Introduction
In the UK, pancreatic ductal adenocarcinoma (PDAC) is the 10th commonest cancer and has an incidence of 9 per 100,000 population1, and biliary tract cancer (BTC) (including intra- and extra-hepatic cholangiocarcinoma and gallbladder cancer) has an incidence of 1–2 cases per 100,000 population2. Long-term survival is poor; 5-year survival is less than 4% for both tumours3,4. Often these tumours are diagnosed late, when patients have advanced disease and curative surgical resection is no longer possible.
Globally the highest incidence of PDAC is seen in Northern Europe and North America5, where the rates are 3 to 4 times higher than in tropical countries6. Overall incidence is increasing5, and as most tumours are sporadic, this rising incidence is attributed to differences in lifestyles and exposure to environmental risk factors7, such as smoking8–15, diabetes mellitus, chronic pancreatitis1,15,16 and obesity17.
In BTC, the variations in incidence seen globally are even more pronounced; and the highest incidence is in northeastern Thailand (96 per 100,000 men)18, which has a population with high levels of chronic typhoid and infestation of liver fluke (Clonorchis sinensis and Opisthorchis viverinni)18. Other BTC risk factors seen in all populations include older age18, primary sclerosing cholangitis19, intraductal stones and rare biliary cystic diseases20. Inflammatory bowel disease, chronic viral hepatitis, cirrhosis, smoking, diabetes, obesity and excess alcohol consumption may also increase the risk of BTC20–22.
Despite improved diagnostic techniques, detecting pancreaticobiliary malignancy remains a significant clinical challenge. A common presentation of these tumours is a biliary stricture with or without a mass lesion. The differential of an indeterminate biliary stricture is broad, and often the associated symptoms and radiological findings overlap between benign and malignant conditions, often making differentiation—particularly between cancer, primary sclerosing cholangitis and IgG4-related disease—impossible without further investigations, typically by endoscopic retrograde cholangiopancreatography (ERCP) or endoscopic ultrasound (EUS)23–25. However, biliary brush cytology is also an imperfect test, although specificity is high (96–100%), sensitivity for malignancy remains low (9–57%) and in early disease when tumours are small, sensitivities are even lower26,27. Therefore, patients frequently require multiple procedures to obtain a final diagnosis28–30.
So there has been growing interest in the development of simple tests to streamline the diagnosis to pancreaticobiliary malignancy and guide appropriate and timely therapy for patients. Identifying better diagnostic tools for PDAC and BTC would also make screening and surveillance possible, particularly in high-risk populations4,8,31. This would enable the detection of tumours at an earlier stage when curative resection is possible, leading to substantial improvements in survival32. This review provides an overview of the latest innovations in diagnostic biomarkers and endoscopic techniques for pancreaticobiliary malignancy.
Methods
We performed a systematic review of the literature by using PubMed, EMBASE and the Cochrane Library. The search was limited to studies published in the English language between January 2013 and March 2017. Medical Subject Headings (MeSH) terms were decided by a consensus of the authors and included “pancreatic cancer” or “cholangiocarcinoma” and “biomarker”. The search was restricted to title, abstract and keywords. Articles that described outcomes for fewer than five patients were excluded. Case reports, abstracts and reviews were excluded. All references were screened for potentially relevant studies not identified in the initial literature search.
The following variables were extracted for each report when available: number of malignant and benign cases, sensitivity, specificity and area under the curve (AUC). One hundred ten articles were included in the final review.
Biomarkers
1. Serum biomarkers and blood tests
Carbohydrate antigen (CA) 19-9 is the most widely used tumour marker in pancreaticobiliary malignancy. Overall sensitivity (78–89%) and specificity (67–87%) are low, and in around 7% of the population who lack the Lewis (a) antigen, CA19-9 will remain negative33. In small tumours, sensitivity decreases further. The marker can also be elevated in a number of other malignant diseases (for example, gastric adenocarcinoma) and benign diseases, particularly those causing jaundice (for example, primary biliary cirrhosis, cholestasis and cholangitis), and in smokers34. In addition, variation has been reported among commercially available assays, which may impact on interpretation35. Therefore, to improve the sensitivity of the marker in current clinical practice, it is always interpreted in the context of cross-sectional imaging findings33.
Other commercially available tumour markers that have a role in diagnosing pancreaticobiliary cancer include carcinoembryonic antigen (CEA) and CA125. CEA is a glycosyl phosphatidyl inositol cell surface-anchored glycoprotein that is involved in cell adhesion. When elevated, it is highly suggestive of colorectal cancer, but it is also increased in about a third of patients with BTC36–38. CA125 is a protein encoded by the MUC16 gene and is a large membrane-associated glycoprotein with a single transmembrane domain. When elevated, it is suggestive of ovarian cancer, but it is also increased in about 40–50% of patients with pancreaticobiliary malignancy, particularly when there is peritoneal involvement38.
Owing to the limitations of existing biomarkers, over the last few years several studies have evaluated various combinations of biomarkers to supplement or ultimately replace existing biomarkers. Biomarker panels using combinations of markers, often including CA19-9, have been particularly successful in detecting small tumours and early disease. Validation studies have also shown that these markers can differentiate PDAC from relevant benign conditions and in some cases detect tumours up to 1 year prior to diagnosis with a specificity of 95% and a sensitivity of 68%7 (Table 1 and Table 2).
Table 1. Serum protein biomarkers for biliary tract cancer, 2013–2017.
Author (year) | Biomarker/ Combination (serum) | Biliary tract cancer, number | Benign lesion/ cholangitis, number | Healthy volunteers, number | Sensitivity | Specificity | Area under the curve |
---|
Single biomarkers |
Han et al. (2013)84 | HDGF | 83 | - | 51 | 66% | 88% | 0.81 |
Ruzzenente et al. (2014)85 | MUC5AC | 49 | 23 | 16 | - | - | 0.91 |
Voigtlander et al. (2014)86 | Angpt-2 | 56 | 111 | - | 74% | 94% | 0.85 |
Lumachi et al. (2014)87 | CA 19-9 | 24 | 25 | - | 74% | 82% | - |
Wang et al. (2014)88 | CA 19-9 | 78 | 78 | 78 | 72% | 96% | - |
Lumachi et al. (2014)87 | CEA | 24 | 25 | - | 52% | 55% | - |
Wang et al. (2014)88 | CEA | 78 | 78 | 78 | 11% | 97% | - |
Wang et al. (2014)88 | CA 125 | 78 | 78 | 78 | 45% | 96% | - |
Lumachi et al. (2014)87 | CYFRA 21-1 | 24 | 25 | - | 76% | 79% | - |
Liu et al. (2015)89 | VEGF-C | 31 | 10 | 10 | 71% | 80% | 0.79 |
Liu et al. (2015)89 | VEGF-D | 31 | 10 | 10 | 74% | 85% | 0.84 |
Huang et al. (2015)90 | CYFRA 21-1 | 134 | 52 | - | 75% | 85% | - |
Lumachi et al. (2014)87 | MMP7 | 24 | 25 | - | 78% | 77% | - |
Nigam et al. (2014)91 | Survivin | 39 (gallbladder cancer) | 30 | 25 | 81% | 80% | - |
Rucksaken et al. (2014)92 | HSP70 | 31
| 12 | 23 | 94%
| 74%
| 0.92
|
Rucksaken et al. (2014)92 | ENO1 | 31 | - | 23 | 81% | 78% | 0.86 |
Rucksaken et al. (2014)92 | RNH1 | 31 | - | 23 | 94% | 67% | 0.84 |
Wang et al. (2014)88 | CA242 | 78 | 78 | 78 | 64% | 99% | - |
Ince et al. (2014)93 | VEGFR3 | 96 | 129 | - | 48% | 82% | 0.62 |
Ince et al. (2014)93 | TAC | 96 | 129 | - | 61% | 60% | 0.60 |
Rucksaken et al. (2017)94 | ORM2 | 70 | 46 | 20 | 92% | 74% | - |
Rose et al. (2016)95 | CEACAM6 | 41 | 42 | - | 87.5% | 69% | 0.74 |
Jiao et al. (2014)96 | Nucleosides | 202 (gallbladder cancer) | 203 | 205 | 91% | 96% | - |
Biomarker combinations |
Lumachi et al. (2014)87 | CEA + CA19-9 + CYFRA 21-1 + MMP7 | 24 | 25 | - | 92% | 96% | |
Table 2. Serum protein biomarkers for pancreatic cancer, 2012–2017.
Author (year) | Biomarker/ Combination (serum) | PDAC, number | Benign controls, number | Healthy volunteers, number | Sensitivity | Specificity | Area under the curve |
---|
Single biomarkers |
Sogawa et al. (2016)97 | C4BPA | 52 | 20 | 40 | 67% | 95% | 0.860 |
Rychlikova et al. (2016)98 | Osteopontin | 64 | 71 | 48 | - | - | - |
Lin et al. (2016)99 | APOA-I | 78 | - | 36 | 96% | 72.2% | 0.880 |
Lin et al. (2016)99 | TF | 78 | - | 36 | 75% | 72.8% | 0.760 |
Guo et al. (2016)100 | Dysbindin | 250 | 80 | 150 | 81.9% | 84.7% | 0.849 |
Han et al. (2015)101 | Dickkopf-1 | 140 | - | 92 | 89.3% | 79.3% | 0.919 |
Qu et al. (2015)102 | DCLK1 | 74 | 74 | - | - | - | 0.740 |
Dong et al. (2015)103 | Survivin | 80 | - | 80 | - | - | - |
Gebauer et al. (2014)104 | EpCAM | 66 | 43 | 104 | 66.7% | 77.5% | - |
Wang et al. (2014)105 | MIC-1 | 807 | 165 | 500 | 65.8% | 96.4% | 0.935 |
Kendrick et al. (2014)106 | IGFBP2 | 84 | 40 | 84 | 22% | 95% | 0.655 |
Kendrick et al. (2014)106 | MSLN | 84 | 40 | 84 | 17% | 95% | 0.668 |
Kang et al. (2014)107 | COL6A3 | 44 | 46 | 30 | - | - | 0.975 |
Willumsen et al. (2013)108 | C1M | 15 | - | 33 | - | - | 0.830 |
Willumsen et al. (2013)108 | C3M | 15 | - | 33 | - | - | 0.880 |
Willumsen et al. (2013)108 | C4M | 15 | - | 33 | - | - | 0.940 |
Willumsen et al. (2013)108 | C4M12a1 | 15 | - | 33 | - | - | 0.890 |
Falco et al. (2013)109 | BAG3 | 52 | - | 44 | 75% | 75% | 0.770 |
Falco et al. (2013)109 | BAG3 | 52 | 17 (chronic pancreatitis) | - | 81% | 77% | 0.810 |
Chen et al. (2013)110 | TTR | 40 | - | 40 | 91% | 47% | 0.730 |
Gold et al. (2013)111 | PAM4 | 298 | - | 79 | 76% | 96% | - |
Gold et al. (2013)111 | PAM4 | 298 | 120 | - | - | - | 0.890 |
Poruk et al. (2013)112 | OPN | 86 | 48 | 86 | - | - | 0.720 |
Poruk et al. (2013)112 | TIMP-1 | 86 | 48 | 86 | - | - | 0.770 |
Lee et al. (2014)113 | CA 19-9 | 41 | 12 | 44 | 80.4% | 70% | 0.833 |
Lee et al. (2014)113 | Human complement factor B (CFB) | 41 | 12 | 44 | 73.1% | 97.9% | 0.958 |
Mixed cohorts |
Ince et al. (2014)93 | CEA | 96 (41 PDAC +25 BTC) | 129 | - | 42.7% | 89.9% | 0.713 |
Ince et al. (2014)93 | CA19-9 | 96 (41 PDAC +25 BTC) | 129 | - | 49% | 84.5% | 0.701 |
Ince et al. (2014)93 | VEGFR3 | 96 (41 PDAC +25 BTC) | 129 | - | 48.4% | 82.9% | 0.622 |
Ince et al. (2014)93 | Total antioxidant capacity | 96 (41 PDAC +25 BTC) | 129 | - | 61.1% | 60.5% | 0.602 |
Abdel-Razik et al. (2016)114 | IGF-1 | 47 (25 PDAC + 18 BTC) | 62 | - | 62% | 51% | 0.605 |
Abdel-Razik et al. (2016)114 | VEGF | 47 (25 PDAC + 18 BTC) | 62 | - | 58.3% | 57.3% | 0.544 |
Biomarker combinations |
Chen et al. (2013)110 | TTR + CA19-9 | 40 | - | 40 | 81% | 85% | 0.910 |
Lee et al. (2014)113 | CA19-9 + CFB | 41 | 12 | 44 | 90.1% | 97.2% | 0.986 |
Sogawa et al. (2016)97 | C4BPA + CA19-9 | 52 | 20 | 40 | 86% | 80% | 0.930 |
Makawita et al. (2013)115 | CA19-9 + REG1B | 100 | - | 92 | - | - | 0.880 |
Makawita et al. (2013)115 | CA19-9 + SYCN + REG1B | 100 | - | 92 | - | - | 0.870 |
Willumsen et al. (2013)108 | C1M + C3M + C4M + C4M12a1 | 15 | - | 33 | - | - | 0.990 |
Shaw et al. (2014)116 | IL10 + IL6 + PDGF + Ca19-9 | 84 | 45 (benign) | - | 93% | 58% | 0.840
|
Shaw et al. (2014)116 | IL8 + IL6 + IL-10 + Ca19-9 | 84 | 32 (chronic pancreatitis) | - | 75% | 91% | 0.880
|
Shaw et al. (2014)116 | IL8 + IL1b + Ca 19-9 | 127 | - | 45 | 94% | 100% | 0.857 |
Brand et al. (2011)117 | Ca-19 + CEA + TIMP-1 | 173 | 70 | 120 | 71% | 89% | - |
Capello et al. (2017)118 | TIMP1 + LRG1 + Ca19-9 | 73 | - | 60 | 0.849% | 0.633% | 0.949 |
Capello et al. (2017)118 | TIMP1 + LRG1 + Ca19-9 | 73 | 74 | - | 0.452% | 0.541% | 0.890 |
Chan et al. (2014)119 | Ca19-9 + Ca125 + LAMC2 | 139 | 65 | 10 | 82% | 74%% | 0.870 |
Makawita et al. (2013)115 | CA19-9 + REG1B | 82 | 41 | 92 | - | - | 0.875 |
Makawita et al. (2013)115 | CA19-9 + SYNC + REG1B | 82 | 41 | 92 | - | - | 0.873 |
Makawita et al. (2013)115 | CA19-9 + AGR2 + REG1B | 82 | 41 | 92 | - | - | 0.869 |
In pancreaticobiliary malignancy and PDAC in particular, metastatic disease occurs at a very early stage in tumour development. This is demonstrated by the fact that patients who underwent resection of small primary tumours (<2 cm) with no clinical evidence of metastatic disease had a 5-year survival after pancreatectomy of less than 18% owing to recurrent metastatic disease39. Tumour development is driven by a series of cumulative genetic abnormalities; therefore, genetic and epigenetic changes have been explored as diagnostic targets in circulating tumour cells, cell-free DNA (cfDNA) and non-coding RNA (Table 3–Table 5). Owing to the position and composition of pancreaticobiliary tumours, tissue samples are frequently acellular, making diagnostics challenging. Recently, the utility of next-generation sequencing was explored as a technique that allows the detection of low-abundance mutations and abnormalities in small amounts of material40. Changes in the metabalome are also being explored as a potential diagnostic tool in pancreaticobiliary malignancy41.
Table 3. Genetic and epigenetic alterations in circulating tumour cells in pancreatic ductal adenocarcinoma and biliary tract cancer, 2013–2017.
Author (year) | Target | Biliary tract cancer, number | Pancreatic ductal adenocarcinoma, number | Benign lesions, number | Healthy volunteers, number | Detected | Sensitivity | Specificity | Area under the curve |
---|
Ankeny et al. (2016)120 | K-ras | - | 72 | 28 | - | - | 75% | 96.4% | 0.867 |
Kulemann et al. (2016)121 | K-ras | - | 21 | - | 10 | 80% (stage IIA/IIB) 91% (stage III/IV) | - | - | - |
Singh et al. (2015)122 | ctDNA, K-ras | - | - | - | - | - | 65.3% | 61.5% | 0.6681 |
Kinugasa et al. (2015)123 | K-ras | - | 141 | 20 | 20 | - | 62.6% | - | - |
Takai et al. (2015)124 | K-ras | - | 259 | - | - | - | 29.2% | - | - |
Sausen et al. (2015)125 | ctDNA | - | 77 | - | - | - | 43% | - | - |
Kulemann et al. (2015)126 | CTC K-ras | - | 11 | - | 9 | 75% (stage IIb) 71% (stage III) | - | - | - |
Zhang et al. (2015)127 | DAPI+, CD45-, CK+, CEP8 > 2+ | - | 22 Validation cohort: 11 | 6
8 | 30
10 | 68.2% |
63.6% |
94.4% |
0.84 |
Wu et al. (2014)128 | K-ras | - | 36 | - | 25 | - | 0 | 0 | - |
Bidard et al. (2013)129 | CK, CD45 | - | 79 | - | - | 11% | - | - | - |
Bobek et al. (2014)130 | DAPI, CK, CEA, Vimentin | - | 24 | - | - | 66.7% | - | - | - |
Rhim et al. (2014)131 | DAPI, CD45, CK, PDX-1 | - | 11 | 21 | 19 | 78% | - | - | - |
Iwanicki-Caron et al. (2013)132 | CTC | - | 40 | - | - | - | 55.5% | 100% | - |
Sheng et al. (2014)133 | CTC | - | 18 | - | - | 94.4% | - | - | - |
Catebacci et al. (2015)134 | CTC (in portal venous blood at EUS) | 2 | 14 | - | - | 100% (pulmonary vein blood) 22.2% (peripheral blood) | - | - | - |
Earl et al. (2015)135 | CTC | - | 35 | - | - | 20% | - | - | - |
Cauley et al. (2015)136 | Circulating epithelial cells | - | 105 | 34 | 9 | 49% | - | - | - |
Kamande et al. (2013)137 | DAPI, CD45, CK | - | 12 | - | - | 100% | - | - | - |
Table 4. Genetic and epigenetic alterations in circulating cell-free DNA pancreatic ductal adenocarcinoma and biliary tract cancer, 2013–2017.
Author (year) | Target | PDAC or BTC | Cancer, number | Benign lesions, number | Healthy volunteers, number | Detected | Sensitivity | Specificity |
---|
Takai et al. (2016)138 | K-ras | PDAC | 107 (non- operable) | - | - | 59% | - | - |
Takai et al. (2015)124 | cfDNA | PDAC | 48 | | | 29% | | |
Hadano et al. (2016)139 | K-ras | PDAC | 105 | - | 20 | 31% | - | - |
Zill et al. (2015)140 | K-ras, TP53, APC, FBXW7, SMAD4 | PDAC | 26 | - | - | - | 92.3% | 100% |
Earl et al. (2015)135 | K-ras | PDAC | 31 | - | - | 26% | - | - |
Kinusaga et al. (2015)123 | G12V, G12D, and G12R in codon 12 of K-ras gene | PDAC | 141 | 20 | 20 | 62% | - | - |
Sausen et al. (2015)125 | cfDNA | PDAC | 77 | - | - | 43% | - | - |
Wu et al. (2014)128 | K-ras | PDAC | 24 | - | 25 | 72% | - | - |
Table 5. Epigenetics: circulating non-coding RNA and DNA methylation markers in pancreatic ductal adenocarcinoma/biliary tract cancer, 2013–2017.
Author (year) | MicroRNA | Biliary tract cancer, number | Pancreatic ductal adenocarcinoma, number | Benign lesions, number | Healthy volunteers, number | Sensitivity | Specificity | Area under the curve |
---|
Circulating non-coding RNA |
Kishimoto et al. (2013)141 | MiR-21 (↑) | 94 94 | - - | - 23 | 50 - | 85% 72.3% | 100% 91.3% | 0.93 0.83 |
Wang et al. (2013)142 | miR-27a-3p + CA19-9(↑) | - | 129 | 103 | 60 | 85.3% | 81.6% | 0.886 |
Kawaguchi et al. (2013)143 | miR-221 (↑), miR-375 (↓) | - | 47 | - | 30 | - | - | 0.762 |
Zhao et al. (2013)144 | miR-192 (↑) | - | 70 | - | 40 | 76% | 55% | 0.63 |
Carleson et al. (2013)145 | MiR-375 (↑) | - | 48 | 47 | - | - | - | 0.72 |
Que et al. (2013)146 | miR-17-5p (↑) miR-21 (↑), | - | 22 | 12 | 8 | - | - | 0.887 0.897 |
Schultz et al. (2014)147 | Index I + CA19-9 Index II + CA19-9 | - | 409 | 25 | 312 | 85% 85% | 88% 86% | 0.93 0.92 |
Silakit et al. (2014)148 | MiR-192 (↑) | 11 | - | - | 9 | 74% | 72% | 0.803 |
Lin et al. (2015)149 | MiR-492 (↑) MiR-663a (↑) | - | 49 | - | 27 | 75% 85% | 70% 80% | 0.787 0.870 |
Chen et al. (2014)150 | miR-182 (↑) | - | 109 | 38 | 50 | 64.1% | 82.6% | 0.775 |
Wang et al. (2015)151 | MiR-150 (↑) | 15 | - | - | 15 | 80% | 58% | 0.764 |
Ganepola et al. (2015)152 | miR-22 (↑), miR-642b (↑) miR-885-5p (↑) | - | 11 | - | 11 | 91% | 91% | 0.970 |
Voigtlander et al. (2015)153 (serum) | MiR-1281 (↑) MiR-126 (↑) MiR-26a (↑) MiR-30b (↑) MiR-122 (↑) | 31 | - | 40 | - | 55% 68% 52% 52% 32% | 90% 93% 93% 88% 90% | 0.83 0.87 0.78 0.78 0.65 |
Voigtlander et al. (2015)153 (bile) | miR-412 (↑) miR-640 (↑) miR-1537 (↑) miR-3189 (↑) | 31 | - | 53 | - | 50% 50% 67% 67% | 89% 92% 90% 89% | 0.81 0.81 0.78 0.80 |
Abue et al. (2015)154 | miR-21 (↑), miR-483-3p (↑) | - | 32 | 12 | 30 | - | - | 0.790 0.754 |
Salter et al. (2015)155 | miR-196a (↑), miR-196b (↑) | - | 19 | 10 | 10 | 100% | 90% | 0.99 |
Kojima et al. (2015)156 | miR-6075, miR-4294, miR-6880-5p, miR-6799-5p, miR-125a-3p, miR-4530, miR-6836-3p, miR-4476 | 98 | 100 | 21 | 150 | 80.3% | 97.6% | 0.953 |
Xu et al. (2015)157 | miR-486-5p (↑) miR-938 (↑) | - | 156 | 142 | 65 | - | - | 0.861 0.693 |
Madhaven et al. (2015)158 | PaCIC + miRNA serum-exosome marker panel | - | - | - | - | 100% | 80% | - |
Komatsu et al. (2015)159 | miR-223 (↑) | - | 71 | - | 67 | 62% | 94.1% | 0.834 |
Alemar et al. (2016)160 | MiR-21 (↑) MiR-34a (↑) | - | 24 | - | 10 | - | - | 0.889 0.865 |
Wu et al. (2016)161 | MiR-150 (↓) | 30 | 30 | 28 | 50 | - | - | - |
Bernuzzi et al. (2016)162 | MiR-483-5p(↑) MiR-194(↑) | 40 | 40 | 70 | 40 | - | - | 0.77 0.74 |
Kim et al. (2016)163 | mRNA – CDH3 (↑) mRNA –IGF2BP3(↑) mRNA – HOXB7 (↑) mRNA – BIRC5 (↑) | - | 21 | 14 | - | 57.1% 76.2% 71.4% 76.2% | 64.3% 100% 57.1% 64.3% | 0.776 0.476 0.898 0.818 |
Duell et al. (2017)164 | MiR-10a (↑) MiR-10b (↑) MiR-21-5p (↑) MiR-30c (↑) MiR-155 (↑) MiR-212 (↑) | - | 225 | - | 225 | - | - | 0.66 0.68 0.64 0.71 0.64 0.64 |
DNA hypermethylation |
Branchi et al. (2016)165 | SHOX2/SEPT9
| 20 | - | - | 100 | 0.45% | 0.99% | 0.752 |
2. Bile and biliary brush biomarkers
Patients with an indeterminate stricture on cross-sectional imaging are typically referred for an ERCP and biliary brushing with or without endobilary biopsy to obtain tissue for diagnosis, with or without therapeutic stenting28. Although these techniques do not compromise resection margins in potentially resectable cases, sensitivity remains low (9–57%) and patients frequently have to undergo multiple procedures to obtain a diagnosis28–30. Bile can be easily obtained at the time of ERCP and, owing to its proximity to the tumour, is a potentially important source of diagnostic biomarkers in these cancers (Table 6). Unfortunately, owing to the invasiveness of ERCP, the role of these biomarkers is limited to diagnosis rather than screening or surveillance in these tumours.
Table 6. Bile and biliary brush biomarkers for pancreatic and biliary tract cancer.
Author (year) | Biomarker | Pancreatic ductal adenocarcinoma, number | Biliary tract cancer, number | Benign lesions, number | Healthy controls, number | Bile or biliary brush | Sensitivity | Specificity | Area under the curve |
---|
Single biomarkers |
Dhar et al. (2013)166 | M2-PK | - | 88 | 79 | 17 | Bile | 90.3% | 84.3% | - |
Navaneethan et al. (2015)167 | M2-PK | - | - | - | - | Bile | 52.9% | 94.1% | 0.77 |
Keane (2017)168 | MCM5 | 24 | 17 | 47 | | Biliary brush | 55.6% | 77.8% | 0.79 |
Danese et al. (2014)169 | MUC5AC | - | 20 | 20 | - | Serum Bile | - | - | 0.94 0.99 |
Farina et al. (2014)170 | CEAM6 | 23 | 6 | 12 | - | Bile | 93% | 83% | 0.92 |
Budzynska et al. (2013)171 | NGAL | 6 | 16 | 18 | - | Bile | 77% | 72% | 0.74 |
Jiao et al. (2014)96 | Nucleosides | | 202 (gallbladder cancer) | 203 | 205 | Bile | 95.3% | 96.4% | - |
Ince et al. (2014)93 | CE | 41 | 25 | 129 | - | Bile | 57.3% | 68.2% | 0.516 |
Ince et al. (2014)93 | CA 19-9 | 41 | 25 | 129 | - | Bile | 74.0% | 34.1% | 0.616 |
Ince et al. (2014)93 | VEGFR3 | 41 | 25 | 129 | - | Bile | 56.2% | 79.1% | 0.663 |
Ince et al. (2014)93 | Total antioxidant capacity | 41 | 25 | 129 | - | Bile | 65.6% | 50.4% | 0.581 |
Abdel-Razik et al. (2016)114 | IGF-1 | 25 | 18 | 62 | - | Bile | 91.4% | 89.5% | 0.943 |
Abdel-Razik et al. (2016)114 | VEGF | 25 | 18 | 62 | - | Bile | 90.3% | 84.9% | 0.915 |
Kim et al. (2016)163 | mRNA – CDH3 (↑) mRNA –IGF2BP3(↑) mRNA – HOXB7 (↑) mRNA – BIRC5 (↑) | - | 21 | 14 | - | Biliary brush | 57.1% 76.2% 71.4% 76.2% | 64.3% 100% 57.1% 64.3% | 0.776 0.476 0.898 0.818 |
3. Urinary biomarkers
Urine provides a very easy and acceptable source for biomarker analysis. In BTC, a 42-peptide panel (consisting mostly of fragments of interstitial collagens) correctly identified 35 of 42 BTC patients with a sensitivity of 83% and a specificity of 79%42. In PDAC, the three-biomarker panel (LYVE-1, REG1A and TFF1) has been validated in a multi-centre cohort of 371 samples. When comparing PDAC stage I–IIA (resectable disease) with healthy urines, the panel achieved AUCs of 0.97 (95% confidence interval of 0.93–1.00). The performance of the urine biomarker panel in discriminating PDAC stage I–IIA was superior to the performance of serum CA19-9 (P=0.006)43 (Table 7).
Table 7. Summary of urine protein biomarkers for pancreatic and biliary tract cancer, 2013–2017.
Author (year) | Biomarker/ Combination (urine) | Pancreatic ductal adenocarcinoma, number | Biliary tract cancer, number | Benign cancer/ Chronic pancreatitis, number | Healthy volunteers, number | Sensitivity | Specificity | Area under the curve |
---|
Single biomarker |
Roy et al. (2014)172 | MMP2 | 51 | - | - | 60 | 70% | 85% | - |
Roy et al. (2014)172 | TIMP-1 | 51 | - | - | 60 | 90% | 70% | - |
Jiao et al. (2014)96 | Nucleosides | - | 202 (gallbladder cancer) | 203 | 205 | 89.4% | 97.1% | - |
Metzger et al. (2013)42 | Urine Proteomic analysis | - | 42 | 81 | - | 83% | 79% | 0.87 |
Biomarker combinations |
Radon et al. (2015)43 | LYVE-1 + REG1A + TFF1 | 192 | - | - | 87 | - | - | 0.89 |
4. Symptoms and cancer decision support tools
Recently, pre-diagnostic symptom profiles have been investigated as an alternative way of detecting hepato-pancreato-biliary (HPB) cancers at an early stage8,9,16,44. It is now recognised that the onset of PDAC and BTC is heralded by a collection of gastrointestinal and constitutional symptoms45. Although overlap occurs with other benign and malignant conditions, certain symptoms such as back pain, lethargy and new-onset diabetes have been identified as particularly suggestive of PDAC. Commonly performed blood tests such as liver function tests, glucose and haemoglobin also typically become abnormal in the months preceding diagnosis46. Therefore, cancer decision support tools have been produced from combinations of symptoms and risk factors. In the UK, they have been introduced into general practices in 15 cancer networks to date8, and their utility is currently being audited47. Modification to existing tools to enhance their diagnostic accuracy can be expected in the future.
Endoscopy
1. Endoscopic ultrasonography
If there is a mass lesion on cross-sectional imaging, endoscopic ultrasonography with fine-needle aspiration (EUS-FNA) provides an alternative method for visualising and sampling the extra-hepatic biliary tree, pancreas, gallbladder or peri-hilar lymph nodes. EUS-FNA has a diagnostic accuracy for PDAC of between 65% and 96%48,49. In BTC, a single-centre study reported a sensitivity of 73%, which was significantly better in distal compared with proximal tumours (81% versus 59% respectively, P=0.04)50. Recently, developed fine core biopsy needles appear to have improved diagnostic accuracy over traditional FNA needles, but randomised trials are awaited49,51,52. Rapid onsite examination by a cytopathologist is used in some centres, particularly in North America, and has been shown to improve the yield of EUS-FNA in individual centres53,54 but this trend has not been borne out in recent randomised controlled trials55.
To improve the diagnostic accuracy of EUS, it can also be combined with novel adjuncts such as contrast agents (SonoVue®), transient elastography (TE) or confocal laser endomicroscopy (CLE). TE allows the measurement of the tissue firmness, which tends to be increased in malignant tissue. In a recent single-centre study from the UK, quantitative strain measurements were found to have high sensitivity but low specificity for the detection of PDAC56. The technology to perform the techniques is available on most modern EUS machines and adds little time to the overall procedure time. The technique can be performed equally well by endosonographers with limited experience57,58 and is particularly advantageous in cases where the diagnosis remains uncertain after standard EUS has been performed59. Contrast-enhanced EUS is performed with agents such as SonoVue® and allows visualisation of the early arterial phase and late parenchymal phase enhancement of the pancreas. Pancreatic tumours are generally hypovascular compared with the surrounding parenchyma60,61. Dynamic contrast EUS is a relatively novel method that allows the non-invasive quantification of the tumour perfusion compared with the pancreatic parenchyma by using software that is now built into a number of EUS scanners. The use of this technology is evolving but is expected to be most applicable when predicting tumour response to chemotherapeutic agents, particularly new drugs against vascular angioneogenesis62,63.
Recently, a needle-based confocal endomicroscope has also been developed which can be passed through a 19G FNA needle to assess indeterminate masses, cysts or lymph nodes. Malignancy in the hepatobilary tract is identified by the presence of irregular vessels, vascular leakage and large dark clumps (Figure 1)64. In a recent study of 25 patients with indeterminate pancreatic masses referred for EUS-FNA, needle-based CLE was shown to be a safe and feasible technique65.

Figure 1. Novel diagnostic adjuncts to ERCP and EUS.
(a) Cholangioscopic view of a malignant hilar stricture with visualisation of the ulcerated, friable biliary mucosa via the Spyglass cholangioscope system (Boston Scientific Corp, Massachusetts, USA). (b) Confocal endomicroscopic image of pancreatic cancer, showing characteristic black clumps. Image was obtained using the Cellvizio AQ-Flex® probe which was introduced to the tumour via 19G FNA needle at the time of EUS.
2. Endoscopic retrograde cholangiopancreatography
ERCP is typically undertaken when imaging demonstrates an indeterminate biliary stricture and tissue acquisition is required for cytological or histological assessment. Biliary brush cytology and endobiliary biopsy have a sensitivity for malignancy of 9–57%29,30,66,67. Most HPB tumours exhibit chromosomal aneuploidy68; therefore, in some centres, fluorescence in situ hybridisation and digital image analysis are used to assess for the presence of DNA abnormalities in brush cytology30,69. Although these techniques have been adopted by only a few centres, the presence of polysomy is highly suggestive of BTC30,69.
Poor diagnostic accuracy in biliary brush and endobiliary samples has been attributed to their being non-targeted samples obtained with only fluoroscopic guidance70. The single-operator cholangioscopy system (SpyGlass, Boston Scientific Corporation, Natick, MA, USA) introduced in 2006 and now superseded by the SpyGlass DS system enables intrabiliary biopsies under direct vision via small disposable forceps (Figure 1). In a recent systematic review, the sensitivity and specificity of cholangioscopy-guided biopsies in the diagnosis of malignant biliary strictures were 60.1% and 98.0%, respectively71. Higher sensitivities are observed for intrinsic biliary malignancy compared with extrinsic compressing tumours72. Several techniques have been employed to augment the visualised mucosa during cholangioscopy, including chromendoscopy with methylene blue73–75, narrow-band imaging76,77 and autofluorescence78.
During ERCP, a “CholangioFlex” confocal probe (Mauna Kea Technologies, Paris, France) can be placed down the working channel of a cholangioscope or duodenoscope to obtain real-time CLE images, which are akin to standard histology (Figure 1). If the images obtained from a point on the biliary mucosa contain dark areas, this is highly suggestive of malignancy79,80. The diagnostic accuracy of probe-based CLE was recently validated in a prospective multi-centre international study with 112 patients (71 with malignant lesions). Tissue sampling alone had a sensitivity, specificity and diagnostic accuracy of 56%, 100% and 72%, respectively. In comparison, ERCP with probe-based CLE had a sensitivity, specificity and diagnostic accuracy of 89%, 71% and 82%, respectively. Diagnostic accuracy increased to 88% when probe-based CLE and tissue sampling results were combined81. CLE is also feasible in the pancreatic duct during pancreaticoscopy but, owing to concerns over pancreatitis, is rarely used. In a case report by Meining et al., the presence of a main duct-intraductal papillary mucinous neoplasia was confirmed by clear views of typical finger-like projections82. Intraductal ultrasound in small studies has also been shown to have a diagnostic accuracy of up to 90%83.
Conclusions
Currently, the most widely used tumour marker in pancreaticobiliary malignancy is CA19-9. However, its use is limited by its elevation in a number of other benign and malignant conditions. Furthermore, it is not produced in approximately 7% of the population who are Lewis antigen–negative and is often undetectable when tumours are small. Over the last few years, a number of very promising biomarker panels have been identified which can detect tumours at an early stage when curative intervention could be possible. These markers are subject to ongoing validation studies but appear likely to be implemented into screening programmes, particularly for high-risk groups, in the near future. Novel endoscopic techniques such as per-oral cholangioscopy and confocal endomicroscopy can enhance the diagnostic accuracy of standard techniques and are increasingly available in large-volume centres worldwide.
Abbreviations
AUC, area under the curve; BTC, biliary tract cancer; CA, carbohydrate antigen; CEA, carcinoembryonic antigen; CLE, confocal laser endomicroscopy; ERCP, endoscopic retrograde cholangiopancreatography; EUS, endoscopic ultrasound; FNA, fine-needle aspiration; HPB, hepato-pancreato-biliary; PDAC, pancreatic ductal adenocarcinoma; TE, transient elastography.
Competing interests
The authors declare that they have no competing interests.
Grant information
SPP is supported in part by National Institutes of Health grant P01CA8420. Part of the work was undertaken at University College London Hospitals/University College London, which received a portion of funding from the Department of Health’s National Institute for Health Research Biomedical Research Centres funding scheme.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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