Compositions and methods for addressing pancreatic cancer risk

Abstract

The present disclosure provides for improved detection and determining of the risk of developing pancreatic cancer, yielding increased lead time detection performance. The present disclosure further provides methods and compositions for identifying the risk of developing pancreatic cancer comprising detecting an expression level of CAI 9-9 in a first sample and in a second sample, when the first sample and the second sample were obtained from the subject at two different time points.

Description

TITLE OF THE INVENTIONCOMPOSITIONS AND METHODS FOR ADDRESSING PANCREATIC CANCER RISK CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the priority of U. S. Provisional Appl. Ser. No. 63 / 730,813, filed December 11, 2024, the entire disclosure of which is incorporated herein by reference.STATEMENT OF GOVERNMENT RIGHTS

[0002] This invention was made with government support under CA239522 awarded by the National Institutes of Health. The government has certain rights in the invention.FIELD OF THE INVENTION

[0003] The present disclosure relates to the field of cancer diagnostics, and more specifically relates to methods and compositions for improving lead time detection of pancreatic ductal adenocarcinoma.BACKGROUND OF THE INVENTION

[0004] Pancreatic ductal adenocarcinoma (PDAC) is characterized by dismal 5-year relative survival rates of less than 15%. The low survival rates are attributed to the majority of PDAC patients (—85%) presenting with locally advanced or metastatic disease. Unequivocal evidence supports that diagnosis of PDAC at earlier, resectable, stages, has a profoundly favorable impact on prognosis. The 5-year relative survival of resected PDAC is as high as -30% in major treatment centers, increasing to 30-60% for tumors <2 cm, and as high as 75% for lesions under 10 mm in size. It is estimated that the initial molecular and cellular events that lead to PDAC occur over the course of two decades, suggesting a wide window of opportunity for early detection.

[0005] There are several challenges associated with PDAC screening, including low incidence in the general population (13 per 100,000), the difficulty of generating a cost-effective screening test with acceptable sensitivity and specificity, the high cost of performing abdominal imaging with CT / MR1 as a screening test, and the emotional toll of a false positive1US_ACTIVE\131856188\V-1result on the patient. Blood-based biomarkers have the potential to better select individuals at high risk of PDAC who will benefit from surveillance and screening for earlier detection.SUMMARY OF THE INVENTION

[0006] In one aspect, the present disclosure provides a method of determining a probability of a subject of being afflicted with or developing pancreatic cancer, the method comprising: (a) determining a pancreatic cancer risk score by: (i) determining an expression level of CAI 9-9 in at least a first sample and a second sample from the subject, wherein the first sample and the second sample were obtained from the subject at two different time points; and (ii) applying a mathematical algorithm to the expression level of CAI 9-9 to produce a pancreatic risk score; and (b) identifying the subject as being at high risk, at normal risk, or at low risk of being afflicted with or developing cancer based on the pancreatic risk score. In one embodiment, the method of the present disclosure may comprise: (a) determining a pancreatic risk score by: (i) determining the expression level of CAI 9-9 and at least one or at least two additional biomarker(s) selected from the group consisting of CAI 25, CEA, LRG1, REG3A, THBS2, T1MP1, and TNFRSF1A in at least the first sample and the second sample from the subject; and (ii) applying a mathematical algorithm to the expression level of CAI 9-9 and the at least one additional biomarker to produce a pancreatic risk score; and (b) identifying the subject as being at high risk, at normal risk, or at low risk of being afflicted with or developing pancreatic cancer based on the pancreatic risk score. In another embodiment, the at least one additional biomarker is T1MP1, LRG1, REG3A, or CAI 25. The at least two additional biomarkers, in another embodiment, are T1MP1 and LRG1 or REG3A and CA125. In yet another embodiment, the mathematical algorithm is:nl*an^i(n+l)PEB- ^1- BtXBn ’whereinZi(n+1)= whereinFfl i+i) is a biomarker level in an ithpatient at a (n+l)thtimepoint,p is about 4.617352,cr2is about 0.03137515, andT2is about 0.7287484;2US_ACTIVE\131856188\V-1S”- z - n11, wherein Zy is the average of a biomarker score across n different time points;Bn -, whereinT2is about 0.72874840,cr2is about 0.03137515, andn is a specific time point of a given test for a given individual in chronological order; and Bi is B„ at a first time point.

[0007] In still yet another embodiment, the mathematical algorithm is: -20.675+ 6.11 l*loglO(CA19-9) or 6.11 l*loglO(CA19-9). In some embodiments, the present disclosure provides a method comprising determining an expression level of CAI 9-9 in at least a first sample, a second sample, and a third sample from the subject, wherein the first sample, the second sample, and the third sample were obtained from the subject three different time points. In one embodiment, the method of the present disclosure may comprise determining an expression level of CAI 9-9 in at least a first sample, a second sample, a third sample, and a fourth sample from the subject, wherein the first sample, the second sample, the third sample, and the fourth sample were obtained from the subject at four different time points. In another embodiment, the method of the present disclosure may comprise determining an expression level of CAI 9-9 in at least a first sample, a second sample, a third sample, a fourth sample, and fifth sample from the subject, wherein the first sample, the second sample, the third sample, the fourth sample, and the fifth sample were obtained from the subject at five different time points.

[0008] In some aspects, the present disclosure provides a method of determining a probability of a subject of being afflicted with or developing pancreatic cancer, the method comprising: (a) determining a pancreatic cancer risk score by: (i) determining an expression level of CA19-9, LRG1, and T1MP1 in at least the first sample and the second sample from the subject; and (ii) applying a mathematical algorithm to the expression level of CAI 9-9, LRG1, and T1MP1 to produce a pancreatic risk score; and (b) identifying the subject as being at high risk, at normal risk, or at low risk of being afflicted with or developing pancreatic cancer on based the pancreatic risk score. In particular embodiments, the mathematical algorithm is:^ whereii(n+l)pEB B̂ₙXBₙ n3US_ACTIVE\131856188\V-1Zi(n+1) =Wherein^i(n+i) is abiomarker level in an ithpatient at a (n+l)thtimepoint,p is about 14.60487,cr2is about 0.06517617, andT2is about 0.119577;11, wherein Zy is the average of a biomarker score across n different time points;Bn= T, -, wherein71° In+ T2’T2is about 0.119577,cr2is about 0.06517617, andn is a specific time point of a given test for a given individual in chronological order; and Bi is B„ at a first time point.

[0009] In one embodiment, the mathematical algorithm is: 1.7005*log10(TIMP1) + 0.93856*log10(LRG1) + 0.60639*log10(CA19-9) or 61.82 + 3.45*[1.7005*log10(TIMP1) + 0.93856*log10(LRG1) + 0.60639*log10(CA19-9)]. In another embodiment, the method of the present disclosure may comprise determining an expression level of CAI 9-9, LRG1, and T1MP1 in at least a first sample, a second sample, and a third sample from the subject, wherein the first sample, the second sample, and the third sample were obtained from the subject three different time points. The method of the present disclosure, in yet another embodiment, may comprise determining an expression level of CA19-9, LRG1, and T1MP1 in at least a first sample, a second sample, a third sample, and a fourth sample from the subject, wherein the first sample, the second sample, the third sample, and the fourth sample were obtained from the subject at four different time points. In still yet another embodiment, the method of the present disclosure may comprise determining an expression level of CAI 9-9, LRG1, and T1MP1 in at least a first sample, a second sample, a third sample, a fourth sample, and fifth sample from the subject, wherein the first sample, the second sample, the third sample, the fourth sample, and the fifth sample were obtained from the subject at five different time points.

[0010] In another aspect, the present disclosure provides a method of determining a probability of a subject of being afflicted with or developing pancreatic cancer, the method comprising:4US_ACTIVE\131856188\V-1(a) determining a pancreatic cancer risk score by: (i) determining an expression level of CAI 9-9, REG3A, and CAI 25 in at least a first sample and a second sample from the subject; and (ii) applying a mathematical algorithm to the expression level of CAI 9-9, REG3A, and CAI 25 to produce a pancreatic risk score; and (b) identifying the subject as being at high risk, at normal risk, or at low risk of being afflicted with or developing pancreatic cancer based on the pancreatic risk score. In one embodiment, the mathematical algorithm is:^i(n+l) In}x^nZ^PEB= -71- B1XBn- ^hereinZhn+1) =wherein^i(n+i) isabiomarker level in an ithpatient at a (n+l)111timepoint,p is about 57.01748,cr2is about 3.615842, andr2is about 72.68247;z..11, wherein Zy is the average of a biomarker score across n different time points;T= -T- -2, whereinn° 7 n+ r2’r2is about 72.68247,cr2is about 3.615842, andn is a specific time point of a given test for a given individual in chronological order; andBi (for the REG3A / CA125 / CA19-9 model) is 0.9526091. In another embodiment, the mathematical algorithm is: 10-logw(CA19-9) + logw(REG3A) + 0.5-logio(CA125). In yet another embodiment, a method of the present disclosure may comprise determining an expression level of CAI 9-9, REG3A, and CAI 25 in at least a first sample, a second sample, and a third sample from the subject, wherein the first sample, the second sample, and the third sample were obtained from the subject three different time points. A method of the present disclosure, in still yet another embodiment, may comprise determining an expression level of CA19-9, REG3A, and CA125 in at least a first sample, a second sample, a third sample, and a fourth sample from the subject, wherein the first sample, the second sample, the third sample,5US_ACTIVE\131856188\V-1and the fourth sample were obtained from the subject at four different time points. In one embodiment, a method of the present disclosure may comprise determining an expression level of CAI 9-9, REG3A, and CA125 in at least a first sample, a second sample, a third sample, a fourth sample, and fifth sample from the subject, wherein the first sample, the second sample, the third sample, the fourth sample, and the fifth sample were obtained from the subject at five different time points.

[0011] Identifying the subject as being at high risk, at normal risk, or at low risk comprises, in one embodiment, may comprise comparing the pancreatic risk score to a predetermined parameter. Identifying the subject as being at high risk, in another embodiment, comprises identifying the subject as having a pancreatic risk score that is greater than the predetermined parameter. Identifying the subject as being at normal risk or at low risk, in yet another embodiment, comprises identifying the subject as having a pancreatic risk score that is equal to or less than the predetermined parameter. The predetermined parameter, in still yet another embodiment, is q( / 6), wherein fo is about 1 - 0.985 and q is a quantile of a standard normal distribution of a test population. In one embodiment, identifying the subject as being at high risk comprises identifying the subject as having a pancreatic risk score of greater than q( / 6), wherein fo is about 1 - 0.985 and q is a quantile of a standard normal distribution of a test population. In another embodiment, identifying the subject as being at low risk or at normal risk comprises identifying the subject as having a pancreatic risk score of less than or equal to q( / b), wherein fo is about 1 - 0.985 and q is a quantile of a standard normal distribution of a test population. In yet another embodiment, the sample is a biological fluid sample, a blood sample, or a serum sample.

[0012] In certain embodiments, the methods of the present disclosure may further comprise, if the subject is identified as being at high risk of being afflicted with or developing pancreatic cancer, performing an appropriate diagnostic procedure on the subject or providing a report recommending that an appropriate diagnostic procedure be performed on the subject. In some embodiments, the methods of the present disclosure may further comprise, if the subjected is identified as being at normal risk or at low risk of being afflicted with or developing pancreatic cancer, repeating a biomarker test of the present disclosure after a time period or providing a report recommending that a biomarker test of the present disclosure be repeated after a time period. In one embodiment, the time period is about 6 months, about 1 year, about 2 years, or about 4 years. Non-limiting examples of diagnostic procedures that may be used according to the embodiments of the present disclosure include computed tomography (CT), contrast-6US_ACTIVE\131856188\V-1enhanced pancreas protocol CT, positron emission tomography (PET), magnetic resonance imaging (MRI), magnetic resonance cholangiopancreatography (MRCP), a biopsy, genetic testing, and additional biomarker testing.

[0013] In yet another aspect, the present disclosure provides a method of determining a probability of a subject of being afflicted with or developing pancreatic cancer, the method comprising: (a) determining an expression level of CAI 9-9 in at least a first sample and a second sample from the subject, wherein the first sample and the second sample were obtained from the subject at two different time points; and (b) identifying the subject as being at high risk, at normal risk, or at low risk of being afflicted with or developing pancreatic cancer based on the expression level of CAI 9-9 in the second sample compared to the first sample. In one embodiment, the present disclosure may comprise (a) determining the expression level of CAI 9-9 and at least one or at least two additional biomarker(s) selected from the group consisting of CA125, CEA, LRG1, REG3A, THBS2, T1MP1, and TNFRSF1A in at least the first sample and the second sample from the subject; and (b) identifying the subject as being at high risk, at normal risk, or at low risk of being afflicted with or developing pancreatic cancer based on the expression level of CAI 9-9 and the at least one additional biomarker in the second sample compared to the first sample. In another embodiment, the at least one additional biomarker is T1MP1, LRG1, REG3A, or CAI 25. The at least two additional biomarkers, in yet another embodiment, are T1MP1 and LRG1 or REG3A and CA125.

[0014] Identifying the subject as being at high risk, in one embodiment, comprises identifying an increase in the expression level of CAI 9-9 in the second sample compared to the first sample. Identifying the subject as being at high risk, in another embodiment, comprises identifying an increase in the expression level of CAI 9-9 in the second sample compared to the first sample that exceeds a predetermined parameter. Identifying the subject as being at normal risk or at low risk, in yet another embodiment, comprises identifying an expression level of CAI 9-9 that decreases or does not increase in the second sample compared to the first sample. Identifying the subject as being at normal risk or at low risk, in still yet another embodiment, comprises identifying an expression level of CAI 9-9 that does not increase beyond a predetermined parameter. In one embodiment, the sample is a biological fluid sample, a blood sample, or a serum sample.

[0015] In some embodiments, the method of the present disclosure may comprise (a) determining an expression level of CAI 9-9 in at least a first sample, a second sample, and a third sample from the subject, wherein the first sample, the second sample, and the third sample 7US_ACTIVE\131856188\V-1were obtained from the subject three different time points; and (b) identifying the subject as being at high risk, at normal risk, or at low risk of being afflicted with or developing pancreatic cancer based on the expression level of CAI 9-9 in the second sample or the third sample compared to the first sample. The method of the present disclosure, in certain embodiments, may further comprise, if the subject is identified as being at high risk of being afflicted with or developing pancreatic cancer, performing an appropriate diagnostic procedure on the subject or providing a report recommending that an appropriate diagnostic procedure be performed on the subject. In some embodiments, the diagnostic procedure is selected from the group consisting of computed tomography (CT), contrast-enhanced pancreas protocol CT, positron emission tomography (PET), magnetic resonance imaging (MRI), magnetic resonance cholangiopancreatography (MRCP), a biopsy, genetic testing, and additional biomarker testing. In particular embodiments, the method of the present disclosure may further comprise, if the subjected is identified as being at normal risk or at low risk of being afflicted with or developing pancreatic cancer, repeating a biomarker test as described herein after a time period or providing a report recommending that the biomarker test be repeated after a time period. In one embodiment, the time period is about 6 months, about 1 year, about 2 years, or about 4 years.BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The following drawings form part of the present specification and are included to further demonstrate certain aspects of the present disclosure. The disclosure may be better understood by reference to one or more of these drawings in combination with the detailed description of specific embodiments presented herein.

[0017] FIG. 1 shows the time-dependent performance estimates of individual protein biomarkers without consideration of repeat testing algorithms. FIG. 1, Panel A - Area under the curve (AUC) estimates. FIG. 1, Panel B - Sensitivity estimates at 98.5% specificity.

[0018] FIG.2 shows time-dependent performance estimates of protein biomarkers comparing parametrical empirical Bayes (PEB) versus single threshold (ST) in terms of area under the curve (AUC) estimates and sensitivity at 98.5% specificity.

[0019] FIG. 3 shows receiver operating characteristic (ROC) curves evaluating the CAI 9-9 performance with the PEB and ST approaches when considering at least one serial measurement within 3 years of a PDAC diagnosis.8US_ACTIVE\131856188\V-1

[0020] FIG. 4 shows receiver operating characteristic (ROC) curves demonstrating CAI 9-9 performance with the PEB and ST approaches for cases diagnosed with localized / regional or metastatic PDAC. At least one biomarker measurement was within 3 years of a clinical diagnosis.

[0021] FIG.5 shows PEB and ST positivity at the 98.5% specificity threshold and considering at least one serial measurement within 3 years of a PDAC diagnosis. FIG. 5, Panel A - Both methods produce positive results on the first biomarker measurement tested, N=3, FIG. 5, Panel B - neither method produces a positive test result, N=25. FIG. 5, Panel C - the first positive result is either not from the first biomarker measurement or is positive by PEB or ST, but not both, N=13.

[0022] FIG. 6 shows receiver operating characteristic (ROC) curves demonstrating the performance of the T1MP1 biomarker for the negative CAI 9-9 population (below the 98.5% specificity threshold cutoff for CAI 9-9). At least one biomarker measurement was within 1 year of a clinical diagnosis.

[0023] FIG. 7 shows a histogram of blood draw to diagnosis.DETAILED DESCRIPTION OF THE INVENTION

[0024] Carbohydrate antigen 19-9 (CAI 9-9) is clinically used for diagnosis in symptomatic patients and for monitoring efficacy of therapy. There are, however, significant limitations to CAI 9-9 that constrain its broader applicability in a screening setting, such as false-negative outcomes in individuals with a Lewis negative genotype and false positive elevations in patients with benign disease. The present disclosure provides a test for determining the risk of developing pancreatic cancer that improves lead time detection performance. The present disclosure surprisingly demonstrates that evaluating changes in biomarker trajectories from repeat measurements significantly improves lead time detection of pancreatic ductal adenocarcinoma (PDAC). The advantage of PEB (i.e., repeat measurement) is the ability to adjust for low values of the CAI 9-9 at baseline whereby incremental increases in CA19-9 may identify those at risk of pancreatic cancer despite being single threshold (ST) negative. For those individuals who are low for CAI 9-9, additional protein biomarkers, as demonstrated with T1MP1, improve sensitivity without loss of specificity. In fact, improvements in AUC estimates (1-13%) were observed for all biomarkers when considering the PEB approach compared to a single threshold (ST) approach. PEBCA19-9yielded an AUC of 0.88 when at least one repeat measurement was within 3 years of clinical diagnosis. At a specificity of 9US_ACTIVE\131856188\V-198.5%, the PEBCA19-9identified 15 of the 41 PDAC cases and signaled positive at an average lead time of 1.09 years. In contrast, the ST approach captured 11 of the 41 PDAC cases with an average positive signal at 0.48 years. Among CAI 9-9 low individuals, a PEB algorithm based on repeat measurements of T1MP1 yielded an additional 14% sensitivity at 98.5% specificity.

[0025] The methods of the present disclosure may be useful for testing patient populations, which include but are not limited to high-risk individuals, including those with germline mutations, strong family history, mucinous pancreatic cysts, and new-onset hyperglycemia and diabetes (NOD). The methods of the present disclosure strongly enhance the positive predictive value of the test and concurrently reduce the overall number of false-positive results. In some embodiments, the methods of the present disclosure may be repeated regularly in high-risk subjects with testing intervals that reflect their degree of risk. An initial rise in CAI 9-9, in certain embodiments, may be sufficient to trigger a PEB positive test that would then prompt more intensive follow-up. In some embodiments, a PEB positive test result may be followed with a diagnostic procedure, non-limiting examples of which include contrast-enhanced pancreas protocol CT, magnetic resonance imaging (MRI), and magnetic resonance cholangiopancreatography (MRCP).

[0026] The present disclosure demonstrates for the first time that repeated monitoring of CAI 9-9 using, in one embodiment, an adaptive PEB algorithm model enhances both sensitivity and lead time for PDAC risk assessment when compared to a single-threshold (ST) approach. The present disclosure further demonstrates that inclusion of additional protein biomarkers, such as T1MP1, improves sensitivity for earlier detection of PDAC among cases with low CAI 9-9.A. Methods and Compositions for Early-Stage Cancer Detection and Improved Lead Time Performance

[0027] In one aspect, the present disclosure provides a method of determining a probability of a subject of being afflicted with or developing pancreatic cancer, comprising determining an expression level of CAI 9-9 in at least a first sample and a second sample from the subject, wherein the first sample and the second sample were obtained from the subject at two different time points; and identifying the subject as being at high risk, at normal risk, or at low risk of being afflicted with or developing pancreatic cancer based on the expression level of CAI 9-9 in the second sample compared to the first sample. In another aspect, the present disclosure10US_ACTIVE\131856188\V-1provides a method of determining a probability of a subject of being afflicted with or developing pancreatic cancer comprising determining a pancreatic cancer risk score by: (i) determining an expression level of CAI 9-9 in at least a first sample and a second sample from the subject, wherein the first sample and the second sample were obtained from the subject at two different time points; and (ii) applying a mathematical algorithm to the expression level of CAI 9-9 to produce a pancreatic risk score, and identifying the subject as being at high risk, at normal risk, or at low risk of being afflicted with or developing cancer based on the pancreatic risk score. In many embodiments, the methods of the present disclosure may improve lead time performance of cancer diagnosis. As used herein the term “lead time” refers to the time between the early detection of cancer and when the cancer would have been diagnosed without screening.

[0028] As used herein, the term “expression level,” refers to the detected, expressed, or accumulated amount of a biomarker. Expression levels can be represented, for example, as the amount or the rate of synthesis of a messenger RNA (mRNA) encoded by a gene, the amount or the rate of synthesis of a polypeptide or protein encoded by a gene, or the amount or the rate of synthesis of a biological molecule accumulated in a cell or biological fluid. In certain embodiments, an expression level may refer to an absolute amount of a molecule in a sample or to a relative amount of the molecule in a sample. Expression levels, in particular embodiments, may be determined under steady-state or non-steady-state conditions. As used herein the term “biomarker” refers to a biological molecule that can be measured to indicate a normal or abnormal process, condition, or disease in an organism. As used herein the term “high risk” as it relates to an individual’s cancer risk refers to an individual who is determined to be at increased risk for having or developing cancer compared to a reference subject or a reference group. In one embodiment, a high-risk individual may have a pancreatic risk score, a biomarker expression level, or an increase in a biomarker expression level in one sample compared to the biomarker expression level of another sample that was obtained at an earlier time point that is greater than a predetermined parameter. In another embodiment, the predetermined parameter is q(fo), wherein fo is (1 -specificity). In certain embodiments, fo is about 1 - 0.985, about 1-0.98, about 1-0.975, about 1-0.97, about 1-0.965, about 1-0.96, about 1-0.955, or about 1-0.95, including all ranges and values derivable therebetween, and q is a quantile of a standard normal distribution of a test population. As used herein the phrase “quantile of a standard normal distribution” refers to a function that is equal to a defined percentage of the area under a distribution. A distribution function indicates a probability with11US_ACTIVE\131856188\V-1which a random variable takes a specific value. In some embodiments the quantile value is predicated by the specificity of the test. In one embodiment, the standard normal quantile corresponds to a chosen false positive rate in the test population. As used herein the term “normal risk” as it relates to an individual’s cancer risk refers to an individual who is determined to be at average risk for having or developing cancer compared to a reference subject or a reference group. As used herein the term “low risk” as it relates to an individual’s cancer risk refers to an individual who is determined to be at decreased risk for having or developing cancer compared to a reference subject or a reference group. In certain embodiments, a normal risk individual or a low risk individual may have a pancreatic risk score, a biomarker expression level, or an increase in a biomarker expression level in one sample compared to the biomarker expression level of another sample that was obtained at an earlier time point that is less than or approximately equal to a predetermined parameter. As used herein the term “reference subject,” “reference group,” or “reference sample” refers to a subject or a group of subjects to which a sample from a test subject may be compared. In some embodiments, a reference subject or reference sample may be used as a control for testing or diagnostic purposes. A reference sample, in certain embodiments, may be obtained from a single subject or may be obtained from a group of subjects to create, example, a pooled sample.

[0029] In yet another embodiment, the present disclosure provides a method of determining a probability of a subject of being afflicted with or developing pancreatic cancer, the method comprising determining the expression level CAI 9-9 and at least one, at least two, at least three, at least four, at least five, at least six, or at least seven additional biomarker(s) selected from the group consisting of cancer antigen 125 (CA125), carcinoembryonic antigen (CEA), leucine -rich alpha-2-glycoprotein 1 (LRG1), regenerating islet-derived protein 3 alpha (REG3A), thrombospondin-2 (THBS2), tissue inhibitor of metalloproteinases- 1 (T1MP1), and tumor necrosis factor receptor 1-A (TNFRSF1A).

[0030] The methods of the present disclosure may be used to identify risk and / or inform treatment or further diagnostic decisions in patients afflicted with or at risk of developing any cancer. In one embodiment, the cancer in pancreatic ductal adenocarcinoma or pancreatic cancer. In certain embodiments, the methods of the present disclosure may include determining biomarker expression levels in samples obtained from a subject at two, three, four, five, six, or more different time points. In some embodiments, the time period between each time point may be about 1 week, about 1 month, about 3 months, about 6 months, about 1 year,12US_ACTIVE\131856188\V-1about 2 years, about 3 years, about 4 years, about 5 years, about 6 years, about 7 years, about 8 years, about 9 year, about 10 years, or more, including all ranges and values derivable therebetween.

[0031] As used herein the term “panel” refers to a group of proteins, RNA molecules, and / or genes. In some embodiments, the group of proteins, RNA molecules, and / or genes may be related by their association with certain cell types, biological functions, phenotypes, or cellular pathways. A panel, in certain embodiments, refers to a group of biomarkers associated with the initiation or progression of cancer. A panel, in particular embodiments, may be considered along with information in addition to information regarding the group of proteins, RNA molecules, and / or genes. Such information may include, but is not limited to, patient demographics or disease characteristics. Types of panels include, but are not limited to, a 2 biomarker panel, a 3 biomarker panel, a 4 biomarker panel, a 5 biomarker panel, a 6 biomarker panel, a 7 biomarker panel, and an 8 biomarker panel. In certain embodiments, the biomarker panel may be a protein biomarker panel, an RNA biomarker panel, or a gene expression biomarker panel. A “pancreatic risk score” as used herein refers to a value calculated based on the expression level of a biomarker or a panel of biomarkers.

[0032] An “upregulated” protein, RNA molecule, or gene, as used herein, refers to a protein, RNA molecule, or gene that demonstrates an increased expression level in response to a given treatment or condition, or in certain subject groups. A “downregulated” protein, RNA molecule, or gene refers to a protein, RNA molecule, or gene that demonstrates a decreased expression level in response to a given treatment or condition, or in certain patient groups. In particular embodiments, the expression level of a protein, RNA molecule, or gene can remain unchanged in response to given treatment or condition. A protein, RNA molecule, or gene from a subject may be upregulated, for example, when the expression level of the protein, RNA molecule, or gene is increased at least about 1%, about 5%, about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, about 100%, about 200%, about 300%, about 500%, about 1,000%, or about 5,000%, including all ranges and values derivable therebetween. Similarly, a protein, RNA molecule, or gene may be downregulated when the expression level of the RNA molecule, protein, or gene is decreased by at least about 1%, about 5%, about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, or about 99%, including all ranges and values derivable therebetween.13US_ACTIVE\131856188\V-1

[0033] The terms “detecting,” “determining,” “measuring,” “evaluating,” “assessing,” and “assaying” as used herein refer to any form of measurement and include detecting or determining whether an element is present or not. These terms include quantitative and / or qualitative determinations. Assays for detecting or determining expression levels are known in the art and any such method may be used according to the embodiments of the present disclosure. Non- limiting examples of such assays include RT-PCR, DNA microarray, RNA-Seq, ELISA, western blot, immunohistochemistry, and protein microarray.

[0034] As used herein the term “sample” refers to a material or mixture of materials containing at least one component of interest. As used herein the term “biological sample” refers to sample obtained from a biological subject. A biological sample of the present disclosure may include, but is not limited to, a body fluid sample, a blood sample, a urine sample, a feces sample, a semen sample, a serum sample, a plasma sample, a saliva sample, a cerebrospinal fluid sample, a cell sample, a tissue sample, and a tumor sample. As used herein the term “healthy” as it relates to a sample, subject, individual, or population refers to a subject, individual, or population that is not afflicted with the disorder or disease being studied. A healthy sample is collected from a subject, individual, or population that is not afflicted with the disease or disorder being studied. In certain embodiments, a healthy subject, individual, or population is not afflicted with or at risk of developing a cancer. In one embodiment, a healthy subject, individual, or population is not afflicted with or at risk of developing pancreatic cancer or pancreatic ductal adenocarcinoma.

[0035] As used herein, the term “calculating” refers to a determination made using mathematics. In some embodiments, calculating may include the use of a scaling value. In one embodiment, a scaling value may be calculated using the equation: ^i(n+i)=J2++'>T2 ’ wherein Ki(n+1) is the biomarker level in the i111patient at the (n+l)111timepoint, p is the population mean (intercept of the model), < T2represents the within subject variance (variance of residual term in the model), and r2represents between- subject variance (variance of the random intercept in the model). In some embodiments, when the biomarker is CAI 9-9, p is about 4.617352. In certain embodiments, when the biomarker is CAI 9-9, < T2is about 0.03137515. In particular embodiments, when the biomarker is CA19-9, r2is about 0.7287484. In some embodiments, when the biomarker is LRG1, p is about 17.31761. In certain embodiments, when the biomarker is LRG1, < T2is about 0.06940621. In particular embodiments, when the biomarker is LRG1, r2is about 0.01302565. In some embodiments,14US_ACTIVE\131856188\V-1when the biomarker is T1MP1, p is about 11.23955. In certain embodiments, when the biomarker is T1MP1, cr2is about 0.1047685. In particular embodiments, when the biomarker is T1MP1, T2is about 0.0617773). In some embodiments, when the biomarker is THBS2, p is about 3.199885. In certain embodiments, when the biomarker is THBS2, cr2is about 0.02783257. In particular embodiments, when the biomarker is THBS2, r2is about 0.08161064. In some embodiments, when the biomarker is CEA, p is about 11.02747. In certain embodiments, when the biomarker is CEA, cr2is about 0.02441896. In particular embodiments, when the biomarker is CEA, r2is about 0.07581457. In some embodiments, when the biomarker is CA125, p is about 3.645114. In certain embodiments, when the biomarker is CA125, cr2is about 0.05699307. In particular embodiments, when the biomarker is CA125, T2is about 0.09534623. In some embodiments, when the biomarker is REG3A, p is about 9.020833. In certain embodiments, when the biomarker is REG3A, cr2is about 0.1452996. In particular embodiments, when the biomarker is REG3A, r2is about 0.1539095. In some embodiments, when the biomarker is TNFRSF1A, p is about 7.475794. In certain embodiments, when the biomarker is TNFRSF1A, cr2is about 0.09853163. In particular embodiments, when the biomarker is TNFRSF1A, r2is about 0.07189291. In some embodiments, in a three biomarker panel comprising CAI 9-9, T1MP1, and LRG1, p is about 14.60487. In certain embodiments, in a three biomarker panel comprising CA19-9, T1MP1, and LRG1, cr2is about 0.06517617. In particular embodiments, in a three biomarker panel comprising CA19-9, T1MP1, and LRG1, r2is about 0.119577. In a three biomarker panel comprising CA19-9, REG3A, and CA125, in one embodiment, p is about 57.01748, o2is about 3.615842, and T2is about 72.68247. In another embodiment, p, < T2,andr2, are calculated using data collected from a reference population and a random intercept mixed model. In yet another embodiment, the reference population includes only individuals who have not been diagnosed with cancer.

[0036] In certain embodiments, calculating may include applying a parametrical empirical Bayes (PEB) algorithm, which considers all existing measurements of a panel and adjusts the biomarker threshold to reflect participant history. In one embodiment, the biomarker mean value (p) and variability of measurements within and across non-case participants may be estimated and used to center and scale biomarker measurements. In the PEB algorithm, in some embodiments, a current measurement may be compared against a weighted average of p and the participant’s previous biomarker values. A PEB algorithm, in particular embodiments, requires three parameters to estimate the quantile risk: population mean (intercept of the model,15US_ACTIVE\131856188\V-1p), within-subject variance (variance of residual term in the model, cr2), and between-subject variance (variance of the random intercept in the model, r2). In certain embodiments, the PEB algorithm is:Z^PEB = -V1- B1XBn-, whereinZhn+1) = X++r^’wherein^i(n+i) is abiomarker level in an ithpatient at a (n+l)111timepoint,Using the natural logarithm value transformed of CAI 9-9, the parameters are:p is about 4.617352,cr2is about 0.03137515, andT2is about 0.7287484;J~11, wherein Zy is defined as the average of a biomarker score across n different time points;= zr; -, whereinn" / n+ UT2is about 0.7287484,cr2is about 0.03137515, andn represents the specific time point of a given test for a given individual in chronological order (i.e., the first test measurement is n=l; the first repeat measurement is n=2, the second repeat measurement is n=3, etc.); andBi is B„ at a first time point.

[0037] In certain embodiments, Bnis a function of n (i.e., the first test measurement is Bi; the first repeat measurement is B2, the second repeat measurement is B3, etc.). In further embodiments, a pancreatic risk score may be calculated using the values for p, r2, and / or cr2, for the biomarkers CA19-9, LRG1, T1MP1, THBS2, CEA, CA125, REG3A, and / or TNFRSF1A provided in Table 1.

[0038] In some embodiments, a pancreatic risk score may be calculated using the equation: -20.675+6.11 l*loglO(CA19-9) or 6.11 l*loglO(CA19-9). In other embodiments, a pancreatic risk score may be calculated using the equation: 61.82 + 3.45*[1.7005*logl0(TlMPl) +16US_ACTIVE\131856188\V-10.93856*logl0(LRGl) + 0.60639*logl0(CA19-9)] or 1.7005*logl0(TIMPl) + 0.93856*logl0(LRGl) + 0.60639*logl0(CA19-9).

[0039] In yet another aspect, the present disclosure provides a kit for determining the probability of a subject of being afflicted with or developing pancreatic cancer or pancreatic ductal adenocarcinoma, the kit comprising a plurality of antigen binding proteins specific for a plurality of biomarkers selected from the group consisting of CA19-9, CA125, CEA, LRG1, REG3A, THBS2, TIMP1, and TNFRSF1 A. In certain embodiments, the kit comprises: at least two, at least three, at least four, at least five, at least six, at least seven, or at least eight antigen binding proteins specific for at least two, at least three, at least four, at least five, at least six, at least seven, or at least eight biomarkers selected from the group consisting of CAI 9-9, CA125, CEA, LRG1, REG3A, THBS2, TIMP1, and TNFRSF1A. In one embodiment, the kit comprises a first antigen binding protein specific for CAI 9-9 and a second antigen binding protein specific for CA125, CEA, LRG1, REG3A, THBS2, TIMP1, or TNFRSF1 A. In another embodiment, the kit comprises a first antigen binding protein specific for CAI 9-9 and a second antigen binding protein specific for LRG1 or TIMP1. In yet another embodiment, the kit comprises a first antigen binding protein specific for CAI 9-9 and a second antigen binding protein specific for TIMP1.

[0040] Antibodies and antigen binding fragments are both members of the broader genus that includes all antigen binding proteins. The term “antibody” as used herein refers to an intact immunoglobulin of any isotype or an antibody fragment that can compete with an intact antibody for specific binding to the target antigen. An “antigen binding fragment” as used herein refers to refers to a portion of a protein which is capable of binding specifically to an antigen. The term “antigen binding protein” as used herein refers to any protein that binds a specified target antigen. In some embodiments of the present disclosure the specified target antigen is a protein selected from the group consisting of CA19-9, CA125, CEA, LRG1, REG3A, THBS2, TIMP1, and TNFRSF1A, or fragments of any thereof. An antigen binding protein includes but is not limited to antibodies and antigen binding fragments. Antibodies of the present disclosure may include but are not limited to mouse, rabbit, goat, chicken, rat, chimeric, humanized, fully human, and bispecific antibodies. The antigen binding proteins, antibodies, and binding fragments of the present disclosure may be produced using any technique known in the art. Non- limiting examples of such techniques include production in hybridomas, production by recombinant DNA techniques, and production by enzymatic or chemical cleavage of intact antibodies. An antibody or antigen binding fragment may include,17US_ACTIVE\131856188\V-1in many embodiments, two full-length heavy chains and two full-length light chains. In some embodiments, an antibody, antigen binding fragment, or an antigen binding protein may include an antibody derivative, an antibody variant, an antibody fragment, or an antibody mutant. Non-limiting examples of antibodies, antigen binding fragments, and antigen binding proteins include monoclonal antibodies, bispecific antibodies, minibodies, domain antibodies, synthetic antibodies, antibody mimetics, chimeric antibodies, humanized antibodies, human antibodies, antibody fusions, antibody conjugates, peptibodies, and fragments thereof.

[0041] The phrase “specifically (or selectively) binds” or “specifically (or selectively) immunoreactive with,” when referring to a protein or peptide, refers to a binding reaction that is determinative of the presence of the protein or complex, often in a heterogeneous population of proteins or complexes. For example, the antigen binding proteins of the present disclosure may specifically bind CA19-9, CA125, CEA, LRG1, REG3A, THBS2, T1MP1, TNFRSF1A, or fragments of any thereof. Such specific antigen binding proteins are known in the art and any such antigen binding protein may be used according to the methods of the present disclosure. Thus, under typical immunoassay conditions, a specified antigen binding protein may bind to a particular protein or complex at least two times the background. In specific embodiments, a specified antigen binding protein may bind a particular protein or complex at least 2, at least 5, at least 10, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 50, at least 55, at least 60, at least 65, at least 70, at least 75, at least 80, at least 85, at least 90, at least 95, or at least 100 times background, including any range or value derivable therebetween. Specific binding to an antigen binding protein under such conditions requires an antigen binding protein that is selected by virtue of its specificity for a particular protein or complex. A variety of assay formats known in the art may be used to select antigen binding protein specifically immunoreactive with a particular protein or complex and any such assay may be used to select an antigen binding protein of the present disclosure. The antigen binding protein of the present disclosure may specifically bind, in particular embodiments, to CAI 9-9, CA125, CEA, LRG1, REG3A, THBS2, T1MP1, TNFRSF1A, or fragments of any thereof. In some embodiments, an antigen binding protein of the present disclosure may cross-react with a small number of highly similar antigens. The term “competes” as used herein in the context of antigen binding proteins that compete for the same epitope refers to the competition between antigen binding proteins as determined by an assay in which the antigen binding protein being tested prevents or reduces specific binding of a reference antigen binding protein to a common antigen. Numerous types of competitive binding assays can be used to determine if one antigen18US_ACTIVE\131856188\V-1binding protein competes with another, for example: solid phase direct or indirect radioimmunoassay (RIA), solid phase direct or indirect enzyme immunoassay (EIA), sandwich competition assay (see, e.g., Stahli et al., 1983, Methods in Enzymology 9:242-253); solid phase direct biotin-avidin EIA (see, e.g., Kirkland et al., 1986, J. Immunol.137:3614-3619) solid phase direct labeled assay, solid phase direct labeled sandwich assay (see, e.g., Harlow and Lane, 1988, Antibodies, A Laboratory Manual, Cold Spring Harbor Press); solid phase direct label RIA using I-125 label (see, e.g., Morel et al., 1988, Molec. lmmunol.25:7-15); solid phase direct biotin-avidin EIA (see, e.g., Cheung, et al., 1990, Virology 176:546-552); and direct labeled RIA (Moldenhauer et al., 1990, Scand. J. Immunol.32:77-82). In certain embodiments, antigen binding proteins identified by a competition assay (competing antigen binding proteins) include antigen binding proteins that bind to the same epitope as the reference antigen binding protein, and antigen binding proteins binding to an adjacent epitope sufficiently proximal to the epitope bound by the reference antigen binding protein for steric hindrance to occur. In particular embodiments, when a competing antigen binding protein is present in excess, the competing antigen binding protein will reduce specific binding of a reference antigen binding protein to a common antigen by at least about 40% to about 45%, about 45% to about 50%, about 50% to about 55%, about 55% to about 60%, about 60% to about 65%, about 65% to about 70%, about 70% to about 75%, about 75% to about 85%, about 80% to about 85%, about 85% to about 90%, about 90% to about 95%, or about 95% to about 99%, including all ranges and values derivable therebetween.

[0042] “Binding affinity” as used herein refers to the strength of the sum total of non-covalent interactions between a single binding site of a molecule (e.g., an antibody) and its binding partner (e.g., an antigen). In some embodiments, the term binding affinity may refer to the intrinsic binding affinity reflecting a 1: 1 interaction between members of a binding pair (e.g., antibody and antigen). The affinity of a molecule X for its partner Y can generally be represented by the dissociation constant (Kd). Binding affinity can be measured by any number of common methods known in the art, and any such method may be used according to the embodiments of the present disclosure. Low-affinity antibodies generally bind antigen slowly and tend to dissociate readily, whereas high-affinity antibodies generally bind antigen faster and tend to remain bound longer.

[0043] The term “antigen” as used herein refers to a substance capable of inducing an adaptive immune response. Antigen binding proteins associated with an adaptive immune response specifically bind to their target antigen. In some embodiments, an antigen may be a molecule19US_ACTIVE\131856188\V-1that binds to antigen-specific receptors but cannot induce an immune response alone. Nonlimiting examples of antigens include proteins, polysaccharides, and lipids. Antigens, in particular embodiments, may include but are not limited to parts of bacteria (coats, capsules, cell walls, flagella, fimbria, and toxins), viruses, and other microorganisms. In some embodiments, antigens also include tumor antigens that antigens generated by mutations in tumors. Antigens may also include immunogens and haptens.

[0044] The term “epitope” as used herein refers to the specific group of atoms or amino acids of an antigen to which an antigen binding protein specifically binds. In some embodiments, an epitope may be a linear epitope or a conformational epitope. A linear epitope, in particular embodiments, may be formed by a continuous sequence of amino acids of the antigen. A conformational epitope, in certain embodiments, may be comprised of discontinuous sections of the amino acid sequence of an antigen. A linear epitope many interact with an antigen binding protein, in particular embodiments, based on primary structure. A conformational epitope may interact with an antigen binding protein, in certain embodiments, based on the 3D structure of the antigen. An epitope, in some embodiments, may be about 3 to about 10, about 4 to about 9, about 4 to about 8, about 4 to about 7, or about 5 to about 6 amino acids in length, including all ranges derivable therebetween. In particular embodiments, two antigen binding proteins may bind the same epitope if they exhibit competitive binding for the antigen.

[0045] A kit of the present disclosure may, in certain embodiments, include components for any suitable assay platform that may be used to determine the expression level of a protein or RNA molecule in a sample. A kit of the present disclosure may include, in certain embodiments, a dipstick, a membrane, a chip, a disk, a test strip, a filter, a microsphere, a slide, a multi- well plate, an optical fiber, or a solid support system for capturing antigen / antigen binding protein complexes. Examples of solid support systems include, but are not limited to, plastic, silicon, metal, resin, glass, membrane, gel, polymer, sheet, polysaccharide, capillary, film, plate, and slide solid support systems. In one embodiment, the kit may further comprise instructions for use.B. Diagnostic Procedures

[0046] In certain aspects, the present disclosure provides methods which include performing a diagnostic procedure on a subject. Such diagnostic procedures include, but are not limited to, computed tomography (CT), contrast-enhanced pancreas protocol CT, positron emission tomography (PET), magnetic resonance imaging (MRI), magnetic resonance20US_ACTIVE\131856188\V-1cholangiopancreatography (MRCP), ultrasonography, a biopsy, genetic testing, additional biomarker testing, and X-ray.

[0047] CT uses ionizing radiation from X-rays to create detailed images of the inside of the body. CT can help identify masses in the body and determine their size, shape, and location. CT may also be useful for visualizing blood vessels that provide the blood supply to a tumor. CT may be useful for the detection of cancers including, but not limited to, bladder cancer, kidney cancer, ovarian cancer, pancreatic cancer, gastric cancer, colorectal cancer, and hematological cancers such as lymphoma or leukemia if they have spread to the lymph nodes, liver, or spleen.

[0048] Contrast-enhanced pancreas protocol CT is a specific type of CT scan that uses intravenous contrast material to produce images of the pancreas. In some embodiment, the contrast material may be a nonionic iodinated contrast material is injected intravenously. Contrast-enhanced pancreas protocol CT is useful for assessing resectability and to stage pancreatic cancer.

[0049] PET is a nuclear imaging test that uses a radioactive tracer to create 3D images of the inside of the body. A PET scan measures metabolic processes, blood flow, and chemical composition. PET may be useful for the detection of cancers that include, but are not limited to, breast cancer, cervical cancer, colorectal cancer, esophageal cancer, head and neck cancer, melanoma, lymphoma, lung cancer, pancreatic cancer, and prostate cancer.

[0050] MR1 uses radio waves and a strong magnetic field to create detailed images of the inside of the body. MR1 can identify tumors, determine their size, shape, and location, and detect metastasis. MR1 may be useful for detecting a number of cancers. Non-limiting examples of which include brain cancer, breast cancer, bone cancer, soft tissue sarcoma, spinal cord cancer, pancreatic cancer, prostate cancer, bladder cancer, uterine cancer, ovarian cancer, colorectal cancer, esophageal cancer, liver cancer, and myeloma.

[0051] MRCP is a medical imaging technique that uses MR1 to produce detailed pictures of the biliary and pancreatic systems. MRCP scans can help diagnose conditions and plan treatment for conditions of the pancreas, gallbladder, bile ducts, and liver, including but not limited to pancreatic cancer.

[0052] Ultrasonography can be used to detect abnormal tissue, distinguish between solid masses and fluid-filled cysts, and provide information regarding the size, shape, and growth21US_ACTIVE\131856188\V-1pattern of a mass. Non-limiting types of cancer that may be detected using ultrasonography include breast cancer, pancreatic cancer, gastric cancer, and ovarian cancer.

[0053] There are many types of biopsies that may be performed to diagnose cancer, and any such biopsy may be used according to the embodiments of the present disclosure. Non- limiting examples of such biopsy types include needle biopsy, vacuum- assisted biopsy, scrape or brush biopsy, punch biopsy, endoscopic biopsy, shave biopsy, incisional biopsy, sentinel lymph node biopsy, bone marrow biopsy, tissue biopsy, and biological fluid biopsy.

[0054] Genetic testing can help to identify if a subject has a genetic mutation that increases the risk of certain cancers. Many genetic tests are known in the art and any such test may be used according to the embodiments of the present disclosure. Non- limiting examples of such tests include multigene tests, single gene tests, and tests that identify chromosomal changes.

[0055] Biomarker testing may be used to detect proteins, genes, or other molecules that may help determine cancer risk. Many biomarker tests are known in the art and any such test may be used according to embodiments of the present disclosure. Non- limiting examples of such tests include single biomarker tests, multi-biomarker tests, whole gene tests, HER2 biomarker tests for breast and gastric cancer, EGFR tests for lung, colorectal, and pancreatic cancer, BRAF tests for melanoma and colorectal cancer, and ALK tests for non-small cell lung cancer.

[0056] As used herein, “subject” or “patient” refers to animals, including humans. For diagnostic or research applications, a wide variety of mammals may be suitable subjects, including rodents (e.g., mice, rats, hamsters), rabbits, primates, and swine, such as inbred pigs and the like. In one embodiment, the subject may be afflicted with or at risk of developing a cancer as described herein.C. Therapeutic Compositions and Methods

[0057] In some aspects, the methods of the present disclosure may further comprise administering a treatment to a subject identified as being at high risk of being afflicted with or developing cancer. Non-limiting examples of such treatments include administering an immune checkpoint inhibitor, a chemotherapy, a radiotherapy, a molecular targeted therapy, an immunotherapy, a hormone therapy, surgery, and a combination of any thereof.

[0058] As used herein the terms “immune checkpoint inhibitor,” “ICI,” “immune checkpoint blockade,” and “ICB” refer to a composition that blocks an immune checkpoint. Immune checkpoints are a normal part of the immune system and prevent an overly robust immune response. When an immune checkpoint is blocked by an immune checkpoint inhibitor,22US_ACTIVE\131856188\V-1immune cells are able to mount a more robust immune response. Such a robust immune response may be beneficial, for example, for killing cancer cells. In some embodiments, an immune checkpoint inhibitor may promote an increased T cell response. Non-limiting examples of immune checkpoint inhibitors include inhibitors of programmed death- 1 (PD- 1 ), programmed death ligand- 1 (PD-L1), cytotoxic T lymphocyte associated antigen 4 (CTLA-4), T cell immunoglobulin and mucin protein-3 (TIM-3), lymphocyte activation gene-3 (LAG-3), programmed death ligand-2 (PD-L2), B and T lymphocyte attenuator (BTLA), T cell immunoreceptor with immunoglobulin and 1T1AM domains (T1G1T), PVR1G (CD112R), VISTA (B7-H5), B7 homolog 4 (B7-H4), CD200, CD328, and CD329. In some embodiments, an immune checkpoint inhibitor may be a small molecule inhibitor, an antibody, an antibody fragment, an antigen binding protein, or an antigen binding fragment. Antibodies of the present disclosure, may include but are not limited to chimeric, humanized, fully human, and bispecific antibodies. An intact antibody may comprise, in certain embodiments, two full-length heavy chains and two full-length light chains. In other embodiments, however, an antibody may include fewer chains. For example, antibodies naturally occurring in camelids can comprise only heavy chains. Antibodies can be derived from a single source or may be chimeric. As used herein the term “chimeric antibody” refers to an antibody that comprises portions that are derived from two different antibodies or an antibody variable region derived from one species paired with a constant region from a different species. The antigen binding proteins, antibodies, and binding fragments of the present disclosure may be produced using any technique known in the art. Non- limiting examples of such techniques include production in hybridomas, production by recombinant DNA techniques, and production by enzymatic or chemical cleavage of intact antibodies. An antibody or antigen binding fragment may include, in many embodiments, two full-length heavy chains and two full-length light chains. In some embodiments, an antibody, antigen binding fragment, or an antigen binding protein may include an antibody derivative, an antibody variant, an antibody fragment, or an antibody mutant. Non-limiting examples of antibodies, antigen binding fragments, and antigen binding proteins include monoclonal antibodies, bispecific antibodies, minibodies, domain antibodies, synthetic antibodies, antibody mimetics, chimeric antibodies, humanized antibodies, human antibodies, antibody fusions, antibody conjugates, peptibodies, and fragments thereof.

[0059] In certain aspects, a method of the present disclosure may comprise administering a chemotherapy, a radiotherapy, a molecular targeted therapy, a hormone therapy, a surgery, an23US_ACTIVE\131856188\V-1immunotherapy, or a combination of any thereof to a subject in need thereof. Any chemotherapy, radiotherapy, molecular targeted therapy, hormone therapy, or immunotherapy known in the art may be administer according to certain embodiments of the present disclosure. Non-limiting examples of a chemotherapy include an alkylating agent, an antitumor antibiotic, an antimetabolite, a topoisomerase inhibitor, and a mitotic inhibitor. An alkylating agent may include, but is not limited to, cisplatin, oxaliplatin, carboplatin, chlorambucil, cyclophosphamide, mechlorethamine, and melphalan. An antitumor antibiotic may include, but is not limited to, daunorubicin, doxorubicin, epirubicin, idarubicin, bleomycin, dactinomycin, mitomycin, mitoxantrone, vincristine, vinblastine, and elsamitrucin. An antimetabolite may include, but is not limited to, 5-fluorouracil, azacitidine, capecitabine, cladribine, clofarabine, cytarabine, decitabine, floxuridine, fludarabine, and gemcitabine. Non-limiting examples of topoisomerase inhibitors include etoposide, topotecan, irinotecan, mitoxantrone, epipodophyllotoxins, benzimidazole, and camptothecin. A mitotic inhibitor may include, but is not limited to, paclitaxel, docetaxel, nab-paclitaxel, cabazitaxel, a pan-Aurora kinase inhibitor, a Chkl inhibitor, and ixabepilone. Non-limiting types of radiation that may be used according to particular embodiments of the present disclosure include external beam radiation, internal radiation, and systemic radiation. A molecular targeted therapy, may include but is not limited to, a small molecule inhibitor and an antigen binding variable domain that binds a receptor tyrosine kinase, a receptor tyrosine kinase ligand, a growth factor receptor, a growth factor receptor ligand, an angiogenic receptor, an angiogenic receptor ligand, a hormone receptor, a hormone receptor ligand, or a lipid. Non-limiting inhibitors of small molecule inhibitors include inhibitors of ALK (crizotinib, ceritinib, alectinib, brigatinib, lorlatinib, and entrectinib), inhibitors EGFR (erlotinib, afatinib, gefitinib, and brigatinib), HER1 / HER2 (lapatinib, capivasertib, neratinib, and tucatinib), BCR-ABL (imatinib), c-kit (imatinib and axitinib), PDGFR (imatinib and axitinib), VEGFR (axitinib, cabozantinib, and fruquintinib), MET (cabozantinib), FLT3 (quizartinib), the proteasome (bortezomib, carfilzomib, marizomib), and CDK4 / 6 (palbociclib, abemaciclib, and ribociclib). A hormone therapy may include, but is not limited to, an aromatase inhibitor, a selective estrogen receptor modulator, an estrogen receptor antagonist, a luteinizing hormone releasing hormone agonist, an anti-androgen, an adrenolytic, or progestin. An immunotherapy may include, but is not limited to, T cell transfer therapy, a cancer vaccine, oncolytic virus therapy, or an immune system modulator, such as an interleukin, a cytokine, a hematopoietic growth factor, or an immunomodulatory drug (i.e.,24US_ACTIVE\131856188\V-1thalidomide, lenalidomide, and pomalidomide). In some embodiments, a surgery may include partial or complete tumor resection.

[0060] In certain aspects, the present disclosure provides pharmaceutical and therapeutic compositions comprising a therapeutic molecule of the present disclosure. Non-limiting examples of such therapeutic molecules include an immune checkpoint inhibitor, a chemotherapy, a radiotherapy, a molecular targeted therapy, an immunotherapy, or a hormone therapy. In some embodiments, the therapeutic molecules of the present disclosure may be combined with a pharmaceutically acceptable carrier. As used herein, a “pharmaceutically acceptable carrier,” “pharmaceutically acceptable adjuvant,” or “adjuvant” refers to reagents, cells, compounds, materials, compositions, and / or dosage forms that are not only compatible with the therapeutic molecules, cells, and / or or other agents to be administered therapeutically, but also are, within the scope of sound medical judgment, suitable for use in contact with the tissues of human beings and animals without excessive toxicity, irritation, allergic response, or other complication commensurate with a reasonable benefit / risk ratio. Also included may be an agent that modifies the effect of other agents and is useful in preparing a therapeutic compound or composition that is generally safe, non-toxic, and neither biologically nor otherwise undesirable. Such an agent may be added to a therapeutic composition to modify the immune response of a subject by boosting the response or to give a higher amount of a therapeutic molecule or cells or provide longer-lasting protection from degradation. Such an agent may include any excipient, diluent, carrier, or adjuvant that is acceptable for pharmaceutical use. Such an agent may be non-naturally occurring, or may be naturally occurring, but not naturally found in combination with other agents in the composition.

[0061] As used herein, a “therapeutic compound” or “therapeutic composition” refers to a composition comprising a therapeutic molecule or a cell of the present disclosure. In particular embodiments, a therapeutic composition of the present disclosure has the activity of inhibiting cancer progression or metastases, preventing an increase in tumor volume, reducing tumor volume, reducing tumor growth, reducing tumor growth rate, eradicating a tumor or cancer cell, prolonging the life of a subject, improving the prognosis of a subject, improving the quality of life of the subject, or the combination of any thereof. Such a compound or composition is meant to encompass a composition suitable for administration to a subject, such as a mammal, particularly a human subject. In general, a therapeutic composition is sterile, and preferably free of contaminants that are capable of eliciting an25US_ACTIVE\131856188\V-1undesirable response within the subject (e.g., the compound(s) in the composition is pharmaceutical grade). Therapeutic compositions may be designed for administration to subjects in need thereof via a number of different routes of administration including oral, intravenous, buccal, rectal, parenteral, intraperitoneal, topical, intradermal, intratracheal, intramuscular, subcutaneous, inhalational, and the like. The appropriate dosage of a composition, as described herein, may be determined based on the type of disease to be treated, the severity and course of the disease, the clinical condition of the individual, clinical history, response to the treatment, and the discretion of the attending physician. In some embodiments, therapeutic compositions provided by the present disclosure may include various “unit doses.” A unit dose is defined as containing a predetermined quantity of the therapeutic composition. The quantity to be administered, and the particular route and formulation, is within the skill of determination of those in the clinical arts. A unit dose need not be administered as a single injection but may comprise continuous infusion over a set period of time. In some aspects, a unit dose comprises a single administrable dose.

[0062] Precise amounts of the therapeutic composition also depend on the judgment of the practitioner and are peculiar to each individual. Factors affecting dose include physical and clinical state of the patient, the route of administration, the intended goal of treatment (alleviation of symptoms versus cure) and the potency, stability and toxicity of the particular therapeutic substance or other therapies a subject may be undergoing.

[0063] A composition, as described herein, may include, in particular embodiments, a combination of therapeutic agents. In some embodiments, a composition as described here may be administered as a single composition or as more than one composition. Different compositions as provided herein, in certain embodiments, may be administered by the same route of administration or by different routes of administration.

[0064] In certain embodiments, the compositions and methods for treating an individual described herein may be combined with any other composition or method of treatment known in the art. The compositions and methods may be administered in any suitable manner known in the art. For example, a first and a second cancer treatment may be administered sequentially (at different times) or concurrently (at the same time). In some aspects, a first and a second cancer treatment may be administered in separate compositions. In certain embodiments, a first and a second cancer treatment may be administered in the same composition.26US_ACTIVE\131856188\V-1

[0065] Therapeutic compounds or compositions may be provided to a subject in a single dose or multiple doses and as such provided in single-dose or multi-dose containers, such as sealed ampules or vials. Such containers may be sealed to preserve sterility of the composition until use. In general, compositions as described herein may be stored as suspensions, solutions, or emulsions in oily or aqueous vehicles. Alternatively, such a composition may be stored in a freeze-dried condition requiring only the addition of a sterile liquid carrier immediately prior to use.

[0066] Such compositions may also comprise buffers (e.g., neutral buffered saline or phosphate buffered saline), carbohydrates (e.g., glucose, mannose, sucrose or dextrans), mannitol, proteins, polypeptides or amino acids such as glycine, antioxidants, bacteriostats, chelating agents such as EDTA or glutathione, adjuvants (e.g., aluminum hydroxide), solutes that render the formulation isotonic, hypotonic, or weakly hypertonic with the blood of a subject, suspending agents, thickening agents, and / or preservatives. Alternatively, compositions of the present disclosure may be formulated as a lyophilizate. Compounds may also be encapsulated within liposomes using methods known in the art.

[0067] For administration, compounds of the present disclosure can be administered at a rate determined by the LD-50 of the molecule or therapeutic compound, and the side-effects thereof at various concentrations, as applied to the mass and overall health of the subject. Administration may be accomplished via single, multiple, or divided doses.

[0068] The term “unit dosage form,” as used herein, refers to physically discrete units suitable as unitary dosages for animal subjects, each unit containing a predetermined quantity of a compound calculated in an amount sufficient to produce the desired effect in association with a pharmaceutically acceptable diluent, carrier, or vehicle. The specifications for unit dosage forms depend on the particular compound employed, the route and frequency of administration, the effect to be achieved, and the pharmacodynamics associated with each compound in the host.

[0069] The phrase “effective amount” refers to a concentration or amount of a therapeutic compound or composition as described herein, reagent, or other agent, which is effective for producing an intended result, including treatment of cancer as described herein. With respect to the administration of a therapeutic compound as disclosed herein, an effective amount may be any effective range or concentration. The exact dose will depend on the purpose of the27US_ACTIVE\131856188\V-1treatment, and one of skill in the art will be able to determine such a dose using techniques known in the art.

[0070] The term "about" is used to indicate that a value includes the standard deviation of the mean for the device or method being employed to determine the value. The use of the term "or" in the claims is used to mean "and / or" unless explicitly indicated to refer to alternatives only or the alternatives are mutually exclusive. When used in conjunction with the word "comprising" or other open language in the claims, the words "a" and "an" denote "one or more," unless specifically noted otherwise. The terms "comprise," "have," and "include" are open-ended linking verbs. Any forms or tenses of one or more of these verbs, such as "comprises," "comprising," "has," "having," "includes," and "including," are also open-ended. For example, any method that "comprises," "has," or "includes" one or more steps is not limited to possessing only those one or more steps and also covers other unlisted steps. Similarly, any system or method that "comprises," "has," or "includes" one or more components is not limited to possessing only those components and covers other unlisted components.

[0071] Other objects, features, and advantages of the present disclosure are apparent from detailed description provided herein. It should be understood, however, that the detailed description and any specific examples provided, while indicating specific embodiments of the disclosure, are given by way of illustration only, since various changes and modifications within the spirit and scope of the disclosure will become apparent to those skilled in the art from this detailed description. Any embodiment of the present disclosure may be used in combination with any other embodiment described herein.

[0072] All references herein are incorporated herein by reference in their entirety.EXAMPLES

[0073] The following examples are included to illustrate embodiments of the present disclosure. It should be appreciated by those of skill in the art that the techniques disclosed in the examples that follow represent techniques discovered by the inventor to function well in the practice of the invention. However, those of skill in the art should, in light of the present disclosure, appreciate that many changes can be made in the specific embodiments which are disclosed and still obtain a like or similar result without departing from the concept, spirit and scope of the invention. More specifically, it will be apparent that certain agents which are both chemically and physiologically related may be substituted for the agents described herein 28US_ACTIVE\131856188\V-1while the same or similar results would be achieved. All such similar substitutes and modifications apparent to those skilled in the art are deemed to be within the spirit, scope and concept of the invention as defined by the appended claims.Example 1: Model Development and Validation.

[0074] The time-dependent predictive performance estimates of CAI 9-9 as well as a panel of eight protein biomarkers (CA125, CEA, LRG1, REG3A, THBS2, T1MP1, TNFRSF1A) irrespective of the longitudinal associations for detection of PDAC was evaluated (FIG. 1). An initial rise in circulating CAI 9-9 was observed 2 years prior to a clinical PDAC diagnosis, yielding an AUC of 0.90 (95% Cl: 0.84-0.95) and sensitivity of 50% at 98.5% specificity when considering cases diagnosed within 1 year of blood draw (FIG. 1). Increases for additional protein biomarkers were observed to occur within 1 year of blood draw, with AUC estimates ranging from 0.68 to 0.89 when considering samples collected within 3 months of diagnosis (FIG. 1).

[0075] It was next evaluated whether an adaptive PEB algorithm, which considers the individual’s biomarker history, would yield improved performance compared to the ST approach. For these analyses, a PEB algorithm was established and tuned for each individual biomarker.

[0076] To model the longitudinal data, a method of parametrical empirical Bayes (PEB) algorithm was used, which consider all existing measurements of the biomarkers and adjusts the biomarker threshold to reflect the participant’s history. To implement PEB, the following steps were taken:1. Using only patients that have not developed cancer, a random intercept mixed model was fit using the Ime package in R statistical software (r-project.org).2. Three parameters were calculated from step 1 including: population mean (intercept of the model, p), within-subject variance (variance of residual term in the model, er2) and between-subject variance (variance of the random intercept in the model, r2). Table 1 provides detailed parameters for the PEB algorithm for each of the biomarkers tested.3. Considering Ti('n+1) as the biomarker level in the i111patient at the (n+l)111timepoint, produces scaling value z using the following equation:„ _ ^i(n+l)—PZi(n+1) - / 'W2+ T229US_ACTIVE\131856188\V-1then define:.2Bn= 2Then the PEB rule is:y”}_ zZKn+l) ~ >< Bn71 - x Bn4. An individual called positive if Ziin+-1)pEB> q( / d) where fo is an input parameter in the algorithm that corresponds to ( 1 -specificity) and q is the quantile of the standard normal distribution.30US_ACTIVE\131856188\V-1Table 1. Detailed parameters for the PEB algorithm for each biomarker tested.Natural logarithm value transformedParameter 3 MP score* CA19-9 LRG1 TIMP1 THBS2 CEA CAI 25 REG3A TNFRSF1A Mu 14.60487 4.617352 17.31761 11.23955 3.199885 11.02747 3.645114 9.020833 7.475794 Tau20.119577 0.7287484 0.01302565 0.0617773 0.08161064 0.07581457 0.09534623 0.153909 0.07189291 Sigma20.06517617 0.03137515 0.06940621 0.1047685 0.02783257 0.02441896 0.05699307 0.145299 0.09853163 ICC 0.6472257 0.9587236 0.1580172 0.6290672 0.7456895 0.7563793 0.6258807 0.514387 0.421846 * 3MP: 1.7005*logl0(TIMPl) + 0.93856*logl0(LRGl) + 0.60639*logl0(CA19-9)31US_ACTIVE\131856188\V-1

[0077] False-positive rate (FPR) was estimated at the screening level, defined as the proportion of positive results among all the screenings conducted in the control group. The screening-level specificity was defined as 1-FPR. The true-positive rate (TPR) or sensitivity was estimated at the patient level, which was defined as the proportion of PDAC cases with at least 1 positive biomarker test, consistent with previous applications of this method. For the ROC curve, the full range of specificities was used, and the sensitivities were estimated and then the area under the curve (AUC) was calculated. For analysis, the FPR was set at 1.5% (specificity of 98.5%) as an acceptable specificity for PDAC screening. Analyses were performed using R software version 4.2.0 (R Project for Statistical Computing). P-value and confidence interval were estimated using 1,000 bootstrap samplings.

[0078] In the entire specimen set, the PEB approach consistently yielded higher predictive performance estimates for all biomarkers compared to the ST approach, with AUC improvements ranging from 1 to 13% (Table 2). A PEB algorithm based on CA19-9, herein referred to as PEBCA19-9, achieved the highest predictive performance (AUCPEB: 0.75 versus AUCST: 0.62) (FIG. 2 and Table 3).Table 2. Performance estimates of individual protein biomarkers with the PEB and ST approaches when considering at least one serial measurement within 3 years of a PDAC diagnosis.Marker PEB AUC ST AUC A AUC (95% CI) CA19-9 0.88 0.82 0.05 (0.01 - 0.09) LRG1 0.73 0.70 0.03 (-0.02 - 0.04) T1MP1 0.73 0.66 0.07 (0.01 - 0.11) THBS2 0.73 0.62 0.11 (0.02 - 0.24) CEA 0.73 0.65 0.08 (-0.01 - 0.19) CA125 0.72 0.61 0.11 (0.02-0.24) REG3A 0.67 0.57 0.10 (0.01 - 0.23) TNFRSF1A 0.66 0.57 0.10 (0.02-0.19)32US_ACTIVE\131856188\V-1Table 3. Lead time estimation at pre-defined 98.5% specificity for PEB and ST methods. At least one biomarker measurement was within 3 years of a clinical diagnosis.Improvement of PEBST remained negative PEB signaled earlier # of Cases 5 # of Cases 0 Lead Time of PEB¥ 1.27 Lead Time of PEB NA (0.88 - 1.62)Lead Time of ST NA Lead Time of ST NA Improvement of STPEB remained negative ST signaled ear ier # of Cases 1 # of Cases 0 Lead Time of PEB NA Lead Time of PEB 0 Lead Time of ST 1.86 Lead Time of ST 0 Overall lead time 1.08 (0.66 - 1.53)improvement¥ years (interquartile range)

[0079] When considering PDAC cases with at least one biomarker measurement within 3 years of diagnosis (N= 41) and all the non-case participants, the PEBCA19-9algorithm yielded an additional 6% improvement in AUC compared to the ST approach (AUCPEB:0.88 VS AUCST:0.82; P-value<0.05) (FIG. 3). Benefit of the PEB algorithm was similar when stratifying cases into those that were clinically diagnosed with localized / regional (N= 11) or distant disease (N= 13) with respective AUC improvements of 0.05 (AUCPEB:0.91 VS AUCST:0.86; -vnZue=0.08) and 0.08 (AUCPEB:0.86 VS AUCST:0.78; P-value<0.05) (FIG. 4).

[0080] Of the 41 cases with at least one blood draw collected within 3 years of diagnosis and at a 98.5% specificity threshold, 16 (39%) had at least one ‘positive’ test (i.e. exceeded the cut point value corresponding to >98.5% specificity) by PEBCA19-9and / or STCA19-9of which 3 tested positive with PEB / ST on the first CAI 9-9 biomarker measurement (FIG. 5). At the 98.5% specificity threshold, PEBCA19-9had sensitivity of 37% (correctly identified 15 out of 41) whereas the STCA19-9approach yielded sensitivity of 27% (correctly identified 11 out of 41) (Table 4). Amongst the 15 cases that were signaled ‘positive’ by PEBCA19-9, the average ‘positive’ test signal occurred at 1.09 years (1QR: 0.54 - 1.67 years) prior to clinical diagnosis compared to 0.48 years (1QR: 0.00 - 1.36) for STCA19-9method (FIG. 5; Table 3). This shows overall improvement of 222 days (7.4 months) when incorporating longitudinal information. Notably, the PEBCA19-9algorithm signaled a ‘positive’ result in 5 (31%) cases that remained ST negative with at an average signal detection time of 1.27 years (interquartile range (1QR): 0.88- 1.62) before diagnosis (Table 3). Only one case was ST positive and remained PEB33US_ACTIVE\131856188\V-1negative (Table 3). Further sub-stratification of CAI 9-9 across different smoking status, sex and stages of the disease showed a similar improvement of PEBCA19-9vs STCA19-9method when comparing AUC and sensitivity at 98.5% specificity (Table 4).34US_ACTIVE\131856188\V-1Table 4. Performance estimates of the CA19-9 stratified by last time point of blood collection for PEB versus ST method.PEB - longitudinal analysis ST- single threshold method Last time point of the# of Case # of non-case Sensitivity at 98.5% Sensitivity at 98.5% biomarker AUC AUCParticipants Participants specificity specificity measurement¥At least 1 draw within 114 168 0.93 0.64 0.92 0.50 year of diagnosisAt least 1 draw within 230 168 0.90 0.47 0.87 0.37 years of diagnosisAt least 1 draw within 341 168 0.88 0.37 0.82 0.27 years of diagnosisAt least 1 draw within 453 168 0.83 0.28 0.76 0.21 years of diagnosisAt least 1 draw within 570 168 0.82 0.24 0.73 0.17 years of diagnosisAll time points 167 168 0.75 0.13 0.62 0.0935US_ACTIVE\131856188\V-1Table 5. Performance of CA19-9 using the PEB and ST method amongst different subgroups. At least one biomarker measurement was within 3 years of a clinical diagnosis.PEB - longitudinal analysisAUC performance Sensitivity at 98.5% specificity Subgroup Case Participants Non-case Participants CA19-9 CA19-9 Localized / Regional 11 168 0.91 0.27Distant 13 168 0.86 0.46Ever Smoker 22 94 0.88 0.36Never Smoker 19 74 0.88 0.37Male 16 82 0.85 0.38Female 25 86 0.9 0.4ST - single thresholdAUC performance Sensitivity at 98.5% specificity Subgroup Case Participants Non-case Participants CA19-9 CA19-9 Localized / Regional 11 168 0.86 0.27Distant 13 168 0.78 0.31Ever Smoker 22 94 0.82 0.32Never Smoker 19 74 0.82 0.21Male 16 82 0.79 0.38Female 25 86 0.84 0.2436US_ACTIVE\131856188\V-1

[0081] No protein biomarker yielded higher performance than CAI 9-9 based on either the PEB or ST approach. However, when focusing on those individuals below the 98.5% specificity threshold cutoff for CAI 9-9, a PEB algorithm based on repeat measurements of T1MP1 yielded an additional 14% sensitivity without loss of specificity (FIG. 6).

[0082] Using longitudinal samples from the PLCO (Prostate, Lung, Colorectal, and Ovarian) cohort, biomarker trajectories among 70 pancreatic cancer cases (each with >2 serial blood draws) and 168 non-cancer controls (each with >2 serial draws) were evaluated. The results of this study demonstrated that incorporating CAI 25 and REG3A into a longitudinal modeling framework alongside CAI 9-9 improved early detection performance, increasing sensitivity by 5% at 99% specificity compared with CAI 9-9 alone. The resulting longitudinal risk score was calculated using the following algorithm: Score = 10-log10(CA19-9) + log10(REG3A) + 0.5-log10(CA125). Longitudinal risk updating was performed using the PEB (Parametric Empirical Bayes) algorithm with the following parameters:• p = 57.01748• c>2= 3.615842• T2= 72.68247• Bi = 0.9526091.A performance summary of this study is shown in Table 6.Table 6. Performance of CA19-9 in combination with CA125 and REG3A the PEB.Model Sensitivity SpecificityCAI 9-9 (longitudinal) 0.25 0.99CAI 9-9 + CA125 + REG3A (longitudinal) 0.30 0.99Example 2: Exemplary Methods and Specimen Sets

[0083] The PLCO Cancer Screening Trial was a randomized multicenter trial in the United States aimed at evaluating the impact of early detection procedures for prostate, lung, colorectal, and ovarian cancer on disease-specific mortality. Detailed information regarding the PLCO cohort is provided in Fahrmann et al., Journal of Clinical Oncology, 40 (2022) 876-883 and Irajizad et al., Journal of Clinical Oncology, 0 JCO.22.02424.

[0084] Pancreatic cancer cases were identified by self-report in annual mail-in surveys, state cancer registries, death certificates, physician referrals and reports from next of kin for deceased individuals. All medical and pathologic records related to pancreatic cancer37US_ACTIVE\131856188\V-1diagnosis and supporting documentation were obtained and confirmed by PLCO staff. Pancreatic cancers were classified as localized, regional, distant, or unstaged using the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) historic staging system.

[0085] The study included 242 PDAC cases that were diagnosed on average 6.98 years (interquartile range (1QR): 3.66-11.28 years) after blood draw and 242 non-case participants in the PLCO study that did not develop any form of cancer during study follow-up (FIG. 7). Non-case participants were matched to case participants based on age, sex, and number of blood draws per participants (Table 7; Table 8). Of the 242 cases and 242 non-case participants, 168 (69.4%) from each group had at least two sequential blood draws (Table 7).Table 7. Patient characteristics for PLCO participants.Case Participants Non-case Participants N, cases 242 242 Sex, N (%)Male 127 (52) 127 (52) Female 115 (48) 115 (48) Age, (median, IQR) 63.0 (59.0 - 67.0) 63.0 (59.0 - 67.0) Smokers, N (%)Never 100 (41) 111 (46) Current 44 (18) 19 (8) Former 98 (41) 112 (46) Stage, N (%) - Localized / Regional 44 (18) - Distant 80 (33) - Unknown -118 (49)Table 8. Numbers of participants by number of serial biomarker measurements.Number of Case Non-caseTotal Participants Serial Samples Participants Participants3 103 103 206 2 65 65 130 1 74 74 148 Total 242 242 48438US_ACTIVE\131856188\V-1

[0086] Plasma concentration for CA19-9, CA125, CEA, IGFBP2, LRG1, REG3A, T1MP1, TNFRSF1A, and THBS2 were determined using bead-based ELISA assays using Luminex assay technology ([CAI 9-9] HCCBP1-58MAG, [LRG1] HCVD6MAG-67K, [T1MP1] HTMP1MAG-54K, Millipore; LXSAHM-04 [CA125, CEA, REG3A, and TNFRSF1A], and DTSP20 [THBS2] from R& D systems).

[0087] For all ELISA experiments, each sample was assayed in singlet and chemiluminescence measured with a SpectraMax m5 microplate reader (Molecular Devices, Sunnyvale, CA). Samples were analyzed in a blinded fashion and the ratio of case: control was equilibrated across each analytical plate to mitigate potential bias. An internal control sample was included in every plate and each value of the samples was divided by the mean value of the internal control in the same plate to correct for interplate variability.* * *

[0088] All of the methods disclosed and claimed herein can be made and executed without undue experimentation in light of the present disclosure. While the compositions and methods of this disclosure have been described in terms of preferred embodiments or aspects, it will be apparent to those of skill in the art that variations may be applied to the methods and in the steps or in the sequence of steps of the method described herein without departing from the concept, spirit, and scope of the invention. More specifically, it will be apparent that certain agents which are both chemically and physiologically related may be substituted for the agents described herein while the same or similar results would be achieved. All such similar substitutes and modifications apparent to those skilled in the art are deemed to be within the spirit, scope and concept of the invention as defined by the appended claims.39US_ACTIVE\131856188\V-1

Claims

CLAIMS1. A method of determining a probability of a subject of being afflicted with or developing pancreatic cancer, the method comprising:(a) determining a pancreatic cancer risk score by:(i) determining an expression level of CAI 9-9 in at least a first sample and a second sample from said subject, wherein the first sample and the second sample were obtained from the subject at two different time points; and(ii) applying a mathematical algorithm to the expression level of CAI 9-9 to produce a pancreatic risk score; and(b) identifying the subject as being at high risk, at normal risk, or at low risk of being afflicted with or developing cancer based on said pancreatic risk score.

2. The method of claim 1, comprising:(a) determining a pancreatic risk score by:(i) determining the expression level of CAI 9-9 and at least one or at least two additional biomarker(s) selected from the group consisting of CA125, CEA, LRG1, REG3A, THBS2, T1MP1, and TNFRSF1A in at least the first sample and the second sample from said subject; and(ii) applying a mathematical algorithm to the expression level of CAI 9-9 and the at least one additional biomarker to produce a pancreatic risk score; and(b) identifying the subject as being at high risk, at normal risk, or at low risk of being afflicted with or developing pancreatic cancer based on said pancreatic risk score.

3. The method of claim 2, wherein:(a) the at least one additional biomarker is T1MP1, LRG1, REG3A, or CAI 25; or (b) the at least two additional biomarkers are T1MP1 and LRG1 or REG3A and CA125.

4. The method of claim 1, wherein the mathematical algorithm is:whereinB̂ₙXBₙ40US_ACTIVE\131856188\V-1Zi(n+1) =Wherein^i(n+i) isabiomarker level in an ithpatient at a (n+l)111timepoint,p is about 4.617352,cr2is about 0.03137515, andT2is about 0.7287484;S”J- ~11’, wherein Zy is the average of a biomarker score across n different time points;Bn =<72 / +T2’WhereinT2is about 0.72874840,cr2is about 0.03137515, andn is a specific time point of a given test for a given individual in chronological order; andBi is B„ at a first time point.

5. The method of claim 1, wherein the mathematical algorithm is: -20.675 + 6.11 l*loglO(CA19-9) or 6.11 l*loglO(CA19-9).

6. The method of claim 1, wherein identifying the subject as being at high risk, at normal risk, or at low risk comprises comparing the pancreatic risk score to a predetermined parameter.

7. The method of claim 6, wherein identifying the subject as being at high risk comprises identifying the subject as having a pancreatic risk score that is greater than the predetermined parameter.

8. The method of claim 6, wherein identifying the subject as being at normal risk or at low risk comprises identifying the subject as having a pancreatic risk score that is equal to or less than the predetermined parameter.

9. The method of claim 6, wherein the predetermined parameter is q( / o), wherein fi is about 1 - 0.985 and q is a quantile of a standard normal distribution of a test population.

10. The method of claim 6, wherein identifying the subject as being at high risk comprises identifying the subject as having a pancreatic risk score of greater than q( / o), wherein fo is about 1 - 0.985 and q is a quantile of a standard normal distribution of a test population.41US_ACTIVE\131856188\V-111. The method of claim 6, wherein identifying the subject as being at low risk or at normal risk comprises identifying the subject as having a pancreatic risk score of less than or equal to q( / o), wherein fi is about 1 - 0.985 and q is a quantile of a standard normal distribution of a test population.

12. The method of claim 1, wherein the method further comprises:if the subject is identified as being at high risk of being afflicted with or developing pancreatic cancer, performing an appropriate diagnostic procedure on the subject or providing a report recommending that an appropriate diagnostic procedure be performed on the subject; orif the subjected is identified as being at normal risk or at low risk of being afflicted with or developing pancreatic cancer, repeating said step (a) and said step (b) after a time period or providing a report recommending that said step (a) and said step (b) be repeated after a time period.

13. The method of claim 12, wherein the diagnostic procedure is selected from the group consisting of computed tomography (CT), contrast-enhanced pancreas protocol CT, positron emission tomography (PET), magnetic resonance imaging (MRI), magnetic resonance cholangiopancreatography (MRCP), a biopsy, genetic testing, and additional biomarker testing.

14. The method of claim 12, wherein the time period is about 6 months, about 1 year, about 2 years, or about 4 years.

15. The method of claim 1, the method comprising:determining an expression level of CAI 9-9 in at least a first sample, a second sample, and a third sample from said subject, wherein the first sample, the second sample, and the third sample were obtained from the subject three different time points;determining an expression level of CAI 9-9 in at least a first sample, a second sample, a third sample, and a fourth sample from said subject, wherein the first sample, the second sample, the third sample, and the fourth sample were obtained from the subject at four different time points; ordetermining an expression level of CAI 9-9 in at least a first sample, a second sample, a third sample, a fourth sample, and fifth sample from said subject, wherein the first sample,42US_ACTIVE\131856188\V-1the second sample, the third sample, the fourth sample, and the fifth sample were obtained from the subject at five different time points.

16. The method of claim 1, wherein the sample is a biological fluid sample, a blood sample, or a serum sample.

17. A method of determining a probability of a subject of being afflicted with or developing pancreatic cancer, the method comprising:(a) determining a pancreatic cancer risk score by:(i) determining an expression level of CAI 9-9, LRG1, and T1MP 1 in at least a first sample and a second sample from said subject; and(ii) applying a mathematical algorithm to the expression level of CAI 9-9, LRG1, and T1MP1 to produce a pancreatic risk score; and(b) identifying the subject as being at high risk, at normal risk, or at low risk of being afflicted with or developing pancreatic cancer based on said pancreatic risk score.

18. The method of claim 17, wherein the mathematical algorithm is:Zi(n+D-Z^PEB= - - ^hereinZv ^i(n+l) I1iKn+i) =Vff2+ T2■ whereinKj(n+i) is a biomarker level in an ithpatient at a (n+l)111timepoint,p is about 14.60487,cr2is about 0.06517617, andT2is about 0.119577;— —, wherein Zy is the average of a biomarker score across n different time points;TBn= -2, wherein71 / n + T2T2is about 0.119577,cr2is about 0.06517617, and43US_ACTIVE\131856188\V-1n is a specific time point of a given test for a given individual in chronological order; andBi is B„ at a first time point.

19. The method of claim 17, wherein the mathematical algorithm is: 1.7005*log10(TIMP1) + 0.93856*log10(LRG1) + 0.60639*log10(CA19-9) or 61.82 + 3.45*[1.7005*log10(TIMP1) + 0.93856*log10(LRG1) + 0.60639*log10(CA19-9)].

20. The method of claim 17, wherein identifying the subj ect as being at high risk, at normal risk, or at low risk comprises comparing the pancreatic risk score to a predetermined parameter.

21. The method of claim 20, wherein identifying the subject as being at high risk comprises identifying the subject as having a pancreatic risk score that is greater than the predetermined parameter.

22. The method of claim 20, wherein identifying the subject as being at normal risk or at low risk comprises identifying the subject as having a pancreatic risk score that is equal to or less than the predetermined parameter.

23. The method of claim 20, wherein the predetermined parameter is q( / 6), wherein fo is about 1 - 0.985 and q is a quantile of a standard normal distribution of a test population.

24. The method of claim 20, wherein identifying the subject as being at high risk comprises identifying the subject as having a pancreatic risk score of greater than q( / o), wherein fo is about 1 - 0.985 and q is a quantile of a standard normal distribution of a test population.

25. The method of claim 20, wherein identifying the subject as being at low risk or at normal risk comprises identifying the subject as having a pancreatic risk score of less than or equal to q( / 6), wherein fo is about 1 - 0.985 and q is a quantile of a standard normal distribution of a test population.

26. The method of claim 17, wherein the method further comprises:if the subject is identified as being at high risk of being afflicted with or developing pancreatic cancer, performing an appropriate diagnostic procedure on the subject or providing a report recommending that an appropriate diagnostic procedure be performed on the subject; orif the subjected is identified as being at normal risk or at low risk of being afflicted with or developing pancreatic cancer, repeating said step (a) and said step (b) after a time period or44US_ACTIVE\131856188\V-1providing a report recommending that said step (a) and said step (b) be repeated after a time period.

27. The method of claim 26, wherein the diagnostic procedure is selected from the group consisting of computed tomography (CT), contrast-enhanced pancreas protocol CT, positron emission tomography (PET), magnetic resonance imaging (MRI), magnetic resonance cholangiopancreatography (MRCP), a biopsy, genetic testing, and additional biomarker testing.

28. The method of claim 26, wherein the time period is about 6 months, about 1 year, about 2 years, or about 4 years.

29. The method of claim 17, the method comprising:determining an expression level of CAI 9-9, LRG1, and T1MP 1 in at least a first sample, a second sample, and a third sample from said subject, wherein the first sample, the second sample, and the third sample were obtained from the subject three different time points; determining an expression level of CAI 9-9, LRG1, and T1MP 1 in at least a first sample, a second sample, a third sample, and a fourth sample from said subject, wherein the first sample, the second sample, the third sample, and the fourth sample were obtained from the subject at four different time points; ordetermining an expression level of CAI 9-9, LRG1, and T1MP 1 in at least a first sample, a second sample, a third sample, a fourth sample, and fifth sample from said subject, wherein the first sample, the second sample, the third sample, the fourth sample, and the fifth sample were obtained from the subject at five different time points.

30. A method of determining a probability of a subject of being afflicted with or developing pancreatic cancer, the method comprising:(a) determining a pancreatic cancer risk score by:(i) determining an expression level of CAI 9-9, REG3A, and CA125 in at least a first sample and a second sample from said subject; and(ii) applying a mathematical algorithm to the expression level of CAI 9-9, REG3A, and CAI 25 to produce a pancreatic risk score; and(b) identifying the subject as being at high risk, at normal risk, or at low risk of being afflicted with or developing pancreatic cancer based on said pancreatic risk score.45US_ACTIVE\131856188\V-131. The method of claim 30, wherein the mathematical algorithm is:Zi(n+1)PEB ^ / 1 B^xBnwhereinZi(n+= -Y^=y wherein^i(n+i) isabiomarker level in an ithpatient at a (n+l)111timepoint,p is about 57.01748,cr2is about 3.615842, andT2is about 72.68247;J~11, wherein Zy is the average of a biomarker score across n different time points;Bn= -, wherein71° In+ T2’T2is about 72.68247,cr2is about 3.615842, andn is a specific time point of a given test for a given individual in chronological order; andBi is 0.9526091.

32. The method of claim 30, wherein the mathematical algorithm is: 10-logw(CA19-9) + log10(REG3A) + 0.5-logio(CA125).

33. The method of claim 30, wherein identifying the subject as being at high risk, at normal risk, or at low risk comprises comparing the pancreatic risk score to a predetermined parameter.

34. The method of claim 33, wherein identifying the subject as being at high risk comprises identifying the subject as having a pancreatic risk score that is greater than the predetermined parameter.

35. The method of claim 33, wherein identifying the subject as being at normal risk or at low risk comprises identifying the subject as having a pancreatic risk score that is equal to or less than the predetermined parameter.46US_ACTIVE\131856188\V-136. The method of claim 33, wherein the predetermined parameter is q( / 6), wherein / 6 is about 1 - 0.985 and q is a quantile of a standard normal distribution of a test population.

37. The method of claim 33, wherein identifying the subject as being at high risk comprises identifying the subject as having a pancreatic risk score of greater than q( / b), wherein fo is about 1 - 0.985 and q is a quantile of a standard normal distribution of a test population.

38. The method of claim 33, wherein identifying the subject as being at low risk or at normal risk comprises identifying the subject as having a pancreatic risk score of less than or equal to q( / 6), wherein fi is about 1 - 0.985 and q is a quantile of a standard normal distribution of a test population.

39. The method of claim 30, wherein the method further comprises:if the subject is identified as being at high risk of being afflicted with or developing pancreatic cancer, performing an appropriate diagnostic procedure on the subject or providing a report recommending that an appropriate diagnostic procedure be performed on the subject; orif the subjected is identified as being at normal risk or at low risk of being afflicted with or developing pancreatic cancer, repeating said step (a) and said step (b) after a time period or providing a report recommending that said step (a) and said step (b) be repeated after a time period.

40. The method of claim 39, wherein the diagnostic procedure is selected from the group consisting of computed tomography (CT), contrast-enhanced pancreas protocol CT, positron emission tomography (PET), magnetic resonance imaging (MRI), magnetic resonance cholangiopancreatography (MRCP), a biopsy, genetic testing, and additional biomarker testing.

41. The method of claim39, wherein the time period is about 6 months, about 1 year, about 2 years, or about 4 years.

42. The method of claim 30, the method comprising:determining an expression level of CA19-9, REG3A, and CA125 in at least a first sample, a second sample, and a third sample from said subject, wherein the first sample, the second sample, and the third sample were obtained from the subject three different time points;determining an expression level of CA19-9, REG3A, and CA125 in at least a first sample, a second sample, a third sample, and a fourth sample from said subject, wherein the47US_ACTIVE\131856188\V-1first sample, the second sample, the third sample, and the fourth sample were obtained from the subject at four different time points; ordetermining an expression level of CA19-9, REG3A, and CA125 in at least a first sample, a second sample, a third sample, a fourth sample, and fifth sample from said subject, wherein the first sample, the second sample, the third sample, the fourth sample, and the fifth sample were obtained from the subject at five different time points.

43. A method of determining a probability of a subject of being afflicted with or developing pancreatic cancer, the method comprising:(a) determining an expression level of CAI 9-9 in at least a first sample and a second sample from said subject, wherein the first sample and the second sample were obtained from the subject at two different time points; and(b) identifying the subject as being at high risk, at normal risk, or at low risk of being afflicted with or developing pancreatic cancer based on the expression level of CAI 9-9 in said second sample compared to said first sample.

44. The method of claim 43, comprising:(a) determining the expression level of CAI 9-9 and at least one or at least two additional biomarker(s) selected from the group consisting of CAI 25, CEA, LRG1, REG3A, THBS2, T1MP1, and TNFRSF1A in at least the first sample and the second sample from said subject; and(b) identifying the subject as being at high risk, at normal risk, or at low risk of being afflicted with or developing pancreatic cancer based on the expression level of CAI 9-9 and the at least one additional biomarker in said second sample compared to said first sample.

45. The method of claim 44, wherein:(a) the at least one additional biomarker is T1MP1, LRG1, REG3A, or CAI 25; or (b) the at least two additional biomarkers are T1MP1 and LRG1 or REG3A and CA125.

46. The method of claim 43, wherein identifying the subject as being at high risk comprises identifying an increase in the expression level of CAI 9-9 in the second sample compared to the first sample.48US_ACTIVE\131856188\V-147. The method of claim 43, wherein identifying the subject as being at high risk comprises identifying an increase in the expression level of CAI 9-9 in the second sample compared to the first sample that exceeds a predetermined parameter.

48. The method of claim 43, wherein identifying the subject as being at normal risk or at low risk comprises identifying an expression level of CAI 9-9 that decreases or does not increase in the second sample compared to the first sample.

49. The method of claim 43, wherein identifying the subject as being at normal risk or at low risk comprises identifying an expression level of CAI 9-9 that does not increase beyond a predetermined parameter.

50. The method of claim 43, wherein the sample is a biological fluid sample, a blood sample, or a serum sample.

51. The method of claim 43, the method comprising:(a) determining an expression level of CAI 9-9 in at least a first sample, a second sample, and a third sample from said subject, wherein the first sample, the second sample, and the third sample were obtained from the subject three different time points; and(b) identifying the subject as being at high risk, at normal risk, or at low risk of being afflicted with or developing pancreatic cancer based on the expression level of CAI 9-9 in said second sample or said third sample compared to said first sample.

52. The method of claim 43, wherein the method further comprises:if the subject is identified as being at high risk of being afflicted with or developing pancreatic cancer, performing an appropriate diagnostic procedure on the subject or providing a report recommending that an appropriate diagnostic procedure be performed on the subject; orif the subjected is identified as being at normal risk or at low risk of being afflicted with or developing pancreatic cancer, repeating said step (a) and said step (b) after a time period or providing a report recommending that said step (a) and said step (b) be repeated after a time period.

53. The method of claim 52, wherein the diagnostic procedure is selected from the group consisting of computed tomography (CT), contrast-enhanced pancreas protocol CT, positron emission tomography (PET), magnetic resonance imaging (MRI), magnetic resonance49US_ACTIVE\131856188\V-1cholangiopancreatography (MRCP), a biopsy, genetic testing, and additional biomarker testing.

54. The method of claim 52, wherein the time period is about 6 months, about 1 year, about 2 years, or about 4 years.50US_ACTIVE\131856188\V-1

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