Sage Journals: Discover world-class research

Abstract

Although pancreatic cancer is only the twelfth most common type of cancer in the world, it features a very unfavorable prognosis. The mortality rate almost equals the incidence rate, corroborating the very poor prognosis of pancreatic cancer. The 5-year survival rate for all stages of pancreatic ductal adenocarcinoma is only 7%. Surgical resection represents the only potentially curative treatment option for pancreatic ductal adenocarcinoma patients but is often not feasible due to the advanced stage of the disease upon diagnosis. For advanced disease, palliative chemotherapy is the treatment of choice although the regimens available to date are untargeted and have extensive side-effect profiles, making them unsuitable for patients with a low performance status. For this reason, early detection of pancreatic cancer is essential in order to provide patients with an optimal therapeutic approach. Up to the present day, carbohydrate antigen 19-9 is the only diagnostic marker approved by the U.S. Food and Drug Administration but its diagnostic potential is limited due to its restricted sensitivity and specificity, supporting the urgent need for novel biomarkers. In addition, prognostic and treatment-predictive biomarkers might provide essential information regarding personalized treatment decisions for individual patients. In this article, we aim to review current and future diagnostic, prognostic, and treatment-predictive biomarkers for pancreatic cancer.

Keywords

MicroRNAs macrophage inhibitory cytokine 1 PAM4 gemcitabine FOLFIRINOX human equilibrative nucleoside transporter 1

Introduction

Pancreatic cancer is the twelfth most common cancer in the world with a global incidence of 2.4 to 8.6 cases per 100,000 people per year. The incidence is highest in the developed world and among men.¹ About 85% of all pancreatic cancers are pancreatic ductal adenocarcinoma (PDAC).² The mortality rate of 2.3 to 8.3 deaths per 100,000 people per year almost equals the incidence rate, emphasizing the very poor prognosis of the disease.¹ In recent years, the 5-year survival rate for all stages of PDAC only marginally improved and reaches only 7% for all stages.³ The only potentially curative treatment option is surgical resection, partly combined with neoadjuvant and/or adjuvant chemoradiotherapy, but only 15%–20% of all patients are eligible for this treatment option due to advanced stage of disease at the time of diagnosis.² For locally advanced and metastatic pancreatic cancer, systemic chemotherapy is the treatment of choice, whereas the importance of radiotherapy has been controversially discussed.² Nevertheless, systemic chemotherapy for PDAC is non-targeted and features a wide range of side-effects.⁴

Due to its unfavorable prognosis and the lack of effective treatment options at the later stages of disease, early diagnosis of pancreatic cancer is essential to optimize possible treatment options and to improve the outcome of patients suffering from PDAC. Furthermore, treatment-predictive and prognostic biomarkers represent a valuable tool to divide pancreatic cancer patients into different subgroups in order to provide them with an optimal personalized therapeutic approach according to their likelihood to benefit from a specific surgical, chemotherapeutic, or conservative treatment. Up to the present day, carbohydrate antigen (CA) 19-9 is the only available biomarker for PDAC approved by the U.S. Food and Drug Administration (FDA), but it features a poor diagnostic sensitivity and specificity.^5,6

For this review on currently available and potential future biomarkers for the diagnosis, prognosis, and treatment prediction of pancreatic adenocarcinoma, studies indexed in MEDLINE between 1990 and 2016 (April) were reviewed. The terms “pancreatic cancer,” “PDAC,” “biomarker,” “diagnostic,” “prognostic,” “therapeutic,” and combinations of these terms were used.

Diagnostic biomarkers

Due to its unfavorable prognosis, particularly at late stage of disease progression, early detection of pancreatic cancer is essential to provide patients with a curative therapeutic approach. To date, no ideal diagnostic biomarker for early detection of PDAC exists. The following section reviews the advantages and limitations of present and future diagnostic biomarkers for pancreatic cancer. Table 1 gives a brief summary on the discussed markers and shows their sensitivity and specificity if applicable.

Table 1.

Summary of biomarkers for the diagnosis of pancreatic adenocarcinoma.

Biomarker	Expression pattern	Sensitivity(%)	Specificity (%)	Sample type	Reference
CA19-9	↑	75.5	77.6	Serum	Zhang et al.⁶
		81	81	Serum	Su et al.⁷
		78	77	Serum	Gu et al.⁸
CA50	↑	46.3	90.9	Serum	Liao et al.⁹
CA50	↑	71.1	93.5	Serum	Wu et al.¹⁰
CA72-4	↑	63.4	75.2	Serum	Jiang et al.¹¹
CA125	↑	66.8	83.3	Serum	Cwik et al.¹²
CA242	↑	67.8	83	Serum	Zhang et al.⁶
CA242	↑	59.4	72.7	Serum	Liao et al.⁹
CEA	↑	39.5	81.3	Serum	Zhang et al.⁶
MIC-1	↑	79.0	86.0	Serum	Chen et al.¹³
PAM4	↑	76.0	85.0	Serum	Gold et al.¹⁴
S100A6	↑	88.2	90.0	EUS-FNA cytology	Zihao et al.¹⁵
OPN	↑	80.0	97.0	Serum	Koopmann et al.¹⁶
OPN, TIMP-1, and CA19-9	↑, ↑, ↑	87.0	91.0	Serum	Poruk et al.¹⁷
Osteoprotegrin, ICAM-1, and CA19-9	↑, ↑, ↑	88.0	90.0	Serum	Brand et al.¹⁸
miR-21	↑	n/a	n/a	Serum	Liu et al.¹⁹
	↑	90.0	66.7	Stool samples	Yang et al.²⁰
	↑	n/a	n/a	Pancreatic juice and tissue	Sadakari et al.²¹
	↑	n/a	n/a	Pancreatic tissue	Dillhoff et al.²²
miR-155	↑	n/a	n/a	Plasma	Wang et al.²³
	↑	76.7	73.3	Stool samples	Yang et al.²⁰
	↑	n/a	n/a	Pancreatic juice and tissue	Sadakari et al.²¹
miR-196a	↑	n/a	n/a	Plasma	Wang et al.²³
miR-210	↑	n/a	n/a	Plasma	Ho et al.²⁴
miR-216	↓	n/a	n/a	Pancreatic tissue	Szafranska et al.²⁵
miR-216, miR-21, and miR-155	↓, ↑, ↑	83.3	83.3	Stool samples	Yang et al.²⁰
miR-217	↓	n/a	n/a	Pancreatic tissue	Szafranska et al.²⁵
miR-143 and miR-30e	↑ and ↑	83.3	96.2	Urine	Debernardi et al.²⁶
miR-205, miR-210, miR-492, and miR-1427	all ↑	88.0	87.0	Pancreatic juice	Wang et al.²⁷

CA: carbohydrate antigen; CEA: carcinoembryonic antigen; MIC-1: macrophage inhibitory cytokine 1; PAM4: monoclonal antibody reactive to MUC5AC (clivatuzumab); EUS-FNA: endoscopic ultrasound-guided fine-needle aspiration; OPN: osteopontin; TIMP-1: tissue inhibitor of metalloproteinase 1; ICAM-1: intercellular adhesion molecule 1; miR: microRNA; ↑: upregulated; ↓: downregulated.

CA19-9, carcinoembryonic antigen, and other carbohydrate antigens

CA19-9 is the most extensively studied biomarker for PDAC and the only one approved by the FDA.⁵ Nevertheless, in terms of diagnostics, it can be considered as a rather poor biomarker for pancreatic cancer as it is also elevated in other medical conditions, such as acute cholangitis, liver cirrhosis, pancreatitis, and obstructive jaundice.^28–31 The median sensitivity and specificity of CA19-9 for the diagnosis of PDAC are only 75.5% and 77.6%, respectively,⁶ and with a low positive predictive value of 0.5%–0.9%, which does not qualify it as a useful screening parameter.³¹ Furthermore, patients with Lewis-null blood type do not produce CA19-9. Given the fact that approximately 5%–10% of Caucasians are of this phenotype, the diagnostic power of CA19-9 is even more inferior.^5,32 The diagnostic potential of CA242 and carcinoembryonic antigen (CEA) for early detection of PDAC is also limited. Zhang et al.⁶ just recently described a median sensitivity/specificity of 67.8%/83% for CA242 and 39.5%/81.3% for CEA, respectively. Similar negative results were found for CA50, CA195, CA72-4, and CA125.³³ Although the combination of CA19-9 and CA242 resulted in a higher sensitivity of 89% (with no impact on the specificity), none of these conventional biomarkers has proven to be a specific and reliable tool for early detection of PDAC.⁶

MicroRNAs and other non-coding RNAs

MicroRNAs (miRNAs) are a group of small non-coding RNAs consisting of 18–25 nucleotides that are capable of regulating post-transcriptional modifications of multiple genes.^34,35 In recent years, the usage of miRNA as a detection marker for different types of cancer has increasingly gained importance.^36,37 As a potential biomarker for pancreatic cancer, miRNAs have been most intensively investigated in pancreatic tumor tissue, blood samples, pancreatic juice, stool, and urine.³⁸ Among others, the most promising findings have been made for miR-21, miR-155, miR-196a, and miR-210, which were shown to be upregulated in pancreatic tissue,^{22,25,39–41} serum samples,^{19,23,24,42,43} fecal specimen,^20,44 and pancreatic juice^21,45,46 from PDAC patients. In contrast, miR-216 and miR-217 seemed to be consistently downregulated in pancreas tissue, stool samples, and pancreatic juice.^{20,25,45–47} Since the acquisition of pancreatic tissue samples and pancreatic juice requires invasive diagnostic approaches, these methods are not suitable for a diagnostic screening test. Thus, non-invasive approaches like serum and stool samples should be preferred as a diagnostic tool. Just recently, urinary miRNA-concentrations as a further non-invasive marker for early detection of pancreatic cancer were investigated. The group showed that urine levels of miR-143, miR-223, and miR-30e were significantly increased in Stage I PDAC when compared with healthy individuals, showing a sensitivity of 83.3% and a specificity of 96.2% for the combinational use of miR-143 and miR-30e.²⁶ Comparable results were shown for fecal specimens by Yang et al.²⁰ who described a sensitivity and specificity of 83.3% each by combining miR-21, miR-155, and miR-216 as a potential screening tool for detection of PDAC.

Particular attention should be paid to miR-21, miR-155, and miR-196a since different studies have shown an upregulation of these miRNAs in tissue samples of intraductal papillary mucinous neoplasm (IPMN) and pancreatic intraepithelial neoplasia (PanIN), suggesting their use as promising biomarkers especially for early detection of precursor lesions with malignant potential.^40,48–52 So far, no consistent data on the existence of these miRNAs in serum or stool samples of patients with precursor lesions are available, indicating the need for future studies to evaluate the role of a non-invasive way to detect miRNAs as early detection markers.

Besides microRNAs, other non-coding RNAs have been identified that might play a potential role as a detection marker for PDAC. Long non-coding RNAs (lncRNAs) are a group of RNAs that are not translated into proteins and consist of more than 200 nucleotides.⁵³ Although the biological functions of these RNAs have not been fully elucidated to date, a growing body of evidence exists, suggesting significant changes in the expression of lncRNAs (HOTAIR, MALAT-1, Gas5, MEG3, HULC, BC008363, and HSATII) in pancreatic cancer tissue and cell lines.⁵⁴ Particularly, the expression of HSATII appears to be highly upregulated in pancreatic cancer and pre-cancer tissues compared to healthy pancreatic tissue.⁵⁵ Currently, there is limited data on whether lncRNAs are as stable as miRNAs and could therefore function as a useful serum/plasma marker for PDAC.

Small ncRNAs are a group of RNAs that consist of upto 200 nucleotides, including small nucleolar RNA (snoRNA), small nuclear RNA (snRNA), and Piwi-interacting RNA (piRNA).⁵⁴ Although different small ncRNAs have been shown to be highly abundant in serum and plasma samples of various cancer types^56–59 and U2snRNA was shown to be highly expressed in PDAC patients,⁶⁰ no specific small ncRNA for pancreatic cancer has been identified so far.⁵⁴

Macrophage inhibitory cytokine 1 and PAM4

Macrophage inhibitory cytokine 1 (MIC-1) is a distant member of the transforming growth factor beta (TGF-β) superfamily that is overexpressed in different cancer entities.^61,62 Koopmann and colleagues were the first to analyze MIC-1 serum levels from 50 patients with resectable pancreatic adenocarcinoma. The receiver operating characteristic (ROC) curve analysis showed that MIC-1 was significantly better than CA19-9 in differentiating patients with pancreatic cancer from healthy controls (area under the curve (AUC) of 0.99 and 0.78, respectively; p = 0.003), but not in distinguishing pancreatic cancer from chronic pancreatitis (AUC of 0.81 and 0.74, respectively; p = 0.63).⁶³ In a recently published meta-analysis including a total of 1235 pancreatic cancer patients, Chen et al.¹³ described a pooled sensitivity of 79% with a specificity of 86% for serum measurements of MIC-1. Similar results have been observed by other groups who suggested that the lack of diagnostic specificity of MIC-1 might be enhanced using a combination of MIC-1 and CA19-9.^64–66 Furthermore, MIC-1 had a sensitivity of 63.1% in detecting patients with CA19-9-negative PDAC.⁶⁵

PAM4, a monoclonal antibody known as clivatuzumab, is reactive to Mucin 5AC (MUC5AC), a secretory mucin, which is expressed de novo in early pancreatic neoplasia and retained throughout disease progression.^67–69 In a large study analyzing sera from 596 patients with confirmed PDAC, other types of cancer, benign diseases of the pancreas and healthy controls, Gold et al.¹⁴ demonstrated an overall sensitivity of 76% for PAM4 detection of PDAC with a high specificity of 85% when compared to benign pancreatic disease with a positive likelihood ratio of 4.93. In comparison to CA19-9, showing a sensitivity of 77% with a specificity of only 68% and a positive likelihood ratio of 2.85 (p = 0.026 compared to PAM4), PAM4 seems to be a biomarker of equal if not better validity for the detection of pancreatic carcinoma in their study. Again, the combined PAM4 and CA19-9 biomarker assay demonstrated the best results with a sensitivity and specificity of 84% and 82%, respectively.¹⁴

Other diagnostic biomarkers

S100A6 is a calcium-binding protein that was shown to be overexpressed in pancreatic cancer.⁷⁰ Although its function in the pathogenesis of PDAC has not been fully elucidated to date, Ohuchida et al.⁷¹ found that S100A6 levels measured in the pancreatic juice significantly discriminated between patients with chronic pancreatitis and PDAC or IPMN (area under the ROC curve for PDAC and IPMN is 0.864 and 0.749, respectively). Similar observations were made for S100A6 expression levels in endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) specimens of PDAC patients.¹⁵ To date, no data on circulating S100A6 serum levels are available.

Osteopontin (OPN), a secretory protein with a variety of functions, including inflammatory reaction and apoptosis, has been found to be upregulated in PDAC and was linked to invasiveness as well as metastatic growth of pancreatic cancer cells.^72,73 Moreover, serum OPN levels in PDAC patients are significantly elevated compared to healthy controls.⁷⁴ In a cohort of 50 PDAC patients, Koopmann et al.¹⁶ showed that elevated serum levels of OPN had a sensitivity of 80% with a specificity of 97% for the detection of pancreatic cancer. Although OPN appeared to differentiate patients with PDAC from chronic pancreatitis patients and even distinguished early stage PDAC from healthy controls, the diagnostic accuracy of OPN was not superior to CA19-9 in a larger study published by Poruk et al.¹⁷ However, using a diagnostic algorithm that combines OPN, CA19-9, and tissue inhibitor of metalloproteinase 1 (TIMP-1), the group obtained a superior diagnostic sensitivity and specificity for PDAC of 87% and 91%, respectively, thus providing a panel that improves diagnostic accuracy compared to any of these three biomarkers individually.¹⁷

Brand et al.¹⁸ attained comparable results with a serum biomarker panel consisting of osteoprotegrin (OPG), intercellular adhesion molecule 1 (ICAM-1), and CA19-9 showing a sensitivity and specificity of 88% and 90%, respectively, for diagnosis of PDAC. Nevertheless, contradictory observations were made for serum protein levels of ICAM-1 and TIMP-1. In a study comprising 100 patients with PDAC, chronic pancreatitis, and benign jaundice due to gall stones, levels of ICAM-1 and TIMP-1 were found to be upregulated in patients with jaundice due to both PDAC and gall stones but neither protein did qualify for the detection of early PDAC.⁷⁵

Genetic and epigenetic markers

KRAS is the most frequently mutated oncogene in pancreatic cancer showing somatic mutations clustered in specific hotspot regions in more than 90% of cases.^76–78 Other mutated genes, which are most commonly found in PDAC include TP53 (tumor protein p53), SMAD4, and CDKN2A (cyclin dependent kinase inhibitor 2A).⁷⁹ A recently published meta-analysis examining the accuracy of KRAS gene mutation analysis for diagnosing PDAC revealed that a combination of KRAS mutation analysis and the cytological analysis of an EUS-FNA specimen can increase the sensitivity from 80.6% to 88.7%, compared to EUS-FNA alone, with a pooled specificity of 92%.⁸⁰ Especially for cases where cytology is inconclusive, KRAS seems to be of diagnostic value.⁸⁰ Considering rather low levels of cell-free circulating tumor DNA (ctDNA) in the patients’ serum, the application of KRAS mutation analysis as a non-invasive diagnostic biomarker seems to be challenging. However, Kinugasa et al.⁸¹ demonstrated that the overall survival of patients with KRAS mutations in ctDNA was significantly shorter compared to patients without mutations, suggesting this non-invasive method as a novel strategy for the diagnosis of PDAC as well as for predicting survival. Contrary results were published by Singh et al.,⁸² who did not find a significant correlation between serum KRAS mutation status and different clinicopathological parameters or survival. Thus, additional studies are needed to investigate the potential role of KRAS mutation analysis as a diagnostic biomarker. Mutations of TP53 and the loss of SMAD4 emerge during later stages of disease and are most commonly found in grade 3 pancreatic intraepithelial neoplasia and invasive carcinoma.⁸³ For this reason, their use as a potential biomarker for early disease detection is rather limited.

Circulating tumor cells

Although hematogenous dissemination of tumor cells is generally thought to occur at an advanced stage of cancer progression, it was shown that in a genetic mouse model of PDAC pancreatic cells were detectable in the bloodstream prior to tumor formation.⁸⁴ In line, a subsequently performed study provided evidence that circulating epithelial cells were also detectable in 73% of patients with PDAC but in none of the patients without cystic lesions or pancreatic cancer.⁸⁵

Treatment predictive markers

Disease specific and effective treatment options for pancreatic cancer are limited. Therefore, treatment-predictive markers might represent a possibility to improve treatment response and to individually tailor personalized treatments for patients suffering from PDAC. Different markers have been proposed to optimize the treatment of pancreatic cancer:

Gemcitabine markers

Gemcitabine can be referred to as the standard chemotherapy for patients with metastatic pancreatic cancer as well as for patients with resected pancreatic cancer in an adjuvant setting.⁴ The intracellular uptake of hydrophilic gemcitabine is mainly dependent on physiologic nucleoside transporter proteins.^86,87 Both known subgroups of this protein family, equilibrative nucleoside transporters (ENTs), and concentrative nucleoside transporters (CNTs) have been suggested as biomarkers for gemcitabine treatment.

Human ENT1

There is a growing body of evidence promoting human ENT1 (hENT1) as a predictive and prognostic marker for gemcitabine treatment. Using immunohistochemistry to analyze hENT1 expression in biopsies of patients treated with gemcitabine for advanced pancreatic cancer, Spratlin et al.⁸⁸ showed a superior median survival of 13 versus 4 months (p = 0.01) for patients with uniformly detectable hENT1 immunostaining. Similar results were obtained in a cohort of 198 patients with early stage pancreatic cancer receiving gemcitabine or 5-fluorouracil (5-FU) as part of their systemic adjuvant therapy. Overall and disease-free survival in the gemcitabine but not in the 5-FU arm was significantly improved in patients with high hENT1 protein expression determined by immunohistochemistry assessment on tissue microarrays.⁸⁹ These findings were recently confirmed in a group of 380 pancreatic cancer patients from the European Study Group for Pancreatic Cancer (ESPAC)-3 trial.⁹⁰

Besides immunohistochemical analyses, evaluation of hENT1 gene expression has also been demonstrated as a diagnostic tool. Using quantitative reverse transcription polymerase chain reaction (qPCR) for tumor tissues of patients with different stages of pancreatic cancer, Giovannetti et al.⁹¹ found that patients with high levels of hENT1 had a significantly longer overall survival of 25.7 months compared to 8.5 months in the lower expression subgroup. These results were corroborated by in vitro experiments, showing an increased sensitivity to gemcitabine in pancreatic cancer cell lines with high hENT1 gene expression.⁹²

Yamada et al.⁹³ analyzed pre-treatment hENT1 expression levels in EUS-FNA specimens obtained from patients with different stages of PDAC and compared them to resected specimens after gemcitabine-based chemoradiotherapy. They confirmed that hENT1 expression was an independent prognostic factor in all patients and in the subgroup that underwent resection. Interestingly, hENT1-negative patients with resection at advanced disease stage had a similar prognosis to those without resection.⁹³ Thus, analysis of pre-treatment hENT1 expression levels obtained by EUS-FNA might function as a potential biomarker in order to select a subgroup of patients eligible for surgical treatment.

In an ongoing interventional randomized clinical trial, the Alberta Health Service (AHS) Cancer Control aims at assessing whether hENT1 is capable of predicting a treatment response to gemcitabine in advanced pancreas cancer and whether a combination of 5-FU, leucovorin, and oxaliplatin (FOLFOX) instead of gemcitabine might be a superior treatment option for patients with low hENT1 expression (ClinicalTrials.gov Identifier: NCT01586611).

Human CNT3 and deoxycytidine kinase

Human CNTs (hCNTs) represent the second group of gemcitabine membrane transporters, using the sodium gradient to shift gemcitabine across the plasma membrane.⁹⁴ Analyzing tumor blocks from 45 PDAC patients treated with gemcitabine-based radiochemotherapy after curative resection, Maréchal et al.⁸⁶ showed significantly longer 3-year survival rates for patients with high hCNT3 expression compared to patients with low hCNT3 expression (54.6% vs 26.1%; p = 0.028). The combination of hCNT3 and hENT1 as treatment-predictive biomarkers seems to be even more superior, with a 3-year survival rate of 81.1% for hCNT3^high/hENT1^high patients, supporting the combined use of those biomarkers rather than using one alone.⁸⁶ Deoxycytidine kinase (dCK) is the rate-limiting enzyme, which phosphorylates and thereby converts gemcitabine into its active form.⁹⁵ In a cohort of 40 pancreatic cancer patients receiving gemcitabine-based adjuvant chemotherapy, high dCK messenger RNA (mRNA) levels significantly correlated with longer disease-free survival.⁹⁶ Given the limited data available to date, further clinical investigations are needed to assess the potential use of hCNT3 and dCK as treatment-predictive biomarkers for gemcitabine therapy.

FOLFIRINOX markers

FOLFIRINOX is a chemotherapy regimen for advanced stage PDAC combining leucovorin, 5-FU, irinotecan, and oxaliplatin.⁹⁷ Although many clinical trials have shown a superior efficacy of FOLFIRINOX over standard gemcitabine, its use is limited to patients with good performance status (PS) due to increased systemic toxicity.^97–99 Given the unfavorable side-effect profile, treatment-predictive biomarkers are essential to determine a subgroup of patients that will benefit most from FOLFIRINOX therapy.

Carboxylesterase 2 (CES2) is an enzyme that converts irinotecan, one of the components of FOLFIRINOX, into its active form SN-38.¹⁰⁰ CES2 expression in PDAC tumor tissue has been suggested as potential predictive biomarker.¹⁰¹ Capello et al.¹⁰¹ showed that high-expression levels of CES2 in tumor tissue were associated with longer overall and progression-free survival in resectable and borderline resectable patients treated with neoadjuvant FOLFIRINOX.

Moreover, genome analyses of pancreatic cancer might function as a predictive marker for platinum-based chemotherapy, such as oxaliplatin as part of the FOLFIRINOX regimen. Using whole-genome sequencing, Waddell et al.⁷⁹ described an above-average response to platinum therapy in individuals with inactivation of DNA maintenance genes such as BRCA1, BRCA2, or PALB2. BRCA1 and PALB2 have been proposed as potential response predictors of pancreatic cancer to DNA damaging agents in the past and might therefore play a role as markers for personalized therapy decisions in future.¹⁰²

Nab-paclitaxel markers

Nab-paclitaxel (albumin-bound paclitaxel particles) has been shown to significantly improve overall survival, progression-free survival, and response rate when added to the standard gemcitabine treatment regimen in patients with metastatic pancreatic cancer.¹⁰³ Secreted protein acidic and rich in cysteine (SPARC), also known as osteonectin, is a glycoprotein highly expressed in different human malignancies, and its deregulation has often been correlated with disease progression and a poor prognosis.¹⁰⁴ In head and neck cancer, SPARC overexpression has been associated with tumor response to nab-paclitaxel in a small group of patients,¹⁰⁵ suggesting that tumoral SPARC-albumin interactions facilitate an accumulation of albumin in the tumor, thereby increasing the efficacy of albumin-bound paclitaxel.¹⁰⁵ Despite promising early data,¹⁰⁶ Hidalgo et al.¹⁰⁷ showed that SPARC expression in the tumor stroma, tumor epithelia, and patient’s plasma is not associated with the efficacy of nab-paclitaxel and gemcitabine in patients with metastatic pancreatic cancer. To our knowledge no other reliable treatment-predictive markers for nab-paclitaxel therapy has been described to date.

Prognostic biomarkers

For a disease as lethal as pancreatic cancer, prognostic factors are indispensable to provide a personalized treatment for PDAC patients with an individualized balance between treatment efficacy and side effects.

Eastern Cooperative Oncology Group PS

The Eastern Cooperative Oncology Group (ECOG) PS has been described as a prognostic marker for different malignancies.^108,109 For pancreatic cancer, an unfavorable PS is an independent negative prognostic marker.^110,111 Especially with regard to an optimal treatment choice, ECOG PS should be considered as a valuable marker because PDAC patients with poor PS usually do not benefit from combination or intensified chemotherapy regimens such as FOLFIRINOX.¹¹² Furthermore, patient’s age significantly influences survival in pancreatic cancer.^113,114 However, the physiological age needs to be differentiated from the chronological age when it comes to treatment decisions.^112,115

SPARC

SPARC—already discussed as treatment-predictive biomarker for nab-paclitaxel treatment—also has a prognostic value. As already shown for a variety of other cancers,^116–118 SPARC overexpression in PDAC patients correlates with a poor prognosis.¹¹⁹ Interestingly, in pancreatic cancer, the location of SPARC expression seems to play a decisive role. Infante et al.¹²⁰ published a study demonstrating that overexpression of SPARC by peritumoral fibroblasts but not by tumor cells themselves predicts prognosis for patients with pancreatic cancer. Patients with positive tumor stroma for SPARC had a significantly shorter median survival compared to patients with SPARC negative stroma (15 vs 30 months; p < 0.001). Similar prognostic capabilities were published for SPARC mRNA levels measured in tissue samples of pancreatic adenocarcinoma patients.¹²¹

CA19-9

As the most extensively studied and validated biomarker for PDAC, CA19-9 has also been examined in terms of its prognostic value. In a large meta-analysis, Ballehaninna and Chamberlain³¹ concluded that normal (<37 U/mL) or moderately elevated pre-operative CA19-9 serum levels (<100 U/mL) independently predict improved overall survival, whereas elevated CA19-9 serum levels (>100 U/mL) are associated with a poor prognosis. Furthermore, the group stated that post-operative normalization or a downward trend of CA19-9 serum levels after pancreatic resection is associated with prolonged survival, while constantly elevated CA19-9 levels following pancreatic resection reflect residual disease and thereby predict a poor survival.³¹

MicroRNAs

Besides the emerging role of miRNAs as a potential diagnostic biomarker for PDAC, their use as a prognostic tool has also been evaluated. A recently published meta-analysis including 1525 patients treated for PDAC demonstrated a significantly shortened overall and disease-free survival in patients with high tumoral miR-21. In addition, a poor overall survival was found for high levels of miR-155 and miR-203 as well as low miR-34a levels.¹²² In addition, among others, low-expression levels of miR-218 and miR-494 in tumor tissue as well as high miR-221/222 and miR-744 levels in tumor tissue and plasma, respectively, have been found to predict poor survival in PDAC patients.^123–126

Prognostic indices

Combination of various prognostic markers has led to the establishment of prognostic indices. Using a combination of five parameters (PS, hemoglobin, leucocyte count, neutrophil–lymphocyte ratio, and CEA), Park et al.¹²⁷ divided patients with metastatic pancreatic cancer into three risk groups and showed a median overall survival of 11.7, 6.2, and 1.3 months for the low, intermediate, and high-risk groups, respectively (p < 0.001). For patients with advanced disease receiving palliative chemotherapy, a prognostic index model of three clinical parameters (ECOG PS, CA19-9 serum levels, and C-reactive protein (CRP) serum levels) was published by Xue et al. The median overall survival for the low-risk and high-risk group was 9.9 and 5.3 months, respectively (p < 0.001) with estimated 1-year survival rates of 40.5% (low-risk group) and 5.9% (high-risk group; p < 0.05).¹²⁸

Markers of disease recurrence

Soluble iC3b (siC3b), the cleavage product of complement factor C3b, has been investigated as a marker to predict disease recurrence in patients with adjuvant treatment after resection of PDAC.¹²⁹ The inactivated complement component is expressed on apoptotic cells, including pancreatic cancer cells.¹³⁰ Märten and colleagues¹²⁹ showed that siC3b plasma levels were increased upto 4 months before radiologically defined recurrence with a sensitivity and specificity resulting in an AUC of 0.85. The combination of siC3b and CA19-9 leads to an AUC of 0.92.

Conclusion

Pancreatic cancer remains one of the most devastating diagnoses to date. Only in case of early diagnosis, curative treatment approaches are available. Thus, diagnostic modalities allowing tumor detection at an early time-point might have a decisive role in the treatment of patients with PDAC. However, at present, no serum-based (and therefore easily accessible) biomarker has a sufficient sensitivity or specificity to be used in clinical routine. Recent research in the context of PDAC and other cancers suggest that innovative molecules such as circulating miRNAs might overcome these limitations of the present protein-based biomarkers. Besides being used in the context of diagnosis, treatment-predictive biomarkers and prognostic biomarkers might have an essential role in providing an optimal and personalized treatment to all the patients with pancreatic cancer. Despite major scientific efforts during the last decades, the ideal biomarker for these purposes has not been identified yet. There is a large number of promising biomarkers, including various tumor and serum proteins, microRNAs, as well as genetic markers that might fulfill these requirements in future. Especially the combination of different markers as diagnostic or prognostic indices appears promising. Nevertheless, further studies are needed to validate novel biomarkers and to further evaluate potential future markers. Thus, in spite of recent progresses in the therapy of patients with PDAC, serum-based biomarkers might not only help to allow an early diagnosis but also to estimate patients’ prognosis and to guide patients’ therapy through the different available therapeutic options.

Footnotes

Acknowledgements

C.R. and T.L. share senior authorship.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The work carried out in the laboratory of T.L. was supported by the German Cancer Aid (Deutsche Krebshilfe 110043 and a Mildred Scheel Professorship); the German Research Foundation (LU 1360/3-1); SFB-TRR57 (P06); the Ernst Jung Foundation, Hamburg; and a grant from the medical faculty of the RWTH Aachen. Moreover, this work was supported by project grants from the German Research Foundation (DFG RO 4317/4-1) and a START grant from the medical faculty RWTH Aachen to C.R.

References

Ferlay

Soerjomataram

Dikshit

. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 2014; 136: E359–E386.

Ryan

Hong

Bardeesy

. Pancreatic adenocarcinoma. N Engl J Med 2014; 371: 2140–2141.

American Cancer Society. Cancer facts & figures 2015, http://old.cancer.org/acs/groups/content/@editorial/documents/document/acspc-044552.pdf

Kamisawa

Wood

Itoi

. Pancreatic cancer. Lancet 2016; 388: 73–85.

Goonetilleke

Siriwardena

. Systematic review of carbohydrate antigen (CA 19-9) as a biochemical marker in the diagnosis of pancreatic cancer. Eur J Surg Oncol 2007; 33: 266–270.

Zhang

Yang

. Tumor markers CA19-9, CA242 and CEA in the diagnosis of pancreatic cancer: a meta-analysis. Int J Clin Exp Med 2015; 8: 11683–11691.

S-B

Qin

S-Y

Chen

. Carbohydrate antigen 19-9 for differential diagnosis of pancreatic carcinoma and chronic pancreatitis. World J Gastroenterol 2015; 21: 4323–4333.

Wang

Huang

. Diagnostic value of combining CA 19-9 and K-ras gene mutation in pancreatic carcinoma: a meta-analysis. Int J Clin Exp Med 2014; 7: 3225–3234.

Liao

Zhao

Y-P

Yang

Y-C

. Combined detection of serum tumor markers for differential diagnosis of solid lesions located at the pancreatic head. Hepatobiliary Pancreat Dis Int 2007; 6: 641–645.

10.

. Evaluation of the diagnostic value of serum tumor markers, and fecal k-ras and p53 gene mutations for pancreatic cancer. Chin J Dig Dis 2006; 7: 170–174.

11.

Jiang

Tao

Zou

. Detection of serum tumor markers in the diagnosis and treatment of patients with pancreatic cancer. Hepatobiliary Pancreat Dis Int 2004; 3: 464–468.

12.

Cwik

Wallner

Skoczylas

. Cancer antigens 19-9 and 125 in the differential diagnosis of pancreatic mass lesions. Arch Surg 2006; 141: 968–973;discussion 974.

13.

Chen

Y-Z

Liu

Zhao

Y-X

. Diagnostic performance of serum macrophage inhibitory cytokine-1 in pancreatic cancer: a meta-analysis and meta-regression analysis. DNA Cell Biol 2014; 33: 370–377.

14.

Gold

Gaedcke

Ghadimi

. PAM4 enzyme immunoassay alone and in combination with CA 19-9 for the detection of pancreatic adenocarcinoma. Cancer 2013; 119: 522–528.

15.

Zihao

Jie

Yan

. Analyzing S100A6 expression in endoscopic ultrasonography-guided fine-needle aspiration specimens: a promising diagnostic method of pancreatic cancer. J Clin Gastroenterol 2013; 47: 69–75.

16.

Koopmann

Fedarko

Jain

. Evaluation of osteopontin as biomarker for pancreatic adenocarcinoma. Cancer Epidemiol Biomarkers Prev 2004; 13: 487–491.

17.

Poruk

Firpo

Scaife

. Serum osteopontin and tissue inhibitor of metalloproteinase 1 as diagnostic and prognostic biomarkers for pancreatic adenocarcinoma. Pancreas 2013; 42: 193–197.

18.

Brand

Nolen

Zeh

. Serum biomarker panels for the detection of pancreatic cancer. Clin Cancer Res 2011; 17: 805–816.

19.

Liu

Chen

. Serum microRNA expression profile as a biomarker in the diagnosis and prognosis of pancreatic cancer. Clin Chem 2012; 58: 610–618.

20.

Yang

J-Y

Sun

Y-W

Liu

D-J

. MicroRNAs in stool samples as potential screening biomarkers for pancreatic ductal adenocarcinoma cancer. Am J Cancer Res 2014; 4: 663–673.

21.

Sadakari

Ohtsuka

Ohuchida

. MicroRNA expression analyses in preoperative pancreatic juice samples of pancreatic ductal adenocarcinoma. JOP 2010; 11: 587–592.

22.

Dillhoff

Liu

Frankel

. MicroRNA-21 is overexpressed in pancreatic cancer and a potential predictor of survival. J Gastrointest Surg 2008; 12: 2171–2176.

23.

Wang

Chen

Chang

. MicroRNAs in plasma of pancreatic ductal adenocarcinoma patients as novel blood-based biomarkers of disease. Cancer Prev Res 2009; 2: 807–813.

24.

Huang

Cao

. Circulating miR-210 as a novel hypoxia marker in pancreatic cancer. Transl Oncol 2010; 3: 109–113.

25.

Szafranska

Davison

John

. MicroRNA expression alterations are linked to tumorigenesis and non-neoplastic processes in pancreatic ductal adenocarcinoma. Oncogene 2007; 26: 4442–4452.

26.

Debernardi

Massat

Radon

. Noninvasive urinary miRNA biomarkers for early detection of pancreatic adenocarcinoma. Am J Cancer Res 2015; 5: 3455–3466.

27.

Wang

Raimondo

Guha

. Circulating microRNAs in pancreatic juice as candidate biomarkers of pancreatic cancer. J Cancer 2014; 5: 696–705.

28.

Satake

Kanazawa

Kho

. Evaluation of serum pancreatic enzymes, carbohydrate antigen 19-9, and carcinoembryonic antigen in various pancreatic diseases. Am J Gastroenterol 1985; 80: 630–636.

29.

Steinberg

. The clinical utility of the CA 19-9 tumor-associated antigen. Am J Gastroenterol 1990; 85: 350–355.

30.

Perkins

Slater

Sanders

. Serum tumor markers. Am Fam Physician 2003; 68: 1075–1082.

31.

Ballehaninna

Chamberlain

. The clinical utility of serum CA 19-9 in the diagnosis, prognosis and management of pancreatic adenocarcinoma: an evidence based appraisal. J Gastrointest Oncol 2012; 3: 105–119.

32.

von Rosen

Linder

Harmenberg

. Serum levels of CA 19-9 and CA 50 in relation to Lewis blood cell status in patients with malignant and benign pancreatic disease. Pancreas 1993; 8: 160–165.

33.

Bünger

Laubert

Roblick

. Serum biomarkers for improved diagnostic of pancreatic cancer: a current overview. J Cancer Res Clin Oncol 2011; 137: 375–389.

34.

Lagos-Quintana

Rauhut

Lendeckel

. Identification of novel genes coding for small expressed RNAs. Science 2001; 294: 853–858.

35.

Bartel

. MicroRNAs: target recognition and regulatory functions. Cell 2009; 136: 215–233.

36.

Getz

Miska

. MicroRNA expression profiles classify human cancers. Nature 2005; 435: 834–838.

37.

Rosenfeld

Aharonov

Meiri

. MicroRNAs accurately identify cancer tissue origin. Nat Biotechnol 2008; 26: 462–469.

38.

Hernandez

Lucas

. MicroRNA in pancreatic ductal adenocarcinoma and its precursor lesions. World J Gastrointest Oncol 2016; 8: 18–29.

39.

Bloomston

Frankel

Petrocca

. MicroRNA expression patterns to differentiate pancreatic adenocarcinoma from normal pancreas and chronic pancreatitis. JAMA 2007; 297: 1901–1908.

40.

Caponi

Funel

Frampton

. The good, the bad and the ugly: a tale of miR-101, miR-21 and miR-155 in pancreatic intraductal papillary mucinous neoplasms. Ann Oncol 2013; 24: 734–741.

41.

Schultz

Werner

Willenbrock

. MicroRNA expression profiles associated with pancreatic adenocarcinoma and ampullary adenocarcinoma. Mod Pathol 2012; 25: 1609–1622.

42.

Liu

Gao

. Aberrant expression miR-196a is associated with abnormal apoptosis, invasion, and proliferation of pancreatic cancer cells. Pancreas 2013; 42: 1169–1181.

43.

Cote

Gore

McElyea

. A pilot study to develop a diagnostic test for pancreatic ductal adenocarcinoma based on differential expression of select miRNA in plasma and bile. Am J Gastroenterol 2014; 109: 1942–1952.

44.

Ren

Gao

Liu

J-Q

. Differential signature of fecal microRNAs in patients with pancreatic cancer. Mol Med Rep 2012; 6: 201–209.

45.

Hong

Park

. MicroRNA expression profiling of diagnostic needle aspirates from surgical pancreatic cancer specimens. Ann Surg Treat Res 2014; 87: 290–297.

46.

Link

Becker

Goel

. Feasibility of fecal microRNAs as novel biomarkers for pancreatic cancer. PLoS ONE 2012; 7: e42933

47.

Lee

Gusev

Jiang

. Expression profiling identifies microRNA signature in pancreatic cancer. Int J Cancer 2007; 120: 1046–1054.

48.

Habbe

Koorstra

J-BM

Mendell

. MicroRNA miR-155 is a biomarker of early pancreatic neoplasia. Cancer Biol Ther 2009; 8: 340–346.

49.

Slater

Strauch

Rospleszcz

. MicroRNA-196a and -196b as potential biomarkers for the early detection of familial pancreatic cancer. Transl Oncol 2014; 7: 464–471.

50.

Ryu

Hong

S-M

Karikari

. Aberrant MicroRNA-155 expression is an early event in the multistep progression of pancreatic adenocarcinoma. Pancreatology 2010; 10: 66–73.

51.

Du Rieu

Torrisani

Selves

. MicroRNA-21 is induced early in pancreatic ductal adenocarcinoma precursor lesions. Clin Chem 2010; 56: 603–612.

52.

Xue

Abou Tayoun

Abo

. MicroRNAs as diagnostic markers for pancreatic ductal adenocarcinoma and its precursor, pancreatic intraepithelial neoplasm. Cancer Genet 2013; 206: 217–221.

53.

Kung

JTY

Colognori

Lee

. Long noncoding RNAs: past, present, and future. Genetics 2013; 193: 651–669.

54.

Kishikawa

. Circulating RNAs as new biomarkers for detecting pancreatic cancer. World J Gastroenterol 2015; 21: 8527–8540.

55.

Ting

Lipson

Paul

. Aberrant overexpression of satellite repeats in pancreatic and other epithelial cancers. Science 2011; 331: 593–596.

56.

Liao

Mei

. Small nucleolar RNA signatures as biomarkers for non-small-cell lung cancer. Mol Cancer 2010; 9: 198.

57.

Cui

Lou

Zhang

. Detection of circulating tumor cells in peripheral blood from patients with gastric cancer using piRNAs as markers. Clin Biochem 2011; 44: 1050–1057.

58.

Mazières

Catherinne

Delfour

. Alternative processing of the U2 small nuclear RNA produces a 19-22nt fragment with relevance for the detection of non-small cell lung cancer in human serum. PLoS ONE 2013; 8: e60134.

59.

Kuhlmann

Baraniskin

Hahn

. Circulating U2 small nuclear RNA fragments as a novel diagnostic tool for patients with epithelial ovarian cancer. Clin Chem 2014; 60: 206–213.

60.

Baraniskin

Nöpel-Dünnebacke

Ahrens

. Circulating U2 small nuclear RNA fragments as a novel diagnostic biomarker for pancreatic and colorectal adenocarcinoma. Int J Cancer 2013; 132: E48–E57.

61.

Bootcov

Bauskin

Valenzuela

. MIC-1, a novel macrophage inhibitory cytokine, is a divergent member of the TGF-β superfamily. Proc Natl Acad Sci U S A 1997; 94: 11514–11519.

62.

Buckhaults

Rago

St Croix

. Secreted and cell surface genes expressed in benign and malignant colorectal tumors. Cancer Res 2001; 61: 6996–7001.

63.

Koopmann

Rosenzweig

CNW

Zhang

. Serum markers in patients with resectable pancreatic adenocarcinoma: macrophage inhibitory cytokine 1 versus CA19-9. Clin Cancer Res 2006; 12: 442–446.

64.

Mohamed

Soliman

Ismail

. Evaluation of circulating ADH and MIC-1 as diagnostic markers in Egyptian patients with pancreatic cancer. Pancreatology 15: 34–39.

65.

Wang

Tian

. Macrophage inhibitory cytokine 1 (MIC-1/GDF15) as a novel diagnostic serum biomarker in pancreatic ductal adenocarcinoma. BMC Cancer 2014; 14: 578.

66.

Kaur

Chakraborty

Baine

. Potentials of plasma NGAL and MIC-1 as biomarker(s) in the diagnosis of lethal pancreatic cancer. PLoS ONE 2013; 8: e55171.

67.

Gold

Lew

Maliniak

. Characterization of monoclonal antibody PAM4 reactive with a pancreatic cancer mucin. Int J Cancer 1994; 57: 204–210.

68.

Gold

Karanjawala

Modrak

. PAM4-reactive MUC1 is a biomarker for early pancreatic adenocarcinoma. Clin Cancer Res 2007; 13: 7380–7387.

69.

Liu

Chang

C-H

Gold

. Identification of PAM4 (clivatuzumab)-reactive epitope on MUC5AC: a promising biomarker and therapeutic target for pancreatic cancer. Oncotarget 2015; 6: 4274–4285.

70.

Vimalachandran

Greenhalf

Thompson

. High nuclear S100A6 (Calcyclin) is significantly associated with poor survival in pancreatic cancer patients. Cancer Res 2005; 65: 3218–3225.

71.

Ohuchida

Mizumoto

. S100A6 is increased in a stepwise manner during pancreatic carcinogenesis: clinical value of expression analysis in 98 pancreatic juice samples. Cancer Epidemiol Biomarkers Prev 2007; 16: 649–654.

72.

Kolb

Kleeff

Guweidhi

. Osteopontin influences the invasiveness of pancreatic cancer cells and is increased in neoplastic and inflammatory conditions. Cancer Biol Ther 2005; 4: 740–746.

73.

Zhivkova-Galunska

Adwan

Eyol

. Osteopontin but not osteonectin favors the metastatic growth of pancreatic cancer cell lines. Cancer Biol Ther 2010; 10: 54–64.

74.

J-J

H-Y

. Diagnostic significance of serum osteopontin level for pancreatic cancer: a meta-analysis. Genet Test Mol Biomarkers 2014; 18: 580–586.

75.

Jenkinson

Elliott

Menon

. Evaluation in pre-diagnosis samples discounts ICAM-1 and TIMP-1 as biomarkers for earlier diagnosis of pancreatic cancer. J Proteomics 2015; 113: 400–402.

76.

Almoguera

Shibata

Forrester

. Most human carcinomas of the exocrine pancreas contain mutant c-K-ras genes. Cell 1988; 53: 549–554.

77.

Smit

Boot

Smits

. KRAS codon 12 mutations occur very frequently in pancreatic adenocarcinomas. Nucleic Acids Res 1988; 16: 7773–7782.

78.

Wood

Hruban

. Pathology and molecular genetics of pancreatic neoplasms. Cancer J 2012; 18: 492–501.

79.

Waddell

Pajic

Patch

A-M

. Whole genomes redefine the mutational landscape of pancreatic cancer. Nature 2015; 518: 495–501.

80.

Fuccio

Hassan

Laterza

. The role of K-ras gene mutation analysis in EUS-guided FNA cytology specimens for the differential diagnosis of pancreatic solid masses: a meta-analysis of prospective studies. Gastrointest Endosc 2013; 78: 596–608.

81.

Kinugasa

Nouso

Miyahara

. Detection of K-ras gene mutation by liquid biopsy in patients with pancreatic cancer. Cancer 2015; 121(13): 2271–2280.

82.

Singh

Gupta

Pandey

. High levels of cell-free circulating nucleic acids in pancreatic cancer are associated with vascular encasement, metastasis and poor survival. Cancer Invest 2015; 33: 78–85.

83.

Maitra

Adsay

Argani

. Multicomponent analysis of the pancreatic adenocarcinoma progression model using a pancreatic intraepithelial neoplasia tissue microarray. Mod Pathol 2003; 16: 902–912.

84.

Rhim

Mirek

Aiello

. EMT and dissemination precede pancreatic tumor formation. Cell 2012; 148: 349–361.

85.

Rhim

Thege

Santana

. Detection of circulating pancreas epithelial cells in patients with pancreatic cystic lesions. Gastroenterology 2014; 146: 647–651.

86.

Maréchal

Mackey

Lai

. Human equilibrative nucleoside transporter 1 and human concentrative nucleoside transporter 3 predict survival after adjuvant gemcitabine therapy in resected pancreatic adenocarcinoma. Clin Cancer Res 2009; 15: 2913–2919.

87.

García-Manteiga

Molina-Arcas

Casado

. Nucleoside transporter profiles in human pancreatic cancer cells: role of hCNT1 in 2′,2′-difluorodeoxycytidine-induced cytotoxicity. Clin Cancer Res 2003; 9: 5000–5008.

88.

Spratlin

Sangha

Glubrecht

. The absence of human equilibrative nucleoside transporter 1 is associated with reduced survival in patients with gemcitabine-treated pancreas adenocarcinoma. Clin Cancer Res 2004; 10: 6956–6961.

89.

Farrell

Elsaleh

Garcia

. Human equilibrative nucleoside transporter 1 levels predict response to gemcitabine in patients with pancreatic cancer. Gastroenterology 2009; 136: 187–195.

90.

Greenhalf

Ghaneh

Neoptolemos

. Pancreatic cancer hENT1 expression and survival from gemcitabine in patients from the ESPAC-3 trial. J Natl Cancer Inst 2014; 106: 347.

91.

Giovannetti

Del Tacca

Mey

. Transcription analysis of human equilibrative nucleoside transporter-1 predicts survival in pancreas cancer patients treated with gemcitabine. Cancer Res 2006; 66: 3928–3935.

92.

Mori

Ishikawa

Ichikawa

. Human equilibrative nucleoside transporter 1 is associated with the chemosensitivity of gemcitabine in human pancreatic adenocarcinoma and biliary tract carcinoma cells. Oncol Rep 2007; 17: 1201–1205.

93.

Yamada

Mizuno

Uchida

. Human equilibrative nucleoside transporter 1 expression in endoscopic ultrasonography-guided fine-needle aspiration biopsy samples is a strong predictor of clinical response and survival in the patients with pancreatic ductal adenocarcinoma undergoing gem. Pancreas 2016; 45(5): 761–771.

94.

Mackey

Mani

Selner

. Functional nucleoside transporters are required for gemcitabine influx and manifestation of toxicity in cancer cell lines. Cancer Res 1998; 58: 4349–4357.

95.

Sebastiani

Ricci

Rubio-Viqueira

. Immunohistochemical and genetic evaluation of deoxycytidine kinase in pancreatic cancer: relationship to molecular mechanisms of gemcitabine resistance and survival. Clin Cancer Res 2006; 12: 2492–2497.

96.

Fujita

Ohuchida

Mizumoto

. Gene expression levels as predictive markers of outcome in pancreatic cancer after gemcitabine-based adjuvant chemotherapy. Neoplasia 2010; 12: 807–817.

97.

Conroy

Desseigne

Ychou

. FOLFIRINOX versus gemcitabine for metastatic pancreatic cancer. N Engl J Med 2011; 364: 1817–1825.

98.

Gourgou-Bourgade

Bascoul-Mollevi

Desseigne

. Impact of FOLFIRINOX compared with gemcitabine on quality of life in patients with metastatic pancreatic cancer: results from the PRODIGE 4/ACCORD 11 randomized trial. J Clin Oncol 2013; 31: 23–29.

99.

Vaccaro

Sperduti

Milella

. FOLFIRINOX versus gemcitabine for metastatic pancreatic cancer. N Engl J Med 2011; 365: 768–769; author reply 769.

100.

Khanna

Morton

Danks

. Proficient metabolism of irinotecan by a human intestinal carboxylesterase. Cancer Res 2000; 60: 4725–4728.

101.

Capello

Lee

Wang

. Carboxylesterase 2 as a determinant of response to irinotecan and neoadjuvant FOLFIRINOX therapy in pancreatic ductal adenocarcinoma. J Natl Cancer Inst 2015; 107: 132.

102.

Villarroel

Rajeshkumar

Garrido-Laguna

. Personalizing cancer treatment in the age of global genomic analyses: pALB2 gene mutations and the response to DNA damaging agents in pancreatic cancer. Mol Cancer Ther 2011; 10: 3–8.

103.

Von Hoff

Ervin

Arena

. Increased survival in pancreatic cancer with nab-paclitaxel plus gemcitabine. N Engl J Med 2013; 369: 1691–1703.

104.

Sato

Fukushima

Maehara

. SPARC/osteonectin is a frequent target for aberrant methylation in pancreatic adenocarcinoma and a mediator of tumor-stromal interactions. Oncogene 2003; 22: 5021–5030.

105.

Desai

Trieu

Damascelli

. SPARC expression correlates with tumor response to albumin-bound paclitaxel in head and neck cancer patients. Transl Oncol 2009; 2: 59–64.

106.

von Hoff

Ramanathan

Borad

. SPARC correlation with response to gemcitabine (G) plus nab-paclitaxel (nab-P) in patients with advanced metastatic pancreatic cancer: a phase I/II study. In: 2009 ASCO annual meet, Orlando, Florida, USA, 29 May–2 June 2009, Abstract 4525, 2009.

107.

Hidalgo

Plaza

Musteanu

. SPARC expression did not predict efficacy of nab-paclitaxel plus gemcitabine or gemcitabine alone for metastatic pancreatic cancer in an exploratory analysis of the phase III MPACT trial. Clin Cancer Res 2015; 21: 4811–4818.

108.

Oken

Creech

Tormey

. Toxicity and response criteria of the Eastern Cooperative Oncology Group. Am J Clin Oncol 1982; 5: 649–655.

109.

Sørensen

Klee

Palshof

. Performance status assessment in cancer patients: an inter-observer variability study. Br J Cancer 1993; 67: 773–775.

110.

Peixoto

Speers

McGahan

. Prognostic factors and sites of metastasis in unresectable locally advanced pancreatic cancer. Cancer Med 2015; 4: 1171–1177.

111.

Louvet

Labianca

Hammel

. Gemcitabine in combination with oxaliplatin compared with gemcitabine alone in locally advanced or metastatic pancreatic cancer: results of a GERCOR and GISCAD phase III trial. J Clin Oncol 2005; 23: 3509–3516.

112.

Sund

Vinci

. Prognostic and predictive markers in pancreatic adenocarcinoma. Dig Liver Dis 2015; 48: 223–230.

113.

Park

Yoon

Kim

Y-T

. Survival and prognostic factors of unresectable pancreatic cancer. J Clin Gastroenterol 2008; 42: 86–91.

114.

Stocken

Hassan

Altman

. Modelling prognostic factors in advanced pancreatic cancer. Br J Cancer 2008; 99: 883–893.

115.

Tas

Sen

Keskin

. Prognostic factors in metastatic pancreatic cancer: older patients are associated with reduced overall survival. Mol Clin Oncol 2013; 1: 788–792.

116.

Wang

C-S

Lin

K-H

Chen

S-L

. Overexpression of SPARC gene in human gastric carcinoma and its clinic-pathologic significance. Br J Cancer 2004; 91: 1924–1930.

117.

Yamashita

Upadhay

Mimori

. Clinical significance of secreted protein acidic and rich in cystein in esophageal carcinoma and its relation to carcinoma progression. Cancer 2003; 97: 2412–2419.

118.

Watkins

Douglas-Jones

Bryce

. Increased levels of SPARC (osteonectin) in human breast cancer tissues and its association with clinical outcomes. Prostaglandins Leukot Essent Fatty Acids 2005; 72: 267–272.

119.

Han

Cao

Chen

M-B

. Prognostic value of SPARC in patients with pancreatic cancer: a systematic review and meta-analysis. PLoS ONE 2016; 11: e0145803.

120.

Infante

Matsubayashi

Sato

. Peritumoral fibroblast SPARC expression and patient outcome with resectable pancreatic adenocarcinoma. J Clin Oncol 2007; 25: 319–325.

121.

Miyoshi

Sato

Ohuchida

. SPARC mRNA expression as a prognostic marker for pancreatic adenocarcinoma patients. Anticancer Res 2010; 30: 867–871.

122.

Frampton

Krell

Jamieson

. microRNAs with prognostic significance in pancreatic ductal adenocarcinoma: a meta-analysis. Eur J Cancer 2015; 51: 1389–1404.

123.

B-S

Liu

Yang

W-L

. Reduced miRNA-218 expression in pancreatic cancer patients as a predictor of poor prognosis. Genet Mol Res 2015; 14: 16372–16378.

124.

. Correlation of miR-494 expression with tumor progression and patient survival in pancreatic cancer. Genet Mol Res 2015; 14: 18153–18159.

125.

Passadouro

Pedroso de Lima

Faneca

. MicroRNA modulation combined with sunitinib as a novel therapeutic strategy for pancreatic cancer. Int J Nanomedicine 2014; 9: 3203–3217.

126.

Miyamae

Komatsu

Ichikawa

. Plasma microRNA profiles: identification of miR-744 as a novel diagnostic and prognostic biomarker in pancreatic cancer. Br J Cancer 2015; 113: 1467–1476.

127.

Park

Lee

Park

. Prognostic Scoring Index for patients with metastatic pancreatic adenocarcinoma. Cancer Res Treat 2016; 48: 1253–1263.

128.

Xue

Zhu

Wan

. A prognostic index model to predict the clinical outcomes for advanced pancreatic cancer patients following palliative chemotherapy. J Cancer Res Clin Oncol 2015; 141: 1653–1660.

129.

Märten

Büchler

Werft

. Soluble iC3b as an early marker for pancreatic adenocarcinoma is superior to CA19.9 and radiology. J Immunother 33: 219–224.

130.

Hoimes

Moyer

Saif

. Biomarkers for early detection and screening in pancreatic cancer. Highlights from the 45th ASCO annual meeting. Orlando, FL, USA. May 29-June 2, 2009. JOP 2009(10): 352–356.

Current and future biomarkers for pancreatic adenocarcinoma

Abstract

Keywords

Introduction

Diagnostic biomarkers

CA19-9, carcinoembryonic antigen, and other carbohydrate antigens

MicroRNAs and other non-coding RNAs

Macrophage inhibitory cytokine 1 and PAM4

Other diagnostic biomarkers

Genetic and epigenetic markers

Circulating tumor cells

Treatment predictive markers

Gemcitabine markers

Human ENT1

Human CNT3 and deoxycytidine kinase

FOLFIRINOX markers

Nab-paclitaxel markers

Prognostic biomarkers

Eastern Cooperative Oncology Group PS

SPARC

CA19-9

MicroRNAs

Prognostic indices

Markers of disease recurrence

Conclusion

Footnotes

Acknowledgements

Declaration of conflicting interests

Funding

References