pancreatic or biliary-tract cancer can be a serious diagnosis, as many patients present too late for surgery. Cancer cells have been found to release small messenger molecules called that regulate cancer genes called microRNAs (miRNAs). The goal of this observational study is to learn about the role of miRNAs from bile and blood samples in patients with pancreatic cancer and bile duct cancer. The main question\[s\] it aims to answer are: * Can this detect patients presenting with jaundice (yellow-skin) undergoing endoscopy? * Can this distinguish between the types of cancer? Participants will have blood and bile samples collected prior to diagnosis and their clinical pathway will be followed up for 6 months.
Biliary strictures may be benign or malignant. The major aetiology of a malignant biliary stricture includes a primary tumour or local extension, such as cholangiocarcinoma (CCA) or pancreatic ductal adenocarcinoma (PDAC). There is still ongoing debate about adequate diagnostics in bile duct strictures of unknown aetiology. The application of endoscopic retrograde cholangio-pancreatography (ERCP) is considered to be an essential tool in bile duct strictures. The advantage of ERCP is the ability to achieve biliary decompression and take transpapillary specimens for histological or cytological analysis at the same time. Unfortunately, biliary brush cytology and/or biopsies are often insensitive at detecting malignancy, especially PDAC. MicroRNAs (miRNAs) are newly recognised, small pieces of genetic code that are crucial for regulating cancer genes. It has been previously shown that miRNA "signatures" in tissue and biofluids can be used to detect and prognosticate PDAC. Bile is potentially a rich source of novel biomarkers for PDAC and BTC due to its intimate proximity to the malignant lesion. This prospective, non-randomised, observational, single-blinded study will investigate, as its primary endpoint, whether candidate biliary miRNAs can differentiate between benign and malignant pancreaticobiliary disease/strictures in patients undergoing ERCP. In order to also generate hypotheses, this study also has several exploratory endpoints for translational research. This will focus on patients with presenting with PDAC, correlating miRNA levels with clinicopathologic factors and survival outcomes, in order to establish bile miRNAs as predictive and prognostic biomarkers. Moreover, the study will aim to elucidate the molecular mechanisms and source of the dysregulated biliary miRNAs in PDAC. In summary, there is currently no effective method to differentiate malignant from benign biliary strictures, or the ability to stratify these tumours pre-operatively based on their biological subtype and aggressiveness. The proposed study aims to improve the speed and accuracy of diagnosing these tumours by using measuring bile-based miRNA signatures. Importantly, it has been shown that biliary miRNAs can be easily extracted and analysed, and these molecules are stable in clinical settings. The development of clinically useful biliary miRNA biomarkers will result in considerable patient benefits.
Study Type
OBSERVATIONAL
Enrollment
229
Samples were prepared for small RNA sequencing using Qiagen's QIAseq small RNA Library Prep kit, quality controlled using an Agilent Bioanalyzer 2100 and sequenced on an NextSeq 500 system (Illumina, San Diego, USA) using the default single-end 75 base pair protocol to include integrated unique molecular indices (UMIs). Validation was undertaken using Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) either using target-specific stem-loop primer assays (TaqMan) or universal reverse transcription (RT) and locked nucleic acid (LNA) enhanced specific primers with SYBR green I assay detection
Imperial College Healthcare NHS Trust
London, United Kingdom
Diagnostic Value as measured by Area under the Curve (AUC) for candidate miRNAs
Expression values for each miRNA is calculated and used to undertake Multiple logistic regression was undertaken for candidates individually and in combination using GraphPad Prism. Data was prepared as a binary outcome and all main effects are included in the model. Optimum cut-offs were determined using thresholds obtained from the ROC curve at the maximum Youden index. Youden's J statistic (also called Youden's index) is a single statistic that ranges from 0 to 1 and is determined by the formula (Specificity + Sensitivity -1). Where multiple hypotheses were tested, an appropriate Benjamini-Hochberg (False Discovery Rate) correction was applied to give an adjusted p-value. A p (adjusted) value of \<0.05 was considered to be statistically significant.
Time frame: On the day of ERCP
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