Purpose: Serum–biomarker based screening for pancreatic cancer could greatly improve survival in appropriately targeted high-risk populations.

Experimental Design: Eighty-three circulating proteins were analyzed in sera of patients diagnosed with pancreatic ductal adenocarcinoma (PDAC, n = 333), benign pancreatic conditions (n = 144), and healthy control individuals (n = 227). Samples from each group were split randomly into training and blinded validation sets prior to analysis. A Metropolis algorithm with Monte Carlo simulation (MMC) was used to identify discriminatory biomarker panels in the training set. Identified panels were evaluated in the validation set and in patients diagnosed with colon (n = 33), lung (n = 62), and breast (n = 108) cancers.

Results: Several robust profiles of protein alterations were present in sera of PDAC patients compared to the Healthy and Benign groups. In the training set (n = 160 PDAC, 74 Benign, 107 Healthy), the panel of CA 19–9, ICAM-1, and OPG discriminated PDAC patients from Healthy controls with a sensitivity/specificity (SN/SP) of 88/90%, while the panel of CA 19–9, CEA, and TIMP-1 discriminated PDAC patients from Benign subjects with an SN/SP of 76/90%. In an independent validation set (n = 173 PDAC, 70 Benign, 120 Healthy), the panel of CA 19–9, ICAM-1 and OPG demonstrated an SN/SP of 78/94% while the panel of CA19–9, CEA, and TIMP-1 demonstrated an SN/SP of 71/89%. The CA19–9, ICAM-1, OPG panel is selective for PDAC and does not recognize breast (SP = 100%), lung (SP = 97%), or colon (SP = 97%) cancer.

Conclusions: The PDAC-specific biomarker panels identified in this investigation warrant additional clinical validation to determine their role in screening targeted high-risk populations. Clin Cancer Res; 17(4); 805–16. ©2010 AACR.

See commentary p. 635

Translational Relevance

Specific challenges associated with pancreatic cancer including ubiquitous symptomatic presentation, deep anatomical location, and aggressive etiology have greatly hindered efforts to combat the disease. Although the low incidence of the disease in the general population renders population-based screening impractical, screening could greatly improve survival in appropriately targeted high-risk populations. The current absence of reliable biomarker testing for pancreatic cancer mandates the development of novel strategies for identifying and characterizing additional biomarkers. The evaluation of serum biomarker levels in patients diagnosed with pancreatic cancer and a spectrum of benign pancreatic conditions presented here provides compelling evidence for the emerging role of blood-based screening in the clinical management of this disease. These findings not only include the identification of multimarker panels capable of discriminating pancreatic cancer from benign conditions and healthy controls with high sensitivity and specificity, but also offer improved insight into the complex network of factors involved in pancreatic tumorigenesis.

Pancreatic cancer is the fourth leading cause of cancer death in the United States. In 2010, an estimated 43,140 people will be diagnosed with pancreatic cancer with a staggering 36,800 perishing from the disease (1). Although a variety of tumors can arise in the pancreas, the vast majority of pancreatic tumors, 85–90%, are represented by a specific histological subtype termed pancreatic ductal adenocarcinoma (PDAC, ref. 2). The poor prognosis from PDAC is largely due to our inability to detect the cancer at an early stage when the option of curative resection remains available. Factors contributing to this difficulty include the inaccessible location of the pancreas deep in the abdomen, late-presenting clinical manifestations (e.g., weight loss, epigastric pain, or obstructive jaundice), and the early development of metastasis. As a result, the majority of pancreatic cancer patients present with unresectable disease leading to a median survival of 6 months and an overall 5-year survival of <5% (3). In contrast, those few who present with small, surgically resectable cancers have a realistic chance of cure and a 5-year survival rate of 20–30% (4). Owing to the low prevalence of PDAC, it is currently neither advisable nor cost effective to screen the general population (5). Efforts are focused on early screening of selected high-risk-cohorts (more than 10-fold increased risk), who account for approximately 10% of patients with PDAC. These mainly consist of patients with a genetic predisposition for developing PDAC including individuals with a family history in multiple family members, and patients with hereditary pancreatitis, Peutz–Jeghers syndrome, hereditary breast-ovarian cancer syndrome, or familial atypical multiple mole melanoma (6). It has been calculated that a screening test with a sensitivity (SN) and specificity (SP) above or near 90% would benefit high-risk groups (5).

There are no reliable screening tests, either molecular or imaging based, for detecting pancreatic cancer in asymptomatic persons and the deep anatomic location of the pancreas makes detection of small localized tumors unlikely during routine abdominal examination. Tumor resolution is a critical factor in the early detection of pancreatic cancer as tumors as small as 2 cm in diameter are frequently associated with metastatic disease (7). Commonly used imaging studies (e.g., abdominal CT or MRI) in the setting of a high clinical suspicion of having PDAC are inadequate for diagnosing pancreatic cancer at an early stage since they do not reliably detect pancreatic tumors <1–2 cm in size (8). More accurate tests such as endoscopic retrograde cholangiopancreatography (ERCP) and endoscopic ultrasound (EUS) are inappropriate for screening asymptomatic patients due to their invasiveness, cost, and attendant clinical risks (9). The mucin-associated carbohydrate antigen CA 19–9 is a biomarker of PDAC with limited clinical utility in the screening setting. CA 19–9 has demonstrated modest effectiveness in the screening of symptomatic individuals on an outpatient basis with a median SN of 79% (range, 70–90%) and median SP of 82% (range, 68–91%), however it has been shown to be ineffective in the mass screening of asymptomatic subjects (10). The principal limitations of CA 19–9 include its frequent elevation associated with nonmalignant conditions such as pancreatitis and obstructive jaundice, and its inability to detect many early stage malignancies (11). CA 19–9 is also unsuitable for use in the estimated 5–10% of patients who are carriers of the Lewis-negative genotype and develop tumors that do not express the antigen (12).

These limitations of CA 19–9 have led investigators to search for alternative biomarkers for use in screening for PDAC. Such alternative serum biomarkers include TPA/TPS, macrophage inhibitory cytokine-1 (MIC-1), IGFBP-1, haptoglobin, SAA, TIMP-1, osteopontin (OPN), HE4, NGAL and others, however none of these have been clinically proven to be superior to CA 19–9 (11), (13)–(19). Several groups have also reported on the performance of combinations of these markers(19)–(21). These multiplexed biomarker approaches have demonstrated improved SN and SP for the detection of pancreatic cancer. For example, in a recent study, a panel of 7 proteins (ALCAM, ICAM-1, LCN2, TIMP-1, REG1A, REG3, and IGFBP-4) with or without the addition of CA 19–9, selected based on findings in a mouse model, was able to discriminate human pancreatic cancer cases from matched controls in a small group of presymptomatic and prediagnostic blood samples (19). In efforts to diagnose pancreatic cancer from benign and healthy controls, TIMP-1 was evaluated along with its target MMP-9 (21), while serum levels of OPN were capable of discriminating resectable PDAC from controls with an SN/SP of 80/97% (20).

Biomarker profiles indicative of a specific cancer include not only those factors produced by the tumor itself but also represent the systemic response to the growing tumor including acute phase reactants, inflammatory cytokines, growth and angiogenic factors, etc. Additionally, it is likely that levels of proteins secreted or released by the tumor will correlate together and therefore mitigate the advantage of their use in combination. We hypothesize that combinations of biomarkers originating from multiple tumoral and extratumoral sites could offer superior diagnostic ability. Proteins representing the systemic response to malignancy may also reflect advancements in tumor development since the tumor relies on these exogenous factors for growth and spreading. In this study, we utilized an extensive array of bead-based assays for a broad range of circulating proteins to evaluate serological alterations present in a diverse group of patients diagnosed with PDAC, nonmalignant pancreatic disease, and healthy controls. We identify a number of significant differences in biomarker profiles associated with each diagnosis. Our underlying objective was the identification of a panel of serum biomarkers capable of detecting PDAC with high SN and SP. Such a panel would offer a noninvasive means of screening in appropriately targeted high-risk populations.

Patient populations

The study population was comprised of 333 patients with histologically diagnosed PDAC, 144 patients with benign pancreatic conditions including acute or chronic pancreatitis, benign pancreatic cysts, or other benign pancreatic neoplasms, 227 healthy controls without a history of pancreatic diseases, and patients diagnosed with colon (n = 33), lung (n = 62), and breast (n = 108) cancer (Table 1). The diagnoses of the patients with benign pancreatic diseases were clinical and guided by standard radiological imaging tests and determined by an experienced pancreatic expert. None of the patients with benign pancreatic diseases or the healthy controls had a history of any malignancies. Samples were obtained prior to any treatments from multiple sources (University of Pittsburgh, Sloan-Kettering Cancer Center, NorthShore University HealthSystems (NUH)–formerly known as Evanston Northwestern Healthcare, University of Alabama Birmingham, Fox Chase Cancer Center, Gynecological Oncology Group, Duke University) and were annotated with information regarding age, diagnosis, disease stage, histology, and grade. Written informed consent was obtained from each subject and the local institutional review boards approved the protocols for use of each sample collection. All collection sites utilized a standardized protocol for sample collection. Samples were stored at −70°C or colder and shipped on dry ice overnight to UPCI. No more than 2 freeze/thaws were allowed. The Healthy, Benign, and PDAC subjects were randomly assigned to either the training or validation sets (Table 1) and validation samples were blinded until completion of the multivariate analysis.

Table 1.

Clinical characteristics of study population

Training set
HealthyBenignPDAC
Source Age range (median) Gender Source Age range (median) Gender Dx Source Age range (median) Gender Stage 
UPCI 24–69 M = 36 UAB 32–83 M = 23 C = 31 NUH 49–91 M = 29 I = 2 
n = 45 (53) F = 9 n = 57 (55) F = 34 P = 17 n = 57 (73) F = 28 II = 18 
      O = 9    III = 1 
          IV = 34 
          U = 2 
FCCC 39–87 M = 1 NUH 23–83 M = 6 C = 1 UAB 42–89 M = 20 I = 1 
n = 35 (66) F = 34 n = 17 (66) F = 11 P = 8 n = 49 (68) F = 29 II = 3 
      O = 8    III = 2 
          IV = 3 
          U = 40 
NUH 33–77 M = 6     SKCC 45–83 M = 17 I = 0 
n = 19 (55) F = 13     n = 36 (64) F = 19 II = 12 
          III = 5 
          IV = 6 
          U = 13 
GOG 18–61 M = 0     UPCI 49–87 M = 9 I = 1 
n = 8 (44) F = 8     n = 18 (70) F = 9 II = 0 
          III = 8 
          IV = 1 
          U = 8 
Total 18–87 M = 43 Total Benign 23–83 M = 29 C = 32 Total 42–91 M = 75 I = 4 
Healthy (58) F = 64 n = 74 (56) F = 45 P = 25 PDAC (69) F = 85 II = 33 
n = 107      O = 17 n = 160   III = 16 
          IV = 44 
          U = 63 
Validation Set 
Healthy   Benign    PDAC    
Source Age range (median) Gender Source Age range (median) Gender Dx Source Age range (median) Gender Stage 
UPCI 20–76 M = 34 UAB 43–85 M = 35 C = 16 UAB 44–91 M = 36 I = 0 
n = 46 (51) F = 12 n = 54 (57) F = 19 P = 19 n = 55 (67) F = 29 II = 6 
      O = 19    III = 4 
          IV = 6 
          U = 39 
FCCC 37–84 M = 6 NUH 35–86 M = 7 C = 2 SKCC 45–83 M = 22 I = 0 
n = 37 (65) F = 31 n = 16 (75) F = 9 P = 8 n = 52 (65) F = 30 II = 16 
      O = 6    III = 10 
          IV = 2 
          U = 24 
NUH 34–87 M = 5     NUH 42–92 M = 22 I = 3 
n = 27 (54) F = 22     n = 46 (74) F = 24 II = 14 
          III = 0 
          IV = 29 
          U = 0 
GOG 39–56 M = 0     UPCI 29–82 M = 8 I = 1 
n = 10 (51) F = 10     n = 20 (64) F = 12 II = 1 
          III = 6 
          IV = 4 
          U = 8 
Total 20–87 M = 45 Total Benign 35–86 M = 42 C = 18 Total 29–92 M = 88 I = 4 
Healthy (54) F = 75 n = 70 (66) F = 28 P = 27 PDAC (68) F = 95 II = 37 
n = 120      O = 25 n = 173   III = 20 
          IV = 41 
          U = 71 
Non-PDAC Cancer Set 
Colon   Lung    Breast    
Source Age range (median) Gender Source Age range (median) Gender Dx Source Age range (median) Gender Dx 
UPCI 47–89 M = 18 UPCI 46–99 M = 30 Ad = 33 Duke 52–82 F = 108 IDC = 108 
n = 33 (64.5) F = 15 n = 62 (68) F = 32 Sq = 29 n = 108 (63)   
Training set
HealthyBenignPDAC
Source Age range (median) Gender Source Age range (median) Gender Dx Source Age range (median) Gender Stage 
UPCI 24–69 M = 36 UAB 32–83 M = 23 C = 31 NUH 49–91 M = 29 I = 2 
n = 45 (53) F = 9 n = 57 (55) F = 34 P = 17 n = 57 (73) F = 28 II = 18 
      O = 9    III = 1 
          IV = 34 
          U = 2 
FCCC 39–87 M = 1 NUH 23–83 M = 6 C = 1 UAB 42–89 M = 20 I = 1 
n = 35 (66) F = 34 n = 17 (66) F = 11 P = 8 n = 49 (68) F = 29 II = 3 
      O = 8    III = 2 
          IV = 3 
          U = 40 
NUH 33–77 M = 6     SKCC 45–83 M = 17 I = 0 
n = 19 (55) F = 13     n = 36 (64) F = 19 II = 12 
          III = 5 
          IV = 6 
          U = 13 
GOG 18–61 M = 0     UPCI 49–87 M = 9 I = 1 
n = 8 (44) F = 8     n = 18 (70) F = 9 II = 0 
          III = 8 
          IV = 1 
          U = 8 
Total 18–87 M = 43 Total Benign 23–83 M = 29 C = 32 Total 42–91 M = 75 I = 4 
Healthy (58) F = 64 n = 74 (56) F = 45 P = 25 PDAC (69) F = 85 II = 33 
n = 107      O = 17 n = 160   III = 16 
          IV = 44 
          U = 63 
Validation Set 
Healthy   Benign    PDAC    
Source Age range (median) Gender Source Age range (median) Gender Dx Source Age range (median) Gender Stage 
UPCI 20–76 M = 34 UAB 43–85 M = 35 C = 16 UAB 44–91 M = 36 I = 0 
n = 46 (51) F = 12 n = 54 (57) F = 19 P = 19 n = 55 (67) F = 29 II = 6 
      O = 19    III = 4 
          IV = 6 
          U = 39 
FCCC 37–84 M = 6 NUH 35–86 M = 7 C = 2 SKCC 45–83 M = 22 I = 0 
n = 37 (65) F = 31 n = 16 (75) F = 9 P = 8 n = 52 (65) F = 30 II = 16 
      O = 6    III = 10 
          IV = 2 
          U = 24 
NUH 34–87 M = 5     NUH 42–92 M = 22 I = 3 
n = 27 (54) F = 22     n = 46 (74) F = 24 II = 14 
          III = 0 
          IV = 29 
          U = 0 
GOG 39–56 M = 0     UPCI 29–82 M = 8 I = 1 
n = 10 (51) F = 10     n = 20 (64) F = 12 II = 1 
          III = 6 
          IV = 4 
          U = 8 
Total 20–87 M = 45 Total Benign 35–86 M = 42 C = 18 Total 29–92 M = 88 I = 4 
Healthy (54) F = 75 n = 70 (66) F = 28 P = 27 PDAC (68) F = 95 II = 37 
n = 120      O = 25 n = 173   III = 20 
          IV = 41 
          U = 71 
Non-PDAC Cancer Set 
Colon   Lung    Breast    
Source Age range (median) Gender Source Age range (median) Gender Dx Source Age range (median) Gender Dx 
UPCI 47–89 M = 18 UPCI 46–99 M = 30 Ad = 33 Duke 52–82 F = 108 IDC = 108 
n = 33 (64.5) F = 15 n = 62 (68) F = 32 Sq = 29 n = 108 (63)   

Sources: UPCI – University of Pittsburgh Cancer Institute; FCCC – Fox Chase Cancer Center; NUH – NorthShore University HealthSystems; GOG – Gynecological Oncology Group; UAB – University of Alabama-Birmingham; SKCC – Sloan-Kettering Cancer Center; Duke – Duke University; Benign Dx: C – Benign Cyst; P – Pancreatitis; O – Other benign neoplasm; PDAC Dx: U – stage unknown; Ad – adenocarcinoma; Sq – squamous cell carcinoma; IDC – invasive ductal carcinoma

Sources of bead-based immunoassays

The xMAP™ bead-based technology (Luminex Corp.) permits multiplexed analysis of several analytes in 1 sample. Eighty-three bead-based xMAP™ immunoassays for a diverse set of serum biomarkers were utilized in this study (Table 2). The immunoassays were either obtained from commercial suppliers or developed by the UPCI Luminex Core Facility as described previously (22). Overall, 14 different multiplexed panels were used. For the UPCI core-developed assays, the intraassay variability of each assay was 3.5–5% and interassay variability was 11–15%. Additional quality control data for each core-developed assay, including correlation with commercial ELISA can be found on the UPCI Luminex Core Facility website. All assays were performed at UPCI.

Table 2.

Complete list of multiplexed biomarker assays

Biological groupsMarkers
Tumor markers Alpha-fetoprotein (AFP), CA 19–9, CA 125, CA 15–3, CA72–4, CEA 
Hormones Adrenocorticotropic hormone (ACTH), follicle stimulating hormone (FSH), growth hormone (GH), insulin, luteinizing hormone (LH), prolactin (PRL), parathyroid hormone (PTH), thyroid stimulating hormone (TSH), pancreatic polypeptide (PP), peptide YY (PYY, total) 
Apoptotic factors Cytokeratin 19 (Cyfra 21–1), DR5, sFas, sFasL 
Cell Adhesion ICAM-1, VCAM-1 
Proteases/inhibitors Kallikrein 10, MMP-2, 3, 9, TIMP-1–4 
Cytokines/chemokines/receptors CD40L, eotaxin-1, fractalkine, IFNγ, IL-10, IL-12p70, IL-13, IL-1b, IL-1Rα, IL-2, IL-2R, IL-4, IL-5, IL-6, IL-6R, IL-7, IL-8, IP-10, macrophage inhibitory factor (MIF), MIP-1b, TGFα, TNFα, TNF-RI, TNF-RII 
Growth/angiogenesis factors/receptors Angiostatin, EGFR, endostatin, ErbB2, GM-CSF, IGFBP-1, thrombospondin 
Adipokines Adiponectin, leptin 
Apolipoproteins ApoAI, ApoAII, ApoB, ApoCII, ApoCIII, ApoE 
Other CRP, ghrelin (active), gastric inhibitory polypeptide (GIP, total), glucagon like peptide 1(GLP-1, active), HE4, mesothelin, osteocalcin (OC), osteoprotegerin (OPG), osteopontin (OPN), serum amyloid A (SAA), serum amyloid P (SAP), transglutaminase II (TG II), total plasminogen activator inhibitor 1 (tPAI 1), myeloperoxidase (MPO) 
Biological groupsMarkers
Tumor markers Alpha-fetoprotein (AFP), CA 19–9, CA 125, CA 15–3, CA72–4, CEA 
Hormones Adrenocorticotropic hormone (ACTH), follicle stimulating hormone (FSH), growth hormone (GH), insulin, luteinizing hormone (LH), prolactin (PRL), parathyroid hormone (PTH), thyroid stimulating hormone (TSH), pancreatic polypeptide (PP), peptide YY (PYY, total) 
Apoptotic factors Cytokeratin 19 (Cyfra 21–1), DR5, sFas, sFasL 
Cell Adhesion ICAM-1, VCAM-1 
Proteases/inhibitors Kallikrein 10, MMP-2, 3, 9, TIMP-1–4 
Cytokines/chemokines/receptors CD40L, eotaxin-1, fractalkine, IFNγ, IL-10, IL-12p70, IL-13, IL-1b, IL-1Rα, IL-2, IL-2R, IL-4, IL-5, IL-6, IL-6R, IL-7, IL-8, IP-10, macrophage inhibitory factor (MIF), MIP-1b, TGFα, TNFα, TNF-RI, TNF-RII 
Growth/angiogenesis factors/receptors Angiostatin, EGFR, endostatin, ErbB2, GM-CSF, IGFBP-1, thrombospondin 
Adipokines Adiponectin, leptin 
Apolipoproteins ApoAI, ApoAII, ApoB, ApoCII, ApoCIII, ApoE 
Other CRP, ghrelin (active), gastric inhibitory polypeptide (GIP, total), glucagon like peptide 1(GLP-1, active), HE4, mesothelin, osteocalcin (OC), osteoprotegerin (OPG), osteopontin (OPN), serum amyloid A (SAA), serum amyloid P (SAP), transglutaminase II (TG II), total plasminogen activator inhibitor 1 (tPAI 1), myeloperoxidase (MPO) 

Multiplex biomarker analysis

The bead-based multiplex serum assays were performed in 96-well microplate format. All purchased assays were performed according to appropriate manufacturer's protocols. In-house assays, sample analysis, and curve-fitting were performed as previously described (22). All biomarker data were normalized prior to statistical analysis according to a scaling procedure developed by our group to specifically account for variation acquired throughout repeated experiments. A complete description of this procedure is provided in the Supplementary Material.

Statistical analysis of data

Descriptive statistics for serum concentrations of each of the tested biomarkers were calculated for each subject group using GraphPad Prism software (GraphPad Software, Inc.). A 1-way analysis of variance (ANOVA) with Tukey's multiple comparison test was used to determine significance of any observed differences in serum biomarker concentrations between the groups. The minimum level of significance was taken as P < 0.05. The false discovery rate (FDR) was controlled at 5% according to the method described by Benjamini and Hochberg (23). Briefly, the individual P-values for each biomarker comparison were ranked from most to least significant. The ranked, unadjusted P-values were then compared to the statistic i × q/m, where i is the P-value rank, q is the FDR (0.05), and m is the total number of biomarker comparisons tested.

Multivariate analysis

All development of statistical models for distinguishing cases from controls was restricted to the training set until 1 panel and 1 model of combining the candidate biomarkers in the panel were selected. A Metropolis algorithm with Monte Carlo simulation (MMC) was utilized for analysis of the data as previously described (22). Using this algorithm, all possible panels consisting of 2, 3, and 4 biomarkers were evaluated for SN at 90% SP in the training set. For each panel size, the 500 panels with the best SN at 95% SP on the full data set we re-estimated the SN with cross-validation. For cross-validation, 20% of subjects were randomly excluded from the data set and the rest was used as training set to build the optimal Scoring Function (SF). The resultant model was applied to the excluded subjects, and this process was repeated 400 times, which was sufficient to obtain a smooth averaged receiver operator characteristic (ROC) curve. For each comparison (PDAC vs. Healthy, PDAC vs. Benign), a single multimarker panel demonstrating the best SN at 90% SP as determined from the ROC curves was validated in an independent, blinded validation set. Following the classification of each sample in the validation set, the samples were unblinded and the diagnosis assigned by the MMC algorithm was compared to the actual diagnosis for calculation of SN/SP. The top performing multimarker panels for the discrimination of PDAC versus Healthy were further evaluated for cancer selectivity in patients diagnosed with colon, lung, and breast cancer.

Univariate analysis of biomarker levels in patients diagnosed with PDAC, benign pancreatic disease, and healthy controls

Serum levels of each biomarker were compared between the PDAC, Benign, and Healthy subject groups and the presented data reflect the inclusion of all subjects, after unblinding, in order to increase the statistical power of comparisons. Of the 83 biomarkers evaluated, 44 were found to differ significantly between the PDAC patients and the Healthy and Benign groups (Table 3). Of these 44 biomarkers, 35 were found to be increased in the PDAC group in comparison to the Healthy group, while 9 were found to be decreased. In the comparison between the Benign and PDAC patient groups, 20 biomarkers were observed at higher concentrations in the PDAC group while 5 biomarkers were found in lower levels.

Table 3.

Circulating levels of significant biomarkers in patients diagnosed with pancreatic cancer, benign pancreatic disease, and healthy control individuals

HealthyBenignPDACPDAC vs. Healthy1PDAC vs. Benign1
ClassBiomarkerUnitsMeanMeanMeanp-valuep-value
Tumor Markers CA 19–9 U/ml 5.42 2.61 56.04 < 0.001 < 0.001 
 CA-125 U/ml 27.4 26.9 1073.1 < 0.001 < 0.001 
Hormones GH ng/ml 1.93 1.17 2.40 < 0.001 < 0.001 
 Prolactin ng/ml 6.18 23.66 14.10 < 0.001 < 0.001 
 PTH pg/ml 27.2 43.5 38.6 < 0.001 ns 
Apoptosis Markers Cytokeratin 19 (Cyfra 21–1) pg/ml 20.16 122.8 7579.0 < 0.01 ns 
 sFas ng/ml 8.88 9.27 13.62 < 0.001 < 0.05 
 sFasL pg/ml 50.0 47.5 40.3 < 0.001 < 0.05 
Invasion/Adhesion Mediators MMP-2 ng/ml 178.4 192.7 198.3 < 0.01 ns 
 MMP-3 ng/ml 11.8 22.2 49.8 < 0.001 ns 
 MMP-9 ng/ml 116.9 164.1 164.7 < 0.01 ns 
 ICAM-1 ng/ml 177.1 535.4 1011.3 < 0.001 < 0.05 
 TIMP-1 ng/ml 116.2 184.0 331.5 < 0.001 < 0.001 
 TIMP-2 ng/ml 102.5 101.9 113.8 < 0.01 < 0.01 
 TIMP-3 ng/ml 13.8 11.1 21.6 < 0.001 < 0.01 
 TIMP-4 ng/ml 1.54 1.75 2.13 < 0.001 < 0.001 
Cytokines/Chemokines/Receptors IL-2R pg/ml 561.8 969.2 1607.1 < 0.001 < 0.05 
 IL-8 pg/ml 10.5 16.7 37.7 < 0.001 ns 
 IL-6 pg/ml 17.5 18.5 21.5 < 0.01 ns 
 IP-10 pg/ml 93.8 94.7 115.5 < 0.001 < 0.001 
 MPO ng/ml 72.2 120.4 125.4 < 0.001 ns 
 TNF-α pg/ml 6.14 7.60 7.14 < 0.05 ns 
 TNF-RI ng/ml 6.09 8.10 10.60 < 0.001 < 0.01 
 TNF-RII ng/ml 4.26 5.31 9.02 < 0.001 ns 
Growth/Angiogenesis Factors Angiostatin μg/ml 6.60 6.00 6.04 < 0.01 ns 
 EGFR ng/ml 10.4 10.0 8.9 < 0.001 < 0.05 
 Endostatin ng/ml 146.5 169.3 195.4 < 0.001 < 0.05 
 ErbB2 ng/ml 1.10 1.21 1.65 < 0.001 ns 
 IGFBP-1 ng/ml 16.04 39.06 50.73 < 0.001 ns 
 Thrombospondin μg/ml 16.05 16.73 14.07 < 0.01 ns 
Adipokines Adiponectin μg/ml 26.3 24.3 33.3 < 0.001 < 0.01 
 Leptin ng/ml 5.47 3.80 3.10 < 0.05 ns 
Apolipoproteins ApoAI μg/ml 226.9 211.8 103.6 < 0.001 < 0.001 
 ApoAII ng/ml 3.23 2.60 2.10 < 0.001 < 0.01 
 ApoCIII ng/ml 231.3 223.3 195.6 < 0.001 ns 
 ApoE ng/ml 63.0 66.5 95.9 < 0.001 < 0.05 
Other OC ng/ml 5.62 7.37 4.87 < 0.001 < 0.001 
 OPG pg/ml 441.7 719.5 824.1 < 0.001 < 0.01 
 OPN ng/ml 1.48 6.52 16.15 < 0.001 < 0.001 
 CRP mg/ml 16.45 67.87 120.50 < 0.001 < 0.001 
 GLP-1 (active) pg/ml 18.59 17.91 24.07 < 0.05 ns 
 HE4 ng/ml 3.63 2.07 5.11 < 0.001 < 0.05 
 SAA μg/ml 100.2 1012.4 2769.2 < 0.001 < 0.001 
 Transglutaminase II ng/ml 24.67 48.18 62.52 < 0.05 ns 
HealthyBenignPDACPDAC vs. Healthy1PDAC vs. Benign1
ClassBiomarkerUnitsMeanMeanMeanp-valuep-value
Tumor Markers CA 19–9 U/ml 5.42 2.61 56.04 < 0.001 < 0.001 
 CA-125 U/ml 27.4 26.9 1073.1 < 0.001 < 0.001 
Hormones GH ng/ml 1.93 1.17 2.40 < 0.001 < 0.001 
 Prolactin ng/ml 6.18 23.66 14.10 < 0.001 < 0.001 
 PTH pg/ml 27.2 43.5 38.6 < 0.001 ns 
Apoptosis Markers Cytokeratin 19 (Cyfra 21–1) pg/ml 20.16 122.8 7579.0 < 0.01 ns 
 sFas ng/ml 8.88 9.27 13.62 < 0.001 < 0.05 
 sFasL pg/ml 50.0 47.5 40.3 < 0.001 < 0.05 
Invasion/Adhesion Mediators MMP-2 ng/ml 178.4 192.7 198.3 < 0.01 ns 
 MMP-3 ng/ml 11.8 22.2 49.8 < 0.001 ns 
 MMP-9 ng/ml 116.9 164.1 164.7 < 0.01 ns 
 ICAM-1 ng/ml 177.1 535.4 1011.3 < 0.001 < 0.05 
 TIMP-1 ng/ml 116.2 184.0 331.5 < 0.001 < 0.001 
 TIMP-2 ng/ml 102.5 101.9 113.8 < 0.01 < 0.01 
 TIMP-3 ng/ml 13.8 11.1 21.6 < 0.001 < 0.01 
 TIMP-4 ng/ml 1.54 1.75 2.13 < 0.001 < 0.001 
Cytokines/Chemokines/Receptors IL-2R pg/ml 561.8 969.2 1607.1 < 0.001 < 0.05 
 IL-8 pg/ml 10.5 16.7 37.7 < 0.001 ns 
 IL-6 pg/ml 17.5 18.5 21.5 < 0.01 ns 
 IP-10 pg/ml 93.8 94.7 115.5 < 0.001 < 0.001 
 MPO ng/ml 72.2 120.4 125.4 < 0.001 ns 
 TNF-α pg/ml 6.14 7.60 7.14 < 0.05 ns 
 TNF-RI ng/ml 6.09 8.10 10.60 < 0.001 < 0.01 
 TNF-RII ng/ml 4.26 5.31 9.02 < 0.001 ns 
Growth/Angiogenesis Factors Angiostatin μg/ml 6.60 6.00 6.04 < 0.01 ns 
 EGFR ng/ml 10.4 10.0 8.9 < 0.001 < 0.05 
 Endostatin ng/ml 146.5 169.3 195.4 < 0.001 < 0.05 
 ErbB2 ng/ml 1.10 1.21 1.65 < 0.001 ns 
 IGFBP-1 ng/ml 16.04 39.06 50.73 < 0.001 ns 
 Thrombospondin μg/ml 16.05 16.73 14.07 < 0.01 ns 
Adipokines Adiponectin μg/ml 26.3 24.3 33.3 < 0.001 < 0.01 
 Leptin ng/ml 5.47 3.80 3.10 < 0.05 ns 
Apolipoproteins ApoAI μg/ml 226.9 211.8 103.6 < 0.001 < 0.001 
 ApoAII ng/ml 3.23 2.60 2.10 < 0.001 < 0.01 
 ApoCIII ng/ml 231.3 223.3 195.6 < 0.001 ns 
 ApoE ng/ml 63.0 66.5 95.9 < 0.001 < 0.05 
Other OC ng/ml 5.62 7.37 4.87 < 0.001 < 0.001 
 OPG pg/ml 441.7 719.5 824.1 < 0.001 < 0.01 
 OPN ng/ml 1.48 6.52 16.15 < 0.001 < 0.001 
 CRP mg/ml 16.45 67.87 120.50 < 0.001 < 0.001 
 GLP-1 (active) pg/ml 18.59 17.91 24.07 < 0.05 ns 
 HE4 ng/ml 3.63 2.07 5.11 < 0.001 < 0.05 
 SAA μg/ml 100.2 1012.4 2769.2 < 0.001 < 0.001 
 Transglutaminase II ng/ml 24.67 48.18 62.52 < 0.05 ns 

1p-value representing significance determined by 1-way ANOVA with Tukey's multiple comparison test; ns – not significant.

We noted a discrepancy in the age distribution between the Healthy, Benign, and PDAC subjects with the Healthy and Benign groups tending to be younger (Table 1). To further assess this discrepancy, we conducted an analysis of age-related biomarker levels within the Healthy and Benign subjects. None of the biomarkers differed significantly between the age-defined subgroups (cutoff of 55 years) of Benign subjects. Significant differences were identified between subgroups of healthy controls defined as ≥55 years of age and <55 years of age for endostatin, LH, MIF, and TNF-RI (Supplementary Table S1 and Fig. S1).

Evaluation of source bias

To analyze bias associated with variations in sample collection procedures at different centers, circulating levels of the 12 most informative biomarkers—CA 19–9, OPG, OPN, ICAM-1, TIMP-1–4, SAA, ApoA1, TIMP-2, and CRP—were analyzed in serum samples obtained from healthy individuals by 1-way ANOVA with Tukey's Multiple Comparison Test (Supplementary Fig. S2). Only CRP was significant in this analysis, however the magnitudes of the biases (6–7%) were small compared to the differences between cancers and control subjects (86%). All observed trends in biomarker differences observed in the univariate analysis of PDAC versus Healthy were consistently present throughout the sampling sites. We concluded from this analysis that source bias was a minimal factor in our analysis.

Multivariate analysis of biomarker levels

The PDAC, Benign, and Healthy subject groups were each split into 2 sets termed training and validation on a random basis (Table 1). All development of multimarker panels using the MMC algorithm was restricted to the training sets. Our analysis identified the highest performing 2, 3, and 4-biomarker panels trained on either the PDAC (cases) versus Healthy (controls) groups or the PDAC (cases) versus Benign (controls) groups. The 21 best 3-biomarker panels for each comparison are shown in Table 4 along with the performance of CA19–9 alone. Each of these panels outperformed all possible 2-biomarker combinations while the addition of a fourth biomarker did not improve performance (Supplementary Table S2). Seven of the panels were identified in both comparisons and these are designated by italics in Table 4. One optimal panel was chosen for each comparison which demonstrated the highest SN at 90% SP. The performance of each of these panels in the training set was compared to CA 19–9 alone by ROC analysis (Fig. 1A, B). In the comparison of PDAC versus Healthy, the selected combination of CA19–9, ICAM-1, and OPG demonstrated improved performance over CA 19–9 alone in terms of areas under curve (AUC, 0.93 vs. 0.83) as well as SN (87.5% vs. 57.2%) and correct classification of stage I/II PDAC (83.4% vs. 44.7%) at 90% SP. In the comparison of PDAC versus Benign, the selected combination of CA 19–9, CEA, and TIMP-1 demonstrated an improvement over CA 19–9 alone in terms of AUC (0.86 vs. 0.82) and SN (75.8% vs. 56.4%) at 90% SP.

Figure 1.

Receiver operator characteristic (ROC) curves for diagnosis of PDAC versus Healthy controls and Benign cases. A and B, the diagnostic performance of the CA 19–9, ICAM-1, OPG combination (solid line) and CA 19–9 alone (dotted line) for the discrimination of PDAC versus Healthy in the training set (A) and in the independent validation set (B). C and D, the diagnostic performance of the CA 19–9, CEA, TIMP-1 combination (solid line), and CA 19–9 alone (dotted line) for the discrimination of PDAC versus Benign disease in the training set (A) and in an independent validation set (B). Areas under curve (AUC) with 95% CI are presented.

Figure 1.

Receiver operator characteristic (ROC) curves for diagnosis of PDAC versus Healthy controls and Benign cases. A and B, the diagnostic performance of the CA 19–9, ICAM-1, OPG combination (solid line) and CA 19–9 alone (dotted line) for the discrimination of PDAC versus Healthy in the training set (A) and in the independent validation set (B). C and D, the diagnostic performance of the CA 19–9, CEA, TIMP-1 combination (solid line), and CA 19–9 alone (dotted line) for the discrimination of PDAC versus Benign disease in the training set (A) and in an independent validation set (B). Areas under curve (AUC) with 95% CI are presented.

Close modal
Table 4.

Top performing biomarker panels identified by MMC algorithm applied to the training set

PDAC vs. HealthyPDAC vs. Benign
%SN at 90% SP%SN at 90% SP
CA 19–9   57.2 CA 19–9   56.4 
CA 19–9 ICAM-1 OPG 88.1 CA 19–9 CEA TIMP-1 75.8 
CA 19–9 TIMP-1 OPG 87.5 CA 19–9 TIMP-3 TIMP-4 75.6 
CA 19–9 OPN TIMP-1 86.9 CA 19–9 TIMP-4 CRP 74.4 
CA 19–9 ICAM-1 TIMP-1 86.3 CA 19–9 SAA ApoAI 73.1 
CA 19–9 CA-125 TIMP-1 85 CA 19–9 SAA CRP 72.5 
CA 19–9 ICAM-1 OPN 85 CA 19–9 Tg II TIMP-4 72.5 
CA 19–9 SAA TIMP-1 84.4 CA 19–9 ApoAI TIMP-3 71.9 
CA 19–9 CEA TIMP-1 84.4 CA 19–9 TIMP-3 CRP 71.9 
CA 19–9 TIMP-1 CRP 83.8 CA 19–9 SAA CA-125 71.3 
CA 19–9 TIMP-1 TIMP-2 83.8 CA 19–9 CEA TIMP-4 71.3 
CA 19–9 Tg II TIMP-1 83.8 CA 19–9 ICAM-1 SAA 71.3 
CA 19–9 OPG CRP 83.1 CA 19–9 TIMP-1 TIMP-4 71.3 
CA 19–9 TIMP-1 TIMP-4 82.5 CA 19–9 CA-125 TIMP-1 70.6 
CA 19–9 ApoAI TIMP-1 82.5 CA 19–9 CEA SAA 70.6 
CA 19–9 OPN CRP 82.5 CA 19–9 SAA TIMP-1 70.6 
CA 19–9 TIMP-1 TIMP-3 81.9 CA 19–9 OPG CRP 70.6 
CA 19–9 ICAM-1 SAA 81.3 CA 19–9 SAA TIMP-4 70.6 
CA 19–9 ICAM-1 Apo AI 80.6 CA 19–9 ICAM-1 TIMP-1 70 
CA 19–9 ICAM-1 CRP 80 CA 19–9 TIMP-2 TIMP-4 70 
CA 19–9 ICAM-1 Tg II 78.8 CA 19–9 SAA TIMP-3 70 
CA 19–9 ICAM-1 TIMP-4 77.5 CA 19–9 ApoAI TIMP-4 69.4 
PDAC vs. HealthyPDAC vs. Benign
%SN at 90% SP%SN at 90% SP
CA 19–9   57.2 CA 19–9   56.4 
CA 19–9 ICAM-1 OPG 88.1 CA 19–9 CEA TIMP-1 75.8 
CA 19–9 TIMP-1 OPG 87.5 CA 19–9 TIMP-3 TIMP-4 75.6 
CA 19–9 OPN TIMP-1 86.9 CA 19–9 TIMP-4 CRP 74.4 
CA 19–9 ICAM-1 TIMP-1 86.3 CA 19–9 SAA ApoAI 73.1 
CA 19–9 CA-125 TIMP-1 85 CA 19–9 SAA CRP 72.5 
CA 19–9 ICAM-1 OPN 85 CA 19–9 Tg II TIMP-4 72.5 
CA 19–9 SAA TIMP-1 84.4 CA 19–9 ApoAI TIMP-3 71.9 
CA 19–9 CEA TIMP-1 84.4 CA 19–9 TIMP-3 CRP 71.9 
CA 19–9 TIMP-1 CRP 83.8 CA 19–9 SAA CA-125 71.3 
CA 19–9 TIMP-1 TIMP-2 83.8 CA 19–9 CEA TIMP-4 71.3 
CA 19–9 Tg II TIMP-1 83.8 CA 19–9 ICAM-1 SAA 71.3 
CA 19–9 OPG CRP 83.1 CA 19–9 TIMP-1 TIMP-4 71.3 
CA 19–9 TIMP-1 TIMP-4 82.5 CA 19–9 CA-125 TIMP-1 70.6 
CA 19–9 ApoAI TIMP-1 82.5 CA 19–9 CEA SAA 70.6 
CA 19–9 OPN CRP 82.5 CA 19–9 SAA TIMP-1 70.6 
CA 19–9 TIMP-1 TIMP-3 81.9 CA 19–9 OPG CRP 70.6 
CA 19–9 ICAM-1 SAA 81.3 CA 19–9 SAA TIMP-4 70.6 
CA 19–9 ICAM-1 Apo AI 80.6 CA 19–9 ICAM-1 TIMP-1 70 
CA 19–9 ICAM-1 CRP 80 CA 19–9 TIMP-2 TIMP-4 70 
CA 19–9 ICAM-1 Tg II 78.8 CA 19–9 SAA TIMP-3 70 
CA 19–9 ICAM-1 TIMP-4 77.5 CA 19–9 ApoAI TIMP-4 69.4 

Note: Panels listed in italics identified in both comparisons, panels listed in bold selected for independent validation.

Next, the 2 optimal panels were validated in an independent blinded validation set (Table 1) containing Healthy, Cancer, and Benign samples (Fig. 1C, D). Each validation sample was diagnosed as cancer or control (noncancer) using the scoring function (SF) assigned to each biomarker panel following the MMC training analysis, and each diagnosis was compared with the clinical diagnosis after unblinding. The diagnostic performance of each panel in the validation set was compared to CA 19–9 alone by ROC analysis (Fig. 1C, D). In the independent validation set, the combination of CA19–9, ICAM-1, and OPG offered SN = 78% at 94.1% SP (AUC = 0.91) for the discrimination of PDAC versus Healthy providing the correct classification of 70.7% of stage I/II cancers compared to SN = 51.4% at 90% SP (AUC = 0.82) and the correct classification of 40% of stage I/II cancers for CA 19–9 alone. In the comparison of PDAC versus Benign, the combination of CA 19–9, CEA, and TIMP-1 demonstrated an improvement over CA 19–9 alone in terms of AUC (0.83 vs. 0.78) and SN/SP (71.2%/88.6% vs. 52.1%/90.2%).

Cancer selectivity of multimarker panels for pancreatic cancer

We utilized the MMC algorithm to evaluate the cancer-specific selectivity of the biomarker panels identified in the multivariate analysis of PDAC versus Healthy (Table 4). Each panel was applied to several groups of sera obtained from patients diagnosed with colon (n = 33), lung (n = 62), and breast (n = 108) cancer and the percentage of each group diagnosed as non-PDAC was determined using the MMC algorithm (Table 5). In this analysis, the validated panel of CA 19–9, ICAM-1, and OPG classified 97% of colon cancer sera, 97% of lung cancer sera, and 100% of breast cancer sera as non-PDAC. Several additional panels performed equally well in this analysis.

Table 5.

Cancer selectivity of multimarker panels for PDAC (%)

ColonLungBreast
Paneln = 33n = 62n = 108
CA 19–9 ICAM-1 OPG 97.0 97.0 100 
CA 19–9 TIMP-1 OPG 97.0 97.0 100 
CA 19–9 OPN TIMP-1 97.0 97.0 100 
CA 19–9 ICAM-1 TIMP-1 97.0 97.0 100 
ColonLungBreast
Paneln = 33n = 62n = 108
CA 19–9 ICAM-1 OPG 97.0 97.0 100 
CA 19–9 TIMP-1 OPG 97.0 97.0 100 
CA 19–9 OPN TIMP-1 97.0 97.0 100 
CA 19–9 ICAM-1 TIMP-1 97.0 97.0 100 

Note: Values list are % classified as non-PDAC.

In the present study, we identify several 3-biomarker panels offering high SN/SP and significant improvement over CA 19–9 alone for the discrimination of PDAC from healthy controls and benign subjects. To the best of our knowledge, these results represent the most advanced validated biomarker discovery effort aimed at the development of improved screening methodologies for the detection of pancreatic cancer. Since the incidence of pancreatic cancer in the general population (∼1% lifetime risk) is too low to warrant screening, strategies are being investigated to define patient cohorts in which the positive predictive value for early-stage pancreatic cancers and advanced precursor lesions is high enough to justify more costly and invasive testing (24). Therefore, our study intended to develop a panel of biomarkers that can be used to screen populations at an increased risk for the development of pancreatic cancer.

At present, high-risk populations are not readily available for the purpose of pancreatic cancer screening. Pancreatic cancer-prone families are the most widely accepted population of this type to date, but only account for 5–10% of all pancreatic adenocarcinomas. Furthermore, the genetic factors underlying this predisposition are uncharacterized in the majority of these families. As it would take many years to follow a cohort of these patients to determine which unaffected individuals in these familial pancreatic cancer kindreds would develop PDAC, it is not practical to use this population for biomarker discovery. However, many centers are actively investigating risk-stratification models that will take into account genetic factors along with epidemiological information, such as smoking and alcohol use, or clinical history including diabetic status or history of gastric ulcer. This should provide appropriate populations with a high enough incidence of pancreatic adenocarcinoma to warrant surveillance.

The multimarker panels identified in our analysis were highly discriminatory for PDAC versus Healthy subjects demonstrating sensitivities ranging from 77–88% with 90% SP and a lower discriminatory power of 70–76% with 90% SP for PDAC versus Benign. The optimal panel identified in the comparison of PDAC versus Healthy comprised of CA 19–9, ICAM-1, and OPG demonstrated significant improvement over CA 19–9 by ROC analysis, whereas the best panel for discrimination of Benign from PDAC did not offer a substantial improvement in terms of SN over CA 19–9 at a level of SP above 80% (Fig. 1). The AUC values we observed for CA 19–9 were within the reported range (0.72–0.84) of several recent studies (25, 26). The panel of CA 19–9, ICAM-1, and OPG also demonstrated a high level of cancer selectivity when applied to colon, lung, and breast cancers. The relative performance of these biomarker panels coupled with the small number of analytes required to achieve that performance make them attractive candidates in the development of early detection strategies.

In the course of our evaluation, we have identified number of proteins that differ significantly in the sera of patients diagnosed with PDAC and healthy controls, and many of these were also significant in comparison to the Benign group with considerable reproducibility in biomarker trends across the multiple sampling sites. To the best of our knowledge, 19 of the observed associations between serum biomarker levels and PDAC have not been described previously: GH, PRL, PTH, sFas, sFasL, MMP-2, TIMP 2–4, MPO, EGFR, ApoAI-II, ApoCIII, OC, GLP-1, HE4, and TGII. Although recent reports have described significant associations between serum levels of OPG (27) and pancreatic cancer, to the best of our knowledge, our report is the first to characterize its diagnostic capacity. The significant biomarkers include representatives from a diverse set of biological families, particularly proteins with functions in such critical aspects of tumor development as growth, angiogenesis, metastasis, inflammation, etc., and encompass an array of factors likely to originate from the developing tumor, the tumor microenvironment and components of the systemic host response to the malignancy.

In the current study, PDAC was associated with circulating alterations of a number of known mediators of inflammatory processes and acute phase reactants. The relationship between tumorigenesis and inflammation has become a central theme in anticancer research and is the focus of increasing interest within the setting of pancreatic cancer. Malignant transformation can be closely associated with chronic infection and inflammation, while elevated levels of proinflammatory proteins could play a role as tumor promoters (28). It has also been clear for some time now that a number of proinflammatory gene products including acute-phase reactants can also be produced by components or tumor microenvironment and tumor cells themselves to further mediate tumor growth (29). For example, elevated circulating levels of SAA could reflect not only hepatic synthesis as part of the acute-phase response, but also increased release of these proteins by cancer cells, and a possible role for SAA in tumor growth, metastasis, and neovascularization has been investigated (30, 31). Therefore, these proinflammatory proteins could potentially be utilized as cancer biomarkers. In fact, it has been demonstrated that serum SAA levels are elevated in a broad spectrum of neoplastic diseases (5, 32) suggesting that it could act at least as a nonspecific tumor marker. The combination of CRP, another acute-phase reactant, and sIFNα/βR was demonstrated to diagnose gastrointestinal and hepatobiliary-pancreatic cancer with an SN/SP of 94.6/88 (33). The production of proinflammatory cytokines by tumor and stromal cells, regulated by NF-kB, has been observed to mediate tumorigenic effects in the pancreas as part of a characteristic desmosplastic reaction (34). We observed altered levels of the NF-kB responsive cytokines TNFα and IL-8 that have been specifically shown to inhibit apoptosis and increase invasiveness of pancreatic cancer cells, respectively (35, 36). Also notably altered was the ROS-mediating enzyme, MPO. MPO has demonstrated involvement in the generation of DNA strand breaks, sister chromatid exchanges, mutations, and the formation of DNA adducts (37), and is associated with smoking, a known risk factor of pancreatic cancer (38).

Our findings also support several hypotheses regarding the role of specialized pathways related to obesity, bone homeostasis, and tissue remodeling in pancreatic cancer. These findings include alterations in number of apolipoproteins and adipokines, modulators of lipid and insulin metabolism. ApoA1 may represent an emerging biomarker of malignancy as circulating levels of ApoA1 have recently been linked to several cancer types including pancreatic (39). The adipokines, adiponectin, and leptin are currently under intense scrutiny stemming from links to obesity, inflammation, and malignancy (40). We observed altered serum levels of OPN, a biomarker previously associated with pancreatic cancer (11), and several other bone-related factors, OC and OPG. OPG is a secreted member of the tumor necrosis factor receptor superfamily, and altered serum levels of OPG have been associated with several malignancies including colorectal cancer, pancreatic cancer, liver metastases, multiple myeloma, Hodgkin's disease, and non-Hodgkin's lymphoma (27). We report dysregulation of several metastasis-related proteins in PDAC. The MMPs and their natural inhibitors, TIMPs, have well-described tumorigenic roles and our observations regarding MMP-2 and TIMPs 1–4 in pancreatic cancer patients are consistent with previous findings (41). The cellular adhesion mediator ICAM-1 has been previously linked to the development of PDAC (42) and was utilized as a part of multimarker panel for the classification of PDAC from healthy controls (19). The expression of ICAM-1 is associated with PDAC cell sensitivity to T–cell based immunotherapy in vitro (43), and prognostic significance of preoperative sICAM-1 levels in several cancers has been demonstrated (44)–(46). Correlation of TG II with metastatic properties of several cancers has been suggested (47, 48), and its role as a tumor marker in the development and progression of pancreatic cancer has been well documented (49, 50). We also observed the dysregulation of a number of growth factors, receptors, and mediators of angiogenesis including EGFR, ErbB2, angiostatin, endostatin, thrombospondin, and IGFBP-1.

In summary, an extensive analysis of circulating biomarkers in patients diagnosed with PDAC and benign pancreatic conditions resulted in a robust profile of alterations in biomarkers in PDAC patients, offering improved insights into the network of factors involved in the process of pancreatic tumorigenesis. A number of the alterations we identify are novel with regard to associations with PDAC as is the expanded use of several previously characterized serum biomarkers for diagnostic purposes. Although the ideal biomarker test would recognize premalignant conditions, to the best of our knowledge, such requisite retrospective samples are presently not available for biomarker discovery. Therefore, the current study was guided by the assumption that a subset of those biomarkers demonstrating differential expression in developed cancer would also be altered in premalignant conditions. Thus, our analysis of biomarker alterations present in serum at the time of diagnosis should pave the way for the subsequent identification of biomarkers of preneoplastic disease. Additionally, biomarker panels discriminating PDAC from benign disease may offer a high level of clinical utility in combination with conventional imaging modalities to provide a diagnostic role as opposed to the aforementioned screening application. This study presents proof-of-principle of the utility of a multiplexed evaluation of circulating biomarkers for the identification and validation of multimarker panels with high classification power for PDAC. Further studies employing this type of approach may result in the identification of more robust panels for both screening and diagnosis of PDAC.

These findings represent an evaluation of biomarkers potentially related to pancreatic cancer and do not promote the use of any individual biomarker considered herein for diagnostic purposes. Moreover, while the nature of our investigation does not permit the identification of specific mechanistic links between any particular biomarker and the development of pancreatic cancer, our results do provide a sound basis for subsequent targeted analyses of pancreatic cancer biomarkers.

No potential conflicts of interest were disclosed.

This work was supported by NIH grants: U01CA117452 (EDRN), RO1 CA098642, R01 CA108990, P50 CA083639, CA086381, CA105009, UPCI Hillman Fellows Award, and The Frieda G. and Saul F. Shapira BRCA Cancer Research Program Award (A.E. Lokshin).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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