Non-Invasive Biomarkers for Earlier Detection of Pancreatic Cancer-A Comprehensive Review

In-depth immune profiling of peripheral blood mononuclear cells in patients with pancreatic ductal adenocarcinoma reveals discriminative immune subpopulations

Pancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis with a 5-year survival of less than 10%. More knowledge of the immune response developed in patients with PDAC is pivotal to develop better combination immune therapies to improve clinical outcome. In this study, we used mass cytometry time-of-flight to undertake an in-depth characterization of PBMCs from patients with PDAC and examine the differences with healthy controls and patients with benign diseases of the biliary system or pancreas. Peripheral blood mononuclear cells from patients with PDAC or benign disease are characterized by the increase of pro-inflammatory cells, as CD86+ classical monocytes and memory T cells expressing CCR6+ and CXCR3+, associated with T helper 1 (Th1) and Th17 immune responses, respectively. However, PBMCs from patients with PDAC present also an increase of CD39+ regulatory T cells and CCR4+CCR6-CXCR3- memory T cells, suggesting Th2 and regulatory responses. Concluding, our results show PDAC develops a multifaceted immunity, where a proinflammatory component is accompanied by regulatory responses, which could inhibit potential antitumor mechanisms.

New techniques to identify the tissue of origin for cancer of unknown primary in the era of precision medicine: progress and challenges

Despite a standardized diagnostic examination, cancer of unknown primary (CUP) is a rare metastatic malignancy with an unidentified tissue of origin (TOO). Patients diagnosed with CUP are typically treated with empiric chemotherapy, although their prognosis is worse than those with metastatic cancer of a known origin. TOO identification of CUP has been employed in precision medicine, and subsequent site-specific therapy is clinically helpful. For example, molecular profiling, including genomic profiling, gene expression profiling, epigenetics and proteins, has facilitated TOO identification. Moreover, machine learning has improved identification accuracy, and non-invasive methods, such as liquid biopsy and image omics, are gaining momentum. However, the heterogeneity in prediction accuracy, sample requirements and technical fundamentals among the various techniques is noteworthy. Accordingly, we systematically reviewed the development and limitations of novel TOO identification methods, compared their pros and cons and assessed their potential clinical usefulness. Our study may help patients shift from empirical to customized care and improve their prognoses.

Meta-analysis of prognostic factors for overall survival and disease-free survival among resected patients with combined hepatocellular carcinoma and cholangiocarcinoma

BACKGROUND

Combined hepatocellular carcinoma and cholangiocarcinoma (CHCC-CC) is a rare subtype of primary liver malignancy and has been treated equally as intra-hepatic cholangiocarcinoma (IHCC) according to the 8th AJCC staging system. Owing to its rarity, its prognostic factors have been rarely explored and defined.

METHODS

PubMed, EMBASE, the Cochrane Library and Web of Science were searched up till January 1st, 2023 and eligible studies were restricted to studies reported prognostic factors of resected CHCC-CC. Standard Parmar modifications were used to determine pooled univariable hazard ratios (HRs).

RESULTS

A total of eleven studies with 1286 patients with resected classical CHCC-CC were finally included. Pooled results indicated that serum tumor biomarkers, including AFP, CA199, and CEA, were prognostic factors for postoperative overall survival (OS) and disease-free survival (DFS). Moreover, liver cirrhosis (P = 0.010), HBV infection (P = 0.030), and HCV infection (P < 0.001) were prognostic factors for OS. Age (HR = 1.03, P = 0.005) was a prognostic factor for DFS. Tumor size (OS: HR = 2, P < 0.001, DFS: HR = 2.15, P < 0.001), tumor number (OS: HR = 2.05, P < 0.001; DFS: HR = 1.96, P = 0.006), surgical margin (OS: HR = 2.33, <0.001001; DFS: HR = 2.35, P < 0.001), node metastasis (OS: HR = 2.96, P < 0.001; DFS: HR = 2.1, P < 0.001), vascular invasion (OS: HR = 2.17, P < 0.001; DFS: HR = 2.64, P < 0.001), and postoperative prophylactic trans-arterial chemotherapy embolization (PPTACE) (OS: HR = 1.67, P = 0.04; DFS: HR = 2.31, P < 0.001) were common prognostic factors for OS and DFS.

CONCLUSION

Various risk factors unmentioned in the 8th AJCC staging system were identified. These promising findings would facilitate a more personalized predictive model and help clinicians to stratify patients with different survival outcomes.

A one-pot isothermal Cas12-based assay for the sensitive detection of microRNAs

The use of microRNAs as clinical cancer biomarkers is hindered by the absence of accurate, fast and inexpensive assays for their detection in biofluids. Here we report a one-step and one-pot isothermal assay that leverages rolling-circle amplification and the endonuclease Cas12a for the accurate detection of specific miRNAs. The assay exploits the cis-cleavage activity of Cas12a to enable exponential rolling-circle amplification of target sequences and its trans-cleavage activity for their detection and for signal amplification. In plasma from patients with pancreatic ductal adenocarcinoma, the assay detected the miRNAs miR-21, miR-196a, miR-451a and miR-1246 in extracellular vesicles at single-digit femtomolar concentrations with single-nucleotide specificity. The assay is rapid (sample-to-answer times ranged from 20 min to 3 h), does not require specialized instrumentation and is compatible with a smartphone-based fluorescence detection and with the lateral-flow format for visual readouts. Simple assays for the detection of miRNAs in blood may aid the development of miRNAs as biomarkers for the diagnosis and prognosis of cancers.

Urine biomarkers enable pancreatic cancer detection up to 2 years before diagnosis

The poor prognosis of pancreatic ductal adenocarcinoma (PDAC) is mainly attributed to late diagnosis. We assessed the predictive performance of our previously reported urine biomarker panel for earlier detection of PDAC (LYVE1, REG1B and TFF1) in prediagnostic samples, alone and in combination with plasma CA19-9. This nested case-control study included 99 PDAC cases with urine samples prospectively collected up to 5 years prior to PDAC diagnosis and 198 matched controls. The samples were obtained from the Shanghai Women's Health Study (SWHS), the Shanghai Men's Health Studies (SMHS) and the Southern Community Cohort Study (SCCS). The urine biomarkers were measured by ELISA. Plasma CA19-9 was quantified by Luminex. Multiple logistic regression and Wilcoxon rank-sum and Mann-Whitney test were used for analysis. The internal validation approach was applied and the validated AUC estimators are reported on. The algorithm of urinary protein panel, urine creatinine and age named PancRISK, displayed similar AUC as CA19-9 up to 1 year before PDAC diagnosis (AUC = 0.79); however, the combination enhanced the AUCs to 0.89, and showed good discriminative ability (AUC = 0.77) up to 2 years. The combination showed sensitivity (SN) of 72% at 90% specificity (SP), and SP of 59% at 90% SN up to 1 year and 60% SN with 80% SP and 53% SP with 80% SN up to 2 years before PDAC diagnosis. Adding the clinical information on BMI value resulted in the overall improvement in performance of the PancRISK score. When combined with CA19-9, the urinary panel reached a workable model for detecting PDAC cases up to 2 years prior to diagnosis.

Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis

BACKGROUND & AIMS

Our purpose was to detect pancreatic ductal adenocarcinoma (PDAC) at the prediagnostic stage (3-36 months before clinical diagnosis) using radiomics-based machine-learning (ML) models, and to compare performance against radiologists in a case-control study.

METHODS

Volumetric pancreas segmentation was performed on prediagnostic computed tomography scans (CTs) (median interval between CT and PDAC diagnosis: 398 days) of 155 patients and an age-matched cohort of 265 subjects with normal pancreas. A total of 88 first-order and gray-level radiomic features were extracted and 34 features were selected through the least absolute shrinkage and selection operator-based feature selection method. The dataset was randomly divided into training (292 CTs: 110 prediagnostic and 182 controls) and test subsets (128 CTs: 45 prediagnostic and 83 controls). Four ML classifiers, k-nearest neighbor (KNN), support vector machine (SVM), random forest (RM), and extreme gradient boosting (XGBoost), were evaluated. Specificity of model with highest accuracy was further validated on an independent internal dataset (n = 176) and the public National Institutes of Health dataset (n = 80). Two radiologists (R4 and R5) independently evaluated the pancreas on a 5-point diagnostic scale.

RESULTS

Median (range) time between prediagnostic CTs of the test subset and PDAC diagnosis was 386 (97-1092) days. SVM had the highest sensitivity (mean; 95% confidence interval) (95.5; 85.5-100.0), specificity (90.3; 84.3-91.5), F1-score (89.5; 82.3-91.7), area under the curve (AUC) (0.98; 0.94-0.98), and accuracy (92.2%; 86.7-93.7) for classification of CTs into prediagnostic versus normal. All 3 other ML models, KNN, RF, and XGBoost, had comparable AUCs (0.95, 0.95, and 0.96, respectively). The high specificity of SVM was generalizable to both the independent internal (92.6%) and the National Institutes of Health dataset (96.2%). In contrast, interreader radiologist agreement was only fair (Cohen's kappa 0.3) and their mean AUC (0.66; 0.46-0.86) was lower than each of the 4 ML models (AUCs: 0.95-0.98) (P < .001). Radiologists also recorded false positive indirect findings of PDAC in control subjects (n = 83) (7% R4, 18% R5).

CONCLUSIONS

Radiomics-based ML models can detect PDAC from normal pancreas when it is beyond human interrogation capability at a substantial lead time before clinical diagnosis. Prospective validation and integration of such models with complementary fluid-based biomarkers has the potential for PDAC detection at a stage when surgical cure is a possibility.

Artificial Intelligence-Driven Diagnosis of Pancreatic Cancer

Pancreatic cancer is among the most challenging forms of cancer to treat, owing to its late diagnosis and aggressive nature that reduces the survival rate drastically. Pancreatic cancer diagnosis has been primarily based on imaging, but the current state-of-the-art imaging provides a poor prognosis, thus limiting clinicians' treatment options. The advancement of a cancer diagnosis has been enhanced through the integration of artificial intelligence and imaging modalities to make better clinical decisions. In this review, we examine how AI models can improve the diagnosis of pancreatic cancer using different imaging modalities along with a discussion on the emerging trends in an AI-driven diagnosis, based on cytopathology and serological markers. Ethical concerns regarding the use of these tools have also been discussed.

Early diagnosis of pancreatic cancer: What strategies to avoid a foretold catastrophe

While great strides in improving survival rates have been made for most cancers in recent years, pancreatic ductal adenocarcinoma (PDAC) remains one of the solid tumors with the worst prognosis. PDAC mortality often overlaps with incidence. Surgical resection is the only potentially curative treatment, but it can be performed in a very limited number of cases. In order to improve the prognosis of PDAC, there are ideally two possible ways: the discovery of new strategies or drugs that will make it possible to treat the tumor more successfully or an earlier diagnosis that will allow patients to be operated on at a less advanced stage. The aim of this review was to summarize all the possible strategies available today for the early diagnosis of PDAC and the paths that research needs to take to make this goal ever closer. All the most recent studies on risk factors and screening modalities, new laboratory tests including liquid biopsy, new imaging methods and possible applications of artificial intelligence and machine learning were reviewed and commented on. Unfortunately, in 2022 the results for this type of cancer still remain discouraging, while a catastrophic increase in cases is expected in the coming years. The article was also written with the aim of highlighting the urgency of devoting more attention and resources to this pathology in order to reach a solution that seems more and more unreachable every day.

Role of artificial intelligence in early detection and screening for pancreatic adenocarcinoma

Pancreatic adenocarcinoma remains to be one of the deadliest malignancies in the world despite treatment advancement over the past few decades. Its low survival rates and poor prognosis can be attributed to ambiguity in recommendations for screening and late symptom onset, contributing to its late presentation. In the recent years, artificial intelligence (AI) as emerged as a field to aid in the process of clinical decision making. Considerable efforts have been made in the realm of AI to screen for and predict future development of pancreatic ductal adenocarcinoma. This review discusses the use of AI in early detection and screening for pancreatic adenocarcinoma, and factors which may limit its use in a clinical setting.

CD63-positive extracellular vesicles are potential diagnostic biomarkers of pancreatic ductal adenocarcinoma

Background

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest gastrointestinal cancers with a 5-year survival rate of less than 10%. Biomarkers for early PDAC detection are useful in treating patients with PDAC. Extracellular vesicles (EVs) are lipid-bound vesicles that are potential biomarkers of various diseases such as PDAC. In this study, we quantitatively measured the serum levels of EVs (CD63⁺-EVs) or platelet-derived EVs (CD41⁺- and CD61⁺-EVs) and evaluated their potential use as biomarkers of PDAC.

Methods

We measured the serum levels of CD63⁺-, CD41⁺-, CD61⁺-EVs using sandwich enzyme-linked immunosorbent assay based on Tim4 with specificity for phosphatidylserine on EVs in age- and sex-matched healthy controls (HCs, n = 39) and patients with PDAC (n = 39). We also examined the effect of tumor burden on the serum EV levels after surgical resection (n = 28). CA19-9, a clinical PDAC biomarker, was also measured for comparison.

Results

Serum levels of CD63⁺-EVs, CD41⁺-EVs, and CD61⁺-EVs were significantly increased in patients with PDAC compared to HCs. Receiver operating characteristic analysis revealed that CD63⁺-EVs exhibited the highest diagnostic performance to discriminate patients with PDAC from HCs (area under the curve (AUC): 0.846), which was comparable to CA19-9 (AUC: 0.842). CA19-9 showed lower AUC values in early stages (I–II, AUC: 0.814) than in late stages (III–IV, AUC: 0.883) PDAC. Conversely, CD63⁺-EVs, CD41⁺-EVs, and CD61⁺-EVs showed comparable AUCs between early- and late-stage PDAC. The combined use of CA19-9 and CD63⁺-EVs showed a higher diagnostic performance for early-stage PDAC (AUC: 0.903) than CA19-9. The serum levels of CD63⁺-EVs, CD41⁺-EVs, CD61⁺-EVs, and CA19-9 decreased significantly after surgical resection, demonstrating that EVs are increased in sera of patients depending on the tumor burden.

Conclusions

The serum levels of CD63⁺-EVs and platelet-derived EVs (CD41⁺-EVs, CD61⁺-EVs) are increased in patients with PDAC than HCs. Since CD63⁺-EVs showed a high AUC to discriminate patients with PDAC from HCs; they might be useful as potential biomarkers for PDAC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-022-02228-7.

NetRank Recovers Known Cancer Hallmark Genes as Universal Biomarker Signature for Cancer Outcome Prediction

Gene expression can serve as a powerful predictor for disease progression and other phenotypes. Consequently, microarrays, which capture gene expression genome-wide, have been used widely over the past two decades to derive biomarker signatures for tasks such as cancer grading, prognosticating the formation of metastases, survival, and others. Each of these signatures was selected and optimized for a very specific phenotype, tissue type, and experimental set-up. While all of these differences may naturally contribute to very heterogeneous and different biomarker signatures, all cancers share characteristics regardless of particular cell types or tissue as summarized in the hallmarks of cancer. These commonalities could give rise to biomarker signatures, which perform well across different phenotypes, cell and tissue types. Here, we explore this possibility by employing a network-based approach for pan-cancer biomarker discovery. We implement a random surfer model, which integrates interaction, expression, and phenotypic information to rank genes by their suitability for outcome prediction. To evaluate our approach, we assembled 105 high-quality microarray datasets sampled from around 13,000 patients and covering 13 cancer types. We applied our approach (NetRank) to each dataset and aggregated individual signatures into one compact signature of 50 genes. This signature stands out for two reasons. First, in contrast to other signatures of the 105 datasets, it is performant across nearly all cancer types and phenotypes. Second, It is interpretable, as the majority of genes are linked to the hallmarks of cancer in general and proliferation specifically. Many of the identified genes are cancer drivers with a known mutation burden linked to cancer. Overall, our work demonstrates the power of network-based approaches to compose robust, compact, and universal biomarker signatures for cancer outcome prediction.

Clinical Significance of Tumor-Infiltrating Conventional and Plasmacytoid Dendritic Cells in Pancreatic Ductal Adenocarcinoma

Simple Summary

The tumor immune contexture plays a pivotal role for the clinical outcome of cancer patients and the efficacy of various treatment modalities. Dendritic cells (DCs) represent a major component of the tumor immune architecture that can either efficiently promote antitumor immunity or contribute to immunosuppression. Here, we investigated the frequency, spatial organization, and clinical significance of tumor-infiltrating conventional DCs type 1 (cDC1s) and type 2 (cDC2s) and plasmacytoid DCs (pDCs) in pancreatic ductal adenocarcinoma (PDAC). A higher frequency of whole tumor area (WTA)- and tumor stroma (TS)-infiltrating cDC1s, and of intraepithelial tumor-infiltrating cDC2s, was significantly associated with improved survival. Furthermore, a higher density of both WTA- and TS-infiltrating cDC1s and pDCs emerged as an independent prognostic factor for better survival. These results provide evidence that tumor-infiltrating DCs are associated with survival of PDAC patients and may support the design of novel DC-based immunotherapeutic strategies.

Abstract

Dendritic cells (DCs) play a key role in the orchestration of antitumor immunity. Activated DCs efficiently enhance antitumor effects mediated by natural killer cells and T lymphocytes. Conversely, tolerogenic DCs essentially contribute to an immunosuppressive tumor microenvironment. Thus, DCs can profoundly influence tumor progression and clinical outcome of tumor patients. To gain novel insights into the role of human DCs in pancreatic ductal adenocarcinoma (PDAC), we explored the frequency, spatial organization, and clinical significance of conventional DCs type 1 (cDC1s) and type 2 (cDC2s) and plasmacytoid DCs (pDCs) in primary PDAC tissues. A higher density of whole tumor area (WTA)- and tumor stroma (TS)-infiltrating cDC1s was significantly associated with better disease-free survival (DFS). In addition, an increased frequency of intraepithelial tumor-infiltrating cDC2s was linked to better DFS and overall survival (OS). Furthermore, an increased density of WTA- and TS-infiltrating pDCs tended to improve DFS. Moreover, a higher frequency of WTA- and TS-infiltrating cDC1s and pDCs emerged as an independent prognostic factor for better DFS and OS. These findings indicate that tumor-infiltrating DCs can significantly influence the clinical outcome of PDAC patients and may contribute to the design of novel treatment options that target PDAC-infiltrating DCs.

Soluble AXL is a novel blood marker for early detection of pancreatic ductal adenocarcinoma and differential diagnosis from chronic pancreatitis

Background

Early diagnosis is crucial for patients with pancreatic ductal adenocarcinoma (PDAC). The AXL receptor tyrosine kinase is proteolytically processed releasing a soluble form (sAXL) into the blood stream. Here we explore the use of sAXL as a biomarker for PDAC.

Methods

AXL was analysed by immunohistochemistry in human pancreatic tissue samples. RNA expression analysis was performed using TCGA/GTEx databases. The plasma concentrations of sAXL, its ligand GAS6, and CA19-9 were studied in two independent cohorts, the HMar cohort (n = 59) and the HClinic cohort (n = 142), including healthy controls, chronic pancreatitis (CP) or PDAC patients, and in a familial PDAC cohort (n = 68). AXL expression and sAXL release were studied in PDAC cell lines and murine models.

Findings

AXL is increased in PDAC and precursor lesions as compared to CP or controls. sAXL determined in plasma from two independent cohorts was significantly increased in the PDAC group as compared to healthy controls or CP patients. Patients with high levels of AXL have a lower overall survival. ROC analysis of the plasma levels of sAXL, GAS6, or CA19-9 in our cohorts revealed that sAXL outperformed CA19-9 for discriminating between CP and PDAC. Using both sAXL and CA19-9 increased the diagnostic value. These results were validated in murine models, showing increased sAXL specifically in animals developing PDAC but not those with precursor lesions or acinar tumours.

Interpretation

sAXL appears as a biomarker for early detection of PDAC and PDAC–CP discrimination that could accelerate treatment and improve its dismal prognosis.

Funding

This work was supported by grants PI20/00625 (PN), RTI2018-095672-B-I00 (AM and PGF), PI20/01696 (MG) and PI18/01034 (AC) from MICINN-FEDER and grant 2017/SGR/225 (PN) from Generalitat de Catalunya.

Roles of Dclk1 in the pathogenesis, diagnosis, prognosis and treatment of pancreatic cancer: A review

INTRODUCTION

Pancreatic cancer (PC) is a malignant tumor with significantly increased incidence and poor prognosis. Its extremely poor prognosis is generally attributed to its early invasion and metastasis as well as the presence of chemotherapy resistance, which may be related to the potential role of cancer stem cells (CSCs). Doublecortin-like kinase 1 (Dclk1) has been recognized to be a marker of CSCs in PC, showing intimate association with its occurrence, metastasis, and poor prognosis.

AREAS COVERED

A review serves to provide a comprehensive overview of Dclk1 in the pathogenesis, diagnosis, prognosis, and treatment in PC.

EXPERT OPINION

Searching for potential key biomarkers for PC has been an urgent issue to be addressed. The expression of Dclk1 is increasing in PC, and its effect is linked to the mutant Kras, supporting that it may be a potential new target. Therefore, it highlights Dclk1 as a candidate biomarker and therapeutic target in future clinical application.

Challenges of an Emerging Disease: The Evolving Approach to Diagnosing Devil Facial Tumour Disease

Devil Facial Tumour Disease (DFTD) is an emerging infectious disease that provides an excellent example of how diagnostic techniques improve as disease-specific knowledge is generated. DFTD manifests as tumour masses on the faces of Tasmanian devils, first noticed in 1996. As DFTD became more prevalent among devils, karyotyping of the lesions and their devil hosts demonstrated that DFTD was a transmissible cancer. The subsequent routine diagnosis relied on microscopy and histology to characterise the facial lesions as cancer cells. Combined with immunohistochemistry, these techniques characterised the devil facial tumours as sarcomas of neuroectodermal origin. More sophisticated molecular methods identified the origin of DFTD as a Schwann cell, leading to the Schwann cell-specific protein periaxin to discriminate DFTD from other facial lesions. After the discovery of a second facial cancer (DFT2), cytogenetics and the absence of periaxin expression confirmed the independence of the new cancer from DFT1 (the original DFTD). Molecular studies of the two DFTDs led to the development of a PCR assay to differentially diagnose the cancers. Proteomics and transcriptomic studies identified different cell phenotypes among the two DFTD cell lines. Phenotypic differences were also reflected in proteomics studies of extracellular vesicles (EVs), which yielded an early diagnostic marker that could detect DFTD in its latent stage from serum samples. A mesenchymal marker was also identified that could serve as a serum-based differential diagnostic. The emergence of two transmissible cancers in one species has provided an ideal opportunity to better understand transmissible cancers, demonstrating how fundamental research can be translated into applicable and routine diagnostic techniques.

Pancreatic cancer in 2021: What you need to know to win

Pancreatic cancer is one of the solid tumors with the worst prognosis. Five-year survival rate is less than 10%. Surgical resection is the only potentially curative treatment, but the tumor is often diagnosed at an advanced stage of the disease and surgery could be performed in a very limited number of patients. Moreover, surgery is still associated with high post-operative morbidity, while other therapies still offer very disappointing results. This article reviews every aspect of pancreatic cancer, focusing on the elements that can improve prognosis. It was written with the aim of describing everything you need to know in 2021 in order to face this difficult challenge.

Sampling, Logistics, and Analytics of Urine for RT-qPCR-based Diagnostics

Body fluids in the context of cancer diagnosis are the primary source of liquid biopsy, i.e., biomarker detection that includes blood and serum, urine, and saliva. RNA represents a particular class of biomarkers because it is thought to monitor the current status of gene expression in humans, in organs, and if present, also in tumors. In case of bladder cancer, we developed a scheme that describes, in detail, all steps from the collection of urine samples from patients, stabilization of samples, their transportation, storage, and marker analysis by qPCR-based technology. We find that urine samples prepared according to this protocol show stability of RNA over more than 10 days at unchilled temperatures during shipping. A specific procedure of primer design and amplicon evaluation allows a specific assignment of PCR products to human genomics and transcriptomics data collections. In summary, we describe a technical option for the robust acquisition of urine samples and the quantitative detection of RNA-based tumor markers in case of bladder cancer patients. This protocol is for general use, and we describe that it works for any RNA-based tumor marker in urine of cancer patients.

Thrombospondin-2 as a diagnostic biomarker for distal cholangiocarcinoma and pancreatic ductal adenocarcinoma

PURPOSE

Distal cholangiocarcinoma and pancreatic ductal adenocarcinoma are malignancies with poor prognoses that can be difficult to distinguish preoperatively. Thrombospondin-2 has been proposed as a novel diagnostic biomarker for early pancreatic ductal adenocarcinoma. The aim of the present study was to evaluate thrombospondin-2 as a diagnostic and prognostic biomarker in combination with current biomarker CA 19-9 for distal cholangiocarcinoma and pancreatic ductal adenocarcinoma.

METHODS

Thrombospondin-2 was measured in prospectively collected serum samples from patients who underwent surgery with a histopathological diagnosis of distal cholangiocarcinoma (N = 51), pancreatic ductal adenocarcinoma (N = 52) and benign pancreatic diseases (N = 27) as well as healthy blood donors (N = 52) using an enzyme-linked immunosorbent assay.

RESULTS

Thrombospondin-2 levels (ng/ml) were similar in distal cholangiocarcinoma 55 (41-77) and pancreatic ductal adenocarcinoma 48 (35-80) (P = 0.221). Thrombospondin-2 + CA 19-9 had an area under the curve of 0.92 (95% CI 0.88-0.97) in differentiating distal cholangiocarcinoma and pancreatic ductal adenocarcinoma from healthy donors which was superior to CA 19-9 alone (P < 0.001). The diagnostic value of adding thrombospondin-2 to CA 19-9 was larger in early disease stages. Thrombospondin-2 did not provide additional value to CA 19-9 in differentiating the benign disease group; however, heterogeneity was notable in the benign cohort. Three of five patients with autoimmune pancreatitis patients had greatly elevated thrombospondin-2 levels. Thrombospondin-2 levels had no correlation with prognoses.

CONCLUSIONS

Serum thrombospondin-2 in combination with CA 19-9 has potential as a biomarker for distal cholangiocarcinoma and pancreatic cancer.