some embodiments the test value is calculated by weighting each of the panel of genes in a particular way.

Determining Levels of Biomarkers

[0001] In some embodiments, the methods comprise determining from a biological sample of a patient a level of a panel of biomarkers of any one of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14, wherein the panel comprises positive biomarkers, negative biomarkers, or both. The methods may further comprise comparing the level of the panel of biomarkers to threshold levels for each biomarker. The methods may further comprise determining the presence or absence of the panel of biomarkers that have a level either above a threshold level for each particular positive biomarker or below a threshold level for each particular negative biomarker. In related embodiments, the levels of the panel of biomarkers indicates a likelihood of pancreatic cancer. In distinct but related embodiments, the levels of the panel of biomarkers indicates a likelihood of chronic pancreatitis In distinct but related embodiments, the levels of the panel of biomarkers indicates a patient with chronic pancreatitis is likely to have pancreatic cancer.

[0002] In some embodiments, the methods may further comprise determining the level of the biomarkers in a control group. The methods may further comprise comparing the levels of the panel of biomarkers in the patient with the levels measured in the control group. The methods may further comprise determining the levels of the panel of biomarkers in the patient are different than the levels measured in the control group. In related embodiments, the different levels of such biomarkers indicates a likelihood of pancreatic cancer. In related embodiments, the different levels of such biomarkers indicates a likelihood of chronic pancreatitis. In some embodiments, the control group will include healthy, age-matched individuals. In some embodiments, the control group may further include individuals with diabetes or other cancers.

[0003] In some embodiments the methods may further comprise determining the threshold level of the panel of biomarkers by measuring the levels of the panel of biomarkers in individuals known to have early stage pancreatic cancer. In some embodiments the threshold level may be further determined by measuring the levels of biomarkers in individuals known to have late stage pancreatic cancer. In some embodiments the threshold levels may be further determined by measuring the levels of the biomarkers in individuals with diabetes or other cancers. In some embodiments the methods may further comprise determining the threshold level of the panel of biomarkers by measuring the levels of the panel of biomarkers in individuals known to have chronic pancreatitis.

[0004] In some embodiments, the methods may further comprise determining the levels of the panel of biomarkers in a group of individuals with a diagnosis of pancreatic cancer, early stage pancreatic cancer, or late stage pancreatic cancer.

[0005] In some embodiments, the methods further comprise determining from the biological sample of the patient the level of each biomarker of any one of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14.

[0006] In some embodiments, the methods further comprise determining the number of family members of the patient diagnosed with pancreatic cancer and determining the degree of relationship to the patient of any such family member.

[0007] In some embodiments, the methods further comprise determining the presence or absence of a mutation in a BRCA2, PALB2, p l 6, STK1 1 , MLH 1 , MSH2, MSH6, PMS2, or EPCAM gene of the patient, or determining whether the patient has hereditary non-polyposis colorectal cancer or a history of diabetes or chronic pancreatitis.

Confirmatory Testing

[0008] The above methods can be used to indicate an individual has or has a high probability of having pancreatic cancer, thus in some embodiments the results of the biomarker analysis will indicate further testing. In some embodiments, further testing may confirm a diagnosis of pancreatic cancer. In some embodiments the biomarker analysis may be confirmed by Computed Tomography (CT) scan. In some embodiments the biomarker analysis may be confirmed by Somatostatin Receptor Scintigraphy (SRS). In some embodiments the biomarker analysis may be confirmed by Positron Emission Tomography (PET) scan. In some embodiments the biomarker analysis may be confirmed by ultrasonography

(ultrasound). In some embodiments the biomarker analysis may be confirmed by Endoscopic Retrograde Cholangiopancreatography (ERCP). In some embodiments the biomarker analysis may be confirmed by Angiography. In some embodiments the biomarker analysis may be confirmed by biopsy. In some embodiments the biomarker analysis may be confirmed by surgery.

[0009] The above methods can be used to diagnose a patient with pancreatitis based in part on the analysis of the biomarker assay. Thus in some embodiments, the diagnosis of pancreatitis will indicate further testing to confirm a diagnosis of pancreatitis. In some embodiments, further testing may include one or more of the following: abdominal ultrasound, computerized tomography scan (CT scan), endoscopic ultrasound (EUS), magnetic resonance cholangiopancreatography (MRCP), therapeutic endoscopic retrograde cholangiopancreatography (ERCP). In some embodiments the biomarker analysis may be confirmed by biopsy. In some embodiments the biomarker analysis may be confirmed by surgery.

[0010] The above methods can be used to differentially diagnose a patient with either pancreatitis or pancreatic cancer. In some embodiments of these methods, the differential diagnosis of either pancreatitis or pancreatic cancer will indicate further testing to either confirm a diagnosis of pancreatitis or confirm a diagnosis of pancreatic cancer. The further testing to confirm a diagnosis of pancreatitis may include any of the confirmatory pancreatitis testing listed above. The further testing to confirm a diagnosis of pancreatic cancer may include any of the confirmatory pancreatic cancer testing listed above.

Treatment and Altering Treatment

[0011] The above methods can be used to indicate an individual has pancreatic cancer, thus in some embodiments the results of the biomarker analysis will indicate treatment of pancreatic cancer. In some embodiments the biomarker analysis indicates treatment of pancreatic cancer by chemotherapy. In some embodiments the biomarker analysis indicates treatment of pancreatic cancer by surgical resection. In some embodiments the biomarker analysis indicates treatment of pancreatic cancer by radiation therapy. In some embodiments the biomarker analysis indicates treatment of pancreatic cancer by ablative techniques. In some embodiments the biomarker analysis indicates treatment of pancreatic cancer by some combination of chemotherapy, radiation therapy, ablative techniques and/or surgical resection.

[0012] The above methods can be used to indicate an individual has pancreatitis, thus in some embodiments the results of the biomarker analysis will indicate treatment of pancreatitis. In some embodiments the biomarker analysis indicates treatment of pancreatitis by one or more of the following techniques: hospitalization of intravenous fluids, antibiotics, pain medications, and/or nasogastric feeding. In some embodiments treatment of pancreatitis may include therapeutic endoscopic retrograde cholangiopancreatography (ERCP) to perform sphincterotomy, gallstone removal, stent placement, and/or balloon dilatation. In other embodiments, treatment of pancreatitis may include synthetic pancreatic enzymes, avoidance of alcohol, treatment of diabetes and/or specialized diet. In some

embodiments, treatment of pancreatitis may involve surgical removal of all or part of the pancreas.

[0013] The above methods can be used to differentially diagnose a patient with either pancreatitis or pancreatic cancer. Therefore, in some embodiments, the differential diagnosis will indicate a differential treatment of either pancreatitis or pancreatic cancer. In some embodiments in which the differential diagnosis was a diagnosis of pancreatic cancer, the treatment can include any of the disclosed treatments indicated for pancreatic cancer. In some embodiments in which the differential diagnosis was a diagnosis of pancreatitis, the treatment can include any of the disclosed treatments indicated for pancreatitis. In treatments in which the differential diagnosis was a diagnosis of pancreatitis, the treatment may include subsequent, follow-up biomarker analysis to monitor for the development of pancreatic cancer.

Reference Standards for Treatment

[0014] In many embodiments, the levels of one or more biomarkers or the levels of a specific panel of biomarkers in a sample are compared to a reference standard ("reference standard" or "reference level") in order to direct treatment decisions. The reference standard used for any embodiment disclosed herein may comprise average, mean, or median levels of the one or more biomarkers or the levels of the specific panel of biomarkers in a control population. The reference standard may additionally comprise cutoff values or any other statistical attribute of the control population, such as a standard deviations from the mean levels of the one or more biomarkers or the levels of the specific panel of biomarkers.

[0015] In some embodiments, comparing the level of the one or more biomarkers is performed using a cutoff value. In related embodiments, if the level of the one or more biomarkers is greater than the cutoff value, the individual may be diagnosed as having, or being at risk of developing pancreatic cancer or chronic pancreatitis. In other distinct embodiments, if the level of the one or more biomarkers is less than the cutoff value, the individual may be diagnosed as having, or being at risk of developing pancreatic cancer or chronic pancreatitis. Cutoff values may be determined by statistical analysis of the control population to determine which levels represent a high likelihood that an individual does or does not belong to the control population. In some embodiments, comparing the level of the one or more biomarkers is performed using other statistical methods. In related embodiments, comparing comprises logistic or linear regression. In some embodiments, comparing the level of the one or more biomarkers is performed by generating a multivariate model. In other embodiments, comparing comprises computing an odds ratio.

[0016] In some embodiments, the control population may comprise healthy individuals, individuals with chronic pancreatitis, individuals with pancreatic cancer, or a mixed population of individuals with chronic pancreatitis, pancreatic cancer or both.

[0017] In some embodiments, individuals with levels of one or more biomarkers or levels of a specific panel of biomarkers greater than the reference levels would be more likely to have pancreatic cancer or chronic pancreatitis. Therefore, an individual presenting with levels of the one or more biomarkers or levels of the specific panel of biomarkers greater than the reference standard would be a candidate for treatment for pancreatic cancer, treatment for pancreatitis, or with more aggressive treatment for pancreatic cancer or pancreatitis On the other hand, an individual presenting with levels of the one or more biomarkers or levels of the specific panel of biomarkers less than or equal to the reference standard would be less likely to have pancreatic cancer and therefore be a candidate for no treatment for pancreatic cancer, no treatment for pancreatitis, delayed treatment for pancreatic cancer, delayed treatment for pancreatitis or less aggressive treatment for pancreatic canceror pancreatitis.

[0018] In other embodiments, individuals with levels of one or more biomarkers or levels of a specific panel of biomarkers less than the reference levels would be more likely to have pancreatic cancer or chronic pancreatitis. Therefore, an individual presenting with levels of the one or more biomarkers or levels of the specific panel of biomarkers less than the reference standard would be a candidate for treatment for pancreatic cancer, treatment for pancreatitis, or with more aggressive therapy for pancreatic cancer or pancreatitis. On the other hand, an individual presenting with levels of the one or more biomarkers or levels of the specific panel of biomarkers greater than or equal to the reference standard would be less likely to have pancreatic cancer or pancreatitis and therefore be a candidate for no treatment for pancreatic cancer, no treatment for pancreatitis, delayed treatment for pancreatic cancer, delayed treatment for pancreatitis or less aggressive treatment for pancreatic cancer or pancreatitis.

Reference Therapy for Treatment

[0019] In some embodiments, a patient is treated more or less aggressively than a reference therapy. A reference therapy is any therapy that is the standard of care for chronic pancreatitis or pancreatic cancer. The standard of care can vary temporally and geographically, and a skilled person can easily determine the appropriate standard of care by consulting the relevant medical literature.

[0020] In some embodiments, based on a determination that levels of a panel of biomarkers is a) greater than, b) less than, c) equal to, d) greater than or equal to, or e) less than or equal to a reference standard, treatment will be either 1) more aggressive, or 2) less aggressive than a standard therapy.

[0021] In some embodiments, a more aggressive therapy than the standard therapy comprises beginning treatment earlier than in the standard therapy. In some

embodiments, a more aggressive therapy than the standard therapy comprises treating on an accelerated schedule compared to the standard therapy. In some embodiments, a more aggressive therapy than the standard therapy comprises administering additional treatments not called for in the standard therapy.

[0022] In some embodiments, a less aggressive therapy than the standard therapy comprises delaying treatment relative to the standard therapy. In some embodiments, a less aggressive therapy than the standard therapy comprises administering less treatment than in the standard therapy. In some embodiments, a less aggressive therapy than the standard therapy comprises administering treatment on a decelerated schedule compared to the standard therapy. In some embodiments, a less aggressive therapy than the standard therapy comprises administering no treatment.

Treatment of Chronic Pancreatitis

[0023] Health practitioners treat chronic pancreatitis by taking actions to ameliorate the causes or symptoms of the disorder in a patient. Treatment may comprise drug- based or non-drug-based therapies.

[0024] Drug-based therapies may include: selecting and administering one or more drugs to the patient, adjusting the dosage of a drug, adjusting the dosing schedule of a drug, and adjusting the length of the therapy with a drug. Drugs are selected by practitioners based on the nature of the symptoms and the patient's response to any previous treatments. The dosage of a drug can be adjusted as well by the practitioner based on the nature of the drug, the nature of the patient's symptoms, the patient's response to previous treatment, and the patient's response to the drug. The dosing schedule can also be adjusted by the practitioner based on the nature of the drug, the nature of the patient's symptoms, the patient's response to previous treatment, and the patient's response to the drug. Also, the length of the therapy can be adjusted by the practitioner based on the nature of the drug, the nature of the patient's symptoms, the patient's response to previous treatment, and the patient's response to the drug. Additionally, the practitioner can select between a single drug therapy, a dual drug therapy, or a triple drug therapy. In some embodiments, a practitioner may optionally treat the patient with a combination of one or more drugs and one or more non-drug-based therapies.

[0025] In one embodiment, the practitioner begins therapy based on a comparison between a reference level and the levels of one or more biomarkers or the levels of a specific panel of biomarkers in a sample from a patient. In some embodiments, the comparison between a reference level and the levels of one or more biomarkers or the levels of a specific panel of biomarkers comprises using the levels of the biomarkers in a biological sample from the patient to derive a score In one embodiment, therapy comprises the selection and administration of a drug to the patient by the practitioner. In another embodiment, therapy comprises the selection and administration of two drugs to the patient by the practitioner as part of dual therapy. In another embodiment, therapy comprises the selection and administration of three drugs to the patient by the practitioner as part of triple therapy.

[0026] Drugs are commonly used by medical practitioners, and a skilled person may identify the appropriate drug to administer based on the medical literature. In some embodiments, treatment comprises administering to an individual an analgesic agent. In some embodiments, treatment comprises administering to an individual uncoated pancreatic enzymes.

[0027] In addition to or in lieu of drug-based therapies, in some embodiments a practitioner may also treat an individual with non-drug based therapies. In some embodiments, the non-drug based therapy comprises Endoscopic therapy aimed at decompressing an obstructed pancreatic duct. In some embodiments, the non-drug based therapy comprises pancreatic duct drainage. In some embodiments, the non-drug based therapy comprises pancreatic resection. In some embodiments, the non-drug based therapy comprises total pancreatectomy and islet autotransplantation.

[0028] In one embodiment, the practitioner adjusts the therapy based on a comparison between a reference level and the levels of one or more biomarkers or the levels of a specific panel of biomarkers in a sample from a patient. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different combination of drugs. In one embodiment, the practitioner adjusts the therapy by adjusting drug dosage. In one embodiment, the practitioner adjusts the therapy by adjusting dose schedule. In one embodiment, the practitioner adjusts the therapy by adjusting length of therapy. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug combination and adjusting drug dosage. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug combination and adjusting dose schedule. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug combination and adjusting length of therapy. In one embodiment, the practitioner adjusts the therapy by adjusting drug dosage and dose schedule. In one embodiment, the practitioner adjusts the therapy by adjusting drug dosage and adjusting length of therapy. In one embodiment, the practitioner adjusts the therapy by adjusting dose schedule and adjusting length of therapy. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug, adjusting drug dosage, and adjusting dose schedule. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug, adjusting drug dosage, and adjusting length of therapy. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug, adjusting dose schedule, and adjusting length of therapy. In one embodiment, the practitioner adjusts the therapy by adjusting drug dosage, adjusting dose schedule, and adjusting length of therapy. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug, adjusting drug dosage, adjusting dose schedule, and adjusting length of therapy.

[0029] In some embodiments, treatment comprises a less aggressive therapy than a reference therapy. In one embodiment a less aggressive therapy comprises not administering drugs and taking a "watchful waiting" approach. In one embodiment a less aggressive therapy comprises delaying treatment. In one embodiment a less aggressive therapy comprises selecting and administering less potent drugs. In one embodiment a less aggressive therapy comprises decreasing dosage of drugs. In one embodiment a less aggressive therapy comprises decreasing the frequency treatment. In one embodiment a less aggressive therapy comprises shortening length of therapy. In one embodiment, less aggressive therapy comprises selecting and administering less potent drugs and decreasing drug dosage. In one embodiment, less aggressive therapy comprises selecting and administering less potent drugs and decelerating dose schedule. In one embodiment, less aggressive therapy comprises selecting and administering less potent drugs and shortening length of therapy. In one embodiment, less aggressive therapy comprises decreasing drug dosage and decelerating dose schedule. In one embodiment, less aggressive therapy comprises decreasing drug dosage and shortening length of therapy. In one embodiment, less aggressive therapy comprises decelerating dose schedule and shortening length of therapy. In one embodiment, less aggressive therapy comprises selecting and administering less potent drugs, decreasing drug dosage, and decelerating dose schedule. In one embodiment, less aggressive therapy comprises selecting and administering less potent drugs, decreasing drug dosage, and shortening length of therapy. In one embodiment, less aggressive therapy comprises selecting and administering less potent drugs, decelerating dose schedule, and shortening length of therapy. In one embodiment, less aggressive therapy comprises decreasing drug dosage, decelerating dose schedule, and shortening length of therapy. In one embodiment, less aggressive therapy comprises selecting and administering less potent drugs, decreasing drug dosage, decelerating dose schedule, and shortening length of therapy. In some embodiments, a less aggressive therapy comprises administering only non-drug-based therapies.

[0030] In another aspect of the present application, treatment comprises a more aggressive therapy than a reference therapy. In one embodiment a more aggressive therapy comprises earlier administration of drugs. In one embodiment a more aggressive therapy comprises increased dosage of drugs. In one embodiment a more aggressive therapy comprises increased length of therapy. In one embodiment a more aggressive therapy comprises increased frequency of the dose schedule. In one embodiment, more aggressive therapy comprises selecting and administering more potent drugs and increasing drug dosage. In one embodiment, more aggressive therapy comprises selecting and administering more potent drugs and accelerating dose schedule. In one embodiment, more aggressive therapy comprises selecting and administering more potent drugs and increasing length of therapy. In one embodiment, more aggressive therapy comprises increasing drug dosage and accelerating dose schedule. In one embodiment, more aggressive therapy comprises increasing drug dosage and increasing length of therapy. In one embodiment, more aggressive therapy comprises accelerating dose schedule and increasing length of therapy. In one embodiment, more aggressive therapy comprises selecting and administering more potent drugs, increasing drug dosage, and accelerating dose schedule. In one embodiment, more aggressive therapy comprises selecting and administering more potent drugs, increasing drug dosage, and increasing length of therapy. In one embodiment, more aggressive therapy comprises selecting and administering more potent drugs, accelerating dose schedule, and increasing length of therapy. In one embodiment, more aggressive therapy comprises increasing drug dosage, accelerating dose schedule, and increasing length of therapy. In one embodiment, more aggressive therapy comprises selecting and administering more potent drugs, increasing drug dosage, accelerating dose schedule, and increasing length of therapy. In some embodiments, a more aggressive therapy comprises administering a combination of drug- based and non-drug-based therapies.

Treatment of Pancreatic Cancer

[0031] Health practitioners treat pancreatic cancer by taking actions to ameliorate the causes or symptoms of the disorder in a patient. Treatment may comprise drug- based or non-drug-based therapies.

[0032] Drug-based therapies may include: selecting and administering one or more anti-cancer, anti-tumor or chemotherapeutic drugs to the patient, adjusting the dosage of one or more of said drugs, adjusting the dosing schedule of one or more of said drugs, and adjusting the length of the therapy with an one or more of said drugs. Drugs are selected by practitioners based on the nature of the symptoms and the patient's response to any previous treatments. The dosage of a drug can be adjusted as well by the practitioner based on the nature of the drug, the nature of the patient's symptoms, the patient's response to previous treatment, and the patient's response to the drug. The dosing schedule can also be adjusted by the practitioner based on the nature of the drug, the nature of the patient's symptoms, the patient's response to previous treatment, and the patient's response to the drug. Also, the length of the therapy can be adjusted by the practitioner based on the nature of the drug, the nature of the patient's symptoms, the patient's response to previous treatment, and the patient's response to the drug. Additionally, the practitioner can select between a single drug therapy, a dual drug therapy, or a triple drug therapy. In some embodiments, a practitioner may optionally treat the patient with a combination of one or more drugs and one or more non-drug-based therapies.

[0033] In one embodiment, the practitioner begins therapy based on a comparison between a reference level and the levels of one or more biomarkers or the levels of a specific panel of biomarkers in a sample from a patient. In one embodiment, therapy comprises the selection and administration of a drug to the patient by the practitioner. In another embodiment, therapy comprises the selection and administration of two drugs to the patient by the practitioner as part of dual therapy. In another embodiment, therapy comprises the selection and

administration of three drugs to the patient by the practitioner as part of triple therapy.

[0034] Drugs are commonly used by medical practitioners, and a skilled person may identify the appropriate drug to administer based on the medical literature. In some embodiments, treatment comprises administering to an individual a chemotherapeutic agent. In some embodiments, the chemotherapeutic agent is Erlotinib Hydrochloride. In some

embodiments, the chemotherapeutic agent is Fluorouracil. In some embodiments, the chemotherapeutic agent is Gemcitabine Hydrochloride. In some embodiments, the

chemotherapeutic agent is Mitomycin C. In some embodiments, the chemotherapeutic agent is a combination of Gemcitabine and Oxaliplatin.

[0035] In other embodiments, treatment comprises administering to an individual a pain medication.

[0036] In addition to or in lieu of drug-based therapies, in some embodiments a practitioner may also treat an individual with non-drug-based therapies. In some embodiments, the non-drug based therapy comprises surgical resection of the cancer. In some embodiments, the non-drug based therapy comprises stent placement. In some embodiments, the non-drug based therapy comprises radiation therapy. In a related embodiment, the non-drug based therapy comprises pancreatectomy.

[0037] In one embodiment, the practitioner adjusts the therapy based on a comparison between a reference level and the levels of one or more biomarkers or the levels of a specific panel of biomarkers in a sample from a patient. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different combination of drugs. In one embodiment, the practitioner adjusts the therapy by adjusting drug dosage. In one embodiment, the practitioner adjusts the therapy by adjusting dose schedule. In one embodiment, the practitioner adjusts the therapy by adjusting length of therapy. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug combination and adjusting drug dosage. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug combination and adjusting dose schedule. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug combination and adjusting length of therapy. In one embodiment, the practitioner adjusts the therapy by adjusting drug dosage and dose schedule. In one embodiment, the practitioner adjusts the therapy by adjusting drug dosage and adjusting length of therapy. In one embodiment, the practitioner adjusts the therapy by adjusting dose schedule and adjusting length of therapy. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug, adjusting drug dosage, and adjusting dose schedule. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug, adjusting drug dosage, and adjusting length of therapy. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug, adjusting dose schedule, and adjusting length of therapy. In one embodiment, the practitioner adjusts the therapy by adjusting drug dosage, adjusting dose schedule, and adjusting length of therapy. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug, adjusting drug dosage, adjusting dose schedule, and adjusting length of therapy.

[0038] In some embodiments, treatment comprises a less aggressive therapy than a reference therapy. In one embodiment a less aggressive therapy comprises not administering drugs and taking a "watchful waiting" approach. In one embodiment a less aggressive therapy comprises delaying treatment. In one embodiment a less aggressive therapy comprises selecting and administering less potent drugs. In one embodiment a less aggressive therapy comprises decreasing dosage of drugs. In one embodiment a less aggressive therapy comprises decreasing the frequency treatment. In one embodiment a less aggressive therapy comprises shortening length of therapy. In one embodiment, less aggressive therapy comprises selecting and administering less potent drugs and decreasing drug dosage. In one embodiment, less aggressive therapy comprises selecting and administering less potent drugs and decelerating dose schedule. In one embodiment, less aggressive therapy comprises selecting and administering less potent drugs and shortening length of therapy. In one embodiment, less aggressive therapy comprises decreasing drug dosage and decelerating dose schedule. In one embodiment, less aggressive therapy comprises decreasing drug dosage and shortening length of therapy. In one embodiment, less aggressive therapy comprises decelerating dose schedule and shortening length of therapy. In one embodiment, less aggressive therapy comprises selecting and administering less potent drugs, decreasing drug dosage, and decelerating dose schedule. In one embodiment, less aggressive therapy comprises selecting and administering less potent drugs, decreasing drug dosage, and shortening length of therapy. In one embodiment, less aggressive therapy comprises selecting and administering less potent drugs, decelerating dose schedule, and shortening length of therapy. In one embodiment, less aggressive therapy comprises decreasing drug dosage, decelerating dose schedule, and shortening length of therapy. In one embodiment, less aggressive therapy comprises selecting and administering less potent drugs, decreasing drug dosage, decelerating dose schedule, and shortening length of therapy. In some embodiments, a less aggressive therapy comprises administering only non-drug-based therapies. In some embodiment, the practitioner reflexes to further tests such as imaging.

[0039] In another aspect of the present application, treatment comprises a more aggressive therapy than a reference therapy. In one embodiment a more aggressive therapy comprises earlier administration of drugs. In one embodiment a more aggressive therapy comprises increased dosage of drugs. In one embodiment a more aggressive therapy comprises increased length of therapy. In one embodiment a more aggressive therapy comprises increased frequency of the dose schedule. In one embodiment, more aggressive therapy comprises selecting and administering more potent drugs and increasing drug dosage. In one embodiment, more aggressive therapy comprises selecting and administering more potent drugs and accelerating dose schedule. In one embodiment, more aggressive therapy comprises selecting and administering more potent drugs and increasing length of therapy. In one embodiment, more aggressive therapy comprises increasing drug dosage and accelerating dose schedule. In one embodiment, more aggressive therapy comprises increasing drug dosage and increasing length of therapy. In one embodiment, more aggressive therapy comprises accelerating dose schedule and increasing length of therapy. In one embodiment, more aggressive therapy comprises selecting and administering more potent drugs, increasing drug dosage, and accelerating dose schedule. In one embodiment, more aggressive therapy comprises selecting and administering more potent drugs, increasing drug dosage, and increasing length of therapy. In one embodiment, more aggressive therapy comprises selecting and administering more potent drugs, accelerating dose schedule, and increasing length of therapy. In one embodiment, more aggressive therapy comprises increasing drug dosage, accelerating dose schedule, and increasing length of therapy. In one embodiment, more aggressive therapy comprises selecting and administering more potent drugs, increasing drug dosage, accelerating dose schedule, and increasing length of therapy. In some embodiments, a more aggressive therapy comprises administering a combination of drug- based and non-drug-based therapies.

Determining Probabilities of Pancreatic Cancer

[0040] The present disclosure also includes methods of determining the probability of early-stage pancreatic cancer, wherein the methods comprise determining from a biological sample of a patient the level of each biomarker of a panel of biomarkers for pancreatic cancer, wherein the panel comprises positive biomarkers, negative biomarkers, or both. The methods may further comprise comparing the level of each biomarker to threshold levels for each biomarker. The methods may further comprise determining the number of patient biomarkers that have either a level above a threshold level for each particular positive biomarker or a level below a threshold level for each particular negative biomarker. The methods may further comprise calculating the probability of early-stage pancreatic cancer based on the above-determined number.

[0041] In some embodiments, the methods further comprise determining the number of family members of the patient diagnosed with pancreatic cancer and determining the degree of relationship to the patient of any such family member. In such embodiments, calculating the probability of early-stage pancreatic cancer may be further based on the number of family members of the patient diagnosed with pancreatic cancer and the degree of relationship to the patient of any such family member.

[0042] In some embodiments, the methods further comprise determining the magnitude of the difference in level for each biomarker of the patient that is either above a threshold level for that particular positive biomarker or below a threshold level for that particular negative biomarker. In such embodiments, calculating the probability of early-stage pancreatic cancer may be further based on such magnitude of the difference in level. [0043] In some embodiments, calculating the probability of early-stage pancreatic cancer is also based on the weighted significance of each patient biomarker that has either a level above the threshold level for each particular positive biomarker or a level below the threshold level for each particular negative biomarker.

[0044] In some embodiments, the methods further comprise determining the presence or absence of a mutation in a BRCA2, PALB2, p l 6, STK1 1 , MLH 1 , MSH2, MSH6, PMS2, or EPCAM gene of the patient, or determining whether the patient has hereditary non-polyposis colorectal cancer. In such embodiments, calculating the probability of early-stage pancreatic cancer may be further based on the presence or absence of a mutation in a BRCA2, PALB2, p l 6, STK1 1 , MLH 1 , MSH2, MSH6, PMS2, or EPCAM gene or whether the patient has hereditary non-polyposis colorectal cancer.

[0045] In some embodiments, methods of determining the probability of early-stage pancreatic cancer comprise determining the number of family members of the patient diagnosed with pancreatic cancer and determining the degree of relationship to the patient of any such family member; determining the presence or absence of a mutation in a BRCA2, PALB2, p l 6, STK1 1 , MLH 1 , MSH2, MSH6, PMS2, or EPCAM gene of the patient, or determining whether the patient has hereditary non-polyposis colorectal cancer; determining from a biological sample of the patient the level of each biomarker of a panel of biomarkers for pancreatic cancer, wherein the panel comprises positive biomarkers, negative biomarkers, or both; comparing the level of each biomarker to threshold levels for each biomarker; determining the number of patient biomarkers that have either a level above a threshold level for each particular positive biomarker or a level below a threshold level for each particular negative biomarker; determining the magnitude of the difference in level for each biomarker of the patient that is either above a threshold level for that particular positive biomarker or below a threshold level for that particular negative biomarker; and calculating the probability of early-stage pancreatic cancer based on at least (i) the number of family members of the patient diagnosed with pancreatic cancer, (ii) the degree of relationship to the patient of any such family member, (iii) the presence or absence of a mutation in a BRCA2, PALB2, p l 6, STK1 1 , MLH 1 , MSH2, MSH6, PMS2, or EPCAM gene or whether the patient has hereditary non-polyposis colorectal cancer, (iv) the number of patient biomarkers that have either a level above the threshold level for each particular positive biomarkers or a level below the threshold level for each particular negative biomarkers, and (v) the magnitude of the difference in level for each biomarker of the patient that is either above a threshold level for that particular positive biomarker or below a threshold level for that particular negative biomarker.

[0046] In some embodiments an algorithm is used to calculate the probability of pancreatic cancer. In some embodiments a score is calculated from a statistical model as described herein to indicate the probability of pancreatic cancer. In some embodiments the algorithm or score incorporates secondary indications, such as the patient' s family and personal medical history.

Screening for Pancreatic Cancer

[0047] The present disclosure also includes methods of screening for further diagnosis of pancreatic cancer, wherein the methods comprise determining from a biological sample of a patient a level of each biomarker of any one of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14, wherein the panel comprises positive biomarkers, negative biomarkers, or both. The methods may further comprise comparing the level of each biomarker to threshold levels for each biomarker. The methods may further comprise determining the presence or absence of a biomarker that has either a level above a threshold level for each particular positive biomarker or a level below a threshold level for each particular negative biomarker. The methods may further comprise conducting endoscopic ultrasonography or magnetic resonance cholangiopancreatography if such a biomarker is present. In some of such embodiments, the methods further comprise conducting endoscopic ultrasonography or magnetic resonance cholangiopancreatography if the patient has at least 3, 4, 5, 6, 7, 8, 9, 10, 15, 20 or 25 such biomarkers present.

[0048] The present disclosure also includes methods of screening a biological sample, wherein the methods comprise obtaining a biological sample and determining a level in the biological sample of a panel of biomarkers of any one of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14, wherein the panel comprises positive biomarkers, negative biomarkers, or both. The methods may further comprise comparing the level of each biomarker to threshold levels for each biomarker. The methods may further comprise determining the presence or absence of a biomarker that has either a level above a threshold level for each particular positive biomarker or a level below a threshold level for each particular negative biomarker. In some of such embodiments, the methods comprise determining a level in the biological sample of each biomarker of the panel. In any embodiment of a method as presently disclosed, the method may be performed on a sample from an individual who is at higher than population risk for pancratic cancer.

[0049] In some embodiments, the method comprises identifying an individual for screening. Identifying an individual for screening may involve identifying an individual with any of the following indications: history of chronic pancreatitis, episodes of abdominal pain, personal history of heavy alcohol use, hereditary disorder of the pancreas, hereditary

pancreatitis, cystic fibrosis, hypercalcemia, hyperlipidemia, hypertriglyceridemia, and/or autoimmune disorder(s) that increase the likelihood of developing pancreatitis. In some embodiments of these methods, the patient additionally has risk factors for developing pancreatic cancer including any one or more of: a first degree relative with pancreatic cancer, a diagnosis or symptoms of Lynch syndrome, a diagnosis of new-onset diabetes, a diagnosis of diabetes, and/or a diagnosis or symptoms of Peutz-Jeghers syndrome. In some embodiments, a patient has at least one mutation in any one or more of the following genes: BRCA2, PALB2, pl6, STKl 1, MLH1, MSH2, MSH6, PMS2, and/or EPCAM.

Screening for Chronic Pancreatitis

[0050] The present disclosure also includes methods of screening for further diagnosis of chronic pancreatitis, wherein the methods comprise determining from a biological sample of a patient a level of each biomarker of any one of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14, wherein the panel comprises positive biomarkers, negative biomarkers, or both. The methods may further comprise comparing the level of each biomarker to threshold levels for each biomarker. The methods may further comprise determining the presence or absence of a biomarker that has either a level above a threshold level for each particular positive biomarker or a level below a threshold level for each particular negative biomarker. The methods may further comprise conducting endoscopic ultrasonography or magnetic resonance cholangiopancreatography if such a biomarker is present. In some of such embodiments, the methods further comprise conducting endoscopic ultrasonography or magnetic resonance cholangiopancreatography if the patient has at least 3, 4, 5, 6, 7, 8, 9, 10, 15, 20 or 25 such biomarkers present.

[0051] The present disclosure also includes methods of screening a biological sample, wherein the methods comprise obtaining a biological sample and determining a level in the biological sample of a panel of biomarkers of any one of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14, wherein the panel comprises positive biomarkers, negative biomarkers, or both. The methods may further comprise comparing the level of each biomarker to threshold levels for each biomarker. The methods may further comprise determining the presence or absence of a biomarker that has either a level above a threshold level for each particular positive biomarker or a level below a threshold level for each particular negative biomarker. In some of such embodiments, the methods comprise determining a level in the biological sample of each biomarker of the panel. In any embodiment of a method as presently disclosed, the method may be performed on a sample from an individual who is at higher than population risk for chronic pancratitis.

[0052] In some embodiments, the method comprises identifying an individual for screening. Identifying an individual for screening may involve identifying an individual with any of the following indications: history of chronic pancreatitis, episodes of abdominal pain, personal history of heavy alcohol use, hereditary disorder of the pancreas, hereditary

pancreatitis, cystic fibrosis, hypercalcemia, hyperlipidemia, hypertriglyceridemia, and/or autoimmune disorder(s) that increase the likelihood of developing pancreatitis. In some embodiments of these methods, the patient additionally has risk factors for developing pancreatic cancer including any one or more of: a first degree relative with pancreatic cancer, a diagnosis or symptoms of Lynch syndrome, a diagnosis of new-onset diabetes, a diagnosis of diabetes, and/or a diagnosis or symptoms of Peutz-Jeghers syndrome. In some embodiments, a patient has at least one mutation in any one or more of the following genes: BRCA2, PALB2, pl6, STK1 1, MLH1, MSH2, MSH6, PMS2, and/or EPCAM.

Communicating Results

[0053] The results of any analyses according to the invention will often be communicated to physicians, patients, or other interested parties in a transmittable form that can be communicated or transmitted to any of the above parties. Such a form can vary and can be tangible or intangible. The results can be embodied in descriptive statements, diagrams, photographs, charts, images or any other visual forms. For example, graphs of levels for various biomarkers can be used in explaining the results. In some embodiments, a graph of the probability of cancer in the individual related to the reference population is provided. Diagrams showing such information for additional target biomarkers(s) are also useful in indicating some testing results. The statements and visual forms can be recorded on a tangible medium such as papers, computer readable media such as floppy disks, compact disks, etc., or on an intangible medium, e.g., an electronic medium in the form of email or website on internet or intranet. In addition, results can also be recorded in a sound form and transmitted through any suitable medium, e.g., analog or digital cable lines, fiber optic cables, etc., via telephone, facsimile, wireless mobile phone, internet phone and the like.

[0054] Thus, the information and data on a test result can be produced anywhere in the world and transmitted to a different location. As an illustrative example, when a biomarker assay is conducted outside the United States, the information and data on a test result may be generated, cast in a transmittable form as described above, and then imported into the United States. Accordingly, the present invention also encompasses methods and systems for producing a transmittable form of biomarker information for at least one patient sample. The method comprises the steps of (1) measuring the levels of biomarkers in a sample according to methods of the present invention; and (2) embodying the result of the levels of biomarkers in a transmittable form. The transmittable form is a product of such a method.

Computer Applications

[0055] Techniques for analyzing biomarker level data (indeed any data obtained according to the invention) will often be implemented using hardware, software or a combination thereof in one or more computer systems or other processing systems capable of effectuating such analysis.

[0056] The computer-based analysis function can be implemented in any suitable language and/or browsers. For example, it may be implemented with C language and preferably using object-oriented high-level programming languages such as Visual Basic, SmallTalk, C++, and the like. The application can be written to suit environments such as the Microsoft

Windows™ environment including Windows™ 98, Windows™ 2000, Windows™ NT, and the like. In addition, the application can also be written for the Macintosh™, SUN™, UNIX or LINUX environment. In addition, the functional steps can also be implemented using a universal or platform- independent programming language. Examples of such multi-platform programming languages include, but are not limited to, hypertext markup language (HTML), JAVA™, JavaScript™, Flash programming language, common gateway interface/structured query language (CGI/SQL), practical extraction report language (PERL), AppleScript™ and other system script languages, programming language/structured query language (PL/SQL), and the like. Java™- or JavaScript™-enabled browsers such as HotJava™, Microsoft™ Explorer™, or Netscape can be used. When active content web pages are used, they may include Java applets or ActiveX™ controls or other active content technologies.

[0057] The analysis function can also be embodied in computer program products and used in the systems described above or other computer- or internet-based systems.

Accordingly, another aspect of the present invention relates to a computer program product comprising a computer-usable medium having computer-readable program codes or instructions embodied thereon for enabling a processor to carry out gene or protein status analysis. These computer program instructions may be loaded onto a computer or other programmable apparatus to produce a machine, such that the instructions which execute on the computer or other programmable apparatus create means for implementing the functions or steps described above. These computer program instructions may also be stored in a computer-readable memory or medium that can direct a computer or other programmable apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory or medium produce an article of manufacture including instruction means which implement the analysis. The computer program instructions may also be loaded onto a computer or other programmable apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions or steps described above.

[0058] The present disclosure also includes computer-assisted methods of determining the probability of a patient having early-stage pancreatic cancer or chronic pancreatitis, or chronic pancreatitis with pancreatic cancer of some stage wherein the methods comprise providing biomarker information from a biomarker information database module on a data processing device, wherein the biomarker information comprises threshold level information for each biomarker of a panel of biomarkers, wherein the panel of biomarkers comprises positive biomarkers, negative biomarkers, or both, wherein a level above a threshold level for positive biomarkers is indicative of the presence of pancreatic cancer or chronic pancreatitis or chronic pancreatitis with pancreatic cancer of some stage in a patient and a level below a threshold level for negative biomarkers is indicative of the presence of pancreatic cancer or chronic pancreatitis or chronic pancreatitis with pancreatic cancer of some stage in a patient. The methods may further comprise providing patient information from a patient information database module on a data processing device, wherein the patient information comprises biomarker level information for the patient. The methods may further comprise comparing with a comparison module on a data processing device biomarker information and patient information. The methods may further comprise determining with an evaluation module on a data processing device operably connected to the comparison module a number of patient biomarkers with either a level above a threshold level for each particular positive biomarker or a level below a threshold level for each particular negative biomarker. The methods may further comprise determining with a calculation module on a data processing device operably connected to the evaluation module a probability of early-stage pancreatic cancer or chronic pancreatitis or chronic pancreatitis with pancreatic cancer of some stage based on the number of patient biomarkers with either a level above the threshold level for each particular positive biomarker or a level below the threshold level for each particular negative biomarker.

[0059] In some embodiments, the methods further comprise providing to the calculation module family history information from a family history database module on a data processing device, the family history information comprising the number of family members of the patient diagnosed with pancreatic cancer, and wherein determining a probability of early- stage pancreatic cancer is further based on the number of family members of the patient diagnosed with pancreatic cancer. The family history information may comprise the degree of relationship to the patient of each family member diagnosed with pancreatic cancer. The family history database may comprise a patient file history database.

[0060] In some embodiments, the patient information database comprises a patient file history database.

[0061] In some embodiments, the methods further comprise displaying a graphical presentation of the patient's probability of early-stage pancreatic cancer or chronic pancreatitis with a display operably connected to the calculation module.

[0062] In some embodiments, the methods further comprise determining additional suggested diagnostic procedures based on the patient's probability of early-stage pancreatic cancer or chronic pancreatitis with a diagnostic module operably connected to the calculation module.

[0063] Figure 1 illustrates a system for performing computer-assisted methods of determining the probability of a patient having early-stage pancreatic cancer or chronic pancreatitispancreatic cancer. System 100 comprises a data processing device 10 comprising a biomarker information database module 1 1. The biomarker information database module 11 comprises biomarker information comprising threshold level information for each biomarker of a panel of biomarkers, wherein the panel of biomarkers comprises positive biomarkers, negative biomarkers, or both, wherein a level above a threshold level for each particular positive biomarker is indicative of the presence of pancreatic cancer or chronic pancreatitis with pancreatic cancer of some stage in a patient and a level below a threshold level for each particular negative biomarker is indicative of the presence of pancreatic cancer or chronic pancreatitis or chronic pancreatitis with pancreatic cancer of some stage in a patient.

[0064] The system 100 further comprises a data processing device 20 comprising a patient information database module 21. The patient information database module 21 comprises patient information comprising biomarker level information for a patient.

[0065] The system 100 further comprises a data processing device 30 comprising a comparison module 31. The comparison module 31 may be in communication with the biomarker information database module 11 and the patient information database module 21. The comparison module 31 may be configured to compare biomarker information and patient information. The data processing device 30 may further comprise an evaluation module 32 in communication with the comparison module 31. The evaluation module 32 may be configured to determine a number of patient biomarkers with either a level above a threshold level for each particular positive biomarker or a level below a threshold level for each particular negative biomarker. The data processing device 30 may further comprise a calculation module 33 in communication with the evaluation module 32. The calculation module 33 may be configured to determine a probability of early-stage pancreatic cancer or chronic pancreatitis based on the number of patient biomarkers with either a level above the threshold level for each particular positive biomarker or a level below the threshold level for each particular negative biomarker. The data processing device 30 may further comprise a diagnostic module 34 in communication with the calculation module 33. The diagnostic module 34 may be configured to determine additional suggested diagnostic procedures based on a patient's probability of early-stage pancreatic cancer or chronic pancreatitis.

[0066] The system 100 further comprises a data processing device 40 comprising a family history database module 41. The family history database module 41 may comprise family history information comprising the number of family members of a patient diagnosed with pancreatic cancer or chronic pancreatitis. The family history information may also comprise the degree of relationship to a patient of each family member diagnosed with pancreatic cancer or chronic pancreatitis. The family history database module 41 may be in communication with the calculation module 33. The calculation module 33 may be configured to utilize family history information in determining the probability of early-stage pancreatic cancer or chronic pancreatitis of a patient. [0067] The system 100 further comprises a display 50 in communication with the calculation module 33 and configured to display a graphical representation of a patient's probability of early-stage pancreatic cancer or chronic pancreatitis or both.

[0068] In some embodiments, the various features of the methods may be embodied in machine-executable instructions executed by a general-purpose or special-purpose computer (or other electronic device). Alternatively, the features may be performed by hardware components that include specific logic for performing the steps or by a combination of hardware, software, and/or firmware.

[0069] Accordingly, the various components, modules, systems, and/or features disclosed herein may be embodied as modules within a system. Such a system may be implemented in software, firmware, hardware, and/or physical infrastructure. Although not always explicitly named herein, a module may be identified (named) based on a function it performs. For example, a module that is configured to calculate something may comprise specific hardware, software, or firmware and be properly referred to as a "calculation module."

[0070] Embodiments may also be provided as a computer program product including a non-transitory machine-readable medium having stored thereon instructions that may be used to program, or be executed on, a computer (or other electronic device) to perform processes described herein. The machine-readable medium may include, but is not limited to, hard drives, floppy diskettes, optical disks, CD-ROMs, DVD-ROMs, ROMs, RAMs, EPROMs, EEPROMs, magnetic or optical cards, solid-state memory devices, or other types of

media/machine-readable media suitable for storing electronic instructions. Moreover, a computer program product may be run, executed, downloaded, and/or otherwise used locally or remotely via a network.

[0071] It should be understood that references to "a data processing device" may refer to the same device or one or more different devices. For example, certain steps of the computer-assisted methods may be performed on a device controlled by a diagnostic service provider and other steps may be performed on a device controlled by a medical practitioner. Likewise, the data processing devices 10, 20, 30 and 40 may be a single device or, for example, the data processing device 30 may be multiple data processing devices.

[0072] In certain embodiments, the computer-implemented method may be configured to identify a patient as having or not having pancreatic cancer. For example, the computer-implemented method may be configured to inform a physician that a particular patient has pancreatic cancer or a probability of having pancreatic cancer. Alternatively or additionally, the computer-implemented method may be configured to actually suggest a particular course of treatment based on the answers to/results for various queries, or refer the patient for further testing.

Kits

[0073] The present disclosure also includes a kit for detecting the presence of early-stage pancreatic cancer or chronic pancreatitis in a patient, the kit comprising reagents useful, sufficient, or necessary for determining the level of each biomarker of any one of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14.

[0074] In another aspect of the present invention, a kit is provided for practicing the diagnosis of the present invention. The kit may include a carrier for the various components of the kit. The carrier can be a container or support, in the form of, e.g., bag, box, tube, rack, and is optionally compartmentalized. The carrier may define an enclosed confinement for safety purposes during shipment and storage. The kit may include antibodies directed to proteins of a panel of biomarkers in any of Panels A-L. The kit may include oligonucleotides directed to mRNA or cDNA of a panel of biomarkers in any of Panels A-L. In some embodiments the kit comprises reagents (e.g., probes, primers, and or antibodies) for determining the levels of a panel of biomarkers, where said panel comprises at least 5% 10%, 15%, 20%, 25%, 30%, 40%, 50%, 60%, 75%, 80%, 90%, 95%, 99%, or 100% biomarkers from Panels A-L. In some embodiments the kit consists of reagents (e.g., probes, primers, and or antibodies) for determining the expression level of no more than 2500 genes, wherein at least 5, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 150, 200, 250, or more of these biomarkers are from Panels A-L. In some kits for performing quantitative immunoassays, the kit may contain capture antibodies (in some embodiments monoclonal), detection antibodies (in some embodiments monoclonal), antigen control reagents, fluorophores/reporter molecules, buffer, blockers, stabilizers, etc.

Definitions

[0075] In any of the embodiments herein that refer to a panel of biomarkers, the panel of biomarkers may comprise any one of the panels described herein. In one embodiment, the panel is panel A. In another embodiment, the panel is panels B. In another embodiment, the panel is panel C. In another embodiment, the panel is panel D. In another embodiment, the panel is panel E. In another embodiment, the panel is panel F. In another embodiment, the panel is panel G. In another embodiment, the panel is panel H. In another embodiment, the panel is panel I. In another embodiment, the panel is panel J. In another embodiment, the panel is panel K. In another embodiment, the panel is panel L.

[0076] In some embodiments, the panel is any three biomarkers from any one of panels A, B, C, D, E, F, G, H, I, J, K or L. In some embodiments, the panel is any four biomarkers from any one of panels A, B, C, D, E, F, G, H, I, J, K or L. In some embodiments, the panel is any five biomarkers from any one of panels A, B, C, D, E, F, G, H, I, J, K or L.

[0077] In one group of individual embodiments, the panel is one of the 3 biomarker panels disclosed in Megatable 3 , respectively. In one group of individual embodiments, the panel is one of the 3 biomarker panels disclosed in Megatable 6, respectively. In one group of individual embodiments, the panel is one of the 3 biomarker panels disclosed in Megatable 9, respectively. In one group of individual embodiments, the panel is one of the 3 biomarker panels disclosed in Megatable 12, respectively.

[0078] In one group of individual embodiments, the panel is one of the 4 biomarker panels disclosed in Megatable 4, respectively. In one group of individual embodiments, the panel is one of the 4 biomarker panels disclosed in Megatable 7, respectively. In one group of individual embodiments, the panel is one of the 4 biomarker panels disclosed in Megatable 10, respectively. In one group of individual embodiments, the panel is one of the 4 biomarker panels disclosed in Megatable 13 , respectively.

[0079] In one group of individual embodiments, the panel is one of the 5 biomarker panels disclosed in Megatable 5, respectively. In one group of individual embodiments, the panel is one of the 5 biomarker panels disclosed in Megatable 8, respectively. In one group of individual embodiments, the panel is one of the 5 biomarker panels disclosed in Megatable 1 1 , respectively. In one group of individual embodiments, the panel is one of the 5 biomarker panels disclosed in Megatable 14, respectively.

[0080] In some embodiments, the panel is comprised of the top 2, 3, 4, 5,

6, 7, 8, 9, 10, 1 1 , 12, 13 , 14, or 15 biomarkers as determined by a multivariate model. In related embodiments, the biomarkers are ranked by p-value, AUC, q-value, or some combination of these to determine the top biomarkers. In related embodiments, the methodology used to generate the multivariate model comprises random forest, boosting or lasso. In related embodiments, the panel comprises top 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, or 15 of the biomarkers disclosed in any of the columns of Tables 3 - 10 or 14-24.

[0081] In some embodiments, the panel comprises any 2, 3, 4, 5, 6, 7, 8, 9,

10, 1 1 , 12, 13 , 14, or all 15 of the of the biomarkers disclosed in any of the columns of Tables 3 - 10 or 14-24.

[0082] In any of the embodiments herein that refer to at least 3, 4 or 5 biomarkers of a panel, each embodiment includes at least 5 biomarkers, at least 6 biomarkers, at least 7 biomarkers, at least 8 biomarkers, at least 9 biomarkers, at least 10 biomarkers, at least 1 1 biomarkers, at least 12 biomarkers, at least 13 biomarkers, at least 14 biomarkers, at least 15 biomarkers, at least 20 biomarkers, at least 25 biomarkers, at least 30 biomarkers, at least 35 biomarkers, at least 40 biomarkers, at least 45 biomarkers, or at least 50 biomarkers of the panel, depending on the number of biomarkers in the panel, up to and including the total number of biomarkers in the panel.

[0083] In any of the embodiments herein that refer to a "biological sample," the biological sample may be a sample from any bodily fluid, tissue, or substance. In some of such embodiments, the biological sample may comprise or originate from a blood sample, a urine sample, a fecal sample, a saliva sample, or a pancreatic tissue sample. In some of such embodiments, the biological sample is a blood plasma sample.

[0084] In any of the embodiments herein that refer to a "level" of a biomarker, the level may be any direct or indirect measure of the quantity of the biomarker. For example, the level may comprise the concentration of a protein or other gene product corresponding to the biomarker in a biological sample. In another example, the level may comprise the concentration of a transcript that encodes for a protein corresponding to the biomarker. In yet another example, the level may comprise the expression level in a tissue sample of nucleic acids corresponding to the biomarker.

[0085] As used herein, "positive biomarker" refers to a biomarker for which a level above a threshold level for that biomarker is indicative of the presence of pancreatic cancer or chronic pancreatitis in a patient. Likewise, as used herein, "negative biomarker" refers to a biomarker for which a level below a threshold level for that biomarker is indicative of the presence of pancreatic cancer or chronic pancreatitis in a patient. [0086] As used herein, "threshold level" refers to the positive or negative value at which a biomarker becomes indicative of the presence of pancreatic cancer in a patient. Methods of determining biomarker threshold levels are known in the art. For example, the threshold level may be the upper and/or lower boundary of the normal distribution of the level a particular biomarker in a particular population, such as a generic population or a specific population. An example of a specific population is a group of individuals that share a common mutation or heritage. In another example, the threshold level may be specific to a particular individual. In that case, a relative increase or decrease in the level of a particular biomarker over time may be indicative of the presence of pancreatic cancer in the individual.

[0087] As used herein, pancreatic cancer means malignant cells arising from tissue in the pancreas. In some embodiments, pancreatic cancer is adenocarcinoma. In some embodiments, pancreatic cancer is pancreatic ductal adenocarcinoma.

[0088] "Panel A" is defined as: 6Ckine, Adiponectin, Agouti-Related Protein

(AgRP), Aldose Reductase, Alpha-l-acid glycoprotein 1 ( AGP-1), Alpha- 1-Antichymotrypsin (AACT), Alpha- 1 -Antitrypsin (AAT), Alpha- 1 -Microglobulin (A 1 Micro), Alpha-2- Macroglobulin (A2Macro), AlphA-Letoprotein (AFP), Amphiregulin (AR), Angiogenin, Angiopoietin-1 (ANG-1), Angiopoietin-2 (ANG-2), Angiopoietin-related protein 3 (ANGPTL3), Angiotensin-Converting Enzyme (ACE), Angiotensinogen, Antileukoproteinase (ALP), Antithrombin-III (AT-III), Apolipoprotein(a) (Lp(a)), Apolipoprotein A-I (Apo A-I),

Apolipoprotein A-II (Apo A-II), Apolipoprotein A-IV (Apo A-IV), Apolipoprotein B (Apo B), Apolipoprotein C-I (Apo C-I), Apolipoprotein C-III (Apo C-III), Apolipoprotein D (Apo D), Apolipoprotein E (Apo E), Apolipoprotein H (Apo H), AXL Receptor Tyrosine Kinase (AXL), B cell-activating factor (BAFF), B Lymphocyte Chemoattractant (BLC), Beta-2 -Microglobulin (B2M), Beta-microseminoprotein (PSP94), Betacellulin (BTC), Brain-Derived Neurotrophic Factor (BDNF), C-Peptide, C-Reactive Protein (CRP), Cadherin-1 (E-Cad), Cadherin-13 (T- cad), Calbindin, Cancer Antigen 125 (CA-125), Cancer Antigen 15-3 (CA-15-3), Cancer Antigen 19-9 (CA-19-9), Cancer Antigen 72-4 (CA 72-4), Carbonic anhydrase 9 (CA-9), Carcinoembryonic Antigen (CEA), Carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1), Carcinoembryonic antigen-related cell adhesion molecule 6 (CEACAM6), Cartilage Oligomeric Matrix Protein (COMP), Cathepsin B (pro) (CTSB), Cathepsin D, CD 5 Antigen-like (CD5L), CD 40 antigen (CD40), CD40 Ligand (CD40-L), CD 163, Cellular Fibronectin (cFib), Chemerin, Chemokine CC-4 (HCC-4), Chromogranin-A (CgA), Ciliary Neurotrophic Factor (CNTF), Clusterin (CLU), Collagen IV, Complement C3 (C3),

Complement component Clq receptor (ClqRl), Complement Factor H - Related Protein 1 (CFHR1), Cortisol (Cortisol), Creatine Kinase-MB (CK-MB), Cystatin-A, Cystatin-B, Cystatin- C, Decorin, Dickkopf-related protein 1 (DKK-1), Dipeptidyl peptidase IV (DPPIV), E-Selectin, EN-RAGE, Endoglin, Endostatin, Eotaxin-1, Eotaxin-2, Eotaxin-3, Epidermal Growth Factor (EGF), Epidermal Growth Factor Receptor (EGFR), Epiregulin (EPR), Epithelial cell adhesion molecule (EpCam), Epithelial-Derived Neutrophil-Activating Protein 78 (ENA-78), Ezrin, Factor VII, Fas Ligand (FasL), FASLG Receptor (FAS), Fatty Acid-Binding Protein, adipocyte (FABP, adipocyte), Fatty Acid-Binding Protein, heart (FABP, heart), Fatty Acid-Binding Protein, liver (FABP, liver), Ferritin (FRTN), Fetuin-A, Fibrinogen, Fibroblast Growth Factor 4 (FGF-4), Fibroblast Growth Factor basic (FGF -basic), Fibulin-lC (Fib-lC), Ficolin-3, Follicle- Stimulating Hormone (FSH), Galectin-3, Gastric inhibitory polypeptide (GIP), Gelsolin, Glucagon, Glucagon-like Peptide 1, active (GLP-1 active), Glucagon-like Peptide 1, total (GLP- 1 total), Glucose-6-phosphate Isomerase (G6PI), Glutathione S-Transferase alpha (GST-alpha), Glutathione S-Transferase Mu 1 (GST-MI), Glycogen phosphorylase isoenzyme BB (GPBB), Granulocyte Colony-Stimulating Factor (G-CSF), Granulocyte-Macrophage Colony-Stimulating Factor (GM-CSF), Growth/differentiation factor 15 (GDF-15), Growth Hormone (GH), Growth- Regulated alpha protein (GRO-alpha), Haptoglobin, HE4, Heat Shock Protein 60 (HSP-60), Hemopexin, Heparin-Binding EGF-Like Growth Factor (HB-EGF), Hepatocyte Growth Factor (HGF), Hepatocyte Growth Factor receptor (HGF receptor), Hepsin, Human Chorionic

Gonadotropin beta (hCG), Human Epidermal Growth Factor Receptor 2 (HER-2),

Immunoglobulin A (IgA), Immunoglobulin E (IgE), Immunoglobulin M (IgM), Insulin, Insulin- like Growth Factor-Binding Protein 1 (IGFBP-1), Insulin-like Growth Factor-Binding Protein 2 (IGFBP-2), Insulin-like Growth Factor-Binding Protein 3 (IGFBP-3), Insulin-like Growth Factor Binding Protein 4 (IGFBP4), Insulin-like Growth Factor Binding Protein 5 (IGFBP5), Insulin- like Growth Factor Binding Protein 6 (IGFBP6), Intercellular Adhesion Molecule 1 (ICAM-1), Interferon alpha (IFN-alpha), Interferon gamma (IFN-gamma), Interferon gamma Induced Protein 10 (IP- 10), Interferon- inducible T-cell alpha chemoattractant (ITAC), Interleukin-1 alpha (IL-1 alpha), Interleukin-1 beta (IL-1 beta), Interleukin-1 receptor antagonist (IL-lra),

Interleukin-2 (IL-2), Interleukin-2 receptor alpha (IL-2 receptor alpha), Interleukin-3 (IL-3), Interleukin-4 (IL-4), Interleukin-5 (IL-5), Interleukin-6 (IL-6), Interleukin-6 receptor (IL-6r), Interleukin-6 receptor subunit beta (IL-6R beta), Interleukin-7 (IL-7), Interleukin-8 (IL-8), Interleukin-10 (IL-10), Interleukin-12 Subunit p40 (IL-12p40), Interleukin-12 Subunit p70 (IL- 12p70), Interleukin-13 (IL-13), Interleukin-15 (IL-15), Interleukin-16 (IL-16), Interleukin-17 (IL-17), Interleukin-18 (IL-18), Interleukin-18-binding protein (IL-18bp), Interleukin-22 (IL-22), Interleukin-23 (IL-23), Interleukin-31 (IL-31), Kallikrein 5, Kidney Injury Molecule-1 (KIM-1), Lactoferrin (LTF), Lactoylglutathione lyase (LGL), Latency-Associated Peptide of Transforming Growth Factor beta 1 (LAP TGF-bl), Lectin-Like Oxidized LDL Receptor 1 (LOX-1), Leptin, Leptin Receptor (Leptin-R), Leucine-rich alpha-2-glycoprotein (LRG1), Lipocalin-1 (LC 1), Lumican, Luteinizing Hormone (LH), Macrophage Colony-Stimulating Factor 1 (M-CSF), Macrophage-Derived Chemokine (MDC), Macrophage Inflammatory Protein- 1 alpha (MIP- 1 alpha), Macrophage Inflammatory Protein- 1 beta (MIP-1 beta), Macrophage Inflammatory Protein-3 alpha (MIP-3 alpha), Macrophage inflammatory protein 3 beta (MIP-3 beta),

Macrophage Migration Inhibitory Factor (MIF), Macrophage- Stimulating Protein (MSP), Malondialdehy de-Modified Low-Density Lipoprotein (MDA-LDL), Maspin, Mast/stem cell growth factor receptor (SCFR), Matrix Metalloproteinase- 1 (MMP-1), Matrix

Metalloproteinase-3 (MMP-3), Matrix Metalloproteinase-7 (MMP-7), Matrix Metalloproteinase- 9 (MMP-9), Matrix Metalloproteinase-9, total (MMP-9, total), Matrix Metalloproteinase- 10 (MMP-10), Mesothelin (MSLN), MHC class I chain-related protein A (MICA), Midkine, Monocyte Chemotactic Protein 1 (MCP-1), Monocyte Chemotactic Protein 2 (MCP-2), Monocyte Chemotactic Protein 3 (MCP-3), Monocyte Chemotactic Protein 4 (MCP-4), Monokine Induced by Gamma Interferon (MIG), Myeloid Progenitor Inhibitory Factor 1 (MPIF- 1), Myeloperoxidase (MPO), Myoglobin, N-terminal prohormone of brain natriuretic peptide (NT proBNP), Nerve Growth Factor beta (NGF-beta), Neuron-Specific Enolase (NSE), Neuronal Cell Adhesion Molecule (Nr-CAM), Neuropilin-1, Neutrophil Activating Peptide 2 (NAP-2), Neutrophil Gelatinase-Associated Lipocalin (NGAL), Omentin , Osteocalcin, Osteopontin, Osteoprotegerin (OPG), P-Selectin, Pancreatic Polypeptide (PPP), Pancreatic secretory trypsin inhibitor (TATI), Paraoxanase-1 (PON-1), Pentraxin-3 (PTX3), Pepsinogen I (PGI), Peptidase D (PEPD), Peptide YY (PYY), Periostin, Phosphoserine Aminotransferase (PSAT), Pigment Epithelium Derived Factor (PEDF), Placenta Growth Factor (PLGF), Plasminogen Activator Inhibitor 1 (PAI-1), Platelet endothelial cell adhesion molecule

(PECAM-1), Platelet-Derived Growth Factor BB (PDGF-BB), Progesterone, Progranulin, Proinsulin, Intact, Proinsulin, Total, Prolactin (PRL), Prostasin, Prostate-Specific Antigen, Free (PSA-L), Prostate Specific Antigen, total (tPSA), Protein S100-A4 (S100-A4), Pulmonary and Activation-Regulated Chemokine (PARC), Pulmonary surfactant-associated protein D (SP-D), Receptor for advanced glycosylation end products (RAGE), Receptor tyrosine-protein kinase erbB-3 (ErbB3), Resistin, Retinol-binding protein 4 (RBP-4), S 100 calcium-binding protein B (S100-B), Selenoprotein P , Serotransferrin (Transferrin), Serum Amyloid A Protein (SAA), Serum Amyloid P-Component (SAP), Sex Hormone-Binding Globulin (SHBG), Sortilin, Squamous Cell Carcinoma Antigen- 1 (SCCA-1), ST2, Stem Cell Factor (SCF), Stromal cell- derived factor-1 (SDF-1), Superoxide Dismutase 1, soluble (SOD-1), T-Cell-Specific Protein RANTES (RANTES), T Lymphocyte-Secreted Protein 1-309 (1-309), Tamm-Horsfall Urinary Glycoprotein (THP), Tenascin-C (TN-C), Tenascin-X (TN-X), Testosterone, Total, Tetranectin, Thrombin-activable fibrinolysis inhibitor (TAFI), Thrombomodulin (TM), Thrombospondin-1, Thrombospondin-4 (TSP4), Thymus and activation-regulated chemokine (TARC),

Thyroglobulin (TG), Thyroid-Stimulating Hormone (TSH), Thyroxine-Binding Globulin (TBG), Tissue Inhibitor of Metalloproteinases 1 (TIMP-1), Tissue Inhibitor of Metalloproteinases 2 (TIMP-2), Tissue type Plasminogen activator (tPA), TNF -Related Apoptosis-Inducing Ligand Receptor 3 (TRAIL-R3), Transforming Growth Factor alpha (TGF-alpha), Transforming Growth Factor beta- 3 (TGF-beta-3), Transthyretin (TTR), Trefoil Factor 3 (TFF3), Tumor Necrosis Factor alpha (TNF-alpha), Tumor Necrosis Factor beta (TNF-beta), Tumor necrosis factor ligand superfamily member 12 (Tweak), Tumor necrosis factor ligand superfamily member 13

(APRIL), Tumor Necrosis Factor Receptor I (TNF RI), Tumor necrosis factor receptor 2 (TNFR2), Tyrosine kinase with Ig and EGF homology domains 1 (Tie-1), Tyrosine kinase with Ig and EGF homology domains 2 (TIE-2), Urokinase-type Plasminogen Activator (uPA), Urokinase-type plasminogen activator receptor (uPAR), Vascular Cell Adhesion Molecule- 1 (VCAM-1), Vascular Endothelial Growth Factor (VEGF), Vascular endothelial growth factor B (VEGF-B), Vascular Endothelial Growth Factor C (VEGF-C), Vascular endothelial growth factor D (VEGF-D), Vascular Endothelial Growth Factor Receptor 1 (VEGFR-1), Vascular Endothelial Growth Factor Receptor 2 (VEGFR-2), Vascular endothelial growth factor receptor 3 (VEGFR-3), Visceral adipose tissue - derived serpin A12 (Vaspin), Visfatin, Vitamin D- Binding Protein (VDBP), Vitamin K-Dependent Protein S (VKDPS), Vitronectin, von

Willebrand Factor (vWF), and YKL-40

[0089] "Panel B" is defined as : 6Ckine, Alpha- 1 -Antichymotrypsin (AACT),

Alpha- 1 -Antitrypsin (AAT), AlphA-Letoprotein (AFP), Apolipoprotein A-I (Apo A-I), Cancer Antigen 19-9 (CA-19-9), Cancer Antigen 72-4 (CA 72-4), Cathepsin D, E-Selectin, Ferritin (FRTN), Interleukin-8 (IL-8), Leucine-rich alpha-2-glycoprotein (LRG1), Mast/stem cell growth factor receptor (SCFR), Matrix Metalloproteinase-7 (MMP-7), Osteopontin, Pentraxin-3 (PTX3), Pulmonary surfactant-associated protein D (SP-D), Selenoprotein P , Tissue Inhibitor of Metalloproteinases 1 (TIMP-1), and Tumor necrosis factor ligand superfamily member 12 (Tweak).

[0090] "Panel C" is defined as : 6Ckine, Alpha- 1 -Antichymotrypsin (AACT),

Alpha-2-Macroglobulin (A2Macro), Antithrombin-III (AT-III), Apolipoprotein A-I (Apo A-I), B cell-activating factor (BAFF), Cancer Antigen 19-9 (CA-19-9), Chromogranin-A (CgA), Creatine Kinase-MB (CK-MB), E-Selectin, Factor VII, Interleukin-1 alpha (IL-1 alpha), Interleukin-8 (IL-8), Macrophage-Derived Chemokine (MDC), Mast/stem cell growth factor receptor (SCFR), Matrix Metalloproteinase-3 (MMP-3), Osteopontin, Pepsinogen I (PGI), Proinsulin, Total, Pulmonary surfactant-associated protein D (SP-D), Receptor for advanced glycosylation end products (RAGE), Stem Cell Factor (SCF), and Vascular Endothelial Growth Factor Receptor 2 (VEGFR-2).

[0091] "Panel D" is defined as: Alpha- 1 -acid glycoprotein 1 ( AGP-1), Alpha- 1-

Antichymotrypsin (AACT), Alpha- 1 -Antitrypsin (AAT), Apolipoprotein(a) (Lp(a)), Cancer Antigen 19-9 (CA-19-9), Carcinoembryonic antigen-related cell adhesion molecule 6

(CEACAM6), Cathepsin D, EN-RAGE, E-Selectin, Ferritin (FRTN), Gelsolin,

Growth/differentiation factor 15 (GDF-15), Interleukin-8 (IL-8), Leucine-rich alpha-2- glycoprotein (LRG1), Matrix Metalloproteinase-7 (MMP-7), Neuropilin- 1 , Pentraxin-3 (PTX3), Peptide YY (PYY), Tissue Inhibitor of Metalloproteinases 1 (TIMP-1), and Tumor necrosis factor receptor 2 (TNFR2).

[0092] "Panel E" is defined as: Alpha- 1 -Antitrypsin (AAT), Apolipoprotein B

(Apo B), Carcinoembryonic antigen-related cell adhesion molecule 6 (CEACAM6), CD 40 antigen (CD40), EN-RAGE, Growth/differentiation factor 15 (GDF-15), Hemopexin, Hepsin, Immunoglobulin A (IgA), Insulin-like Growth Factor Binding Protein 4 (IGFBP4), Insulin-like Growth Factor-Binding Protein 2 (IGFBP-2), Interferon alpha (IFN-alpha), Interleukin- 13 (IL- 13), Interleukin-2 receptor alpha (IL-2 receptor alpha), Kidney Injury Molecule- 1 (KIM-1), Matrix Metalloproteinase-7 (MMP-7), Pancreatic secretory trypsin inhibitor (TATI), Pepsinogen I (PGI), Proinsulin, Total, Prostate-Specific Antigen, Free (PSA-L), Tissue Inhibitor of

Metalloproteinases 1 (TIMP-1), Trefoil Factor 3 (TFF3), and Tumor necrosis factor ligand superfamily member 13 (APRIL).

[0093] "Panel F" is defined as: Amphiregulin (AR), Angiogenin, Angiopoietin-2

(ANG-2), Angiotensin-Converting Enzyme (ACE), Angiotensinogen, Apolipoprotein(a) (Lp(a)), Apolipoprotein A-I (Apo A-I), Apolipoprotein A-II (Apo A-II), Apolipoprotein A-IV (Apo A- IV), Apolipoprotein B (Apo B), Apolipoprotein C-I (Apo C-I), Apolipoprotein C-III (Apo C-III), Apolipoprotein D (Apo D), Apolipoprotein E (Apo E), Apolipoprotein H (Apo H), AXL

Receptor Tyrosine Kinase (AXL), B cell-activating factor (BAFF), B Lymphocyte

Chemoattractant (BLC), Beta-2-Microglobulin (B2M), Betacellulin (BTC), Angiopoietin- 1 (ANG-1), Antileukoproteinase (ALP), Beta-microseminoprotein (PSP94), Cadherin-1 (E-Cad), Cadherin-13 (T-cad), Carbonic anhydrase 9 (CA-9), Carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1), Carcinoembryonic antigen-related cell adhesion molecule 6 (CEACAM6), Cartilage Oligomeric Matrix Protein (COMP), Cathepsin B (pro) (CTSB), Complement component Clq receptor (ClqRl), Cystatin-A, Cystatin-B, Decorin, Dipeptidyl peptidase IV (DPPIV), Interleukin- 18 -binding protein (IL-18bp), Lactoferrin (LTF), Lipocalin-1 (LC 1), Lumican, Mast/stem cell growth factor receptor (SCFR), Brain-Derived Neurotrophic Factor (BDNF), C-Peptide, C-Reactive Protein (CRP), Calbindin, Cancer Antigen 125 (CA- 125), Cancer Antigen 15-3 (CA-15-3), Cancer Antigen 19-9 (CA-19-9), Cancer Antigen 72-4 (CA 72-4), Carcinoembryonic Antigen (CEA), Cathepsin D, CD5 Antigen-like (CD5L), CD 40 antigen (CD40), CD40 Ligand (CD40-L), Cellular Fibronectin (cFib), Chemokine CC-4 (HCC- 4), Chromogranin-A (CgA), Ciliary Neurotrophic Factor (CNTF), Clusterin (CLU), Collagen IV, Complement C3 (C3), Complement Factor H - Related Protein 1 (CFHR1), Cortisol (Cortisol), Creatine Kinase-MB (CK-MB), Cystatin-C, E-Selectin, EN-RAGE, Endoglin, Endostatin, Eotaxin-1, Eotaxin-2, Eotaxin-3, Epidermal Growth Factor (EGF), Epidermal Growth Factor Receptor (EGFR), Epiregulin (EPR), Epithelial cell adhesion molecule (EpCam), Epithelial- Derived Neutrophil-Activating Protein 78 (ENA-78), Ezrin, Factor VII, Fas Ligand (FasL), FASLG Receptor (FAS), Fatty Acid-Binding Protein, adipocyte (FABP, adipocyte), Fatty Acid- Binding Protein, heart (FABP, heart), Fatty Acid-Binding Protein, liver (FABP, liver), Ferritin (FRTN), Fetuin-A, Fibrinogen, Fibroblast Growth Factor 4 (FGF-4), Fibroblast Growth Factor basic (FGF -basic), Fibulin-lC (Fib-lC), Follicle-Stimulating Hormone (FSH), Galectin-3, Gelsolin, Glucagon, Glucagon-like Peptide 1, active (GLP-1 active), Glucagon-like Peptide 1, total (GLP-1 total), Glucose-6-phosphate Isomerase (G6PI), Glutathione S-Transferase alpha (GST-alpha), Glutathione S-Transferase Mu 1 (GST-MI), Granulocyte Colony-Stimulating Factor (G-CSF), Granulocyte-Macrophage Colony-Stimulating Factor (GM-CSF), Growth Hormone (GH), Growth-Regulated alpha protein (GRO-alpha), Haptoglobin, HE4, Heat Shock Protein 60 (HSP-60), Heparin-Binding EGF-Like Growth Factor (HB-EGF), Hepatocyte Growth Factor (HGF), Hepatocyte Growth Factor receptor (HGF receptor), Hepsin, Human Chorionic Gonadotropin beta (hCG), Human Epidermal Growth Factor Receptor 2 (HER-2),

Immunoglobulin A (IgA), Immunoglobulin E (IgE), Immunoglobulin M (IgM), Insulin, Insulin- like Growth Factor-Binding Protein 1 (IGFBP-1), Insulin-like Growth Factor-Binding Protein 2 (IGFBP-2), Insulin-like Growth Factor-Binding Protein 3 (IGFBP-3), Insulin-like Growth Factor Binding Protein 4 (IGFBP4), Insulin-like Growth Factor Binding Protein 5 (IGFBP5), Insulin- like Growth Factor Binding Protein 6 (IGFBP6), Intercellular Adhesion Molecule 1 (ICAM-1), Interferon gamma (IFN-gamma), Interferon gamma Induced Protein 10 (IP- 10), Interferon- inducible T-cell alpha chemoattractant (IT AC), Interleukin- 1 alpha (IL-1 alpha), Interleukin- 1 beta (IL-1 beta), Interleukin-1 receptor antagonist (IL-lra), Interleukin-2 (IL-2), Interleukin-2 receptor alpha (IL-2 receptor alpha), Interleukin-3 (IL-3), Interleukin-4 (IL-4), Interleukin-5 (IL- 5), Interleukin-6 (IL-6), Interleukin-6 receptor (IL-6r), Interleukin-6 receptor subunit beta (IL-6R beta), Interleukin-7 (IL-7), Interleukin-8 (IL-8), Interleukin-10 (IL-10), Interleukin-12 Subunit p40 (IL-12p40), Interleukin-12 Subunit p70 (IL-12p70), Interleukin-13 (IL-13), Interleukin-15 (IL-15), Interleukin-16 (IL-16), Interleukin-17 (IL-17), Interleukin- 18 (IL-18), Interleukin-23 (IL-23), Kallikrein 5, Kallikrein-7 (KLK-7), Kidney Injury Molecule-1 (KIM-1),

Lactoylglutathione lyase (LGL), Latency-Associated Peptide of Transforming Growth Factor beta 1 (LAP TGF-bl), Lectin-Like Oxidized LDL Receptor 1 (LOX-1), Leptin, Luteinizing Hormone (LH), Macrophage Colony-Stimulating Factor 1 (M-CSF), Macrophage-Derived Chemokine (MDC), Macrophage Inflammatory Protein- 1 alpha (MIP-1 alpha), Macrophage Inflammatory Protein- 1 beta (MIP- 1 beta), Macrophage Inflammatory Protein-3 alpha (MIP-3 alpha), Macrophage inflammatory protein 3 beta (MIP-3 beta), Macrophage Migration Inhibitory Factor (MIF), Macrophage- Stimulating Protein (MSP), Malondialdehyde-Modified Low-Density Lipoprotein (MDA-LDL), Maspin, Matrix Metalloproteinase- 1 (MMP-1), Matrix

Metalloproteinase-3 (MMP-3), Matrix Metalloproteinase-7 (MMP-7), Matrix Metalloproteinase- 9 (MMP-9), Matrix Metalloproteinase-9, total (MMP-9, total), Matrix Metalloproteinase- 10 (MMP-10), Mesothelin (MSLN), MHC class I chain-related protein A (MICA), Monocyte Chemotactic Protein 1 (MCP-1), Monocyte Chemotactic Protein 2 (MCP-2), Monocyte

Chemotactic Protein 3 (MCP-3), Monocyte Chemotactic Protein 4 (MCP-4), Monokine Induced by Gamma Interferon (MIG), Myeloid Progenitor Inhibitory Factor 1 (MPIF-1),

Myeloperoxidase (MPO), Midkine, Myoglobin, Pancreatic secretory trypsin inhibitor (TATI), Peptidase D (PEPD), Platelet endothelial cell adhesion molecule (PECAM-1), Prostate Specific Antigen, total (tPSA), Pulmonary surfactant-associated protein D (SP-D), Tenascin-X (TN-X), Thrombospondin-4 (TSP4), Tissue Inhibitor of Metalloproteinases 2 (TIMP-2), Tyrosine kinase with Ig and EGF homology domains 1 (Tie-1), 6Ckine, Adiponectin, Agouti-Related Protein (AgRP), Aldose Reductase, Alpha- 1-Antichymotrypsin (AACT), Alpha- 1 -Antitrypsin (AAT), Alpha- 1 -Microglobulin (A 1 Micro), Alpha-2-Macroglobulin (A2Macro), AlphA-Letoprotein (AFP), N-terminal prohormone of brain natriuretic peptide (NT proBNP), Nerve Growth Factor beta (NGF-beta), Neuron-Specific Enolase (NSE), Neuronal Cell Adhesion Molecule (Nr- CAM), Neuropilin-1, Neutrophil Gelatinase-Associated Lipocalin (NGAL), Osteopontin, Osteoprotegerin (OPG), Pancreatic Polypeptide (PPP), Pepsinogen I (PGI), Peptide YY (PYY), Phosphoserine Aminotransferase (PSAT), Placenta Growth Factor (PLGF), Plasminogen Activator Inhibitor 1 (PAI-1), Platelet-Derived Growth Factor BB (PDGF-BB), Progesterone, Proinsulin, Intact, Proinsulin, Total, Prolactin (PRL), Prostasin, Prostate-Specific Antigen, Free (PSA-L), Protein S100-A4 (S100-A4), Pulmonary and Activation-Regulated Chemokine (PARC), Receptor for advanced glycosylation end products (RAGE), Receptor tyrosine-protein kinase erbB-3 (ErbB3), Resistin, S 100 calcium-binding protein B (S100-B), Serotransferrin (Transferrin), Serum Amyloid P-Component (SAP), Sex Hormone-Binding Globulin (SHBG), Sortilin, Squamous Cell Carcinoma Antigen- 1 (SCCA-1), Stem Cell Factor (SCF), Stromal cell- derived factor-1 (SDF-1), Superoxide Dismutase 1, soluble (SOD-1), T-Cell-Specific Protein RANTES (RANTES), T Lymphocyte-Secreted Protein 1-309 (1-309), Tamm-Horsfall Urinary Glycoprotein (THP), Tenascin-C (TN-C), Testosterone, Total, Tetranectin, Thrombomodulin (TM), Thrombospondin-1, Thyroglobulin (TG), Thyroid-Stimulating Hormone (TSH),

Thyroxine-B iding Globulin (TBG), Tissue Inhibitor of Metalloproteinases 1 (TIMP-1), Tissue type Plasminogen activator (tPA), TNF-Related Apoptosis-Inducing Ligand Receptor 3 (TRAIL- R3), Transforming Growth Factor alpha (TGF-alpha), Transforming Growth Factor beta-3 (TGF-beta-3), Transthyretin (TTR), Trefoil Factor 3 (TFF3), Tumor Necrosis Factor alpha (TNF-alpha), Tumor Necrosis Factor beta (TNF-beta), Tumor Necrosis Factor Receptor I (TNF RI), Tumor necrosis factor receptor 2 (TNFR2), Tyrosine kinase with Ig and EGF homology domains 2 (TIE-2), Urokinase-type Plasminogen Activator (uPA), Urokinase-type plasminogen activator receptor (uPAR), Vascular Cell Adhesion Molecule- 1 (VCAM-1), Vascular

Endothelial Growth Factor (VEGF), Vascular endothelial growth factor B (VEGF-B), Vascular Endothelial Growth Factor C (VEGF-C), Vascular endothelial growth factor D (VEGF-D), Vascular Endothelial Growth Factor Receptor 1 (VEGFR-1), Vascular Endothelial Growth Factor Receptor 2 (VEGFR-2), Vascular endothelial growth factor receptor 3 (VEGFR-3), Vitamin D-Binding Protein (VDBP), Vitamin K-Dependent Protein S (VKDPS), Vitronectin, von Willebrand Factor (vWF), and YKL-40.

[0094] "Panel G" is defined as: Cancer Antigen 19-9, Matrix

Metalloproteinase-7, Alpha- 1 -Antichymotryps in, Peptide YY and Apolipoprotein A-I.

[0095] "Panel H" is defined as: Matrix Metalloproteinase-7, Pentraxin-3,

Gelsolin , Apolipoprotein a and E-Selectin.

[0096] "Panel I" is defined as: Cancer Antigen 19-9, Pepsinogen I, Mast/stem cell growth factor receptor, E-Selectin and Total Proinsulin.

[0097] "Panel J" is defined as: Pepsinogen I, Interleukin-1 alpha , Mast/stem cell growth factor receptor, Total Proinsulin and Osteopontin. [0098] "Panel K" is defined as: Cancer Antigen 19-9, Alpha- 1-

Antichymotrypsin, Cathepsin D, Apolipoprotein A-I and 6Ckine.

[0099] "Panel L" is defined as: Alpha- 1 -Antichymotrypsin, Cathepsin D,

Matrix Metalloproteinase-7, 6Ckine and Mast/stem cell growth factor receptor.

EXAMPLE 1

[00100] The biomarkers in Panel A (307 biomarkers) were evaluated for their ability ability to discriminate PDAC from healthy control and ChPT. The concentrations of each the biomarkers of Panel A were determined in the plasma of 139 subjects. Plasma samples of 1.0 ml stored in vials were used. Forty seven of the subjects were used as healthy control (CON), forty seven of the subjects were previously diagnosed with chronic pancreatitis (ChPT), and the remaining 42 subjects were previously diagnosed with early stage pancreatic ductal adenocarcinoma (PDAC). Each subject's age and gender were also recorded. The specimens were randomly distributed by disease category across plates and run order to minimize any potential artifacts. Concentrations of the biomarkers were measured in a multiplexed

immunoassay on a Luminex platform.

[00101] Seven analytes were excluded from the analyses because either all of the specimens had the same value or more than 50% of the values were missing. This reduced the number of biomarkers to 300. The raw analyte concentration values were logl O-transformed. These log-transformed values were adjusted by fitting regression models of the form analyte ~ age + gender in the control subjects. These regression models were then applied to all subjects (CON, PDAC, and ChPT) and the residuals from this regression were the adjusted analyte values for subsequent analysis. Additionally, age and gender were included in the models to predict disease status.

[00102] Boxplots for each of the biomarkers of Panel A were calculated for each of the categories: CON, ChPT, and PDAC. P-values were also calculated for each biomarker using ANOVA and Kruskal-Wallis (KW), a non-parametric alternative to ANOVA. The ANOVA analysis tested the hypothesis that the group means of an analyte were the same in the three disease groups. The plots were calculated without adjustment for age or gender. Figure 2 shoes sample boxplots for this analysis. [00103] Data analysis was performed for each of the following model groups: CON and ChPT versus PDAC, CON versus PDAC, ChPT versus PDAC, and CON versus ChPT. Within each model group, the p-value, q-value, ROC, and mean in group were calculated for each individual analyte using any one of the following methods: t-test, ANOVA, Wilcoxon rank-sum test, rank comparison, Kruskal-Wallis, and/or logistic regression. Logistic regression analyses were carried out on the individual analytes with binary disease category as the dependent variable and analyte, age, and gender as the independent variables. The logistic regression analyses used age- and gender-adjusted analyte values, as described above. The area under the ROC curve was calculated for analytes alone and for the analytes in combination with age and gender using the results of the logistic regression. To control for multiplicity, the false discovery rate q-value is given for each p-value for each analyte. The corresponding values are shown in Megatable 1. For the attached Megatable 1 , the column names, column definitions, values, tests, predictors, response variable, datasets, diseases compared, and formulas have the definitions as outlined in Megatable 2. In the attached Megatable 1 , the column names have the definitions as outlined in Table 1.

[00104] Table 1.

[00105] The analysis of CON and ChPT versus PDAC identifies 96 analytes (out of 307 total analytes from Panel A) with both p-values less than 0.05 and false discovery rate q-values of 0. 13 or less. In the analysis of CON versus PDAC, 1 19 analytes were identified with p-values less than 0.05 and q-value less than 0. 1. For ChPT versus PDAC, 51 analytes were identified with q-value less than 0.29. Table 2 shows the twenty analytes with the lowest p-values for CON and ChPT versus PDAC.

[00106] Table 2.

[00107] The data from Example 1 were analyzed with multivariate models to predict disease category. Four modeling methods were used: random forest (rf), boosting, lasso, and logistic regression. The best analytes were selected independently by each of the three modeling methods using estimation of area under the ROC curve (AUC) from 100 bootstraps using the best n variables. These models rank the 15 best biomarkers.

[00108] Figures 4- 1 1 illustrate the results of the multivariate analysis. Figures 3-6 illustrate the results of the multivariate analysis including CA 19-9. Figure 3 illustrates PDAC versus CON and ChPT. Figure 4 illustrates PDAC versus CON. Figure 5 illustrates PDAC versus ChPT. Figure 6 illustrates CON versus ChPT.

[00109] Figures 7- 10 illustrate the results of the multivariate analysis excluding CA 19-9. Figure 7 illustrates PDAC versus CON and ChPT. Figure 8 illustrates PDAC versus CON. Figure 9 illustrates PDAC versus ChPT. Figure 10 illustrates CON versus ChPT.

[00110] Tables 3- 10 contain the results of the best 15 analytes by each method corresponding to Figures 3 - 10, respectively.

[00111] Table 3.

[001121 Table 4.

[00113] Table 5.

[001141 Table 6.

[001151 Table 7.

[00116] Table 8.

[00117] Table 9.

[001181 Table 10.

[00119] Pair-wise Pearson correlation coefficients among significant analytes for Example 1 were calculated and displayed as a heatmap. Figure 1 1 illustrates this heatmap. Analytes were chosen to be included in the heatmap of Pearson correlation coefficients by using each log 10 transformed and age and gender adjusted analyte value (response/dependent variable) and performing ANOVA analysis on the value by using the three disease groups (CON, ChPT, and PDAC) as the explanatory variable (also called the independent variable or predictor). The null hypothesis was that the mean values of the analyte are the same among the three disease groups. P- values and q-values (false discovery rates (FDR)) were also calculated. Analytes with FDR < 0.01 were included in the heatmap.

[00120] Unique candidate biomarkers were then selected from Panel A to comprise Panel B, Panel C, Panel D, and Panel E. Each of Panel B, Panel C, Panel D, and Panel E comprise: i) the union of: the top analytes from each classification algorithm from the multivariate analysis that excluded CA 19-9, ii) the union of top analytes from univariate analysis, and iii) the union of top analytes from each classification algorithm from multivariate analysis including CA 19-9. Panel B, Panel C, Panel D, and Panel E comprise the biomarkers selected from the analyses of PDAC vs. CON and ChPT, PDAC vs. ChPT, PDAC vs. CON, and CON vs. ChPT, respectively. [00121] Combinations of three, four, or five biomarkers were then generated from Panel B, Panel C, Panel D, and Panel E, respectively. Each combination was considered as a model and logistic regression was performed on each model for comparing two diagnoses. For each model, the p-value for each biomarker was calculated and the AUC for the logistic model was calculated. Megatables 3 through 14 show each model, the combination of biomarkers comprising each model, the p-value for each biomarker within the model, and the AUC for each model.

[00122] Megatables 3-5 show panels comprising combinations of 3, 4, or 5 biomarkers for Panel B. Megatable 3 shows combinations of three biomarkers.

Megatable 4 shows combinations of four biomarkers. Megatable 5 shows combinations of five biomarkers.

[00123] Megatables 6-8 show panels comprising combinations of 3, 4, or 5 biomarkers for Panel C. Megatable 6 shows combinations of three biomarkers.

Megatable 7 shows combinations of four biomarkers. Megatable 8 shows combinations of five biomarkers.

[00124] Megatables 9- 1 1 show panels comprising combinations of 3, 4, or 5 biomarkers for Panel D. Megatable 9 shows combinations of three biomarkers.

Megatable 10 shows combinations of four biomarkers. Megatable 1 1 shows

combinations of five biomarkers.

[00125] Megatables 12- 14 show panels comprising combinations of 3, 4, or

5 biomarkers for Panel E. Megatable 12 shows combinations of three biomarkers.

Megatable 13 shows combinations of four biomarkers. Megatable 14 shows

combinations of five biomarkers.

EXAMPLE 2

[00126] The biomarkers in Panel F (273 biomarkers) were evaluated for their ability to discriminate early, late PDAC, healthy controls and ChPT. The concentration of each of the biomarkers of Panel F was determined in the plasma of 144 additional subjects. Plasma samples of 1.0 mL stored in vials were used. Sixty of the subjects were used as healthy control (CON), 16 of the subjects were previously diagnosed with chronic pancreatitis (ChPT), eight subjects were previously diagnosed with stage IA, IB, IIA PDAC (the early-stage samples), and the remaining 60 subjects were previously diagnosed with stage IIB, III, IV PDAC (the late stage samples). Each subject's age and gender were also recorded. Concentrations of the biomarkers were measured in a multiplexed immunoassay using the RBM MAP platform.

[00127] Missing values (NR.) were not imputed and biomarkers were removed with more than 50% missing. The raw concentration data was log-transformed and adjusted for the effects of age and gender by regressing log of each biomarker on age and gender. The residuals from the regression model were used instead of the original values for future processing. The following seven biomarkers had a high proportion of <LOW> values; however, the low values were consistently associated with one of the three disease categories (CON, ChPT, and PDAC), so these biomarkers were included in the statistical analyses:

Antileukoproteinase (ALP), Cadherin 1 (E Cad), Cystatin B, Lactoferrin (LTF), Mast stem cell growth factor receptor (SCFR), Pancreatic secretory trypsin inhibitor (TATI), and Tissue Inhibitor of Metalloproteinases 2 (TIMP2).

[00128] The raw analyte concentration values were log lO-transformed. These log-transformed values were adjusted by fitting regression models of the form analyte ~ age + gender in the control subjects. These regression models were then applied to all subjects (CON, PDAC, and ChPT) and the residuals from this regression were the adjusted analyte values for subsequent analysis. Additionally, age and gender were included in the models to predict disease status.

[00129] Boxplots along with ANOVA and KW logistic regression and Wilcoxon p-values were calculated in similar fashion as in Example 1. Figure 12 show sample boxplots that were generated in this analysis.

[00130] Data analysis was performed for each of the following model groups: CON and ChPT versus PDAC, CON versus PDAC, ChPT versus PDAC and CON versus ChPT. Within each model group, the p-value, q-value, AUC, and mean in group were calculated for each individual analyte using any one of the following methods: t-test, ANOVA, Kruskal-Wallis, Wilcoxon rank-sum test, rank comparison, and/or logistic regression. Logistic regression analyses were carried out on the individual analytes with binary disease category as the dependent variable and analyte, age, and gender as the independent variables. The logistic regression analyses used age- and gender-adjusted analyte values, as described above. The area under the ROC curve was calculated for analytes alone and for the analytes in combination with age and gender using the results of the logistic regression. To control for multiplicity, the false discovery rate q-value is given for each p-value for each analyte. The corresponding values are shown in Megatable 15. For the attached Megatable 15, the column names, column definitions, values, tests, predictors, response variable, datasets, diseases compared, and formulas have the definitions as outlined in Megatable 16.

[00131] For all stages of PDAC, there are 136 analytes (out of 273 total) with p-values less than 0.05 and with q-values of 0.0686 or less. Table 1 1 shows the twenty analytes with the lowest p-values for CON and ChPT versus PDAC for all stages of PDAC.

Table 1 1.

[00132] For early stages of PDAC, there are 61 analytes (out of 273 total) with p-values less than 0.05 and with q-values of 0.20 or less. Table 12 shows the twenty analytes with the lowest p-values for CON and ChPT versus PDAC for early stages of PDAC. Table 12.

[00133] Table 13 shows the twenty analytes with the lowest p-values for CON and ChPT versus PDAC for late stages of PDAC.

Table 13.

[00134] The data from Example 2 were analyzed with multivariate models to predict disease category. Three modeling methods were used: random forest (rf), boosting, and lasso. The best analytes were selected independently by each of the three modeling methods using estimation of area under the ROC curve (AUC) from 100 bootstraps using the best n variables. These models rank the 15 best biomarkers.

[00135] Figures 13-23 illustrate the results of the multivariate analysis. Figures 13 - 16 illustrate the results of the multivariate analysis using samples representing all stages of PDAC. Figure 13 illustrates PDAC versus CON and ChPT. Figure 14 illustrates PDAC versus CON. Figure 15 illustrates PDAC versus ChPT. Figure 16 illustrates CON versus ChPT.

[00136] Figures 17-26 illustrate the results of the multivariate analysis using samples representing late stages of PDAC. Figure 17 illustrates PDAC versus CON and ChPT. Figure 18 illustrates PDAC versus CON. Figure 19 illustrates PDAC versus ChPT. Figure 20 illustrates CON versus ChPT.

[00137] Figures 21 -23 illustrate the results of the multivariate analysis using samples representing early stages of PDAC. Figure 21 illustrates PDAC versus CON and ChPT. Figure 22 illustrates PDAC versus CON. Figure 23 illustrates PDAC versus ChPT.

[00138] Tables 14-24 contain the results of the analyses by each method corresponding to Figures 13 -23, respectively.

[00139] Table 14

[001401 Table 15

[001411 Table 16

[001421 Table 17

[001431 Table 18

[001441 Table 19

[001451 Table 20

[001461 Table 21

[001471 Table 22

[001481 Table 23

[001491 Table 24

[00150] Pair-wise Pearson correlation coefficients among significant analytes for Example 2 were calculated and displayed as a heatmap. Figure 24 illustrates this heatmap. Analytes were chosen to be included in the heatmap of Pearson correlation coefficients by using each log lO transformed and age and gender adjusted analyte value (response/dependent variable) and performing ANOVA analysis on the value by using the three disease groups (CON, ChPT, and PDAC) as the explanatory variable (also called the independent variable or predictor). The null hypothesis was that the mean values of the analyte are the same among the three disease groups. P- values and q-values (false discovery rates (FDR)) were also calculated. Analytes with FDR < 0.01 were included in the heatmap.

[00151] The results from Example 1 were compared with the results from Example 2. Nine of the analytes in Table 2 are also among the twenty analytes with the lowest p-values in Example 2. Table 25 shows results from Example 2 for the twenty analytes with the lowest p-values for CON and ChPT vs. PDAC. Analytes marked with an asterisk are among the top 20 analytes by p-value in both Example 1 and Example 2. Of these top 20 analytes in Example 2, nine are among the top 20 in Example 1 and 17 of 20 are significant (p< 0.05) in Example 1.

[00152] Table 25.

Also among top twenty analytes with lowest p-values in Example 1.

[00153] Hierarchical clustering of PDAC samples sets from Example 1 and

Example 2 was performed based on their analyte values. The analyte values were loglO transformed, age and gender adjusted, and standardized. The cluster dendrogram is illustrated in Figure 25. The resulting tree was divided into 12 clusters. Table 26 shows the counts of disease status in each cluster. Cluster 2 has mostly CON and PDAC, clusters 3 and 4 have mostly PDAC, and cluster 5 is mixed.

[00154] Table 26.

[00155] All publications and patent applications mentioned in the specification are indicative of the level of those skilled in the art to which this invention pertains. All publications and patent applications are herein incorporated by reference to the same extent as if each individual publication or patent application was specifically and individually indicated to be incorporated by reference. The mere mentioning of the publications and patent applications does not necessarily constitute an admission that they are prior art to the instant application.

[00156] The following enumerated embodiments further elucidate the presently disclosed invention.

Embodiment 1. A method of screening for pancreatic cancer, the method comprising: a. determining from a biological sample of a patient the levels of a panel of biomarkers selected from any one of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14, wherein the panel comprises positive biomarkers, negative biomarkers, or both; b. comparing the level of each biomarker to threshold levels for each biomarker; and c. determining the likelihood the patient has pancreatic cancer. Embodiment 2. A method of Embodiment 1 further comprising: a. determining the presence or absence of a biomarker that has either a level above a threshold level for each particular positive biomarker or a level below a threshold level for each particular negative biomarker; and b. conducting endoscopic ultrasonography or magnetic resonance

cholangiopancreatography if the patient has a biomarker that has either a level above the threshold level for each particular positive biomarker or a level below the threshold level for each particular negative biomarker. Embodiment 3. The method of Clam 1, further comprising conducting endoscopic ultrasonography or magnetic resonance cholangiopancreatography if the patient has at least 4 biomarkers that have either a level above the threshold level for that particular positive biomarkers or a level below the threshold level for that particular negative biomarkers.

Embodiment 4. A method of screening a biological sample, the method comprising: a. obtaining a biological sample; b. determining a level in the biological sample of at least 4 biomarkers of any one of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14, wherein the panel comprises positive biomarkers, negative biomarkers, or both; c. comparing the level of each biomarker to threshold levels for each biomarker; and d. determining the presence or absence of a biomarker that has either a level above a threshold level for each particular positive biomarker or a level below a threshold level for each particular negative biomarker.

Embodiment 5. The method of Embodiment 4, further comprising determining a level in the biological sample of each biomarker of the panel.

Embodiment 6. The method of Embodiment 4 wherein the levels of the biomarkers in the sample indicate pancreatic cancer.

Embodiment 7. A method of screening, the method comprising: a. identifying a patient with (i) a first-degree relative diagnosed with pancreatic cancer, (ii) a mutation in a BRCA2, PALB2, pl6, STK1 1, MLH1, MSH2, MSH6, PMS2, or EPCAM gene, (iii) a diagnosis of hereditary non-polyposis colorectal cancer, or (iv) a diagnosis of Lynch syndrome, Peutz-Jeghers syndrome, acute pancreatitis, chronic pancreatitis, or recent onset diabetes, or (v) a combination of any of the foregoing; b. determining a level in the biological sample of each biomarker of any one of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14, wherein the panel comprises positive biomarkers, negative biomarkers, or both; c. comparing the level of each biomarker to threshold levels for each biomarker; and d. determining the presence or absence of a biomarker that either has a level above a threshold level for each particular positive biomarker or a level below a threshold level for each particular negative biomarker.

Embodiment 8. The method of Embodiment 7 wherein determining the presence or absence of a biomarker that either has a level above a threshold level for each particular positive biomarker or a level below a threshold level for each particular negative biomarker indicates the presence of pancreatic cancer.

Embodiment 9. A method of screening, the method comprising detecting in a biological sample of an individual identified as having (i) a first-degree relative diagnosed with pancreatic cancer, (ii) a mutation in a BRCA2, PALB2, pl6, STK11, MLH1, MSH2, MSH6, PMS2, or EPCAM gene, (iii) a diagnosis of hereditary non-polyposis colorectal cancer, (iv) ) a diagnosis of hereditary non-polyposis colorectal cancer, or (iv) a diagnosis of Lynch syndrome, Peutz- Jeghers syndrome, acute pancreatitis, chronic pancreatitis, or recent onset diabetes or (v) a combination of (i)-(iv), the level of each biomarker of any one of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14.

Embodiment 10. The method of Embodiment 9 wherein the levels of biomarkers measured indicate the presence of pancreatic cancer. Embodiment 1 1. A method of screening, the method comprising detecting in a biological sample of an individual identified as having (i) a first-degree relative diagnosed with pancreatic cancer, (ii) a mutation in a BRCA2, PALB2, pi 6, STK11, MLH1, MSH2, MSH6, PMS2, or EPCAM gene, (iii) a diagnosis of hereditary non-polyposis colorectal cancer, (iv) a diagnosis of Lynch syndrome, Peutz-Jeghers syndrome, acute pancreatitis, chronic pancreatitis, or recent onset diabetes or (v) a combination of (i)-(iv), the level of at least 4 biomarkers of any one of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14.

Embodiment 12. The method of Embodiment 1 1 wherein the levels of biomarkers measured indicate the presence of pancreatic cancer.

Embodiment 13. A method of diagnosing pancreatic cancer, the method comprising:

(a) determining from a biological sample of a patient a level of at least 3 biomarkers listed in any one of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14, wherein the panel comprises positive biomarkers, negative biomarkers, or both;

(b) comparing the level of the at least 3 biomarkers to threshold levels for each

biomarker; and

(c) determining the presence or absence of at least 3 biomarkers that have a level either above the threshold level for each particular positive biomarker or below the threshold level for each particular negative biomarker, wherein the presence of at least 3 biomarkers that have a level either above the threshold level for that particular positive biomarker or below the threshold level for that particular negative biomarker indicates a likelihood of pancreatic cancer.

Embodiment 14. The method of Embodiment 13, further comprising determining from the biological sample of the patient a level of each biomarker of the panel.

Embodiment 15. The method of Embodiment 13, further comprising determining a number of family members of the patient diagnosed with pancreatic cancer and determining a degree of relationship to the patient of any such family member. Embodiment 16. The method of Embodiment 13, further comprising determining the presence or absence of a mutation in a BRCA2, PALB2, pl6, STKl 1, MLHl, MSH2, MSH6, PMS2, or EPCAM gene of a patient, or determining whether the patient has hereditary non- polyposis colorectal cancer.

Embodiment 17. The method of Embodiment 13, wherein the patient has been diagnosed with Peutz-Jeghers syndrome, Lynch syndrome, chronic pancreatitis, recent onset diabetes, or acute pancreatitis.

Embodiment 18. The method of Embodiment 13, wherein a pancreatic cancer diagnosis is confirmed by Computed tomography (CT) scan, Somatostatin Receptor Scintigraphy (SRS), Positron Emission Tomography (PET) scan, ultrasonography (ultrasound), Endoscopic

Retrograde Cholangiopancreatography (ERCP), Angiography, or biopsy.

Embodiment 19. The method of Embodiment 13, wherein the patient is subsequently treated with surgical resection, ablative techniques, radiation therapy, or chemotherapy.

Embodiment 20. The method of Embodiment 13 wherein the biological sample is blood, serum or plasma.

Embodiment 21. A method of determining the probability of early-stage pancreatic cancer in a patient, the method comprising: a. determining from a biological sample of a patient a level of each biomarker of a panel of biomarkers for pancreatic cancer, wherein the panel comprises positive biomarkers, negative biomarkers, or both; b. comparing the level of each biomarker to threshold levels for each biomarker; c. determining a number of patient biomarkers that have either a level above a threshold level for each particular positive biomarker or a level below a threshold level for each particular negative biomarker; and d. calculating a probability of early-stage pancreatic cancer based on the number.

Embodiment 22. The method of Embodiment 21, further comprising determining the number of family members of a patient diagnosed with pancreatic cancer and determining a degree of relationship to the patient of any such family member. Embodiment 23. The method of Error! Reference source not found., further comprising determining a magnitude of the difference in level for each biomarker of the patient that is either above a threshold level for that particular positive biomarker or below a threshold level for that particular negative biomarker.

Embodiment 24. The method of Embodiment 21, wherein calculating the probability of early-stage pancreatic cancer is further based on a number of family members of the patient diagnosed with pancreatic cancer and the degree of relationship to the patient of any such family member.

Embodiment 25. The method of Error! Reference source not found., wherein calculating the probability of early-stage pancreatic cancer is further based on a magnitude of the difference in level for each biomarker of the patient that is either above a threshold level for a positive biomarker or below a threshold level for a negative biomarker.

Embodiment 26. The method of Error! Reference source not found., wherein calculating the probability of early-stage pancreatic cancer is also based on a weighted significance of each patient biomarker that has either a level above a threshold level for each particular positive biomarker or a level below a threshold level for each particular negative biomarker.

Embodiment 27. The method of Error! Reference source not found., wherein the panel of biomarkers comprises any one of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14.

Embodiment 28. The method of Error! Reference source not found., wherein the panel of biomarkers comprises at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 1 1, at least 12, at least 13, at least 14, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, or at least 50 biomarkers of any one of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14.

Embodiment 29. The method of Error! Reference source not found., further comprising determining the presence or absence of a mutation in a BRCA2, PALB2, pl6, STK1 1, MLH1, MSH2, MSH6, PMS2, or EPCAM gene of a patient, or determining whether the patient has hereditary non-polyposis colorectal cancer.

Embodiment 30. The method of Error! Reference source not found., wherein calculating the probability of early-stage pancreatic cancer is further based on the presence or absence of a mutation in a BRCA2, PALB2, pl6, STKl 1, MLHl, MSH2, MSH6, PMS2, or EPCAM gene or whether the patient has hereditary non-polyposis colorectal cancer.

Embodiment 31. The method of Embodiment 21 , further comprising determining whether the patient is symptomatic of Lynch syndrome, chronic pancreatitis, recent onset diabetes, or acute pancreatitis.

Embodiment 32. The method of Error! Reference source not found., wherein calculating the probability of early-stage pancreatic cancer is further based on the patient being symptomatic of Lynch syndrome, chronic pancreatitis, recent onset diabetes, or acute pancreatitis.

Embodiment 33. The method of Embodiment 21 , further comprising confirming the probability of early-stage pancreatic cancer by Computed tomography (CT) scan, Somatostatin Receptor Scintigraphy (SRS), Positron Emission Tomography (PET) scan, ultrasonography (ultrasound), Endoscopic Retrograde Cholangiopancreatography (ERCP), Angiography, or biopsy

Embodiment 34. A method of determining a probability of early-stage pancreatic cancer in a patient, the method comprising: a. determining a number of family members of a patient diagnosed with pancreatic cancer and determining a degree of relationship to the patient of any such family member; b. determining the presence or absence of a mutation in a BRCA2, PALB2, pi 6, STKl 1, MLHl, MSH2, MSH6, PMS2, or EPCAM gene of a patient, or determining whether the patient has hereditary non-polyposis colorectal cancer; c. determining from a biological sample of the patient a level of each biomarker of a panel of biomarkers for pancreatic cancer, wherein the panel comprises positive biomarkers, negative biomarkers, or both; d. comparing the level of each biomarker to threshold levels for each biomarker; e. determining a number of patient biomarkers that have either a level above the threshold level for each particular positive biomarker or a level below the threshold level for each particular negative biomarker; f. determining a magnitude of the difference in level for each biomarker of the patient that is either above the threshold level for that particular positive biomarker or below the threshold level for that particular negative biomarker; and g. calculating a probability of early-stage pancreatic cancer based on at least (i) the number of family members of the patient diagnosed with pancreatic cancer, (ii) the degree of relationship to the patient of any such family member, (iii) the presence or absence of a mutation in a BRCA2, PALB2, pl6, STK1 1, MLH1, MSH2, MSH6, PMS2, or EPCAM gene or whether the patient has hereditary non-polyposis colorectal cancer, (iv) the number of patient biomarkers that have either a level above the threshold level for each particular positive biomarker or a level below a threshold level for each particular negative biomarker, and (v) the magnitude of the difference in level for each biomarker of the patient that is either above the threshold level for that particular positive biomarkers or below the threshold level for that particular negative biomarkers.

Embodiment 35. A computer-assisted method of determining a probability of a patient having early-stage pancreatic cancer, the method comprising: a. providing biomarker information from a biomarker information database module on a data processing device, wherein the biomarker information comprises threshold level information for each biomarker of a panel of biomarkers, wherein the panel of biomarkers comprises positive biomarkers, negative biomarkers, or both, wherein a level above the threshold level for each particular positive biomarkers is indicative of the presence of pancreatic cancer in a patient and a level below the threshold level for each particular negative biomarkers is indicative of the presence of pancreatic cancer in a patient; b. providing patient information from a patient information database module on a data processing device, wherein the patient information comprises biomarker level information for the patient; c. comparing with a comparison module on a data processing device biomarker information and patient information; d. determining with an evaluation module on a data processing device operably connected to the comparison module a number of patient biomarkers with either a level above a threshold level for each particular positive biomarker or a level below a threshold level for each particular negative biomarker; and e. determining with a calculation module on a data processing device operably

connected to the evaluation module a probability of early-stage pancreatic cancer based on the determined number.

Embodiment 36. The method of Error! Reference source not found., further comprising providing to the calculation module family history information from a family history database module on a data processing device, the family history information comprising a number of family members of the patient diagnosed with pancreatic cancer, and wherein determining a probability of early-stage pancreatic cancer is further based on the number of family members of the patient diagnosed with pancreatic cancer.

Embodiment 37. The method of Error! Reference source not found., wherein the family history information comprises a degree of relationship to the patient of each family member diagnosed with pancreatic cancer.

Embodiment 38. The method of Embodiment 36, wherein the family history database comprises a patient file history database.

Embodiment 39. The method of Error! Reference source not found., wherein the patient information database comprises a patient file history database.

Embodiment 40. The method of Embodiment 36, further comprising displaying a graphical presentation of the patient's probability of early-stage pancreatic cancer with a display operably connected to the calculation module.

Embodiment 41. The method of Embodiment 36, further comprising determining additional suggested diagnostic procedures based on the patient's probability of early-stage pancreatic cancer with a diagnostic module operably connected to the calculation module.

Embodiment 42. The method of Embodiment 36, wherein the panel of biomarkers comprises any one of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14.

Embodiment 43. A kit for detecting the presence of early-stage pancreatic cancer in a patient, the kit comprising reagents useful, sufficient, or necessary for determining the level of at least three biomarkers in any one of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14.

Embodiment 44. A method of assessing the likelihood pancreatic cancer in a patient, comprising: a. obtaining a biological sample from a patient b. assaying the biological sample to determine the levels of a panel of at least three biomarkers in the biological sample, c. comparing the levels of the biomarkers to a threshold for each biomarker, where some biomarkers are positively associated with pancreatic cancer, and some biomarkers are negatively associated with pancreatic cancer d. determining the levels of the biomarkers in the biological sample are above the threshold for biomarkers that are positively associated with pancreatic cancer or below the threshold for biomarkers that are negatively associated with pancreatic cancer; and e. determining the likelihood the patient has pancreatic cancer.

Embodiment 45. The method of Embodiment 44 wherein the threshold value for each biomarker is determined by measuring the level of each biomarker in control subjects.

Embodiment 46. The method of Embodiment 45 wherein the control subjects are healthy and age- and sex- matched.

Embodiment 47. The method of Embodiment 45 wherein the levels of the biomarkers in the subject are further compared to the levels of biomarkers in subjects who have a diagnosis of diabetes, or non-pancreatic cancer.

Embodiment 48. The method of Embodiment 45 wherein the threshold value for each biomarker is determined by measuring the level of each biomarker in subject with a known early stage pancreatic cancer diagnosis.

Embodiment 49. The method of Embodiment 45 wherein the biomarkers are selected from Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14. Embodiment 50. The method of Embodiment 45 wherein the biomarkers are any 3, 4, 5, 6, 7, 8, 9, or 10 of the biomarkers listed in Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14.

Embodiment 51. The method of Embodiment 45 wherein determining the likelihood of pancreatic cancer is further supported by biopsy, Computed tomography (CT) scan, Somatostatin Receptor Scintigraphy (SRS), Positron Emission Tomography (PET) scan, ultrasonography (ultrasound), Endoscopic Retrograde Cholangiopancreatography (ERCP), or Angiography.

Embodiment 52. The method of Embodiment 44 where in the patient has (i) a first-degree relative diagnosed with pancreatic cancer, (ii) a mutation in a BRCA2, PALB2, pl6, STK1 1, MLHl, MSH2, MSH6, PMS2, or EPCAM gene, (iii) a diagnosis of hereditary non-polyposis colorectal cancer, (iv) a diagnosis of Lynch syndrome, Peutz-Jeghers syndrome, acute pancreatitis, chronic pancreatitis, or recent onset diabetes or (v) a combination of (i)-(iv).

Embodiment 53. The method of Embodiment 44 wherein the levels of the at least three biomarkers indicates an early stage pancreatic cancer diagnosis.

Embodiment 54. The method of embodiment 44 wherein the levels of the at least three biomarkers indicates a diagnosis of stage 1, 2, or 3 pancreatic cancer.

Embodiment 55. The method of Embodiment 44 wherein the patient is treated for pancreatic cancer through surgical resection, chemotherapy, drug therapy, or radiation therapy.

Embodiment 56. The method of Embodiment 44 wherein the sample is blood, serum or plasma.

Embodiment 57. A method of assessing the likelihood of pancreatic cancer in a subject comprising: a. obtaining a biological sample from a subject b. obtaining a biological sample from a control subject or group of control subjects c. measuring the levels a panel of at least three biomarkers in the subject and control subject or control subjects d. comparing the levels of biomarkers in the subject and control subject(s) and e. determining the likelihood of pancreatic cancer in the subject based at least in part on the difference in the levels of the at least three biomarkers in the subject compared to the control subject(s)

Embodiment 58. The method of embodiment 57 wherein the subject is human.

Embodiment 59. The method of embodiment 57 wherein biological sample is blood, serum or plasma.

Embodiment 60. The method of embodiment 57 wherein the control subjects are healthy age matched individuals.

Embodiment 61. The method of embodiment 57 wherein the control subjects have a diagnosis of non-pancreatic cancer, or diabetes.

Embodiment 62. The method of embodiment 57 wherein the subject is symptomatic of Lynch syndrome, chronic pancreatitis, acute pancreatitis, recent onset diabetes, or Peutz-Jeghers syndrome.

Embodiment 63. The method of embodiment 57 wherein the subject has at least one first- degree relative who has been diagnosed with pancreatic cancer.

Embodiment 64. The method of embodiment 57 wherein the subject has a mutation in the pl6, BRCA2, PALB2, or STK11 genes.

Embodiment 65. The method of embodiment 57 wherein biomarkers are selected from any panel A through L.

Embodiment 66. The method of embodiment 57 wherein the biomarkers are selected from biomarkers in Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14 with a t-test p-value equal to or less than 0.05.

Embodiment 67. The method of embodiment 57 wherein the biomarkers are selected from biomarkers in Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14 with an ANOVA p-value equal to or less than 0.05.

Embodiment 68. The method of embodiment 57 wherein the biomarkers are selected from biomarkers in Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14 with a Wilcoxon rank sum p-value equal to or less than 0.05. Embodiment 69. The method of embodiment 57 wherein the biomarkers are selected from biomarkers in Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14 with a Kruskal-Wallis p-value equal to or less than 0.05.

Embodiment 70. The method of embodiment 57 wherein pancreatic cancer in the subject is confirmed by biopsy, Computed tomography (CT) scan, Somatostatin Receptor Scintigraphy (SRS), Positron Emission Tomography (PET) scan, ultrasonography (ultrasound), Endoscopic Retrograde Cholangiopancreatography (ERCP), or Angiography.

Embodiment 71. The method of embodiment 57 wherein the patient is treated by chemotherapy, radiation therapy, or ablative techniques.

Embodiment 72. The method of embodiment 57 wherein said determination of pancreatic cancer is computed by a system where the levels of three or more biomarkers are related to a numerical value, comparing the numerical value with a numerical index database and analyzing whether said numerical index falls within a range of database index results that is predetermined to be indicative of pancreatic cancer.

Embodiment 73. The method of embodiment 57 wherein the results of said comparison are provided in a report.

Embodiment 74. A method of treating a patient comprising:

a. measuring in a biological sample taken from the patient the levels of a panel of biomarkers comprising at least three biomarkers in any of Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14,

b. comparing the levels of the panel of biomarkers in the patient with a threshold level for each biomarker

c. determining whether the level of biomarker in the patient is above or below the threshold;

d. (1) correlating a level of biomarker above the threshold for a positive biomarker or below the threshold for a negative biomarker in any of said panel of biomarkers to an increased likelihood of pancreatic cancer, or (2) correlating a level of biomarker below the threshold for a positive biomarker or above the threshold for a negative biomarker in any of said panel of biomarkers to no increased likelihood of pancreatic cancer; and e. recommending, prescribing, or administering a treatment to the patient for pancreatic cancer.

Embodiment 75. A method of treating pancreatic cancer in a subject comprising: a. obtaining a biological sample from a subject; b. measuring the levels of a panel of at least three biomarkers in the biological sample; c. comparing the levels of the biomarkers in the subject to a threshold; d. diagnosing the patient with pancreatic cancer; and e. treating the patient for pancreatic cancer.

Embodiment 76. The method of embodiment 75 wherein the biological sample is blood, serum or plasma.

Embodiment 77. The method of embodiment 75 wherein the diagnosis of pancreatic cancer is based at least in part on the difference between the biomarker levels in the patient and the threshold level.

Embodiment 78. The method of embodiment 75 wherein the threshold level is determined by measuring the levels of the at least three biomarkers in a control sample.

Embodiment 79. The method of embodiment 78 wherein the control sample is from a healthy, age matched individual or group of individuals.

Embodiment 80. The method of embodiment 78 wherein the threshold level is further determined by measuring the levels of the at least three biomarkers in samples from individuals with a non-pancreatic cancer diagnosis.

Embodiment 81. The method of embodiment 78 wherein the threshold level is further determined by measuring the levels of the at least three biomarkers in samples from individuals diagnosed with diabetes.

Embodiment 82. The method of embodiment 78 wherein the threshold level is further determined by measuring the levels of the at least three biomarkers in samples from individuals with early stage pancreatic cancer diagnosis. Embodiment 83. The method of embodiment 75 wherein the biomarkers are selected from any biomarkers listed in Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14.

Embodiment 84. The method of embodiment 75 wherein the patient has at least one first degree relative with a pancreatic cancer diagnosis

Embodiment 85. The method of embodiment 75 wherein the patient is symptomatic of Lynch syndrome, chronic pancreatitis, new onset diabetes, acute pancreatitis, or Peutz-Jeghers syndrome.

Embodiment 86. The method of embodiment 75 wherein the patient has a germ-line mutation in the pi 6, BRCA2, PALB2, or STK11 genes.

Embodiment 87. The method of embodiment 75 wherein the pancreatic cancer diagnosis is confirmed by biopsy, Computed tomography (CT) scan, Somatostatin Receptor Scintigraphy (SRS), Positron Emission Tomography (PET) scan, ultrasonography (ultrasound), Endoscopic Retrograde Cholangiopancreatography (ERCP), or Angiography.

Embodiment 88. The method of embodiment 75 wherein the treatment for pancreatic cancer is chemotherapy, radiation therapy, or ablative techniques.

Embodiment 89. A method for diagnosing pancreatic cancer in a subject, the method comprising: a. contacting a biological sample from the subject with at least three reagents, each one of which specifically binds to a different biomarker in a panel b. determining whether the selected marker is over expressed or under expressed in the sample, thereby providing a diagnosis for pancreatic cancer in the subject.

Embodiment 90. The method of embodiment 89 wherein the over or under expression is determined by comparing expression level to a predetermined threshold.

Embodiment 91. The method of embodiment 89 wherein the over or under expression of the panel of biomarkers is determined by comparing the levels in the patient to a control group.

Embodiment 92. The method of embodiment 91 wherein the control group is made up of healthy age-matched individuals. Embodiment 93. The method of embodiment 91 wherein the overexpression or underexpression is further determined defined by comparing the levels to a group of individuals diagnosed with early stage pancreatic cancer, late stage pancreatic cancer, other cancers, or diabetes.

Embodiment 94. The method of embodiment 89 wherein the subject has been diagnosed with or is symptomatic of Lynch syndrome, chronic pancreatitis, new onset diabetes, acute pancreatitis, or Peutz-Jeghers syndrome.

Embodiment 95. The method of embodiment 89 wherein the subject has at least one first degree relative with a pancreatic cancer diagnosis.

Embodiment 96. The method of embodiment 89 where in the subject has a germ line germ- line mutation in at least one of the pi 6, BRCA2, PALB2, or STK11 genes.

Embodiment 97. The method of embodiment 89 wherein the biological sample is blood, serum or plasma.

Embodiment 98. The method of embodiment 89 wherein determining an expression level comprises determining protein expression.

Embodiment 99. The method of embodiment 89, wherein the subject is at risk for developing pancreatic cancer.

Embodiment 100. A method of classifying a patient at risk for pancreatic cancer, comprising: a. determining a level of at least three of the biomarkers in a sample obtained from a subject; b. comparing the levels the at least three biomarkers to a cut-off value, wherein the cut-off value was determined by analysis of normalized expression values for the at least three biomarkers in a panel of control samples; c. and characterizing the patient as having a low likelihood of pancreatic cancer if the measured levels of biomarkers are on the same side of the cut-off value as the panel of control samples or characterizing the patient as having a high likelihood of pancreatic cancer if the output value is on the opposite side of the cut-off value as the panel of control samples.

Embodiment 101. A method of determining the likelihood of pancreatic cancer in a patient, comprising: a. determining, in a sample obtained from the patient, an expression level of at least three biomarkers selected from any listed in Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14; b. calculating an output from an algorithm that uses the expression levels of the at least three biomarkers as an input; and c. determining from the algorithm output that the patient has or does not have

pancreatic cancer by comparing the output to a reference standard from control samples.

Embodiment 102. The method of embodiment 101 wherein the levels of biomarkers in the subject are additionally compared to the output to a reference standard from samples from individuals with a pancreatic cancer diagnosis.

Embodiment 103. The method of embodiment 101 wherein the steps of calculating the output from the algorithm, and determining from the algorithm output that the sample is or is not malignant by comparing the output to a reference standard are performed by a suitably programmed computer.

Embodiment 104. The method of embodiment 101, further comprising providing to a user a report comprising the algorithm output or the determination that the patient has a high or low likelihood of having pancreatic cancer.

Embodiment 105. The method of embodiment 101, wherein if the patient is determined as having a high likelihood of pancreatic cancer, the method further comprises selecting the subject for treatment for pancreatic cancer.

Embodiment 106. The method of embodiment 101, further comprising treating the subject for pancreatic cancer. Embodiment 107. The method of embodiment 101, wherein the patient is determined to have a high likelihood of early stage pancreatic cancer.

Embodiment 108. The method of embodiment 101 wherein the patient the algorithm incorporates the patient's family history, and personal medical history into the determination of likelihood of pancreatic cancer.

Embodiment 109. A method of treating pancreatic cancer comprising a. assaying a biological sample from a patient to determine the levels of a panel of biomarkers in the biological sample; b. determining that the levels of a panel of biomarkers is increased or decreased relative to a reference standard; c. administering to said patient, therapy based on the levels of a panel of biomarkers;

Embodiment 1 10. The method of embodiment 103 wherein the panel of biomarkers comprises three or more of Panel A.

Embodiment 1 11. The method of embodiment 103 wherein the panel of biomarkers comprises at least three biomarkers selected from those listed in Panels A through L, or any panel of 3, 4, or 5 biomarkers in Megatables 3 through 14.

Embodiment 1 12. A method of determining and increased likelihood of pancreatic cancer in an individual, comprising a. assaying a tissue sample from the individual by multiplexed immunoassay to determined the normalized levels of a panel of biomarkers;

b. generating a test score from the normalized levels of the panel of biomarkers utilizing a statistical model; and

c. determining that the individual has an increased likelihood of pancreatic cancer based at least in part on the test score falling outside the normal range of the statistical model, wherein the panel of biomarkers comprises Cancer Antigen 19-9 (CA-19-9), Pentraxin-3 (PTX3), Matrix Metalloproteinase-7 (MMP-7), Mast/stem cell growth factor receptor (SCFR), and E-Selectin. Embodiment 1 13. The method of claim 1, wherein the statistical model is generated using lasso, random forest, logistic regression, or boosting.

Embodiment 1 14. The method of claim 1, wherein the statistical model is generated using the normalized levels of the panel of biomarkers in a population comprising healthy individuals and individuals with pancreatic cancer.

Embodiment 1 15. The method of claim 1, wherein the tissue sample is blood, plasma or serum.

Embodiment 1 16. The method of claim 1, wherein the increased liekelihood of pancreatic cancer is confirmed by Computed tomography (CT) scan, Somatostatin Receptor Scintigraphy (SRS), Positron Emission Tomography (PET) scan, ultrasonography (ultrasound), Endoscopic Retrograde Cholangiopancreatography (ERCP), Angiography, or biopsy.

Embodiment 1 17. The method of claim 1, wherein the pancreatic cancer is early stage cancer.

[00157] Although the foregoing invention has been described in some detail by way of illustration and example, for purposes of clarity of understanding, it will be obvious that certain changes and modifications may be practiced within the scope of the appended claims.

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Claims

CLAIMS What is claimed is:

1. A method of determining and increased likelihood of pancreatic cancer in an individual, comprising

a. assaying a tissue sample from the individual by multiplexed immunoassay to determined the normalized levels of a panel of biomarkers;

c. determining that the individual has an increased likelihood of pancreatic cancer based at least in part on the test score falling outside the normal range of the statistical model, wherein the panel of biomarkers comprises Cancer Antigen 19-9 (CA-19-9), Pentraxin-3 (PTX3), Matrix Metalloproteinase-7 (MMP-7), Mast/stem cell growth factor receptor (SCFR), and E-Selectin.

2. The method of claim 1, wherein the statistical model is generated using lasso, random forest, logistic regression, or boosting.

3. The method of claim 1, wherein the statistical model is generated using the normalized levels of the panel of biomarkers in a population comprising healthy individuals and individuals with pancreatic cancer.

4. The method of claim 1 , wherein the tissue sample is blood, plasma or serum.

5. The method of claim 1, wherein the increased liekelihood of pancreatic cancer is confirmed by Computed tomography (CT) scan, Somatostatin Receptor Scintigraphy (SRS), Positron Emission Tomography (PET) scan, ultrasonography (ultrasound), Endoscopic Retrograde Cholangiopancreatography (ERCP), Angiography, or biopsy.

6. The method of claim 1 , wherein the pancreatic cancer is early stage cancer.