Currently, a general population-screening program for PDAC is not cost-effective because of low relative disease incidence and non-availability of simple, cheap, highly accurate non-invasive tests. The main goal of the screening is to identify clinically significant precursor or early stage PDAC. However, since overwhelming majority of premalignant and small PDAC lesions is asymptomatic, we do not have a definite surrogate marker to identify a subset population for screening. Consequently, as one of the approaches in investigating the risks, research has focused on identification of a subset of individuals with a higher-risk for PDAC development in order to elucidate the genetic predilection. Up to 10% of PDAC patients have a familial/genetic basis and they have increased risk of developing both pancreatic and extra-pancreatic malignancies[33-37]. Classic categorization of high-risk patients are based on the highly associated genetic risks defined as those who have significant family history of the cancer or have an inherited PDAC syndrome with a known genetic abnormality (Table 1).

Familial pancreatic cancer: Familial pancreatic cancer (FPC) cohort (cancer in two or more first-degree relatives (FDRs) or in three or more affected family members - including one first-degree relative) is considered a high-risk and a candidate for screening program[47,54,55]. Currently, the genetic foundation for FPC is not fully understood. Various investigations have demonstrated the presence of a germline mutation in the BRCA2 gene[47-49], association of BRCA1[46,56], paladin gene mutation[57] as well as other genes such as apolipoprotein A4, CEA, keratin 19, stratifin, trefoil factor, and S100A6[58,59] in FPC, and more recently identification of PALB2[60], as a pancreatic cancer susceptibility gene. These facts suggest that multiple and heterogeneous factors are likely at play for the genesis of PDAC in this subset.

Analysis of the PDAC kindred data from Johns Hopkins’ National Familial Pancreas Tumor Registry has shown that the risk of PDAC in individuals with two afflicted FDRs is 6.4% and the lifetime risk is 8%-12%; for persons with three afflicted FDRs, the relative and lifetime risks for PDAC increase to 32% and 16%-32%, respectively[36]. Brune et al[61] in their recent article reported a higher risk of PDAC among FPC kindred with a younger onset (age less than 50). Rulyak et al[62] in another study found smoking as a significant risk factor in FPC cohort, especially among males and those under age 50. This factor increases the cancer risk by 2.0-3.7 times and lowers the onset age by 10 years. A risk assessment software tool, PancPRO, has been generated and is available for calculating the risk for individuals with familial pancreatic cancer (http://www4.utsouthwestern.edu/breasthealth/cagene/default.asp)[35].

Screening modalities and the current screening programs: Many of the screening programs have used additional investigational biomarker to complement imaging tests to identify the early lesions. A commonly used marker, CA19-9, is neither sensitive nor specific independently for early PDAC or precursor detection. Kim et al[63] in their study found only 0.9% positive-predictive value using the standard cut-off value (37 U/mL). Other biomarkers investigated recently include MIC-1, CEACAM-1, SPan1, DUPAN, Alpha4GNT, and PAM4, but none is validated for routine clinical use[64]. In another approach, elevated fasting-glucose level has been demonstrated to be associated with sporadic PDAC[65] and is currently utilized by an European registry in high-risk people with mutational analysis of pancreatic juice along with p16 promoter methylation status.

Several international screening programs exist for PDAC in high-risk individuals. “Cancer of the Pancreas Screening Study” (CAPS study), led by John Hopkins University, is the largest screening program that involves 24 American Centers of Excellence. To date, three studies, CAPS 1, CAPS 2 and CAPS 3, have been completed (Table 2).

In the CAPS 1, thirty-eight patients including 31 from a kindred with > 3 affected with PDAC, 6 with 2 affected relatives, and 1 patient with Peutz-Jeghers syndrome (PJS) were studied. Screening protocol with EUS revealed six pancreatic masses: 1 invasive PDAC, 1 benign IPMN, 2 serous cystadenomas, and 2 non-neoplastic lesions. The yield of screening was 5.3% in this study[66]. In the CAPS 2, seventy-eight high-risk patients were studied[67]. In 8 patients, the screening found pancreatic neoplasia, confirmed by surgery or FNA: 6 benign IPMNs, 1 IPMN that progressed to invasive PDAC, and another had high-grade pancreatic intraepithelial neoplasia (PanIN-3). The CAPS 3 was a multicenter prospective cohort study involving annual EUS, MRI screening with assays of DNA and protein markers in serum and pancreatic juice. Over 200 patients were enrolled over a three-year period. The study results on the detection modality comparison demonstrated that the EUS has the highest rate of detection of early neoplastic changes in up to 42.6% of the asymptomatic high-risk group[68].

In another study from the University of Washington, high-risk familial cohorts were screened with EUS, beginning 10 years prior to the earliest index PDAC case. If EUS was normal, they underwent a repeat EUS in 2-3 years. With abnormal EUS findings, they were referred for ERCP and if abnormalities were noted, patients were offered surgical intervention[69]. Among 75 screened subjects, 15 had gone to surgery for abnormal EUS and ERCP findings. All surgical cases revealed premalignant lesions: PanIN-3 in 10 and PanIN-2 in five cases[70]. The study gave a yield of 13% (10 out of 75) for detecting PanIN-3 lesions. A single patient developed unresectable PDAC during the surveillance.

In a German PDAC screening program (FaPaCa), 76 patients were followed using annual EUS, MRCP, and laboratory assays (CDKN2a and BRCA2 genetic analysis, CA19-9 and CEA). Any appreciable lesion was evaluated with EUS. With an abnormal finding, the patient underwent surgical exploration and if malignancy was detected, total pancreatectomy was performed. In 10 cases, lesions were seen on EUS as compared to only seven detected by MR scan. Out of the seven MRCP-positive cases, six underwent resections and the histology showed one PanIN-3, one PanIN-2, one PanIN-1, and three with other benign lesions. This resulted in a diagnostic yield of 1.3% for PanIN-3 detection[71]. Another study from the Netherlands in 44 high-risk subjects demonstrated a 7% detection rate for asymptomatic PDAC and a 16% for premalignant lesions[72].

The International CAPS Consortium have recently met and reported a suggested guideline for current PDAC screening based on the risk[73]. A consensus (≥ 75% agreement by the participants) was reached that the following groups should be offered screening (only to individuals who are surgical candidate): (1) FDRs of the cancer patients from a familial pancreatic cancer cohort with at least two affected FDRs; (2) patients with Peutz-Jeghers syndrome; and (3) p16, BRCA2 and hereditary non-polyposis colorectal cancer mutation carriers with at least single affected FDR. The initial screening should include EUS and/or MRI. However, consensus was not reached on the beginning and the end age of screening/surveillance and the interval of the examination. Their conclusions also included requirement for further studies, and the clinical management should occur at high-volume centers with multidisciplinary teams.

Identification of a higher-PDAC-risk group: As the prevalence of PDAC in the general United States population over the age 55 is approximately 68 per 100000, a candidate discriminant test with a specificity of 98% and a sensitivity of 100% would generate 1999 false-positive test results and 68 true-positives[74]. Thus, relying on a single determinant for distinguishing the PDAC early-stage cases from the general population would necessitate a highly accurate test with a specificity of greater than 99%. More practical approach, then, would be to begin with a subset of population with a higher prevalence, and in conjunction with novel surrogate markers to curtail the at-risk subset, we could begin to identify the group with significantly increased PDAC risk for whom the endoscopic/imaging-based screening strategy could be applied.

An initial approach in selection of the screening population is to utilize selective clinical parameters that could be used to curtail the subset of the general population at increased PDAC risk. For instance, based on the epidemiological evidence, such clinical parameters include hyperglycemia or diabetes, which are noted in 50%-80% of pancreatic cancer patients[75-79]. Though not encompassing all PDAC patients, this subset includes a much larger proportion of PDAC patients for whom we may select further for screening. Similarly, patients with a history of chronic pancreatitis or obesity are reported to have increased PDAC risk during their lifetime[80-85]. Recent findings from molecular biology and animal studies investigating effects of diet-induced obesity in a PDAC mouse model demonstrated increased occurrence of pancreatic inflammation and accelerated pancreatic neoplastic changes, supporting the association of obesity and pancreatic inflammation and PDAC risks[86,87]. Considering that millions are being diagnosed with diabetes or glucose intolerance, chronic pancreatitis, or obesity annually in comparison to PDAC, however, further refinement of the screening patient group is critically needed to justify for developing a larger scale screening protocol.

Initially established as a key methodology in the field of inborn metabolic errors and toxicology, metabolomics have developed over the years to examine a much wider array of low-molecular-weight products or intermediates within the biological state of a cell, tissue, organ, or organism. A metabolome represents a physiological readout of the biochemical state in an individual’s body compartment, and provides the functional terminal signals of the genome and proteome, reflecting more closely the current phenotypic state of an individual in response to the environmental stimuli[88]. Thus, metabolomics data has considerable potential in elucidating cancer-development risks, with its additional capacity for providing temporal molecular information to the ongoing changes originating from genetic PDAC risk alone.

With the recent advancement in the technology and resumed interest in the cancer-associated metabolic abnormality[89,90], application of metabolomics in the cancer field has attracted more attention[91]. Cancer-related metabolic reprogramming, Warburg effect, has been known since nearly a century ago in association with various solid tumors including PDAC[92], as cancer cells undergo energetically inefficient glycolysis even in the presence of oxygen in the environment (aerobic glycolysis)[93]. A number of common cancer mutations including Akt1, HIF (hypoxia-inducible factor), and p53 have been shown to support the Warburg effect through glycolysis and down-regulation of metabolite flux through the Krebs cycle[94-101]. In PDAC, increased phosphorylation or activation of Akt1 has also been reported (illuminating on the importance of enzyme functionality)[102] as well as involvement of HIF1 in the tumor growth via effects on glycolytic process[103,104] and membrane-bound glycoprotein (MUC17) regulation[105] - reflective of activation of metabolic pathways. Further evidences of loss-of-function genetic mutations in key mitochondrial metabolic enzymes such as succinate dehydrogenase and fumarate hydratase, isocitrate dehydrogenase, phosphoglycerate dehydrogenase support carcinogenesis and the Warburg effect[106-110]. Other important alternative pathways in cancer metabolism such as glutaminolysis and pyruvate kinase isoform suppression have been shown to accumulate respective upstream intermediates and reduction of associated end products such as NADPH, ribose-5-phosphate and nucleic acids[111-116]. As such, various groups have reported metabolomics biomarker applications for different cancers[117,118].

As a major organ involved in metabolic regulation in a healthy individual, pancreatic disorder such as malignancy is anticipated to influence the normal metabolism, presenting further rationale and interest in elucidating the implication of malignant transformation and PDAC development. Proteomic analysis of the pancreatic cancer cells demonstrated alteration in proteins involved in metabolic pathways including increased expression of glycolytic and reduced Krebs cycle enzymes, and accumulation of key proteins involved in glutamine metabolism, in support of Warburg effect. These in turn play significant role in nucleotide and amino acid biosynthesis required for sustaining the proliferating cancer cells[119]. Applications of sensitive mass spectrometric techniques in metabolomics study of PDAC detection biomarkers have led to identification of a set of small molecules or metabolites (or biochemical intermediates) that are potent discriminants of developing PDAC and the controls (See Figure 1 as an example of metabolomics based analysis, allowing segregation of PDAC from benign cases). Recent reports from our group as well as others have demonstrated that specific candidate metabolites consisting of amino acids, bile acids, and a number of lipids and fatty acids - suspected to be reflective of tumor proliferation as well as many systemic response yet to be determined - were identified as potential discriminant for blood-based PDAC biomarkers[120-123]. As a further supporting data, elucidation of lipids and fatty acids as discriminant factors from PDAC and benign lesions from the cancer tissue and adjacent normal tissue has been reported recently[124].

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Figure 1 Example of metabolomics based analysis, allowing segregation of pancreatic ductal adenocarcinoma from benign cases. Heat map illustration of discriminant capability of a metabolite set derived from gas chromatography and liquid chromatography/mass spectrometry plasma metabolomics dataset comparing pancreatic ductal adenocarcinoma patients (Red or group 1; n = 110) and benign pancreas (includes benign cysts, chronic pancreatitis, and normal pancreas) (Green or group 2; n = 90). Metabolites are plotted on x-axis, and the cases on the y-axis. Blue color indicates data points with a value smaller than the median of the respective metabolite and the red indicates higher values. This candidate set of metabolites enabled the segregation of the two groups.

By virtue of simultaneously depicting the multiple metabolite levels, metabolomics approach reveals various biochemical pathways that are uniquely involved in malignant conditions and has led to findings such as abnormalities of glycine and its mitochondrial biosynthetic pathway, as a potential therapeutic target in certain cancers[125]. Moreover, in combination with other systems biology approaches such as transcriptomics and proteomics, further refinement in characterization of cancer development and therapeutic targets as well as identification of potential biomarkers could be realized for PDAC. Since many enzymes in a metabolic network determine metabolites’ level and nonlinear quantitative relationship from the genes to the proteome and metabolome levels exist, a metabolome cannot be easily decomposed to a specific single marker, which will designate the cancer state[126]. Thus, in order to delineate a pathological state such as PDAC, multiple metabolomic features might be required for accurate depiction of a developing cancer. Future studies are anticipated to incorporate cancer systems’ biological knowledge, including metabolomics, for optimal designation of PDAC biomarkers, which would be utilized in conjunction with a clinical-parameter-derived population subset for establishing the PDAC screening population. Subsequently, further validation studies for the PDAC biomarkers need to be performed.